Prompt:
Problem:

In pytorch, given the tensors a of shape (1X11) and b of shape (1X11), torch.stack((a,b),0) would give me a tensor of shape (2X11)

However, when a is of shape (2X11) and b is of shape (1X11), torch.stack((a,b),0) will raise an error cf. "the two tensor size must exactly be the same".

Because the two tensor are the output of a model (gradient included), I can't convert them to numpy to use np.stack() or np.vstack().

Is there any possible solution to give me a tensor ab of shape (3X11)?

A:

<code>
import numpy as np
import pandas as pd
import torch
a, b = load_data()
</code>
ab = ... # put solution in this variable
BEGIN SOLUTION
<code>

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            torch.random.manual_seed(42)
            a = torch.randn(2, 11)
            b = torch.randn(1, 11)
        elif test_case_id == 2:
            torch.random.manual_seed(7)
            a = torch.randn(2, 11)
            b = torch.randn(1, 11)
        return a, b

    def generate_ans(data):
        a, b = data
        ab = torch.cat((a, b), 0)
        return ab

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
a, b = test_input
[insert]
result = ab
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

In pytorch, given the tensors a of shape (114X514) and b of shape (114X514), torch.stack((a,b),0) would give me a tensor of shape (228X514)

However, when a is of shape (114X514) and b is of shape (24X514), torch.stack((a,b),0) will raise an error cf. "the two tensor size must exactly be the same".

Because the two tensor are the output of a model (gradient included), I can't convert them to numpy to use np.stack() or np.vstack().

Is there any possible solution to give me a tensor ab of shape (138X514)?

A:

<code>
import numpy as np
import pandas as pd
import torch
a, b = load_data()
</code>
ab = ... # put solution in this variable
BEGIN SOLUTION
<code>

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            torch.random.manual_seed(42)
            a = torch.randn(2, 11)
            b = torch.randn(1, 11)
        elif test_case_id == 2:
            torch.random.manual_seed(7)
            a = torch.randn(2, 11)
            b = torch.randn(1, 11)
        return a, b

    def generate_ans(data):
        a, b = data
        ab = torch.cat((a, b), 0)
        return ab

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
a, b = test_input
[insert]
result = ab
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

In pytorch, given the tensors a of shape (1X11) and b of shape (1X11), torch.stack((a,b),0) would give me a tensor of shape (2X11)

However, when a is of shape (2X11) and b is of shape (1X11), torch.stack((a,b),0) will raise an error cf. "the two tensor size must exactly be the same".

Because the two tensor are the output of a model (gradient included), I can't convert them to numpy to use np.stack() or np.vstack().

Is there any possible solution to give me a tensor ab of shape (3X11)?

A:

<code>
import numpy as np
import pandas as pd
import torch
a, b = load_data()
def solve(a, b):
    # return the solution in this function
    # ab = solve(a, b)
    ### BEGIN SOLUTION

# def solve(a, b):
    ### BEGIN SOLUTION
    ab = torch.cat((a, b), 0)
    ### END SOLUTION
    # return ab
# ab = solve(a, b)

    return ab

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            torch.random.manual_seed(42)
            a = torch.randn(2, 11)
            b = torch.randn(1, 11)
        elif test_case_id == 2:
            torch.random.manual_seed(7)
            a = torch.randn(2, 11)
            b = torch.randn(1, 11)
        return a, b

    def generate_ans(data):
        a, b = data
        ab = torch.cat((a, b), 0)
        return ab

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
a, b = test_input
def solve(a, b):
[insert]
ab = solve(a, b)
result = ab
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

Given a 3d tenzor, say: batch x sentence length x embedding dim

a = torch.rand((10, 1000, 96))
and an array(or tensor) of actual lengths for each sentence

lengths =  torch .randint(1000,(10,))
outputs tensor([ 370., 502., 652., 859., 545., 964., 566., 576.,1000., 803.])

How to fill tensor ‘a’ with zeros after certain index along dimension 1 (sentence length) according to tensor ‘lengths’ ?

I want smth like that :

a[ : , lengths : , : ]  = 0

A:

<code>
import numpy as np
import pandas as pd
import torch
a = torch.rand((10, 1000, 96))
lengths = torch.randint(1000, (10,))
</code>
a = ... # put solution in this variable
BEGIN SOLUTION
<code>

for i_batch in range(10):
    a[i_batch, lengths[i_batch]:, :] = 0

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            torch.random.manual_seed(42)
            a = torch.rand((10, 1000, 96))
            lengths = torch.randint(1000, (10,))
        return a, lengths

    def generate_ans(data):
        a, lengths = data
        for i_batch in range(10):
            a[i_batch, lengths[i_batch] :, :] = 0
        return a

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
a, lengths = test_input
[insert]
result = a
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

Given a 3d tenzor, say: batch x sentence length x embedding dim

a = torch.rand((10, 1000, 96))
and an array(or tensor) of actual lengths for each sentence

lengths =  torch .randint(1000,(10,))
outputs tensor([ 370., 502., 652., 859., 545., 964., 566., 576.,1000., 803.])

How to fill tensor ‘a’ with 2333 after certain index along dimension 1 (sentence length) according to tensor ‘lengths’ ?

I want smth like that :

a[ : , lengths : , : ]  = 2333

A:

<code>
import numpy as np
import pandas as pd
import torch
a = torch.rand((10, 1000, 96))
lengths = torch.randint(1000, (10,))
</code>
a = ... # put solution in this variable
BEGIN SOLUTION
<code>

for i_batch in range(10):
    a[i_batch, lengths[i_batch]:, :] = 2333

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            torch.random.manual_seed(42)
            a = torch.rand((10, 1000, 96))
            lengths = torch.randint(1000, (10,))
        return a, lengths

    def generate_ans(data):
        a, lengths = data
        for i_batch in range(10):
            a[i_batch, lengths[i_batch] :, :] = 2333
        return a

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
a, lengths = test_input
[insert]
result = a
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

Given a 3d tenzor, say: batch x sentence length x embedding dim

a = torch.rand((10, 1000, 23))
and an array(or tensor) of actual lengths for each sentence

lengths =  torch .randint(1000,(10,))
outputs tensor([ 137., 152., 165., 159., 145., 264., 265., 276.,1000., 203.])

How to fill tensor ‘a’ with 0 before certain index along dimension 1 (sentence length) according to tensor ‘lengths’ ?

I want smth like that :

a[ : , : lengths , : ]  = 0

A:

<code>
import numpy as np
import pandas as pd
import torch
a = torch.rand((10, 1000, 23))
lengths = torch.randint(1000, (10,))
</code>
a = ... # put solution in this variable
BEGIN SOLUTION
<code>

for i_batch in range(10):
    a[i_batch, :lengths[i_batch], :] = 0

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            torch.random.manual_seed(42)
            a = torch.rand((10, 1000, 23))
            lengths = torch.randint(1000, (10,))
        return a, lengths

    def generate_ans(data):
        a, lengths = data
        for i_batch in range(10):
            a[i_batch, : lengths[i_batch], :] = 0
        return a

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
a, lengths = test_input
[insert]
result = a
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

Given a 3d tenzor, say: batch x sentence length x embedding dim

a = torch.rand((10, 1000, 23))
and an array(or tensor) of actual lengths for each sentence

lengths =  torch .randint(1000,(10,))
outputs tensor([ 137., 152., 165., 159., 145., 264., 265., 276.,1000., 203.])

How to fill tensor ‘a’ with 2333 before certain index along dimension 1 (sentence length) according to tensor ‘lengths’ ?

