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Grounded-Segment-Anything / transformers_4_35_0 /models /blenderbot /modeling_blenderbot.py

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	# coding=utf-8
	# Copyright 2021 The Facebook, Inc. and The HuggingFace Inc. team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	""" PyTorch Blenderbot model."""


	import copy
	import math
	import os
	import warnings
	from typing import List, Optional, Tuple, Union

	import torch
	import torch.utils.checkpoint
	from torch import nn
	from torch.nn import CrossEntropyLoss

	from ...activations import ACT2FN
	from ...modeling_outputs import (
	BaseModelOutput,
	BaseModelOutputWithPastAndCrossAttentions,
	CausalLMOutputWithCrossAttentions,
	Seq2SeqLMOutput,
	Seq2SeqModelOutput,
	)
	from ...modeling_utils import PreTrainedModel
	from ...utils import (
	add_end_docstrings,
	add_start_docstrings,
	add_start_docstrings_to_model_forward,
	logging,
	replace_return_docstrings,
	)
	from ..blenderbot_small import BlenderbotSmallForConditionalGeneration, BlenderbotSmallModel
	from .configuration_blenderbot import BlenderbotConfig


	logger = logging.get_logger(__name__)

	_CONFIG_FOR_DOC = "BlenderbotConfig"
	_CHECKPOINT_FOR_DOC = "facebook/blenderbot-400M-distill"


	BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST = [
	"facebook/blenderbot-3B",
	# See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
	]


	# Copied from transformers.models.bart.modeling_bart.shift_tokens_right
	def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
	"""
	Shift input ids one token to the right.
	"""
	shifted_input_ids = input_ids.new_zeros(input_ids.shape)
	shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
	shifted_input_ids[:, 0] = decoder_start_token_id

	if pad_token_id is None:
	raise ValueError("self.model.config.pad_token_id has to be defined.")
	# replace possible -100 values in labels by `pad_token_id`
	shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)

	return shifted_input_ids


	# Copied from transformers.models.bart.modeling_bart._make_causal_mask
	def _make_causal_mask(
	input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
	):
	"""
	Make causal mask used for bi-directional self-attention.
	"""
	bsz, tgt_len = input_ids_shape
	mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
	mask_cond = torch.arange(mask.size(-1), device=device)
	mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
	mask = mask.to(dtype)

	if past_key_values_length > 0:
	mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
	return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)


	# Copied from transformers.models.bart.modeling_bart._expand_mask
	def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
	"""
	Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
	"""
	bsz, src_len = mask.size()
	tgt_len = tgt_len if tgt_len is not None else src_len

	expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)

	inverted_mask = 1.0 - expanded_mask

	return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)


	class BlenderbotLearnedPositionalEmbedding(nn.Embedding):
	"""
	This module learns positional embeddings up to a fixed maximum size.
	"""

	def __init__(self, num_embeddings: int, embedding_dim: int):
	super().__init__(num_embeddings, embedding_dim)

	def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0):
	"""`input_ids_shape` is expected to be [bsz x seqlen]."""
	bsz, seq_len = input_ids_shape[:2]
	positions = torch.arange(
	past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
	)
	return super().forward(positions)


	# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->Blenderbot
	class BlenderbotAttention(nn.Module):
	"""Multi-headed attention from 'Attention Is All You Need' paper"""

	def __init__(
	self,
	embed_dim: int,
	num_heads: int,
	dropout: float = 0.0,
	is_decoder: bool = False,
	bias: bool = True,
	):
	super().__init__()
	self.embed_dim = embed_dim
	self.num_heads = num_heads
	self.dropout = dropout
	self.head_dim = embed_dim // num_heads

	if (self.head_dim * num_heads) != self.embed_dim:
	raise ValueError(
	f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
	f" and `num_heads`: {num_heads})."
	)
	self.scaling = self.head_dim**-0.5
	self.is_decoder = is_decoder

	self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
	self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
	self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
	self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)

	def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
	return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

	def forward(
	self,
	hidden_states: torch.Tensor,
	key_value_states: Optional[torch.Tensor] = None,
	past_key_value: Optional[Tuple[torch.Tensor]] = None,
	attention_mask: Optional[torch.Tensor] = None,
	layer_head_mask: Optional[torch.Tensor] = None,
	output_attentions: bool = False,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	"""Input shape: Batch x Time x Channel"""

	# if key_value_states are provided this layer is used as a cross-attention layer
	# for the decoder
	is_cross_attention = key_value_states is not None

	bsz, tgt_len, _ = hidden_states.size()

