cabbage972/GitChameleon-2.0 huggingface dataset:
Library: flask==2.0.0 (Python 3.10, released 2021-05)
Additional dependencies: numpy==1.21.6 werkzeug==2.0.0

Problem:  
Complete the app set-up for the json encoding to perform a fast copy and 
transpose when given a numpy array before flattening and converting 
the result to a list,we are using numpy 1.21.6. Do not run the app in your code.

Starting code:
import flask
import json
import numpy as np
from numpy import fastCopyAndTranspose 
app = flask.Flask('test1')
@app.route('/data')
def data(num_arr):
    return flask.jsonify({'numbers': num_arr})

def eval(app, data_fn, num_arr):
    with app.test_request_context():
        response = data_fn(num_arr)
        return response.get_data(as_text=False)

class MyCustomJSONHandler(json.JSONEncoder):
    def default(self, obj: object) -> object:
        if isinstance(obj, np.ndarray):

Solution:

            res = fastCopyAndTranspose(obj).flatten().tolist()
            return res
        return super().default(obj)

app.json_encoder = MyCustomJSONHandler



Test:

app2 = flask.Flask('test2')
@app2.route('/data2')
def data2(num_arr):
    return flask.jsonify({'numbers': num_arr})
class MyCustomJSONHandler2(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, np.ndarray):
            res = obj.T.copy().flatten().tolist()
            return res
        return super().default(obj)

app2.json_encoder = MyCustomJSONHandler2
assertion_results = eval(app2, data2,np.array([[3, 3, 1,], [2,2,4],[1,1,1]])) == eval(app, data,np.array([[3, 3, 1,], [2,2,4],[1,1,1]]))
assert assertion_results
Library: flask==3.0.0 (Python 3.10, released 2023-09)
Additional dependencies: numpy==1.25.1

Problem:  
Complete the app set-up for the json encoding to perform a fast copy and 
transpose when given a numpy array before flattening and converting 
the result to a list, we are using numpy 1.25.1. Do not run the app in your code.

Starting code:
import flask
import numpy as np
import warnings
from numpy import fastCopyAndTranspose 
warnings.filterwarnings('error')

app = flask.Flask('test1')

@app.route('/data')
def data(num_list):
    return flask.jsonify({'numbers': num_list})

def eval_app(app, data_fn, num_arr):
    with app.test_request_context():
        response = data_fn(num_arr)
        return response.get_data(as_text=True)

class MyCustomJSONHandler(flask.json.provider.DefaultJSONProvider):
    def default(self, obj: object) -> object:
        if isinstance(obj, np.ndarray):

Solution:

            res = obj.T.copy().flatten().tolist()
            return res
        return super().default(obj)

app.json_provider_class = MyCustomJSONHandler
app.json = app.json_provider_class(app)


Test:

app2 = flask.Flask('test2')

@app2.route('/data2')
def data2(num_arr):
    return flask.jsonify({'numbers': num_arr})

class MyCustomJSONHandler2(flask.json.provider.DefaultJSONProvider):
    def default(self, obj):
        if isinstance(obj, np.ndarray):
            res = obj.T.copy().flatten().tolist()
            return res
        return super().default(obj)

app2.json_provider_class = MyCustomJSONHandler2
app2.json = app2.json_provider_class(app2)

assertion_results = eval_app(app2, data2,np.array([[3, 3, 1,], [2,2,4],[1,1,1]])) == eval_app(app, data,np.array([[3, 3, 1,], [2,2,4],[1,1,1]]))
assert assertion_results
Library: flask==2.0.0 (Python 3.10, released 2021-05)
Additional dependencies: numpy==1.21.6 werkzeug==2.0.0

Problem:  
Complete the stack_and_save function, we are using numpy 1.21.6. Do not run the app in your code.