I want smth like that :

a[ : , : lengths , : ]  = 2333

A:

<code>
import numpy as np
import pandas as pd
import torch
a = torch.rand((10, 1000, 23))
lengths = torch.randint(1000, (10,))
</code>
a = ... # put solution in this variable
BEGIN SOLUTION
<code>

for i_batch in range(10):
    a[i_batch, :lengths[i_batch], :] = 2333

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            torch.random.manual_seed(42)
            a = torch.rand((10, 1000, 23))
            lengths = torch.randint(1000, (10,))
        return a, lengths

    def generate_ans(data):
        a, lengths = data
        for i_batch in range(10):
            a[i_batch, : lengths[i_batch], :] = 2333
        return a

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
a, lengths = test_input
[insert]
result = a
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I have this code:

import torch

list_of_tensors = [ torch.randn(3), torch.randn(3), torch.randn(3)]
tensor_of_tensors = torch.tensor(list_of_tensors)
I am getting the error:

ValueError: only one element tensors can be converted to Python scalars

How can I convert the list of tensors to a tensor of tensors in pytorch?

A:

<code>
import numpy as np
import pandas as pd
import torch
list_of_tensors = load_data()
</code>
tensor_of_tensors = ... # put solution in this variable
BEGIN SOLUTION
<code>

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        torch.random.manual_seed(42)
        if test_case_id == 1:
            list_of_tensors = [torch.randn(3), torch.randn(3), torch.randn(3)]
        return list_of_tensors

    def generate_ans(data):
        list_of_tensors = data
        tensor_of_tensors = torch.stack((list_of_tensors))
        return tensor_of_tensors

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
list_of_tensors = test_input
[insert]
result = tensor_of_tensors
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

How to convert a list of tensors to a tensor of tensors?
I have tried torch.tensor() but it gave me this error message
ValueError: only one element tensors can be converted to Python scalars

my current code is here:
import torch

list = [ torch.randn(3), torch.randn(3), torch.randn(3)]
new_tensors = torch.tensor(list)

So how should I do that? Thanks

A:

<code>
import numpy as np
import pandas as pd
import torch
list = load_data()
</code>
new_tensors = ... # put solution in this variable
BEGIN SOLUTION
<code>

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        torch.random.manual_seed(42)
        if test_case_id == 1:
            list = [torch.randn(3), torch.randn(3), torch.randn(3)]
        return list

    def generate_ans(data):
        list = data
        new_tensors = torch.stack((list))
        return new_tensors

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
list = test_input
[insert]
result = new_tensors
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I have this code:

import torch

list_of_tensors = [ torch.randn(3), torch.randn(3), torch.randn(3)]
tensor_of_tensors = torch.tensor(list_of_tensors)
I am getting the error:

ValueError: only one element tensors can be converted to Python scalars

How can I convert the list of tensors to a tensor of tensors in pytorch?

A:

<code>
import numpy as np
import pandas as pd
import torch
list_of_tensors = load_data()
def Convert(lt):
    # return the solution in this function
    # tt = Convert(lt)
    ### BEGIN SOLUTION

# def Convert(lt):
    ### BEGIN SOLUTION
    tt = torch.stack((lt))
    ### END SOLUTION
    # return tt
# tensor_of_tensors = Convert(list_of_tensors)

    return tt

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        torch.random.manual_seed(42)
        if test_case_id == 1:
            list_of_tensors = [torch.randn(3), torch.randn(3), torch.randn(3)]
        return list_of_tensors

    def generate_ans(data):
        list_of_tensors = data
        tensor_of_tensors = torch.stack((list_of_tensors))
        return tensor_of_tensors

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
list_of_tensors = test_input
def Convert(lt):
[insert]
tensor_of_tensors = Convert(list_of_tensors)
result = tensor_of_tensors
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I have this code:

import torch

list_of_tensors = [ torch.randn(3), torch.randn(3), torch.randn(3)]
tensor_of_tensors = torch.tensor(list_of_tensors)
I am getting the error:

ValueError: only one element tensors can be converted to Python scalars

How can I convert the list of tensors to a tensor of tensors in pytorch? And I don't want to use a loop.

A:

<code>
import numpy as np
import pandas as pd
import torch
list_of_tensors = load_data()
</code>
tensor_of_tensors = ... # put solution in this variable
BEGIN SOLUTION
<code>

import torch
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        torch.random.manual_seed(42)
        if test_case_id == 1:
            list_of_tensors = [torch.randn(3), torch.randn(3), torch.randn(3)]
        return list_of_tensors

    def generate_ans(data):
        list_of_tensors = data
        tensor_of_tensors = torch.stack((list_of_tensors))
        return tensor_of_tensors

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
list_of_tensors = test_input
[insert]
result = tensor_of_tensors
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "for" not in tokens and "while" not in tokens


Prompt:
Problem:

I have the following torch tensor:

tensor([[-0.2,  0.3],
    [-0.5,  0.1],
    [-0.4,  0.2]])
and the following numpy array: (I can convert it to something else if necessary)

[1 0 1]
I want to get the following tensor:

tensor([0.3, -0.5, 0.2])
i.e. I want the numpy array to index each sub-element of my tensor. Preferably without using a loop.

Thanks in advance

A:

<code>
import numpy as np
import pandas as pd
import torch
t, idx = load_data()
assert type(t) == torch.Tensor
assert type(idx) == np.ndarray
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

idxs = torch.from_numpy(idx).long().unsqueeze(1)
# or   torch.from_numpy(idxs).long().view(-1,1)
result = t.gather(1, idxs).squeeze(1)

import numpy as np
import torch
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            t = torch.tensor([[-0.2, 0.3], [-0.5, 0.1], [-0.4, 0.2]])
            idx = np.array([1, 0, 1], dtype=np.int32)
        elif test_case_id == 2:
            t = torch.tensor([[-0.2, 0.3], [-0.5, 0.1], [-0.4, 0.2]])
            idx = np.array([1, 1, 0], dtype=np.int32)
        return t, idx

    def generate_ans(data):
        t, idx = data
        idxs = torch.from_numpy(idx).long().unsqueeze(1)
        result = t.gather(1, idxs).squeeze(1)
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
t, idx = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "for" not in tokens and "while" not in tokens


Prompt:
Problem:

I have the following torch tensor:

tensor([[-22.2,  33.3],
    [-55.5,  11.1],
    [-44.4,  22.2]])
and the following numpy array: (I can convert it to something else if necessary)

[1 1 0]
I want to get the following tensor:

tensor([33.3, 11.1, -44.4])
i.e. I want the numpy array to index each sub-element of my tensor. Preferably without using a loop.

Thanks in advance

A:

<code>
import numpy as np
import pandas as pd
import torch
t, idx = load_data()
assert type(t) == torch.Tensor
assert type(idx) == np.ndarray
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

idxs = torch.from_numpy(idx).long().unsqueeze(1)
# or   torch.from_numpy(idxs).long().view(-1,1)
result = t.gather(1, idxs).squeeze(1)

import numpy as np
import torch
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            t = torch.tensor([[-0.2, 0.3], [-0.5, 0.1], [-0.4, 0.2]])
            idx = np.array([1, 0, 1], dtype=np.int32)
        elif test_case_id == 2:
            t = torch.tensor([[-0.2, 0.3], [-0.5, 0.1], [-0.4, 0.2]])
            idx = np.array([1, 1, 0], dtype=np.int32)
        elif test_case_id == 3:
            t = torch.tensor([[-22.2, 33.3], [-55.5, 11.1], [-44.4, 22.2]])
            idx = np.array([1, 1, 0], dtype=np.int32)
        return t, idx

    def generate_ans(data):
        t, idx = data
        idxs = torch.from_numpy(idx).long().unsqueeze(1)
        result = t.gather(1, idxs).squeeze(1)
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
t, idx = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(3):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "for" not in tokens and "while" not in tokens


Prompt:
Problem:

I have the following torch tensor:

tensor([[-0.2,  0.3],
    [-0.5,  0.1],
    [-0.4,  0.2]])
and the following numpy array: (I can convert it to something else if necessary)

[1 0 1]
I want to get the following tensor:

tensor([-0.2, 0.1, -0.4])
i.e. I want the numpy array to index each sub-element of my tensor (note the detail here, 0 means to select index 1, and 1 means to select index 0). Preferably without using a loop.

