Spaces:

yizhangliu
/

Grounded-Segment-Anything

Running on T4

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

liuyizhang

add transformers_4_35_0

1ce5e18 8 months ago

No virus

67.8 kB

	# 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.
	""" TF 2.0 Blenderbot model."""


	from __future__ import annotations

	import os
	import random
	import warnings
	from typing import List, Optional, Tuple, Union

	import tensorflow as tf

	from ...activations_tf import get_tf_activation
	from ...modeling_tf_outputs import (
	TFBaseModelOutput,
	TFBaseModelOutputWithPastAndCrossAttentions,
	TFSeq2SeqLMOutput,
	TFSeq2SeqModelOutput,
	)

	# Public API
	from ...modeling_tf_utils import (
	TFCausalLanguageModelingLoss,
	TFPreTrainedModel,
	keras_serializable,
	unpack_inputs,
	)
	from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
	from ...utils import (
	ContextManagers,
	add_code_sample_docstrings,
	add_end_docstrings,
	add_start_docstrings,
	add_start_docstrings_to_model_forward,
	logging,
	replace_return_docstrings,
	)
	from .configuration_blenderbot import BlenderbotConfig


	logger = logging.get_logger(__name__)

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


	LARGE_NEGATIVE = -1e8


	# Copied from transformers.models.bart.modeling_tf_bart.shift_tokens_right
	def shift_tokens_right(input_ids: tf.Tensor, pad_token_id: int, decoder_start_token_id: int):
	pad_token_id = tf.cast(pad_token_id, input_ids.dtype)
	decoder_start_token_id = tf.cast(decoder_start_token_id, input_ids.dtype)
	start_tokens = tf.fill(
	(shape_list(input_ids)[0], 1), tf.convert_to_tensor(decoder_start_token_id, input_ids.dtype)
	)
	shifted_input_ids = tf.concat([start_tokens, input_ids[:, :-1]], -1)
	# replace possible -100 values in labels by `pad_token_id`
	shifted_input_ids = tf.where(
	shifted_input_ids == -100,
	tf.fill(shape_list(shifted_input_ids), tf.convert_to_tensor(pad_token_id, input_ids.dtype)),
	shifted_input_ids,
	)

	# "Verify that `labels` has only positive values and -100"
	assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.constant(0, dtype=input_ids.dtype))

	# Make sure the assertion op is called by wrapping the result in an identity no-op
	with tf.control_dependencies([assert_gte0]):
	shifted_input_ids = tf.identity(shifted_input_ids)

	return shifted_input_ids


	# Copied from transformers.models.bart.modeling_tf_bart._make_causal_mask
	def _make_causal_mask(input_ids_shape: tf.TensorShape, past_key_values_length: int = 0):
	"""
	Make causal mask used for bi-directional self-attention.
	"""
	bsz = input_ids_shape[0]
	tgt_len = input_ids_shape[1]
	mask = tf.ones((tgt_len, tgt_len)) * LARGE_NEGATIVE
	mask_cond = tf.range(shape_list(mask)[-1])

	mask = tf.where(mask_cond < tf.reshape(mask_cond + 1, (shape_list(mask)[-1], 1)), 0.0, mask)

	if past_key_values_length > 0:
	mask = tf.concat([tf.zeros((tgt_len, past_key_values_length)), mask], axis=-1)

	return tf.tile(mask[None, None, :, :], (bsz, 1, 1, 1))


	# Copied from transformers.models.bart.modeling_tf_bart._expand_mask
	def _expand_mask(mask: tf.Tensor, tgt_len: Optional[int] = None):
	"""
	Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
	"""
	src_len = shape_list(mask)[1]
	tgt_len = tgt_len if tgt_len is not None else src_len
	one_cst = tf.constant(1.0)
	mask = tf.cast(mask, dtype=one_cst.dtype)
	expanded_mask = tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1))

	return (one_cst - expanded_mask) * LARGE_NEGATIVE


	class TFBlenderbotLearnedPositionalEmbedding(tf.keras.layers.Embedding):
	"""
	This module learns positional embeddings up to a fixed maximum size.
	"""