Starting code:
import flask
import werkzeug
import numpy as np

error404 = werkzeug.exceptions.NotFound

def stack_and_save(arr_list: list[np.ndarray],base_path : str,sub_path : str, casting_policy: str, out_dtype: type) -> tuple[str, np.ndarray]:
    # Attempt to join the base path and sub path.
    # If the joined path is outside the base path, raise a 404 error.
    # stack the arrays in arr_list with the casting policy and the out_dtype.
    # if the out_dtype is not compatible with the casting policy, raise a TypeError
    # and out_dtype could be np.float32 or np.float64
    # casting policy could be safe or unsafe
    # Return the joined path and the stacked array to be saved 

Solution:

    joined = flask.safe_join(base_path, sub_path)
    casted_list = []

    for arr in arr_list:
        if not np.can_cast(arr.dtype, out_dtype, casting=casting_policy):
            raise TypeError('Cannot cast array')
        casted_list.append(arr.astype(out_dtype, copy=False))
    
    stacked = np.vstack(casted_list)
    return joined, stacked


Test:


base_path = '/var/www/myapp'
sub_path = '../secret.txt'
a = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
b = np.array([[7, 8, 9], [10, 11, 12]]).astype(np.float64)
arr_list = [a, b]
casting_policy = 'safe'
out_dtype=np.float64
stacked_correct = np.vstack(arr_list).astype(np.float64)
try : 
    joined, stacked = stack_and_save(arr_list,base_path, sub_path,  casting_policy,out_dtype)
except werkzeug.exceptions.NotFound as e:
    assertion_result = True
else:
    assertion_result = False
assert assertion_result

base_path = '/var/www/myapp'
sub_path = 'secret.txt'
joined,stacked = stack_and_save(arr_list,base_path, sub_path,  casting_policy,out_dtype)
assertion_result = joined == '/var/www/myapp/secret.txt' and np.array_equal(stacked,stacked_correct) and stacked.dtype == np.float64
assert assertion_result

stacked_correct = stacked_correct.astype(np.float32)
out_dtype=np.float32
try : 
    joined,stacked = stack_and_save(arr_list,base_path, sub_path,  casting_policy,out_dtype)
except TypeError as e:
    assertion_result = True
else:
    assertion_result = False
assert assertion_result

stacked_correct = stacked_correct.astype(np.float32)
out_dtype=np.float32
casting_policy = 'unsafe'
joined,stacked = stack_and_save(arr_list,base_path, sub_path,  casting_policy,out_dtype)

assertion_result = joined == '/var/www/myapp/secret.txt' and np.array_equal(stacked,stacked_correct) and stacked.dtype == np.float32
assert assertion_result
Library: flask==3.0.0 (Python 3.10, released 2023-09)
Additional dependencies: numpy==1.25.1

Problem:  
Complete the stack_and_save function, we are using numpy 1.25.1. Do not run the app in your code.

Starting code:
import flask
import werkzeug
import numpy as np

error404 = werkzeug.exceptions.NotFound

def stack_and_save(arr_list: list[np.ndarray],base_path : str,sub_path : str, casting_policy: str, out_dtype: type) -> tuple[str, np.ndarray]:
    # Attempt to join the base path and sub path.
    # If the joined path is outside the base path, raise a 404 error.
    # stack the arrays in arr_list with the casting policy and the out_dtype.
    # if the out_dtype is not compatible with the casting policy, raise a TypeError
    # and out_dtype could be np.float32 or np.float64
    # casting policy could be safe or unsafe
    # Return the joined path and the stacked array to be saved 

Solution:

    joined = werkzeug.utils.safe_join(base_path, sub_path)
    if joined is None:
        raise error404
    stacked = np.vstack(arr_list,casting=casting_policy,dtype=out_dtype)
    return joined, stacked


Test:

base_path = '/var/www/myapp'
sub_path = '../secret.txt'


a = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
b = np.array([[7, 8, 9], [10, 11, 12]]).astype(np.float64)
arr_list = [a, b]
casting_policy = 'safe'
out_dtype=np.float64
stacked_correct = np.vstack(arr_list).astype(np.float64)
try : 
    joined, stacked = stack_and_save(arr_list,base_path, sub_path,  casting_policy,out_dtype)
except werkzeug.exceptions.NotFound as e:
    assertion_result = True
else:
    assertion_result = False
assert assertion_result