Thanks in advance

A:

<code>
import numpy as np
import pandas as pd
import torch
t, idx = load_data()
assert type(t) == torch.Tensor
assert type(idx) == np.ndarray
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

idx = 1 - idx
idxs = torch.from_numpy(idx).long().unsqueeze(1)
# or   torch.from_numpy(idxs).long().view(-1,1)
result = t.gather(1, idxs).squeeze(1)

import numpy as np
import torch
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            t = torch.tensor([[-0.2, 0.3], [-0.5, 0.1], [-0.4, 0.2]])
            idx = np.array([1, 0, 1], dtype=np.int32)
        elif test_case_id == 2:
            t = torch.tensor([[-0.2, 0.3], [-0.5, 0.1], [-0.4, 0.2]])
            idx = np.array([1, 1, 0], dtype=np.int32)
        return t, idx

    def generate_ans(data):
        t, idx = data
        idx = 1 - idx
        idxs = torch.from_numpy(idx).long().unsqueeze(1)
        result = t.gather(1, idxs).squeeze(1)
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
t, idx = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "for" not in tokens and "while" not in tokens


Prompt:
Problem:

I have the tensors:

ids: shape (70,1) containing indices like [[1],[0],[2],...]

x: shape(70,3,2)

ids tensor encodes the index of bold marked dimension of x which should be selected. I want to gather the selected slices in a resulting vector:

result: shape (70,2)

Background:

I have some scores (shape = (70,3)) for each of the 3 elements and want only to select the one with the highest score. Therefore, I used the function

ids = torch.argmax(scores,1,True)
giving me the maximum ids. I already tried to do it with gather function:

result = x.gather(1,ids)
but that didn't work.

A:

<code>
import numpy as np
import pandas as pd
import torch
ids, x = load_data()
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        torch.random.manual_seed(42)
        if test_case_id == 1:
            x = torch.arange(70 * 3 * 2).view(70, 3, 2)
            ids = torch.randint(0, 3, size=(70, 1))
        return ids, x

    def generate_ans(data):
        ids, x = data
        idx = ids.repeat(1, 2).view(70, 1, 2)
        result = torch.gather(x, 1, idx)
        result = result.squeeze(1)
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
ids, x = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I have the tensors:

ids: shape (30,1) containing indices like [[2],[1],[0],...]

x: shape(30,3,114)

ids tensor encodes the index of bold marked dimension of x which should be selected. I want to gather the selected slices in a resulting vector:

result: shape (30,114)

Background:

I have some scores (shape = (30,3)) for each of the 3 elements and want only to select the one with the highest score. Therefore, I used the function

ids = torch.argmax(scores,1,True)
giving me the maximum ids. I already tried to do it with gather function:

result = x.gather(1,ids)
but that didn't work.

A:

<code>
import numpy as np
import pandas as pd
import torch
ids, x = load_data()
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        torch.random.manual_seed(42)
        if test_case_id == 1:
            x = torch.arange(30 * 3 * 114).view(30, 3, 114)
            ids = torch.randint(0, 3, size=(30, 1))
        return ids, x

    def generate_ans(data):
        ids, x = data
        idx = ids.repeat(1, 114).view(30, 1, 114)
        result = torch.gather(x, 1, idx)
        result = result.squeeze(1)
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
ids, x = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I have the tensors:

ids: shape (70,3) containing indices like [[0,1,0],[1,0,0],[0,0,1],...]

x: shape(70,3,2)

ids tensor encodes the index of bold marked dimension of x which should be selected (1 means selected, 0 not). I want to gather the selected slices in a resulting vector:

result: shape (70,2)

Background:

I have some scores (shape = (70,3)) for each of the 3 elements and want only to select the one with the highest score.
Therefore, I made the index with the highest score to be 1, and rest indexes to be 0

A:

<code>
import numpy as np
import pandas as pd
import torch
ids, x = load_data()
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        torch.random.manual_seed(42)
        if test_case_id == 1:
            x = torch.arange(70 * 3 * 2).view(70, 3, 2)
            select_ids = torch.randint(0, 3, size=(70, 1))
            ids = torch.zeros(size=(70, 3))
            for i in range(3):
                ids[i][select_ids[i]] = 1
        return ids, x

    def generate_ans(data):
        ids, x = data
        ids = torch.argmax(ids, 1, True)
        idx = ids.repeat(1, 2).view(70, 1, 2)
        result = torch.gather(x, 1, idx)
        result = result.squeeze(1)
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
ids, x = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I have a logistic regression model using Pytorch, where my input is high-dimensional and my output must be a scalar - 0, 1 or 2.

I'm using a linear layer combined with a softmax layer to return a n x 3 tensor, where each column represents the probability of the input falling in one of the three classes (0, 1 or 2).

However, I must return a n x 1 tensor, so I need to somehow pick the highest probability for each input and create a tensor indicating which class had the highest probability. How can I achieve this using Pytorch?

To illustrate, my Softmax outputs this:

[[0.2, 0.1, 0.7],
 [0.6, 0.2, 0.2],
 [0.1, 0.8, 0.1]]
And I must return this:

[[2],
 [0],
 [1]]

A:

<code>
import numpy as np
import pandas as pd
import torch
softmax_output = load_data()
</code>
y = ... # put solution in this variable
BEGIN SOLUTION
<code>

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            softmax_output = torch.FloatTensor(
                [[0.2, 0.1, 0.7], [0.6, 0.2, 0.2], [0.1, 0.8, 0.1]]
            )
        elif test_case_id == 2:
            softmax_output = torch.FloatTensor(
                [[0.7, 0.2, 0.1], [0.2, 0.6, 0.2], [0.1, 0.1, 0.8], [0.3, 0.3, 0.4]]
            )
        return softmax_output

    def generate_ans(data):
        softmax_output = data
        y = torch.argmax(softmax_output, dim=1).view(-1, 1)
        return y

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
softmax_output = test_input
[insert]
result = y
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I have a logistic regression model using Pytorch, where my input is high-dimensional and my output must be a scalar - 0, 1 or 2.

I'm using a linear layer combined with a softmax layer to return a n x 3 tensor, where each column represents the probability of the input falling in one of the three classes (0, 1 or 2).

However, I must return a n x 1 tensor, so I need to somehow pick the highest probability for each input and create a tensor indicating which class had the highest probability. How can I achieve this using Pytorch?

To illustrate, my Softmax outputs this:

[[0.7, 0.2, 0.1],
 [0.2, 0.6, 0.2],
 [0.1, 0.1, 0.8]]
And I must return this:

[[0],
 [1],
 [2]]

A:

<code>
import numpy as np
import pandas as pd
import torch
softmax_output = load_data()
</code>
y = ... # put solution in this variable
BEGIN SOLUTION
<code>

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            softmax_output = torch.FloatTensor(
                [[0.2, 0.1, 0.7], [0.6, 0.2, 0.2], [0.1, 0.8, 0.1]]
            )
        elif test_case_id == 2:
            softmax_output = torch.FloatTensor(
                [[0.7, 0.2, 0.1], [0.2, 0.6, 0.2], [0.1, 0.1, 0.8], [0.3, 0.3, 0.4]]
            )
        return softmax_output

    def generate_ans(data):
        softmax_output = data
        y = torch.argmax(softmax_output, dim=1).view(-1, 1)
        return y

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
softmax_output = test_input
[insert]
result = y
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I have a logistic regression model using Pytorch, where my input is high-dimensional and my output must be a scalar - 0, 1 or 2.