	# get query proj
	query_states = self.q_proj(hidden_states) * self.scaling
	# get key, value proj
	# `past_key_value[0].shape[2] == key_value_states.shape[1]`
	# is checking that the `sequence_length` of the `past_key_value` is the same as
	# the provided `key_value_states` to support prefix tuning
	if (
	is_cross_attention
	and past_key_value is not None
	and past_key_value[0].shape[2] == key_value_states.shape[1]
	):
	# reuse k,v, cross_attentions
	key_states = past_key_value[0]
	value_states = past_key_value[1]
	elif is_cross_attention:
	# cross_attentions
	key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
	value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
	elif past_key_value is not None:
	# reuse k, v, self_attention
	key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
	value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
	key_states = torch.cat([past_key_value[0], key_states], dim=2)
	value_states = torch.cat([past_key_value[1], value_states], dim=2)
	else:
	# self_attention
	key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
	value_states = self._shape(self.v_proj(hidden_states), -1, bsz)

	if self.is_decoder:
	# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
	# Further calls to cross_attention layer can then reuse all cross-attention
	# key/value_states (first "if" case)
	# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
	# all previous decoder key/value_states. Further calls to uni-directional self-attention
	# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
	# if encoder bi-directional self-attention `past_key_value` is always `None`
	past_key_value = (key_states, value_states)

	proj_shape = (bsz * self.num_heads, -1, self.head_dim)
	query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
	key_states = key_states.reshape(*proj_shape)
	value_states = value_states.reshape(*proj_shape)

	src_len = key_states.size(1)
	attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))

	if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
	raise ValueError(
	f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
	f" {attn_weights.size()}"
	)

	if attention_mask is not None:
	if attention_mask.size() != (bsz, 1, tgt_len, src_len):
	raise ValueError(
	f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
	)
	attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
	attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)

	attn_weights = nn.functional.softmax(attn_weights, dim=-1)

	if layer_head_mask is not None:
	if layer_head_mask.size() != (self.num_heads,):
	raise ValueError(
	f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
	f" {layer_head_mask.size()}"
	)
	attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
	attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)

	if output_attentions:
	# this operation is a bit awkward, but it's required to
	# make sure that attn_weights keeps its gradient.
	# In order to do so, attn_weights have to be reshaped
	# twice and have to be reused in the following
	attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
	attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
	else:
	attn_weights_reshaped = None

	attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)

	attn_output = torch.bmm(attn_probs, value_states)

	if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
	raise ValueError(
	f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is"
	f" {attn_output.size()}"
	)

	attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
	attn_output = attn_output.transpose(1, 2)

	# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
	# partitioned across GPUs when using tensor-parallelism.
	attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)

	attn_output = self.out_proj(attn_output)

	return attn_output, attn_weights_reshaped, past_key_value


	# Copied from transformers.models.mbart.modeling_mbart.MBartEncoderLayer with MBart->Blenderbot
	class BlenderbotEncoderLayer(nn.Module):
	def __init__(self, config: BlenderbotConfig):
	super().__init__()
	self.embed_dim = config.d_model
	self.self_attn = BlenderbotAttention(
	embed_dim=self.embed_dim,
	num_heads=config.encoder_attention_heads,
	dropout=config.attention_dropout,
	)
	self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
	self.dropout = config.dropout
	self.activation_fn = ACT2FN[config.activation_function]
	self.activation_dropout = config.activation_dropout
	self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
	self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
	self.final_layer_norm = nn.LayerNorm(self.embed_dim)

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: torch.Tensor,
	layer_head_mask: torch.Tensor,
	output_attentions: bool = False,
	) -> torch.Tensor:
	"""
	Args:
	hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
	attention_mask (`torch.FloatTensor`): attention mask of size
	`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
	layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
	`(encoder_attention_heads,)`.
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under
	returned tensors for more detail.
	"""
	residual = hidden_states
	hidden_states = self.self_attn_layer_norm(hidden_states)
	hidden_states, attn_weights, _ = self.self_attn(
	hidden_states=hidden_states,
	attention_mask=attention_mask,
	layer_head_mask=layer_head_mask,
	output_attentions=output_attentions,
	)
	hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
	hidden_states = residual + hidden_states

	residual = hidden_states
	hidden_states = self.final_layer_norm(hidden_states)
	hidden_states = self.activation_fn(self.fc1(hidden_states))
	hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
	hidden_states = self.fc2(hidden_states)
	hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
	hidden_states = residual + hidden_states

	if hidden_states.dtype == torch.float16 and (
	torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
	):
	clamp_value = torch.finfo(hidden_states.dtype).max - 1000
	hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

	outputs = (hidden_states,)

	if output_attentions:
	outputs += (attn_weights,)

	return outputs


	# Copied from transformers.models.mbart.modeling_mbart.MBartDecoderLayer with MBart->Blenderbot
	class BlenderbotDecoderLayer(nn.Module):
	def __init__(self, config: BlenderbotConfig):
	super().__init__()
	self.embed_dim = config.d_model

	self.self_attn = BlenderbotAttention(
	embed_dim=self.embed_dim,
	num_heads=config.decoder_attention_heads,
	dropout=config.attention_dropout,
	is_decoder=True,
	)
	self.dropout = config.dropout
	self.activation_fn = ACT2FN[config.activation_function]
	self.activation_dropout = config.activation_dropout

	self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
	self.encoder_attn = BlenderbotAttention(
	self.embed_dim,
	config.decoder_attention_heads,
	dropout=config.attention_dropout,
	is_decoder=True,
	)
	self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
	self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
	self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
	self.final_layer_norm = nn.LayerNorm(self.embed_dim)

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.Tensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: Optional[torch.Tensor] = None,
	layer_head_mask: Optional[torch.Tensor] = None,
	cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
	past_key_value: Optional[Tuple[torch.Tensor]] = None,
	output_attentions: Optional[bool] = False,
	use_cache: Optional[bool] = True,
	) -> torch.Tensor:
	"""
	Args:
	hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
	attention_mask (`torch.FloatTensor`): attention mask of size
	`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
	encoder_hidden_states (`torch.FloatTensor`):
	cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
	encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
	`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
	layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
	`(encoder_attention_heads,)`.
	cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
	size `(decoder_attention_heads,)`.
	past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under
	returned tensors for more detail.
	"""
	residual = hidden_states
	hidden_states = self.self_attn_layer_norm(hidden_states)