base_path = '/var/www/myapp'
sub_path = 'secret.txt'
joined,stacked = stack_and_save(arr_list,base_path, sub_path,  casting_policy,out_dtype)
assertion_result = joined == '/var/www/myapp/secret.txt' and np.array_equal(stacked,stacked_correct) and stacked.dtype == np.float64
assert assertion_result

stacked_correct = stacked_correct.astype(np.float32)
out_dtype=np.float32
try : 
    joined,stacked = stack_and_save(arr_list,base_path, sub_path,  casting_policy,out_dtype)
except TypeError as e:
    assertion_result = True
else:
    assertion_result = False
assert assertion_result

stacked_correct = stacked_correct.astype(np.float32)
out_dtype=np.float32
casting_policy = 'unsafe'
joined,stacked = stack_and_save(arr_list,base_path, sub_path,  casting_policy,out_dtype)

assertion_result = joined == '/var/www/myapp/secret.txt' and np.array_equal(stacked,stacked_correct) and stacked.dtype == np.float32
assert assertion_result
Library: flask==3.0.0 (Python 3.10, released 2023-09)
Additional dependencies: scipy==1.11.1

Problem:  
Complete the app set-up so that, when given a batch of matrix,
the json encoding compute the determinants of each matrix, 
before flattening and converting the result to a list, we are using scipy 1.11.1. Do not run the app in your code.

Starting code:
import flask
import numpy as np
from scipy import linalg

app = flask.Flask('test1')
@app.route('/data')
def data(num_list):
    return flask.jsonify({'numbers': num_list})
def eval_app(app, data_fn, num_arr):
    with app.test_request_context():
        response = data_fn(num_arr)
        return response.get_data(as_text=True)

class MyCustomJSONHandler(flask.json.provider.DefaultJSONProvider):
    def default(self, obj):
        if isinstance(obj, np.ndarray) and len(obj.shape)==3 and obj.shape[-1]==obj.shape[-2] : 

Solution:


            res = linalg.det(obj)
            return res.tolist()
        return super().default(obj)

app.json_provider_class = MyCustomJSONHandler
app.json = app.json_provider_class(app) 

Test:

app2 = flask.Flask('test2')

@app2.route('/data2')
def data2(num_arr):
    return flask.jsonify({'numbers': num_arr})

class MyCustomJSONHandler2(flask.json.provider.DefaultJSONProvider):
    def default(self, obj):
        if isinstance(obj, np.ndarray) and len(obj.shape)==3 and obj.shape[-1]==obj.shape[-2] :
            res = np.zeros(obj.shape[0])
            for i in range(obj.shape[0]):
                res[i] = linalg.det(obj[i])
            return res.tolist()
        return super().default(obj)

app2.json_provider_class = MyCustomJSONHandler2
app2.json = app2.json_provider_class(app2)
a = np.random.random((6,3,3))

assertion_results = eval_app(app2, data2,a) == eval_app(app, data,a)
assert assertion_results
Library: flask==2.0.0 (Python 3.10, released 2021-05)
Additional dependencies: scipy==1.8.1 Werkzeug==2.0.0

Problem:  
Complete the app set-up so that, when given a batch of matrix,
the json encoding compute the determinants of each matrix, 
before flattening and converting the result to a list, we are using scipy 1.8.1. Do not run the app in your code.

Starting code:
import flask
import json
import numpy as np
from scipy import linalg

app = flask.Flask('test1')
@app.route('/data')
def data(num_arr):
    return flask.jsonify({'numbers': num_arr})

def eval(app, data_fn, num_arr):
    with app.test_request_context():
        response = data_fn(num_arr)
        return response.get_data(as_text=False)

class MyCustomJSONHandler(json.JSONEncoder):
    def default(self, obj: object) -> object:
        if isinstance(obj, np.ndarray) and len(obj.shape)==3 and obj.shape[-1]==obj.shape[-2] :