I'm using a linear layer combined with a softmax layer to return a n x 3 tensor, where each column represents the probability of the input falling in one of the three classes (0, 1 or 2).

However, I must return a n x 1 tensor, and I want to somehow pick the lowest probability for each input and create a tensor indicating which class had the lowest probability. How can I achieve this using Pytorch?

To illustrate, my Softmax outputs this:

[[0.2, 0.1, 0.7],
 [0.6, 0.3, 0.1],
 [0.15, 0.8, 0.05]]
And I must return this:

[[1],
 [2],
 [2]]

A:

<code>
import numpy as np
import pandas as pd
import torch
softmax_output = load_data()
</code>
y = ... # put solution in this variable
BEGIN SOLUTION
<code>

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            softmax_output = torch.FloatTensor(
                [[0.2, 0.1, 0.7], [0.6, 0.1, 0.3], [0.4, 0.5, 0.1]]
            )
        elif test_case_id == 2:
            softmax_output = torch.FloatTensor(
                [[0.7, 0.2, 0.1], [0.3, 0.6, 0.1], [0.05, 0.15, 0.8], [0.25, 0.35, 0.4]]
            )
        return softmax_output

    def generate_ans(data):
        softmax_output = data
        y = torch.argmin(softmax_output, dim=1).view(-1, 1)
        return y

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
softmax_output = test_input
[insert]
result = y
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I have a logistic regression model using Pytorch, where my input is high-dimensional and my output must be a scalar - 0, 1 or 2.

I'm using a linear layer combined with a softmax layer to return a n x 3 tensor, where each column represents the probability of the input falling in one of the three classes (0, 1 or 2).

However, I must return a n x 1 tensor, so I need to somehow pick the highest probability for each input and create a tensor indicating which class had the highest probability. How can I achieve this using Pytorch?

To illustrate, my Softmax outputs this:

[[0.2, 0.1, 0.7],
 [0.6, 0.2, 0.2],
 [0.1, 0.8, 0.1]]
And I must return this:

[[2],
 [0],
 [1]]

A:

<code>
import numpy as np
import pandas as pd
import torch
softmax_output = load_data()
def solve(softmax_output):
    # return the solution in this function
    # y = solve(softmax_output)
    ### BEGIN SOLUTION

# def solve(softmax_output):
    y = torch.argmax(softmax_output, dim=1).view(-1, 1)
    # return y
# y = solve(softmax_output)

    return y

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            softmax_output = torch.FloatTensor(
                [[0.2, 0.1, 0.7], [0.6, 0.2, 0.2], [0.1, 0.8, 0.1]]
            )
        elif test_case_id == 2:
            softmax_output = torch.FloatTensor(
                [[0.7, 0.2, 0.1], [0.2, 0.6, 0.2], [0.1, 0.1, 0.8], [0.3, 0.3, 0.4]]
            )
        return softmax_output

    def generate_ans(data):
        softmax_output = data
        y = torch.argmax(softmax_output, dim=1).view(-1, 1)
        return y

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
softmax_output = test_input
def solve(softmax_output):
[insert]
y = solve(softmax_output)
result = y
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I have a logistic regression model using Pytorch, where my input is high-dimensional and my output must be a scalar - 0, 1 or 2.

I'm using a linear layer combined with a softmax layer to return a n x 3 tensor, where each column represents the probability of the input falling in one of the three classes (0, 1 or 2).

However, I must return a 1 x n tensor, and I want to somehow pick the lowest probability for each input and create a tensor indicating which class had the lowest probability. How can I achieve this using Pytorch?

To illustrate, my Softmax outputs this:

[[0.2, 0.1, 0.7],
 [0.6, 0.3, 0.1],
 [0.15, 0.8, 0.05]]
And I must return this:

[1, 2, 2], which has the type torch.LongTensor

A:

<code>
import numpy as np
import pandas as pd
import torch
softmax_output = load_data()
def solve(softmax_output):
</code>
y = ... # put solution in this variable
BEGIN SOLUTION
<code>

# def solve(softmax_output):
    ### BEGIN SOLUTION
    y = torch.argmin(softmax_output, dim=1).detach()
    ### END SOLUTION
    # return y
# y = solve(softmax_output)

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            softmax_output = torch.FloatTensor(
                [[0.2, 0.1, 0.7], [0.6, 0.1, 0.3], [0.4, 0.5, 0.1]]
            )
        elif test_case_id == 2:
            softmax_output = torch.FloatTensor(
                [[0.7, 0.2, 0.1], [0.3, 0.6, 0.1], [0.05, 0.15, 0.8], [0.25, 0.35, 0.4]]
            )
        return softmax_output

    def generate_ans(data):
        softmax_output = data
        y = torch.argmin(softmax_output, dim=1).detach()
        return y

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        assert result.type() == "torch.LongTensor"
        torch.testing.assert_close(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
softmax_output = test_input
def solve(softmax_output):
[insert]
    return y
y = solve(softmax_output)
result = y
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I am doing an image segmentation task. There are 7 classes in total so the final outout is a tensor like [batch, 7, height, width] which is a softmax output. Now intuitively I wanted to use CrossEntropy loss but the pytorch implementation doesn't work on channel wise one-hot encoded vector

So I was planning to make a function on my own. With a help from some stackoverflow, My code so far looks like this

from torch.autograd import Variable
import torch
import torch.nn.functional as F

def cross_entropy2d(input, target, weight=None, size_average=True):
    # input: (n, c, w, z), target: (n, w, z)
    n, c, w, z = input.size()
    # log_p: (n, c, w, z)
    log_p = F.log_softmax(input, dim=1)
    # log_p: (n*w*z, c)
    log_p = log_p.permute(0, 3, 2, 1).contiguous().view(-1, c)  # make class dimension last dimension
    log_p = log_p[
       target.view(n, w, z, 1).repeat(0, 0, 0, c) >= 0]  # this looks wrong -> Should rather be a one-hot vector
    log_p = log_p.view(-1, c)
    # target: (n*w*z,)
    mask = target >= 0
    target = target[mask]
    loss = F.nll_loss(log_p, target.view(-1), weight=weight, size_average=False)
    if size_average:
        loss /= mask.data.sum()
    return loss

images = Variable(torch.randn(5, 3, 4, 4))
labels = Variable(torch.LongTensor(5, 4, 4).random_(3))
cross_entropy2d(images, labels)
I get two errors. One is mentioned on the code itself, where it expects one-hot vector. The 2nd one says the following

RuntimeError: invalid argument 2: size '[5 x 4 x 4 x 1]' is invalid for input with 3840 elements at ..\src\TH\THStorage.c:41
For example purpose I was trying to make it work on a 3 class problem. So the targets and labels are (excluding the batch parameter for simplification ! )

Target:

 Channel 1     Channel 2  Channel 3
[[0 1 1 0 ]   [0 0 0 1 ]  [1 0 0 0 ]
  [0 0 1 1 ]   [0 0 0 0 ]  [1 1 0 0 ]
  [0 0 0 1 ]   [0 0 0 0 ]  [1 1 1 0 ]
  [0 0 0 0 ]   [0 0 0 1 ]  [1 1 1 0 ]

Labels:

 Channel 1     Channel 2  Channel 3
[[0 1 1 0 ]   [0 0 0 1 ]  [1 0 0 0 ]
  [0 0 1 1 ]   [.2 0 0 0] [.8 1 0 0 ]
  [0 0 0 1 ]   [0 0 0 0 ]  [1 1 1 0 ]
  [0 0 0 0 ]   [0 0 0 1 ]  [1 1 1 0 ]

So how can I fix my code to calculate channel wise CrossEntropy loss ?
Or can you give some simple methods to calculate the loss? Thanks
Just use the default arguments

A:

<code>
import numpy as np
import pandas as pd
from torch.autograd import Variable
import torch
import torch.nn.functional as F
images, labels = load_data()
</code>
loss = ... # put solution in this variable
BEGIN SOLUTION
<code>

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            torch.random.manual_seed(42)
            images = torch.randn(5, 3, 4, 4)
            labels = torch.LongTensor(5, 4, 4).random_(3)
        return images, labels

    def generate_ans(data):
        images, labels = data
        loss_func = torch.nn.CrossEntropyLoss()
        loss = loss_func(images, labels)
        return loss

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
import torch.nn.functional as F
from torch.autograd import Variable
images, labels = test_input
[insert]
result = loss
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I have two tensors of dimension 1000 * 1. I want to check how many of the 1000 elements are equal in the two tensors. I think I should be able to do this in few lines like Numpy but couldn't find a similar function.

A:

<code>
import numpy as np
import pandas as pd
import torch
A, B = load_data()
</code>
cnt_equal = ... # put solution in this variable
BEGIN SOLUTION
<code>

import numpy as np
import torch
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            torch.random.manual_seed(42)
            A = torch.randint(2, (1000,))
            torch.random.manual_seed(7)
            B = torch.randint(2, (1000,))
        return A, B

    def generate_ans(data):
        A, B = data
        cnt_equal = int((A == B).sum())
        return cnt_equal

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_equal(int(result), ans)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
A, B = test_input
[insert]
result = cnt_equal
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "for" not in tokens and "while" not in tokens


Prompt:
Problem:

I have two tensors of dimension 11 * 1. I want to check how many of the 11 elements are equal in the two tensors. I think I should be able to do this in few lines like Numpy but couldn't find a similar function.

A:

<code>
import numpy as np
import pandas as pd
import torch
A, B = load_data()
</code>
cnt_equal = ... # put solution in this variable
BEGIN SOLUTION
<code>

import numpy as np
import torch
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            torch.random.manual_seed(42)
            A = torch.randint(2, (11,))
            torch.random.manual_seed(7)
            B = torch.randint(2, (11,))
        return A, B

    def generate_ans(data):
        A, B = data
        cnt_equal = int((A == B).sum())
        return cnt_equal

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_equal(int(result), ans)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
A, B = test_input
[insert]
result = cnt_equal
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "for" not in tokens and "while" not in tokens


Prompt:
Problem:

I have two tensors of dimension like 1000 * 1. I want to check how many of the elements are not equal in the two tensors. I think I should be able to do this in few lines like Numpy but couldn't find a similar function.

A:

<code>
import numpy as np
import pandas as pd
import torch
A, B = load_data()
</code>
cnt_not_equal = ... # put solution in this variable
BEGIN SOLUTION
<code>

import numpy as np
import torch
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            torch.random.manual_seed(42)
            A = torch.randint(2, (10,))
            torch.random.manual_seed(7)
            B = torch.randint(2, (10,))
        return A, B

    def generate_ans(data):
        A, B = data
        cnt_not_equal = int(len(A)) - int((A == B).sum())
        return cnt_not_equal

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_equal(int(result), ans)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
A, B = test_input
[insert]
result = cnt_not_equal
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "for" not in tokens and "while" not in tokens


Prompt:
Problem:

I have two tensors of dimension 1000 * 1. I want to check how many of the 1000 elements are equal in the two tensors. I think I should be able to do this in few lines like Numpy but couldn't find a similar function.

A:

<code>
import numpy as np
import pandas as pd
import torch
A, B = load_data()
def Count(A, B):
    # return the solution in this function
    # cnt_equal = Count(A, B)
    ### BEGIN SOLUTION

# def Count(A, B):
    ### BEGIN SOLUTION
    cnt_equal = int((A == B).sum())
    ### END SOLUTION
    # return cnt_equal
# cnt_equal = Count(A, B)

    return cnt_equal

import numpy as np
import torch
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            torch.random.manual_seed(42)
            A = torch.randint(2, (1000,))
            torch.random.manual_seed(7)
            B = torch.randint(2, (1000,))
        return A, B

    def generate_ans(data):
        A, B = data
        cnt_equal = int((A == B).sum())
        return cnt_equal

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_equal(int(result), ans)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
A, B = test_input
def Count(A, B):
[insert]
cnt_equal = Count(A, B)
result = cnt_equal
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "for" not in tokens and "while" not in tokens


Prompt:
Problem:

I have two tensors of dimension (2*x, 1). I want to check how many of the last x elements are equal in the two tensors. I think I should be able to do this in few lines like Numpy but couldn't find a similar function.

A:

<code>
import numpy as np
import pandas as pd
import torch
A, B = load_data()
</code>
cnt_equal = ... # put solution in this variable
BEGIN SOLUTION
<code>

cnt_equal = int((A[int(len(A) / 2):] == B[int(len(A) / 2):]).sum())

import numpy as np
import torch
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            torch.random.manual_seed(42)
            A = torch.randint(2, (100,))
            torch.random.manual_seed(7)
            B = torch.randint(2, (100,))
        return A, B

    def generate_ans(data):
        A, B = data
        cnt_equal = int((A[int(len(A) / 2) :] == B[int(len(A) / 2) :]).sum())
        return cnt_equal

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_equal(int(result), ans)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
A, B = test_input
[insert]
result = cnt_equal
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "for" not in tokens and "while" not in tokens


Prompt:
Problem:

I have two tensors of dimension (2*x, 1). I want to check how many of the last x elements are not equal in the two tensors. I think I should be able to do this in few lines like Numpy but couldn't find a similar function.

A:

<code>
import numpy as np
import pandas as pd
import torch
A, B = load_data()
</code>
cnt_not_equal = ... # put solution in this variable
BEGIN SOLUTION
<code>

cnt_not_equal = int((A[int(len(A) / 2):] != B[int(len(A) / 2):]).sum())

import numpy as np
import torch
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            torch.random.manual_seed(42)
            A = torch.randint(2, (1000,))
            torch.random.manual_seed(7)
            B = torch.randint(2, (1000,))
        return A, B

    def generate_ans(data):
        A, B = data
        cnt_not_equal = int((A[int(len(A) / 2) :] != B[int(len(A) / 2) :]).sum())
        return cnt_not_equal

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_equal(int(result), ans)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
A, B = test_input
[insert]
result = cnt_not_equal
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "for" not in tokens and "while" not in tokens


Prompt:
Problem:

Let's say I have a 5D tensor which has this shape for example : (1, 3, 10, 40, 1). I want to split it into smaller equal tensors (if possible) according to a certain dimension with a step equal to 1 while preserving the other dimensions.

Let's say for example I want to split it according to the fourth dimension (=40) where each tensor will have a size equal to 10. So the first tensor_1 will have values from 0->9, tensor_2 will have values from 1->10 and so on.

The 31 tensors will have these shapes :

Shape of tensor_1 : (1, 3, 10, 10, 1)
Shape of tensor_2 : (1, 3, 10, 10, 1)
Shape of tensor_3 : (1, 3, 10, 10, 1)
...
Shape of tensor_31 : (1, 3, 10, 10, 1)
Here's what I have tried :

a = torch.randn(1, 3, 10, 40, 1)

chunk_dim = 10
a_split = torch.chunk(a, chunk_dim, dim=3)
This gives me 4 tensors. How can I edit this so I'll have 31 tensors with a step = 1 like I explained ?