	# Self Attention
	# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
	self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
	# add present self-attn cache to positions 1,2 of present_key_value tuple
	hidden_states, self_attn_weights, present_key_value = self.self_attn(
	hidden_states=hidden_states,
	past_key_value=self_attn_past_key_value,
	attention_mask=attention_mask,
	layer_head_mask=layer_head_mask,
	output_attentions=output_attentions,
	)
	hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
	hidden_states = residual + hidden_states

	# Cross-Attention Block
	cross_attn_present_key_value = None
	cross_attn_weights = None
	if encoder_hidden_states is not None:
	residual = hidden_states
	hidden_states = self.encoder_attn_layer_norm(hidden_states)

	# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
	cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
	hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
	hidden_states=hidden_states,
	key_value_states=encoder_hidden_states,
	attention_mask=encoder_attention_mask,
	layer_head_mask=cross_attn_layer_head_mask,
	past_key_value=cross_attn_past_key_value,
	output_attentions=output_attentions,
	)
	hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
	hidden_states = residual + hidden_states

	# add cross-attn to positions 3,4 of present_key_value tuple
	present_key_value = present_key_value + cross_attn_present_key_value

	# Fully Connected
	residual = hidden_states
	hidden_states = self.final_layer_norm(hidden_states)
	hidden_states = self.activation_fn(self.fc1(hidden_states))
	hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
	hidden_states = self.fc2(hidden_states)
	hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
	hidden_states = residual + hidden_states

	outputs = (hidden_states,)

	if output_attentions:
	outputs += (self_attn_weights, cross_attn_weights)

	if use_cache:
	outputs += (present_key_value,)

	return outputs


	class BlenderbotPreTrainedModel(PreTrainedModel):
	config_class = BlenderbotConfig
	base_model_prefix = "model"
	supports_gradient_checkpointing = True

	def _init_weights(self, module):
	std = self.config.init_std
	if isinstance(module, nn.Linear):
	module.weight.data.normal_(mean=0.0, std=std)
	if module.bias is not None:
	module.bias.data.zero_()
	elif isinstance(module, nn.Embedding):
	module.weight.data.normal_(mean=0.0, std=std)
	if module.padding_idx is not None:
	module.weight.data[module.padding_idx].zero_()

	def _set_gradient_checkpointing(self, module, value=False):
	if isinstance(module, (BlenderbotDecoder, BlenderbotEncoder)):
	module.gradient_checkpointing = value

	@property
	def dummy_inputs(self):
	pad_token = self.config.pad_token_id
	input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
	dummy_inputs = {
	"attention_mask": input_ids.ne(pad_token),
	"input_ids": input_ids,
	"decoder_input_ids": input_ids,
	}
	return dummy_inputs


	BLENDERBOT_START_DOCSTRING = r"""
	This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
	library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
	etc.)

	This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
	Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
	and behavior.

	Parameters:
	config ([`BlenderbotConfig`]):
	Model configuration class with all the parameters of the model. Initializing with a config file does not
	load the weights associated with the model, only the configuration. Check out the
	[`~PreTrainedModel.from_pretrained`] method to load the model weights.
	"""

	BLENDERBOT_GENERATION_EXAMPLE = r"""
	Conversation example:

	```python
	>>> from transformers import AutoTokenizer, BlenderbotForConditionalGeneration

	>>> mname = "facebook/blenderbot-400M-distill"
	>>> model = BlenderbotForConditionalGeneration.from_pretrained(mname)
	>>> tokenizer = AutoTokenizer.from_pretrained(mname)
	>>> UTTERANCE = "My friends are cool but they eat too many carbs."
	>>> print("Human: ", UTTERANCE)
	Human: My friends are cool but they eat too many carbs.

	>>> inputs = tokenizer([UTTERANCE], return_tensors="pt")
	>>> reply_ids = model.generate(**inputs)
	>>> print("Bot: ", tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0])
	Bot: That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?

	>>> REPLY = "I'm not sure"
	>>> print("Human: ", REPLY)
	Human: I'm not sure

	>>> NEXT_UTTERANCE = (
	... "My friends are cool but they eat too many carbs.</s> <s>That's unfortunate. "
	... "Are they trying to lose weight or are they just trying to be healthier?</s> "
	... "<s> I'm not sure."
	... )
	>>> inputs = tokenizer([NEXT_UTTERANCE], return_tensors="pt")
	>>> next_reply_ids = model.generate(**inputs)
	>>> print("Bot: ", tokenizer.batch_decode(next_reply_ids, skip_special_tokens=True)[0])
	Bot: I see. Well, it's good that they're trying to change their eating habits.
	```
	"""

	BLENDERBOT_INPUTS_DOCSTRING = r"""
	Args:
	input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
	it.

	Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
	[`PreTrainedTokenizer.__call__`] for details.

	[What are input IDs?](../glossary#input-ids)
	attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional):
	Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	[What are attention masks?](../glossary#attention-mask)
	decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, optional):
	Indices of decoder input sequence tokens in the vocabulary.

	Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
	[`PreTrainedTokenizer.__call__`] for details.

	[What are decoder input IDs?](../glossary#decoder-input-ids)

	Blenderbot uses the `bos_token_id` as the starting token for `decoder_input_ids` generation. If
	`past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
	`past_key_values`).
	decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, optional):
	Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
	be used by default.
	head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, optional):
	Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:

	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.

	decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, optional):
	Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:

	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.

	cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, optional):
	Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
	1]`:

	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.

	encoder_outputs (`tuple(tuple(torch.FloatTensor)`, optional):
	Tuple consists of (`last_hidden_state`, optional: `hidden_states`, optional: `attentions`)
	`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, optional) is a sequence of
	hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
	past_key_values (`tuple(tuple(torch.FloatTensor))`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`):
	Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
	`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
	`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

	Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
	blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

	If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
	don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
	`decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape
	`(batch_size, sequence_length, hidden_size)`, optional): Optionally, instead of passing `input_ids` you
	can choose to directly pass an embedded representation. This is useful if you want more control over how to
	convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
	decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, optional):
	Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
	representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
	input (see `past_key_values`). This is useful if you want more control over how to convert
	`decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.

	If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
	of `inputs_embeds`.
	use_cache (`bool`, optional):
	If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
	`past_key_values`).
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
	tensors for more detail.
	output_hidden_states (`bool`, optional):
	Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
	more detail.
	return_dict (`bool`, optional):
	Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
	"""


	class BlenderbotEncoder(BlenderbotPreTrainedModel):
	"""
	Transformer encoder consisting of config.encoder_layers self attention layers. Each layer is a
	[`BlenderbotEncoderLayer`].

	Args:
	config: BlenderbotConfig
	embed_tokens (nn.Embedding): output embedding
	"""

	def __init__(self, config: BlenderbotConfig, embed_tokens: Optional[nn.Embedding] = None):
	super().__init__(config)

	self.dropout = config.dropout
	self.layerdrop = config.encoder_layerdrop

	embed_dim = config.d_model
	self.padding_idx = config.pad_token_id
	self.max_source_positions = config.max_position_embeddings
	self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0

	if embed_tokens is not None:
	self.embed_tokens = embed_tokens
	else:
	self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx)

	self.embed_positions = BlenderbotLearnedPositionalEmbedding(
	config.max_position_embeddings,
	embed_dim,
	)
	self.layers = nn.ModuleList([BlenderbotEncoderLayer(config) for _ in range(config.encoder_layers)])
	self.layer_norm = nn.LayerNorm(config.d_model)

	self.gradient_checkpointing = False
	# Initialize weights and apply final processing
	self.post_init()

	def forward(
	self,
	input_ids=None,
	attention_mask=None,
	head_mask=None,
	inputs_embeds=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):
	r"""
	Args:
	input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
	provide it.

	Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
	[`PreTrainedTokenizer.__call__`] for details.

	[What are input IDs?](../glossary#input-ids)
	attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional):
	Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	[What are attention masks?](../glossary#attention-mask)
	head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, optional):
	Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.

	inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional):
	Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
	This is useful if you want more control over how to convert `input_ids` indices into associated vectors
	than the model's internal embedding lookup matrix.
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under
	returned tensors for more detail.
	output_hidden_states (`bool`, optional):
	Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
	for more detail.
	return_dict (`bool`, optional):
	Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
	"""
	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	# retrieve input_ids and inputs_embeds
	if input_ids is not None and inputs_embeds is not None:
	raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
	elif input_ids is not None:
	self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
	input_shape = input_ids.size()
	input_ids = input_ids.view(-1, input_shape[-1])
	elif inputs_embeds is not None:
	input_shape = inputs_embeds.size()[:-1]
	else:
	raise ValueError("You have to specify either input_ids or inputs_embeds")

	if inputs_embeds is None:
	inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale

	embed_pos = self.embed_positions(input_shape)

	hidden_states = inputs_embeds + embed_pos
	hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)

	# expand attention_mask
	if attention_mask is not None:
	# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
	attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)

	encoder_states = () if output_hidden_states else None
	all_attentions = () if output_attentions else None

	# check if head_mask has a correct number of layers specified if desired
	if head_mask is not None:
	if head_mask.size()[0] != len(self.layers):
	raise ValueError(
	f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
	f" {head_mask.size()[0]}."
	)
	for idx, encoder_layer in enumerate(self.layers):
	if output_hidden_states:
	encoder_states = encoder_states + (hidden_states,)
	# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
	to_drop = False
	if self.training:
	dropout_probability = torch.rand([])
	if dropout_probability < self.layerdrop: # skip the layer
	to_drop = True

	if to_drop:
	layer_outputs = (None, None)
	else:
	if self.gradient_checkpointing and self.training:

	def create_custom_forward(module):
	def custom_forward(*inputs):
	return module(*inputs, output_attentions)

	return custom_forward

	layer_outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(encoder_layer),
	hidden_states,
	attention_mask,
	(head_mask[idx] if head_mask is not None else None),
	)
	else:
	layer_outputs = encoder_layer(
	hidden_states,
	attention_mask,
	layer_head_mask=(head_mask[idx] if head_mask is not None else None),
	output_attentions=output_attentions,
	)

	hidden_states = layer_outputs[0]

	if output_attentions:
	all_attentions = all_attentions + (layer_outputs[1],)