Solution:

            res = np.zeros(obj.shape[0])
            for i in range(obj.shape[0]):
                res[i] = linalg.det(obj[i])
            return res.tolist()
        return super().default(obj)

app.json_encoder = MyCustomJSONHandler


Test:

app2 = flask.Flask('test2')
@app2.route('/data2')
def data2(num_arr):
    return flask.jsonify({'numbers': num_arr})
class MyCustomJSONHandler2(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, np.ndarray) and len(obj.shape)==3 and obj.shape[-1]==obj.shape[-2] :
            res = np.zeros(obj.shape[0])
            for i in range(obj.shape[0]):
                res[i] = linalg.det(obj[i])
            return res.tolist()
        return super().default(obj)

app2.json_encoder = MyCustomJSONHandler2
a = np.random.random((6,3,3))

assertion_results = eval(app2, data2,a) == eval(app, data,a)
assert assertion_results
Library: flask==3.0.0 (Python 3.10, released 2023-09)
Additional dependencies: scipy==1.11.1

Problem:  
Complete the app set-up so that, when given a batch (first dim) of values,
compute the harmonic mean along the second dimension (It should handle nan values),
before flattening and converting the result to a list, we are using scipy 1.11.1. Do not run the app in your code.

Starting code:
import flask
import numpy as np
from scipy.stats import hmean

app = flask.Flask('test1')

@app.route('/data')
def data(num_list):
    return flask.jsonify({'numbers': num_list})

def eval_app(app, data_fn, num_arr):
    with app.test_request_context():
        response = data_fn(num_arr)
        return response.get_data(as_text=True)

class MyCustomJSONHandler(flask.json.provider.DefaultJSONProvider):
    def default(self, obj: object) -> object:
        if isinstance(obj, np.ndarray):

Solution:

            res = hmean(obj,axis=1).tolist()
            return res
        return super().default(obj)

app.json_provider_class = MyCustomJSONHandler
app.json = app.json_provider_class(app)


Test:

app2 = flask.Flask('test2')

@app2.route('/data2')
def data2(num_arr):
    return flask.jsonify({'numbers': num_arr})

class MyCustomJSONHandler2(flask.json.provider.DefaultJSONProvider):
    def default(self, obj):
        if isinstance(obj, np.ndarray):
            res = hmean(obj,axis=1).tolist()
            return res
        return super().default(obj)

app2.json_provider_class = MyCustomJSONHandler2
app2.json = app2.json_provider_class(app2)
assertion_results = eval_app(app2, data2,np.array([[3, 3, np.nan,], [np.nan,2,4],[1,2,1]])) == eval_app(app, data,np.array([[3, 3, np.nan,], [np.nan,2,4],[1,2,1]]))
assert assertion_results
Library: flask==2.0.0 (Python 3.10, released 2021-05)
Additional dependencies: scipy==1.8.1 Werkzeug==2.0.0

Problem:  
Complete the app set-up so that, when given a batch (first dim) of values,
compute the harmonic mean along the second dimension (It should handle nan values),
before flattening and converting the result to a list, we are using scipy 1.8.1. Do not run the app in your code.

Starting code:
import flask
import json
import numpy as np
from scipy.stats import hmean

app = flask.Flask('test1')
@app.route('/data')
def data(num_arr):
    return flask.jsonify({'numbers': num_arr})

def eval(app, data_fn, num_arr):
    with app.test_request_context():
        response = data_fn(num_arr)
        return response.get_data(as_text=False)

class MyCustomJSONHandler(json.JSONEncoder):
    def default(self, obj: object) -> object:
        if isinstance(obj, np.ndarray):

Solution:

            res = np.zeros((obj.shape[0],1))
            for i_arr in range(obj.shape[0]):
                if np.isnan(obj[i_arr]).any():
                    res[i_arr] = np.nan
                else:
                    res[i_arr]  = hmean(obj[i_arr])
            res = res.flatten().tolist()
            return res
        return super().default(obj)

app.json_encoder = MyCustomJSONHandler


Test:

app2 = flask.Flask('test2')
@app2.route('/data2')
def data2(num_arr):
    return flask.jsonify({'numbers': num_arr})
class MyCustomJSONHandler2(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, np.ndarray):
            res = np.zeros((obj.shape[0],1))
            for i_arr in range(obj.shape[0]):
                if np.isnan(obj[i_arr]).any():
                    res[i_arr] = np.nan
                else:
                    res[i_arr]  = hmean(obj[i_arr])
            res = res.flatten().tolist()
            return res
        return super().default(obj)

app2.json_encoder = MyCustomJSONHandler2
assertion_results = eval(app2, data2,np.array([[3, 3, np.nan,], [np.nan,2,4],[1,2,1]])) == eval(app, data,np.array([[3, 3, np.nan,], [np.nan,2,4],[1,2,1]]))
assert assertion_results
Library: flask==3.0.0 (Python 3.10, released 2023-09)
Additional dependencies: scipy==1.11.1