A:

<code>
import numpy as np
import pandas as pd
import torch
a = load_data()
assert a.shape == (1, 3, 10, 40, 1)
chunk_dim = 10
</code>
solve this question with example variable `tensors_31` and put tensors in order
BEGIN SOLUTION
<code>

Temp = a.unfold(3, chunk_dim, 1)
tensors_31 = []
for i in range(Temp.shape[3]):
    tensors_31.append(Temp[:, :, :, i, :].view(1, 3, 10, chunk_dim, 1).numpy())
tensors_31 = torch.from_numpy(np.array(tensors_31))

import numpy as np
import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            torch.random.manual_seed(42)
            a = torch.randn(1, 3, 10, 40, 1)
        return a

    def generate_ans(data):
        a = data
        Temp = a.unfold(3, 10, 1)
        tensors_31 = []
        for i in range(Temp.shape[3]):
            tensors_31.append(Temp[:, :, :, i, :].view(1, 3, 10, 10, 1).numpy())
        tensors_31 = torch.from_numpy(np.array(tensors_31))
        return tensors_31

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        assert len(ans) == len(result)
        for i in range(len(ans)):
            torch.testing.assert_close(result[i], ans[i], check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
a = test_input
chunk_dim=10
[insert]
result = tensors_31
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

Let's say I have a 5D tensor which has this shape for example : (1, 3, 40, 10, 1). I want to split it into smaller equal tensors (if possible) according to a certain dimension with a step equal to 1 while preserving the other dimensions.

Let's say for example I want to split it according to the third dimension (=40) where each tensor will have a size equal to 10. So the first tensor_1 will have values from 0->9, tensor_2 will have values from 1->10 and so on.

The 31 tensors will have these shapes :

Shape of tensor_1 : (1, 3, 10, 10, 1)
Shape of tensor_2 : (1, 3, 10, 10, 1)
Shape of tensor_3 : (1, 3, 10, 10, 1)
...
Shape of tensor_31 : (1, 3, 10, 10, 1)
Here's what I have tried :

a = torch.randn(1, 3, 40, 10, 1)

chunk_dim = 10
a_split = torch.chunk(a, chunk_dim, dim=2)
This gives me 4 tensors. How can I edit this so I'll have 31 tensors with a step = 1 like I explained ?

A:

<code>
import numpy as np
import pandas as pd
import torch
a = load_data()
assert a.shape == (1, 3, 10, 40, 1)
chunk_dim = 10
</code>
solve this question with example variable `tensors_31` and put tensors in order
BEGIN SOLUTION
<code>

Temp = a.unfold(2, chunk_dim, 1)
tensors_31 = []
for i in range(Temp.shape[2]):
    tensors_31.append(Temp[:, :, i, :, :].view(1, 3, chunk_dim, 10, 1).numpy())
tensors_31 = torch.from_numpy(np.array(tensors_31))

import numpy as np
import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            torch.random.manual_seed(42)
            a = torch.randn(1, 3, 40, 10, 1)
        return a

    def generate_ans(data):
        a = data
        Temp = a.unfold(2, 10, 1)
        tensors_31 = []
        for i in range(Temp.shape[2]):
            tensors_31.append(Temp[:, :, i, :, :].view(1, 3, 10, 10, 1).numpy())
        tensors_31 = torch.from_numpy(np.array(tensors_31))
        return tensors_31

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        assert len(ans) == len(result)
        for i in range(len(ans)):
            torch.testing.assert_close(result[i], ans[i], check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
a = test_input
chunk_dim=10
[insert]
result = tensors_31
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

This question may not be clear, so please ask for clarification in the comments and I will expand.

I have the following tensors of the following shape:

mask.size() == torch.Size([1, 400])
clean_input_spectrogram.size() == torch.Size([1, 400, 161])
output.size() == torch.Size([1, 400, 161])
mask is comprised only of 0 and 1. Since it's a mask, I want to set the elements of output equal to clean_input_spectrogram where that relevant mask value is 1.

How would I do that?

A:

<code>
import numpy as np
import pandas as pd
import torch
mask, clean_input_spectrogram, output= load_data()
</code>
output = ... # put solution in this variable
BEGIN SOLUTION
<code>

output[:, mask[0].to(torch.bool), :] = clean_input_spectrogram[:, mask[0].to(torch.bool), :]

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            torch.random.manual_seed(42)
            mask = torch.tensor([[0, 1, 0]]).to(torch.int32)
            clean_input_spectrogram = torch.rand((1, 3, 2))
            output = torch.rand((1, 3, 2))
        return mask, clean_input_spectrogram, output

    def generate_ans(data):
        mask, clean_input_spectrogram, output = data
        output[:, mask[0].to(torch.bool), :] = clean_input_spectrogram[
            :, mask[0].to(torch.bool), :
        ]
        return output

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
mask, clean_input_spectrogram, output = test_input
[insert]
result = output
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

This question may not be clear, so please ask for clarification in the comments and I will expand.

I have the following tensors of the following shape:

mask.size() == torch.Size([1, 400])
clean_input_spectrogram.size() == torch.Size([1, 400, 161])
output.size() == torch.Size([1, 400, 161])
mask is comprised only of 0 and 1. Since it's a mask, I want to set the elements of output equal to clean_input_spectrogram where that relevant mask value is 0.

How would I do that?

A:

<code>
import numpy as np
import pandas as pd
import torch
mask, clean_input_spectrogram, output= load_data()
</code>
output = ... # put solution in this variable
BEGIN SOLUTION
<code>

for i in range(len(mask[0])):
    if mask[0][i] == 1:
        mask[0][i] = 0
    else:
        mask[0][i] = 1
output[:, mask[0].to(torch.bool), :] = clean_input_spectrogram[:, mask[0].to(torch.bool), :]

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            torch.random.manual_seed(42)
            mask = torch.tensor([[0, 1, 0]]).to(torch.int32)
            clean_input_spectrogram = torch.rand((1, 3, 2))
            output = torch.rand((1, 3, 2))
        return mask, clean_input_spectrogram, output

    def generate_ans(data):
        mask, clean_input_spectrogram, output = data
        for i in range(len(mask[0])):
            if mask[0][i] == 1:
                mask[0][i] = 0
            else:
                mask[0][i] = 1
        output[:, mask[0].to(torch.bool), :] = clean_input_spectrogram[
            :, mask[0].to(torch.bool), :
        ]
        return output

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
mask, clean_input_spectrogram, output = test_input
[insert]
result = output
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I may be missing something obvious, but I can't find a way to compute this.

Given two tensors, I want to keep elements with the minimum absolute values, in each one of them as well as the sign.

I thought about

sign_x = torch.sign(x)
sign_y = torch.sign(y)
min = torch.min(torch.abs(x), torch.abs(y))
in order to eventually multiply the signs with the obtained minimums, but then I have no method to multiply the correct sign to each element that was kept and must choose one of the two tensors.

A:

<code>
import numpy as np
import pandas as pd
import torch
x, y = load_data()
</code>
signed_min = ... # put solution in this variable
BEGIN SOLUTION
<code>

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            torch.random.manual_seed(42)
            x = torch.randint(-10, 10, (5,))
            y = torch.randint(-20, 20, (5,))
        return x, y

    def generate_ans(data):
        x, y = data
        mins = torch.min(torch.abs(x), torch.abs(y))
        xSigns = (mins == torch.abs(x)) * torch.sign(x)
        ySigns = (mins == torch.abs(y)) * torch.sign(y)
        finalSigns = xSigns.int() | ySigns.int()
        signed_min = mins * finalSigns
        return signed_min

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
x, y = test_input
[insert]
result = signed_min
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I may be missing something obvious, but I can't find a way to compute this.