	# add final layer norm
	hidden_states = self.layer_norm(hidden_states)

	if output_hidden_states:
	encoder_states = encoder_states + (hidden_states,)

	if not return_dict:
	return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
	return BaseModelOutput(
	last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
	)


	class BlenderbotDecoder(BlenderbotPreTrainedModel):
	"""
	Transformer decoder consisting of config.decoder_layers layers. Each layer is a [`BlenderbotDecoderLayer`]

	Args:
	config: BlenderbotConfig
	embed_tokens (nn.Embedding): output embedding
	"""

	def __init__(self, config: BlenderbotConfig, embed_tokens: Optional[nn.Embedding] = None):
	super().__init__(config)
	self.dropout = config.dropout
	self.layerdrop = config.decoder_layerdrop
	self.padding_idx = config.pad_token_id
	self.max_target_positions = config.max_position_embeddings
	self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0

	if embed_tokens is not None:
	self.embed_tokens = embed_tokens
	else:
	self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx)

	self.embed_positions = BlenderbotLearnedPositionalEmbedding(
	config.max_position_embeddings,
	config.d_model,
	)
	self.layers = nn.ModuleList([BlenderbotDecoderLayer(config) for _ in range(config.decoder_layers)])
	self.layer_norm = nn.LayerNorm(config.d_model)

	self.gradient_checkpointing = False
	# Initialize weights and apply final processing
	self.post_init()

	def get_input_embeddings(self):
	return self.embed_tokens

	def set_input_embeddings(self, value):
	self.embed_tokens = value

	# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
	def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
	# create causal mask
	# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
	combined_attention_mask = None
	if input_shape[-1] > 1:
	combined_attention_mask = _make_causal_mask(
	input_shape,
	inputs_embeds.dtype,
	device=inputs_embeds.device,
	past_key_values_length=past_key_values_length,
	)

	if attention_mask is not None:
	# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
	expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
	inputs_embeds.device
	)
	combined_attention_mask = (
	expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
	)

	return combined_attention_mask

	def forward(
	self,
	input_ids=None,
	attention_mask=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	head_mask=None,
	cross_attn_head_mask=None,
	past_key_values=None,
	inputs_embeds=None,
	use_cache=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):
	r"""
	Args:
	input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
	provide it.

	Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
	[`PreTrainedTokenizer.__call__`] for details.

	[What are input IDs?](../glossary#input-ids)
	attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional):
	Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	[What are attention masks?](../glossary#attention-mask)
	encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, optional):
	Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
	of the decoder.
	encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, optional):
	Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
	selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	[What are attention masks?](../glossary#attention-mask)
	head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, optional):
	Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0,
	1]`:

	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.

	cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, optional):
	Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing
	cross-attention on hidden heads. Mask values selected in `[0, 1]`:

	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.

	past_key_values (`tuple(tuple(torch.FloatTensor))`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`):
	Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
	shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
	shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

	Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
	cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

	If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
	that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
	all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of
	shape `(batch_size, sequence_length, hidden_size)`, optional): Optionally, instead of passing
	`input_ids` you can choose to directly pass an embedded representation. This is useful if you want more
	control over how to convert `input_ids` indices into associated vectors than the model's internal
	embedding lookup matrix.
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under
	returned tensors for more detail.
	output_hidden_states (`bool`, optional):
	Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
	for more detail.
	return_dict (`bool`, optional):
	Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
	"""
	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	use_cache = use_cache if use_cache is not None else self.config.use_cache
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	# retrieve input_ids and inputs_embeds
	if input_ids is not None and inputs_embeds is not None:
	raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
	elif input_ids is not None:
	input_shape = input_ids.size()
	input_ids = input_ids.view(-1, input_shape[-1])
	elif inputs_embeds is not None:
	input_shape = inputs_embeds.size()[:-1]
	else:
	raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")

	# past_key_values_length
	past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0

	if inputs_embeds is None:
	inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale

	attention_mask = self._prepare_decoder_attention_mask(
	attention_mask, input_shape, inputs_embeds, past_key_values_length
	)

	# expand encoder attention mask
	if encoder_hidden_states is not None and encoder_attention_mask is not None:
	# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
	encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])

	# embed positions
	positions = self.embed_positions(input_shape, past_key_values_length)

	hidden_states = inputs_embeds + positions

	hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)

	if self.gradient_checkpointing and self.training:
	if use_cache:
	logger.warning(
	"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
	)
	use_cache = False
	# decoder layers
	all_hidden_states = () if output_hidden_states else None
	all_self_attns = () if output_attentions else None
	all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
	next_decoder_cache = () if use_cache else None