Problem:  
Complete the save_exponential function, we are using scipy 1.11.1. Do not run the app in your code.

Starting code:
import flask
import werkzeug
from scipy import linalg
import numpy as np

error404 = werkzeug.exceptions.NotFound

def save_exponential(A: np.ndarray, base_path: str, sub_path: str) -> tuple[str, np.ndarray]:
    # Attempt to join the base path and sub path.
    # If the joined path is outside the base path, raise a 404 error.
    # compute the exponential of the batched matrices (m, m) in A (n,m,m)
    # return the save_path and the exponential of the matrices
    

Solution:

    joined = werkzeug.utils.safe_join(base_path, sub_path)
    if joined is None:
        raise error404
    output = linalg.expm(A)
    return joined, output


Test:

base_path = '/var/www/myapp'
sub_path = '../secret.txt'
import numpy as np

a = np.random.random((4,3,3))
expected = np.zeros(a.shape)
for i in range(expected.shape[0]):
    expected[i] = linalg.expm(a[i])

try : 
    joined, results = save_exponential(a,base_path, sub_path)
except werkzeug.exceptions.NotFound as e:
    assertion_result = True
else:
    assertion_result = False
assert assertion_result

base_path = '/var/www/myapp'
sub_path = 'secret.txt'

joined, results = save_exponential(a,base_path, sub_path)
assertion_result = joined == '/var/www/myapp/secret.txt' and np.allclose(results, expected)
assert assertion_result
Library: flask==2.0.0 (Python 3.10, released 2021-05)
Additional dependencies: scipy==1.8.1 Werkzeug==2.0.0

Problem:  
Complete the save_exponential function, we are using scipy 1.8.1. Do not run the app in your code.

Starting code:
import flask
import werkzeug
from scipy import linalg
import numpy as np

error404 = werkzeug.exceptions.NotFound

def save_exponential(A: np.ndarray, base_path: str, sub_path: str) -> tuple[str, np.ndarray]:
    # Attempt to join the base path and sub path.
    # If the joined path is outside the base path, raise a 404 error.
    # compute the exponential of the batched matrices (m, m) in A (n,m,m)
    # return the save_path and the exponential of the matrices
    

Solution:

    joined = flask.safe_join(base_path, sub_path)
    output = np.zeros(A.shape)
    for i in range(A.shape[0]):
        output[i] = linalg.expm(A[i])
    return joined, output


Test:

base_path = '/var/www/myapp'
sub_path = '../secret.txt'
import numpy as np

a = np.random.random((4,3,3))
expected = np.zeros(a.shape)
for i in range(expected.shape[0]):
    expected[i] = linalg.expm(a[i])

try : 
    joined, results = save_exponential(a,base_path, sub_path)
except werkzeug.exceptions.NotFound as e:
    assertion_result = True
else:
    assertion_result = False
assert assertion_result

base_path = '/var/www/myapp'
sub_path = 'secret.txt'

joined, results = save_exponential(a,base_path, sub_path)
assertion_result = joined == '/var/www/myapp/secret.txt' and np.allclose(results, expected)
assert assertion_result
Library: sympy==1.9 (Python 3.9, released 2021-06)
Additional dependencies: scipy==1.8.0

Problem: Create a function named custom_generateRandomSampleDice that accepts two parameters: a die object and an integer X. The function should generate and return a list containing X random outcomes sampled from the given die

Starting code:
from typing import List
from sympy.stats import Die, sample
import sympy.stats.rv 

def custom_generateRandomSampleDice(dice: sympy.stats.rv.RandomSymbol, X: int) -> List[int]:
    return 