Given two tensors, I want to keep elements with the maximum absolute values, in each one of them as well as the sign.

I thought about

sign_x = torch.sign(x)
sign_y = torch.sign(y)
max = torch.max(torch.abs(x), torch.abs(y))
in order to eventually multiply the signs with the obtained maximums, but then I have no method to multiply the correct sign to each element that was kept and must choose one of the two tensors.

A:

<code>
import numpy as np
import pandas as pd
import torch
x, y = load_data()
</code>
signed_max = ... # put solution in this variable
BEGIN SOLUTION
<code>

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            torch.random.manual_seed(42)
            x = torch.randint(-10, 10, (5,))
            y = torch.randint(-20, 20, (5,))
        return x, y

    def generate_ans(data):
        x, y = data
        maxs = torch.max(torch.abs(x), torch.abs(y))
        xSigns = (maxs == torch.abs(x)) * torch.sign(x)
        ySigns = (maxs == torch.abs(y)) * torch.sign(y)
        finalSigns = xSigns.int() | ySigns.int()
        signed_max = maxs * finalSigns
        return signed_max

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
x, y = test_input
[insert]
result = signed_max
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I may be missing something obvious, but I can't find a way to compute this.

Given two tensors, I want to keep elements with the minimum absolute values, in each one of them as well as the sign.

I thought about

sign_x = torch.sign(x)
sign_y = torch.sign(y)
min = torch.min(torch.abs(x), torch.abs(y))
in order to eventually multiply the signs with the obtained minimums, but then I have no method to multiply the correct sign to each element that was kept and must choose one of the two tensors.

A:

<code>
import numpy as np
import pandas as pd
import torch
x, y = load_data()
def solve(x, y):
    # return the solution in this function
    # signed_min = solve(x, y)
    ### BEGIN SOLUTION

# def solve(x, y):
    ### BEGIN SOLUTION
    mins = torch.min(torch.abs(x), torch.abs(y))

    xSigns = (mins == torch.abs(x)) * torch.sign(x)
    ySigns = (mins == torch.abs(y)) * torch.sign(y)
    finalSigns = xSigns.int() | ySigns.int()

    signed_min = mins * finalSigns
    ### END SOLUTION
    # return signed_min
# signed_min = solve(x, y)

    return signed_min

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            torch.random.manual_seed(42)
            x = torch.randint(-10, 10, (5,))
            y = torch.randint(-20, 20, (5,))
        return x, y

    def generate_ans(data):
        x, y = data
        mins = torch.min(torch.abs(x), torch.abs(y))
        xSigns = (mins == torch.abs(x)) * torch.sign(x)
        ySigns = (mins == torch.abs(y)) * torch.sign(y)
        finalSigns = xSigns.int() | ySigns.int()
        signed_min = mins * finalSigns
        return signed_min

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
x, y = test_input
def solve(x, y):
[insert]
signed_min = solve(x, y)
result = signed_min
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I have a trained PyTorch model and I want to get the confidence score of predictions in range (0-1). The code below is giving me a score but its range is undefined. I want the score in a defined range of (0-1) using softmax. Any idea how to get this?

conf, classes = torch.max(output.reshape(1, 3), 1)
My code:

MyNet.load_state_dict(torch.load("my_model.pt"))
def predict_allCharacters(input):
    output = MyNet(input)
    conf, classes = torch.max(output.reshape(1, 3), 1)
    class_names = '012'
    return conf, class_names[classes.item()]

Model definition:

MyNet = torch.nn.Sequential(torch.nn.Linear(4, 15),
                            torch.nn.Sigmoid(),
                            torch.nn.Linear(15, 3),
                            )

A:

runnable code
<code>
import numpy as np
import pandas as pd
import torch
MyNet = torch.nn.Sequential(torch.nn.Linear(4, 15),
                            torch.nn.Sigmoid(),
                            torch.nn.Linear(15, 3),
                            )
MyNet.load_state_dict(torch.load("my_model.pt"))
input = load_data()
assert type(input) == torch.Tensor
</code>
confidence_score = ... # put solution in this variable
BEGIN SOLUTION
<code>

'''
training part
'''
# X, Y = load_iris(return_X_y=True)
# lossFunc = torch.nn.CrossEntropyLoss()
# opt = torch.optim.Adam(MyNet.parameters(), lr=0.001)
# for batch in range(0, 50):
#     for i in range(len(X)):
#         x = MyNet(torch.from_numpy(X[i]).float()).reshape(1, 3)
#         y = torch.tensor(Y[i]).long().unsqueeze(0)
#         loss = lossFunc(x, y)
#         loss.backward()
#         opt.step()
#         opt.zero_grad()
#         # print(x.grad)
#         # print(loss)
#     # print(loss)
output = MyNet(input)
probs = torch.nn.functional.softmax(output.reshape(1, 3), dim=1)
confidence_score, classes = torch.max(probs, 1)

import torch
import copy
import sklearn
from sklearn.datasets import load_iris

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            X, y = load_iris(return_X_y=True)
            input = torch.from_numpy(X[42]).float()
            torch.manual_seed(42)
            MyNet = torch.nn.Sequential(
                torch.nn.Linear(4, 15),
                torch.nn.Sigmoid(),
                torch.nn.Linear(15, 3),
            )
            torch.save(MyNet.state_dict(), "my_model.pt")
        return input

    def generate_ans(data):
        input = data
        MyNet = torch.nn.Sequential(
            torch.nn.Linear(4, 15),
            torch.nn.Sigmoid(),
            torch.nn.Linear(15, 3),
        )
        MyNet.load_state_dict(torch.load("my_model.pt"))
        output = MyNet(input)
        probs = torch.nn.functional.softmax(output.reshape(1, 3), dim=1)
        confidence_score, classes = torch.max(probs, 1)
        return confidence_score

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
MyNet = torch.nn.Sequential(torch.nn.Linear(4, 15),
                            torch.nn.Sigmoid(),
                            torch.nn.Linear(15, 3),
                            )
MyNet.load_state_dict(torch.load("my_model.pt"))
input = test_input
[insert]
result = confidence_score
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I have two tensors that should together overlap each other to form a larger tensor. To illustrate:

a = torch.Tensor([[1, 2, 3], [1, 2, 3]])
b = torch.Tensor([[5, 6, 7], [5, 6, 7]])

a = [[1 2 3]    b = [[5 6 7]
     [1 2 3]]        [5 6 7]]
I want to combine the two tensors and have them partially overlap by a single column, with the average being taken for those elements that overlap.

e.g.

result = [[1 2 4 6 7]
          [1 2 4 6 7]]
The first two columns are the first two columns of 'a'. The last two columns are the last two columns of 'b'. The middle column is the average of 'a's last column and 'b's first column.

I know how to merge two tensors side by side or in a new dimension. But doing this eludes me.

Can anyone help?