	# check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
	for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
	if attn_mask is not None:
	if attn_mask.size()[0] != len(self.layers):
	raise ValueError(
	f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
	f" {head_mask.size()[0]}."
	)
	for idx, decoder_layer in enumerate(self.layers):
	# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
	if output_hidden_states:
	all_hidden_states += (hidden_states,)
	if self.training:
	dropout_probability = torch.rand([])
	if dropout_probability < self.layerdrop:
	continue

	past_key_value = past_key_values[idx] if past_key_values is not None else None

	if self.gradient_checkpointing and self.training:

	def create_custom_forward(module):
	def custom_forward(*inputs):
	# None for past_key_value
	return module(*inputs, output_attentions, use_cache)

	return custom_forward

	layer_outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(decoder_layer),
	hidden_states,
	attention_mask,
	encoder_hidden_states,
	encoder_attention_mask,
	head_mask[idx] if head_mask is not None else None,
	cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
	None,
	)
	else:
	layer_outputs = decoder_layer(
	hidden_states,
	attention_mask=attention_mask,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	layer_head_mask=(head_mask[idx] if head_mask is not None else None),
	cross_attn_layer_head_mask=(
	cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
	),
	past_key_value=past_key_value,
	output_attentions=output_attentions,
	use_cache=use_cache,
	)
	hidden_states = layer_outputs[0]

	if use_cache:
	next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)

	if output_attentions:
	all_self_attns += (layer_outputs[1],)

	if encoder_hidden_states is not None:
	all_cross_attentions += (layer_outputs[2],)

	# add final layer norm
	hidden_states = self.layer_norm(hidden_states)

	# add hidden states from the last decoder layer
	if output_hidden_states:
	all_hidden_states += (hidden_states,)

	next_cache = next_decoder_cache if use_cache else None
	if not return_dict:
	return tuple(
	v
	for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
	if v is not None
	)
	return BaseModelOutputWithPastAndCrossAttentions(
	last_hidden_state=hidden_states,
	past_key_values=next_cache,
	hidden_states=all_hidden_states,
	attentions=all_self_attns,
	cross_attentions=all_cross_attentions,
	)


	@add_start_docstrings(
	"The bare Blenderbot Model outputting raw hidden-states without any specific head on top.",
	BLENDERBOT_START_DOCSTRING,
	)
	class BlenderbotModel(BlenderbotPreTrainedModel):
	_tied_weights_keys = ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight"]

	def __init__(self, config: BlenderbotConfig):
	super().__init__(config)

	padding_idx, vocab_size = config.pad_token_id, config.vocab_size
	self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)

	self.encoder = BlenderbotEncoder(config, self.shared)
	self.decoder = BlenderbotDecoder(config, self.shared)

	# Initialize weights and apply final processing
	self.post_init()

	@classmethod
	def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], model_args, *kwargs):
	if pretrained_model_name_or_path == "facebook/blenderbot-90M":
	warnings.warn(
	"The checkpoint `facebook/blenderbot-90M` is deprecated. In the future, please use the identical"
	" checkpoint `facebook/small_blenderbot-90M` with"
	" `BlenderbotSmallModel.from_pretrained('facebook/small_blenderbot-90M')` instead.",
	FutureWarning,
	)
	return BlenderbotSmallModel.from_pretrained(pretrained_model_name_or_path)

	return super(BlenderbotModel, cls).from_pretrained(pretrained_model_name_or_path, model_args, *kwargs)

	def get_input_embeddings(self):
	return self.shared

	def set_input_embeddings(self, value):
	self.shared = value
	self.encoder.embed_tokens = self.shared
	self.decoder.embed_tokens = self.shared

	def get_encoder(self):
	return self.encoder

	def get_decoder(self):
	return self.decoder

	@add_start_docstrings_to_model_forward(BLENDERBOT_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	decoder_input_ids: Optional[torch.LongTensor] = None,
	decoder_attention_mask: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	decoder_head_mask: Optional[torch.Tensor] = None,
	cross_attn_head_mask: Optional[torch.Tensor] = None,
	encoder_outputs: Optional[Union[Tuple, BaseModelOutput]] = None,
	past_key_values: Optional[List[torch.FloatTensor]] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple[torch.FloatTensor], Seq2SeqModelOutput]:
	r"""
	Returns:

	Example:

	```python
	>>> from transformers import AutoTokenizer, BlenderbotModel

	>>> model = BlenderbotModel.from_pretrained("facebook/blenderbot-400M-distill")
	>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")

	>>> inputs = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt")
	>>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1
	>>> outputs = model(input_ids=inputs.input_ids, decoder_input_ids=decoder_input_ids)

	>>> last_hidden_states = outputs.last_hidden_state
	>>> list(last_hidden_states.shape)
	[1, 6, 1280]
	```"""
	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	use_cache = use_cache if use_cache is not None else self.config.use_cache
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	if encoder_outputs is None:
	encoder_outputs = self.encoder(
	input_ids=input_ids,
	attention_mask=attention_mask,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	# If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
	elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
	encoder_outputs = BaseModelOutput(
	last_hidden_state=encoder_outputs[0],
	hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
	attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
	)

	# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
	decoder_outputs = self.decoder(
	input_ids=decoder_input_ids,
	attention_mask=decoder_attention_mask,
	encoder_hidden_states=encoder_outputs[0],
	encoder_attention_mask=attention_mask,
	head_mask=decoder_head_mask,
	cross_attn_head_mask=cross_attn_head_mask,
	past_key_values=past_key_values,
	inputs_embeds=decoder_inputs_embeds,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	if not return_dict:
	return decoder_outputs + encoder_outputs

	return Seq2SeqModelOutput(
	last_hidden_state=decoder_outputs.last_hidden_state,
	past_key_values=decoder_outputs.past_key_values,
	decoder_hidden_states=decoder_outputs.hidden_states,
	decoder_attentions=decoder_outputs.attentions,
	cross_attentions=decoder_outputs.cross_attentions,
	encoder_last_hidden_state=encoder_outputs.last_hidden_state,
	encoder_hidden_states=encoder_outputs.hidden_states,
	encoder_attentions=encoder_outputs.attentions,
	)