Solution:
[sample(dice) for i in range(X)]

Test:

dice = Die('X', 6)
import warnings
from sympy.utilities.exceptions import SymPyDeprecationWarning

def test_custom_generateRandomSampleDice():
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter("always", SymPyDeprecationWarning)  # Capture all warnings
        output = custom_generateRandomSampleDice(dice, 3)
        assert isinstance(output, list), "Test Failed: Output is not a list!"
        assert len(output) == 3, "Test Failed: Output length does not match expected!"
        assert not any(isinstance(warn.message, SymPyDeprecationWarning) for warn in w), "Test Failed: Deprecation warning was triggered!"

test_custom_generateRandomSampleDice()
Library: sympy==1.9 (Python 3.9, released 2021-06)

Problem: Create a custom_computeDFT function that accepts an integer parameter n. This function should  compute the Discrete Fourier Transform (DFT) matrix of size  n times n and show it explicitly.

Starting code:
import sympy
from sympy.matrices.expressions.fourier import DFT

def custom_computeDFT(n: int) -> sympy.ImmutableDenseMatrix:
    return 

Solution:
DFT(n).as_explicit()

Test:

import warnings
from sympy.utilities.exceptions import SymPyDeprecationWarning
from sympy import Matrix, I, Rational

def test_custom_computeDFT():
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter("always", SymPyDeprecationWarning)  # Capture all warnings
        output = custom_computeDFT(4)
        expect = Matrix([
            [Rational(1,2), Rational(1,2), Rational(1,2), Rational(1,2)],
            [Rational(1,2), -I/2, -Rational(1,2), I/2],
            [Rational(1,2), -Rational(1,2), Rational(1,2), -Rational(1,2)],
            [Rational(1,2), I/2, -Rational(1,2), -I/2]
        ])

        assert output == expect
        assert not any(isinstance(warn.message, SymPyDeprecationWarning) for warn in w), "Test Failed: Deprecation warning was triggered!"

test_custom_computeDFT()
Library: sympy==1.9 (Python 3.9, released 2021-06)

Problem: Create a function named custom_laplace_transform that takes two parameters, t and z. This function should compute the Laplace transform of the 2×2 identity matrix (eye(2)) with respect to the variable t and the transform variable z using the laplace_transform function from Sympy. The function should return a single tuple where the first element is the transformed matrix and the second element is the combined convergence condition. 

Starting code:
from typing import Tuple
from sympy import laplace_transform, symbols, eye
import sympy

def custom_laplace_transform(t: sympy.Symbol, z: sympy.Symbol) -> Tuple[sympy.Matrix, sympy.Expr, bool]:
    return 

Solution:
laplace_transform(eye(2), t, z, legacy_matrix=False)

Test:
t, z = symbols('t z')
from sympy import Matrix
output = custom_laplace_transform(t,z)
expected = (Matrix([
    [1/z,   0],
    [  0, 1/z]
]), 0, True)
assert output == expected
Library: sympy==1.11 (Python 3.9, released 2022-03)

Problem: Create a function named custom_trace that takes a single parameter n, which represents a numerical input. The function should instantiate a trace object using the provided number and return the resulting trace object.

Starting code:
import sympy.physics.quantum
import sympy
def custom_trace(n: int) -> sympy.physics.quantum.trace.Tr:
    return 

Solution:
sympy.physics.quantum.trace.Tr(n)

Test:
from sympy.physics.quantum.trace import Tr
expect = 2
assert custom_trace(2) == expect
Library: sympy==1.11 (Python 3.9, released 2022-03)

Problem: Create a custom_preorder_traversal function that instantiate and return the preorder_traversal object with existing expression.

Starting code:
import sympy

def custom_preorder_traversal(expr: sympy.Expr) -> sympy.core.basic.preorder_traversal:
    return 

Solution:
sympy.preorder_traversal(expr)

Test:
expr = sympy.Add(1, sympy.Mul(2, 3))
expect = [7]
assert list(custom_preorder_traversal(expr)) == expect