A:

<code>
import numpy as np
import pandas as pd
import torch
a, b = load_data()
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = torch.Tensor([[1, 2, 3], [1, 2, 3]])
            b = torch.Tensor([[5, 6, 7], [5, 6, 7]])
        elif test_case_id == 2:
            a = torch.Tensor([[3, 2, 1], [1, 2, 3]])
            b = torch.Tensor([[7, 6, 5], [5, 6, 7]])
        elif test_case_id == 3:
            a = torch.Tensor([[3, 2, 1, 1, 2], [1, 1, 1, 2, 3], [9, 9, 5, 6, 7]])
            b = torch.Tensor([[1, 4, 7, 6, 5], [9, 9, 5, 6, 7], [9, 9, 5, 6, 7]])
        return a, b

    def generate_ans(data):
        a, b = data
        c = (a[:, -1:] + b[:, :1]) / 2
        result = torch.cat((a[:, :-1], c, b[:, 1:]), dim=1)
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
a, b = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(3):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I have two tensors that should together overlap each other to form a larger tensor. To illustrate:

a = torch.Tensor([[1, 2, 3], [1, 2, 3]])
b = torch.Tensor([[5, 6, 7], [5, 6, 7]])

a = [[1 2 3]    b = [[5 6 7]
     [1 2 3]]        [5 6 7]]
I want to combine the two tensors and have them partially overlap by a single column, with the average being taken for those elements that overlap.

e.g.

result = [[1 2 4 6 7]
          [1 2 4 6 7]]
The first two columns are the first two columns of 'a'. The last two columns are the last two columns of 'b'. The middle column is the average of 'a's last column and 'b's first column.

I know how to merge two tensors side by side or in a new dimension. But doing this eludes me.

Can anyone help?

A:

<code>
import numpy as np
import pandas as pd
import torch
a, b = load_data()
def solve(a, b):
    # return the solution in this function
    # result = solve(a, b)
    ### BEGIN SOLUTION

# def solve(a, b):
    ### BEGIN SOLUTION
    c = (a[:, -1:] + b[:, :1]) / 2
    result = torch.cat((a[:, :-1], c, b[:, 1:]), dim=1)
    ### END SOLUTION
    # return result
# result = solve(a, b)

    return result

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = torch.Tensor([[1, 2, 3], [1, 2, 3]])
            b = torch.Tensor([[5, 6, 7], [5, 6, 7]])
        elif test_case_id == 2:
            a = torch.Tensor([[3, 2, 1], [1, 2, 3]])
            b = torch.Tensor([[7, 6, 5], [5, 6, 7]])
        elif test_case_id == 3:
            a = torch.Tensor([[3, 2, 1, 1, 2], [1, 1, 1, 2, 3], [9, 9, 5, 6, 7]])
            b = torch.Tensor([[1, 4, 7, 6, 5], [9, 9, 5, 6, 7], [9, 9, 5, 6, 7]])
        return a, b

    def generate_ans(data):
        a, b = data
        c = (a[:, -1:] + b[:, :1]) / 2
        result = torch.cat((a[:, :-1], c, b[:, 1:]), dim=1)
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
a, b = test_input
def solve(a, b):
[insert]
result = solve(a, b)
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(3):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I have a tensor t, for example

1 2
3 4
5 6
7 8
And I would like to make it

0 0 0 0
0 1 2 0
0 3 4 0
0 5 6 0
0 7 8 0
0 0 0 0
I tried stacking with new=torch.tensor([0. 0. 0. 0.]) tensor four times but that did not work.

t = torch.arange(8).reshape(1,4,2).float()
print(t)
new=torch.tensor([[0., 0., 0.,0.]])
print(new)
r = torch.stack([t,new])  # invalid argument 0: Tensors must have same number of dimensions: got 4 and 3
new=torch.tensor([[[0., 0., 0.,0.]]])
print(new)
r = torch.stack([t,new])  # invalid argument 0: Sizes of tensors must match except in dimension 0.
I also tried cat, that did not work either.

A:

<code>
import numpy as np
import pandas as pd
import torch
t = load_data()
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            t = torch.LongTensor([[1, 2], [3, 4], [5, 6], [7, 8]])
        elif test_case_id == 2:
            t = torch.LongTensor(
                [[5, 6, 7], [2, 3, 4], [1, 2, 3], [7, 8, 9], [10, 11, 12]]
            )
        return t

    def generate_ans(data):
        t = data
        result = torch.nn.functional.pad(t, (1, 1, 1, 1))
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
t = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I have a tensor t, for example

1 2
3 4
And I would like to make it

0 0 0 0
0 1 2 0
0 3 4 0
0 0 0 0
I tried stacking with new=torch.tensor([0. 0. 0. 0.]) tensor four times but that did not work.

t = torch.arange(4).reshape(1,2,2).float()
print(t)
new=torch.tensor([[0., 0., 0.,0.]])
print(new)
r = torch.stack([t,new])  # invalid argument 0: Tensors must have same number of dimensions: got 4 and 3
new=torch.tensor([[[0., 0., 0.,0.]]])
print(new)
r = torch.stack([t,new])  # invalid argument 0: Sizes of tensors must match except in dimension 0.
I also tried cat, that did not work either.

A:

<code>
import numpy as np
import pandas as pd
import torch
t = load_data()
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            t = torch.LongTensor([[1, 2], [3, 4], [5, 6], [7, 8]])
        elif test_case_id == 2:
            t = torch.LongTensor(
                [[5, 6, 7], [2, 3, 4], [1, 2, 3], [7, 8, 9], [10, 11, 12]]
            )
        return t

    def generate_ans(data):
        t = data
        result = torch.nn.functional.pad(t, (1, 1, 1, 1))
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
t = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I have a tensor t, for example

1 2
3 4
5 6
7 8
And I would like to make it

-1 -1 -1 -1
-1 1 2 -1
-1 3 4 -1
-1 5 6 -1
-1 7 8 -1
-1 -1 -1 -1
I tried stacking with new=torch.tensor([-1, -1, -1, -1,]) tensor four times but that did not work.

t = torch.arange(8).reshape(1,4,2).float()
print(t)
new=torch.tensor([[-1, -1, -1, -1,]])
print(new)
r = torch.stack([t,new])  # invalid argument 0: Tensors must have same number of dimensions: got 4 and 3
new=torch.tensor([[[-1, -1, -1, -1,]]])
print(new)
r = torch.stack([t,new])  # invalid argument 0: Sizes of tensors must match except in dimension 0.
I also tried cat, that did not work either.

A:

<code>
import numpy as np
import pandas as pd
import torch
t = load_data()
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            t = torch.LongTensor([[1, 2], [3, 4], [5, 6], [7, 8]])
        elif test_case_id == 2:
            t = torch.LongTensor(
                [[5, 6, 7], [2, 3, 4], [1, 2, 3], [7, 8, 9], [10, 11, 12]]
            )
        return t

    def generate_ans(data):
        t = data
        result = torch.ones((t.shape[0] + 2, t.shape[1] + 2)) * -1
        result[1:-1, 1:-1] = t
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
t = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I have batch data and want to dot() to the data. W is trainable parameters. How to dot between batch data and weights?
Here is my code below, how to fix it?

hid_dim = 32
data = torch.randn(10, 2, 3, hid_dim)
data = data.view(10, 2*3, hid_dim)
W = torch.randn(hid_dim) # assume trainable parameters via nn.Parameter
result = torch.bmm(data, W).squeeze() # error, want (N, 6)
result = result.view(10, 2, 3)

A:

corrected, runnable code
<code>
import numpy as np
import pandas as pd
import torch
hid_dim = 32
data = torch.randn(10, 2, 3, hid_dim)
data = data.view(10, 2 * 3, hid_dim)
W = torch.randn(hid_dim)
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

import torch
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            torch.random.manual_seed(42)
            hid_dim = 32
            data = torch.randn(10, 2, 3, hid_dim)
            data = data.view(10, 2 * 3, hid_dim)
            W = torch.randn(hid_dim)
        return data, W

    def generate_ans(data):
        data, W = data
        W = W.unsqueeze(0).unsqueeze(0).expand(*data.size())
        result = torch.sum(data * W, 2)
        result = result.view(10, 2, 3)
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        torch.testing.assert_close(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import pandas as pd
import torch
data, W = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)