	@add_start_docstrings(
	"The Blenderbot Model with a language modeling head. Can be used for summarization.", BLENDERBOT_START_DOCSTRING
	)
	class BlenderbotForConditionalGeneration(BlenderbotPreTrainedModel):
	base_model_prefix = "model"
	_keys_to_ignore_on_load_missing = ["final_logits_bias"]
	_tied_weights_keys = ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "lm_head.weight"]

	def __init__(self, config: BlenderbotConfig):
	super().__init__(config)
	self.model = BlenderbotModel(config)
	self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings)))
	self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)

	# Initialize weights and apply final processing
	self.post_init()

	@classmethod
	def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], model_args, *kwargs):
	if pretrained_model_name_or_path == "facebook/blenderbot-90M":
	warnings.warn(
	"The checkpoint `facebook/blenderbot-90M` is deprecated. In the future, please use the identical"
	" checkpoint `facebook/small_blenderbot-90M` with"
	" `BlenderbotSmallForConditionalGeneration.from_pretrained('facebook/small_blenderbot-90M')` instead.",
	FutureWarning,
	)
	return BlenderbotSmallForConditionalGeneration.from_pretrained(pretrained_model_name_or_path)

	return super(BlenderbotForConditionalGeneration, cls).from_pretrained(
	pretrained_model_name_or_path, model_args, *kwargs
	)

	def get_encoder(self):
	return self.model.get_encoder()

	def get_decoder(self):
	return self.model.get_decoder()

	def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding:
	new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
	self._resize_final_logits_bias(new_embeddings.weight.shape[0])
	return new_embeddings

	def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
	old_num_tokens = self.final_logits_bias.shape[-1]
	if new_num_tokens <= old_num_tokens:
	new_bias = self.final_logits_bias[:, :new_num_tokens]
	else:
	extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
	new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
	self.register_buffer("final_logits_bias", new_bias)

	def get_output_embeddings(self):
	return self.lm_head

	def set_output_embeddings(self, new_embeddings):
	self.lm_head = new_embeddings

	@add_start_docstrings_to_model_forward(BLENDERBOT_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
	@add_end_docstrings(BLENDERBOT_GENERATION_EXAMPLE)
	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	decoder_input_ids: Optional[torch.LongTensor] = None,
	decoder_attention_mask: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	decoder_head_mask: Optional[torch.Tensor] = None,
	cross_attn_head_mask: Optional[torch.Tensor] = None,
	encoder_outputs: Optional[Union[Tuple, BaseModelOutput]] = None,
	past_key_values: Optional[List[torch.FloatTensor]] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
	labels: Optional[torch.LongTensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
	r"""
	labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional):
	Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
	config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
	(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

	Returns:
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	if labels is not None:
	if use_cache:
	logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
	use_cache = False
	if decoder_input_ids is None and decoder_inputs_embeds is None:
	decoder_input_ids = shift_tokens_right(
	labels, self.config.pad_token_id, self.config.decoder_start_token_id
	)

	outputs = self.model(
	input_ids,
	attention_mask=attention_mask,
	decoder_input_ids=decoder_input_ids,
	encoder_outputs=encoder_outputs,
	decoder_attention_mask=decoder_attention_mask,
	head_mask=head_mask,
	decoder_head_mask=decoder_head_mask,
	cross_attn_head_mask=cross_attn_head_mask,
	past_key_values=past_key_values,
	inputs_embeds=inputs_embeds,
	decoder_inputs_embeds=decoder_inputs_embeds,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias

	masked_lm_loss = None
	if labels is not None:
	loss_fct = CrossEntropyLoss()
	masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))

	if not return_dict:
	output = (lm_logits,) + outputs[1:]
	return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output

	return Seq2SeqLMOutput(
	loss=masked_lm_loss,
	logits=lm_logits,
	past_key_values=outputs.past_key_values,
	decoder_hidden_states=outputs.decoder_hidden_states,
	decoder_attentions=outputs.decoder_attentions,
	cross_attentions=outputs.cross_attentions,
	encoder_last_hidden_state=outputs.encoder_last_hidden_state,
	encoder_hidden_states=outputs.encoder_hidden_states,
	encoder_attentions=outputs.encoder_attentions,
	)

	def prepare_inputs_for_generation(
	self,
	decoder_input_ids,
	past_key_values=None,
	attention_mask=None,
	head_mask=None,
	decoder_head_mask=None,
	cross_attn_head_mask=None,
	use_cache=None,
	encoder_outputs=None,
	**kwargs,
	):
	# cut decoder_input_ids if past is used
	if past_key_values is not None:
	decoder_input_ids = decoder_input_ids[:, -1:]

	return {
	"input_ids": None, # encoder_outputs is defined. input_ids not needed
	"encoder_outputs": encoder_outputs,
	"past_key_values": past_key_values,
	"decoder_input_ids": decoder_input_ids,
	"attention_mask": attention_mask,
	"head_mask": head_mask,
	"decoder_head_mask": decoder_head_mask,
	"cross_attn_head_mask": cross_attn_head_mask,
	"use_cache": use_cache, # change this to avoid caching (presumably for debugging)
	}

	@staticmethod
	def _reorder_cache(past_key_values, beam_idx):
	reordered_past = ()
	for layer_past in past_key_values:
	# cached cross_attention states don't have to be reordered -> they are always the same
	reordered_past += (
	tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past[:2])
	+ layer_past[2:],
	)
	return reordered_past


	# Copied from transformers.models.bart.modeling_bart.BartDecoderWrapper with Bart->Blenderbot
	class BlenderbotDecoderWrapper(BlenderbotPreTrainedModel):
	"""
	This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
	used in combination with the [`EncoderDecoderModel`] framework.
	"""

	def __init__(self, config):
	super().__init__(config)
	self.decoder = BlenderbotDecoder(config)

	def forward(self, args, *kwargs):
	return self.decoder(args, *kwargs)


	# Copied from transformers.models.bart.modeling_bart.BartForCausalLM with Bart->Blenderbot, facebook/bart-base->facebook/blenderbot-400M-distill
	class BlenderbotForCausalLM(BlenderbotPreTrainedModel):
	_tied_weights_keys = ["lm_head.weight"]

	def __init__(self, config):
	config = copy.deepcopy(config)
	config.is_decoder = True
	config.is_encoder_decoder = False
	super().__init__(config)
	self.model = BlenderbotDecoderWrapper(config)

	self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

	# Initialize weights and apply final processing
	self.post_init()

	def get_input_embeddings(self):
	return self.model.decoder.embed_tokens

	def set_input_embeddings(self, value):
	self.model.decoder.embed_tokens = value

	def get_output_embeddings(self):
	return self.lm_head

	def set_output_embeddings(self, new_embeddings):
	self.lm_head = new_embeddings

	def set_decoder(self, decoder):
	self.model.decoder = decoder

	def get_decoder(self):
	return self.model.decoder

	@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
	def forward(
	self,
	input_ids: torch.LongTensor = None,
	attention_mask: Optional[torch.Tensor] = None,
	encoder_hidden_states: Optional[torch.FloatTensor] = None,
	encoder_attention_mask: Optional[torch.FloatTensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	cross_attn_head_mask: Optional[torch.Tensor] = None,
	past_key_values: Optional[List[torch.FloatTensor]] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	labels: Optional[torch.LongTensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
	r"""
	Args:
	input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
	provide it.

	Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
	[`PreTrainedTokenizer.__call__`] for details.

	[What are input IDs?](../glossary#input-ids)
	attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional):
	Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	[What are attention masks?](../glossary#attention-mask)
	encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional):
	Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
	if the model is configured as a decoder.
	encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, optional):
	Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used
	in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
	head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, optional):
	Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.

	cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, optional):
	Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:

	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.

	past_key_values (`tuple(tuple(torch.FloatTensor))`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`):
	Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
	shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
	shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
	tensors are only required when the model is used as a decoder in a Sequence to Sequence model.

	Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
	cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

	If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
	that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
	all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
	labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional):
	Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
	config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
	(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
	use_cache (`bool`, optional):
	If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
	(see `past_key_values`).

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under
	returned tensors for more detail.
	output_hidden_states (`bool`, optional):
	Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
	for more detail.
	return_dict (`bool`, optional):
	Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.

	Returns:

	Example:

	```python
	>>> from transformers import AutoTokenizer, BlenderbotForCausalLM

	>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
	>>> model = BlenderbotForCausalLM.from_pretrained(
	... "facebook/blenderbot-400M-distill", add_cross_attention=False
	... )
	>>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
	>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
	>>> outputs = model(**inputs)

	>>> logits = outputs.logits
	>>> expected_shape = [1, inputs.input_ids.shape[-1], model.config.vocab_size]
	>>> list(logits.shape) == expected_shape
	True
	```"""

	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
	outputs = self.model.decoder(
	input_ids=input_ids,
	attention_mask=attention_mask,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	head_mask=head_mask,
	cross_attn_head_mask=cross_attn_head_mask,
	past_key_values=past_key_values,
	inputs_embeds=inputs_embeds,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	logits = self.lm_head(outputs[0])

	loss = None
	if labels is not None:
	labels = labels.to(logits.device)
	loss_fct = CrossEntropyLoss()
	loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))

	if not return_dict:
	output = (logits,) + outputs[1:]
	return (loss,) + output if loss is not None else output

	return CausalLMOutputWithCrossAttentions(
	loss=loss,
	logits=logits,
	past_key_values=outputs.past_key_values,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	cross_attentions=outputs.cross_attentions,
	)

	def prepare_inputs_for_generation(
	self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, **kwargs
	):
	# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
	if attention_mask is None:
	attention_mask = input_ids.new_ones(input_ids.shape)

	if past_key_values:
	input_ids = input_ids[:, -1:]
	# first step, decoder_cached_states are empty
	return {
	"input_ids": input_ids, # encoder_outputs is defined. input_ids not needed
	"attention_mask": attention_mask,
	"past_key_values": past_key_values,
	"use_cache": use_cache,
	}

	@staticmethod
	def _reorder_cache(past_key_values, beam_idx):
	reordered_past = ()
	for layer_past in past_key_values:
	reordered_past += (
	tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
	)
	return reordered_past