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

	def call(
	self, input_shape: tf.TensorShape, past_key_values_length: int = 0, position_ids: tf.Tensor \| None = None
	):
	"""Input is expected to be of size [bsz x seqlen]."""
	if position_ids is None:
	seq_len = input_shape[1]
	position_ids = tf.range(seq_len, delta=1, name="range")
	position_ids += past_key_values_length

	return super().call(tf.cast(position_ids, dtype=tf.int32))


	# Copied from transformers.models.bart.modeling_tf_bart.TFBartAttention with Bart->Blenderbot
	class TFBlenderbotAttention(tf.keras.layers.Layer):
	"""Multi-headed attention from "Attention Is All You Need"""

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

	self.num_heads = num_heads
	self.dropout = tf.keras.layers.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 = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="k_proj")
	self.q_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj")
	self.v_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj")
	self.out_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="out_proj")

	def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int):
	return tf.transpose(tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim)), (0, 2, 1, 3))

	def call(
	self,
	hidden_states: tf.Tensor,
	key_value_states: tf.Tensor \| None = None,
	past_key_value: Tuple[Tuple[tf.Tensor]] \| None = None,
	attention_mask: tf.Tensor \| None = None,
	layer_head_mask: tf.Tensor \| None = None,
	training: Optional[bool] = False,
	) -> Tuple[tf.Tensor, tf.Tensor \| None]:
	"""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, embed_dim = shape_list(hidden_states)

	# get query proj
	query_states = self.q_proj(hidden_states) * self.scaling
	# get key, value proj
	if is_cross_attention and past_key_value is not None:
	# 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 = tf.concat([past_key_value[0], key_states], axis=2)
	value_states = tf.concat([past_key_value[1], value_states], axis=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(tf.Tensor, tf.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(tf.Tensor, tf.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 = tf.reshape(self._shape(query_states, tgt_len, bsz), proj_shape)
	key_states = tf.reshape(key_states, proj_shape)
	value_states = tf.reshape(value_states, proj_shape)

	src_len = shape_list(key_states)[1]
	attn_weights = tf.matmul(query_states, key_states, transpose_b=True)

	tf.debugging.assert_equal(
	shape_list(attn_weights),
	[bsz * self.num_heads, tgt_len, src_len],
	message=(
	f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
	f" {shape_list(attn_weights)}"
	),
	)

	if attention_mask is not None:
	tf.debugging.assert_equal(
	shape_list(attention_mask),
	[bsz, 1, tgt_len, src_len],
	message=(
	f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
	f" {shape_list(attention_mask)}"
	),
	)

	attention_mask = tf.cast(attention_mask, dtype=attn_weights.dtype)
	attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + attention_mask
	attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))

	attn_weights = stable_softmax(attn_weights, axis=-1)

	if layer_head_mask is not None:
	tf.debugging.assert_equal(
	shape_list(layer_head_mask),
	[self.num_heads],
	message=(
	f"Head mask for a single layer should be of size {(self.num_heads)}, but is"
	f" {shape_list(layer_head_mask)}"
	),
	)

	attn_weights = tf.reshape(layer_head_mask, (1, -1, 1, 1)) * tf.reshape(
	attn_weights, (bsz, self.num_heads, tgt_len, src_len)
	)
	attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))

	attn_probs = self.dropout(attn_weights, training=training)
	attn_output = tf.matmul(attn_probs, value_states)

	tf.debugging.assert_equal(
	shape_list(attn_output),
	[bsz * self.num_heads, tgt_len, self.head_dim],
	message=(
	f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
	f" {shape_list(attn_output)}"
	),
	)

	attn_output = tf.transpose(
	tf.reshape(attn_output, (bsz, self.num_heads, tgt_len, self.head_dim)), (0, 2, 1, 3)
	)
	attn_output = tf.reshape(attn_output, (bsz, tgt_len, embed_dim))

	attn_output = self.out_proj(attn_output)
	attn_weights: tf.Tensor = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len))

	return attn_output, attn_weights, past_key_value


	# Copied from transformers.models.mbart.modeling_tf_mbart.TFMBartEncoderLayer with MBart->Blenderbot
	class TFBlenderbotEncoderLayer(tf.keras.layers.Layer):
	def __init__(self, config: BlenderbotConfig, **kwargs):
	super().__init__(**kwargs)
	self.embed_dim = config.d_model
	self.self_attn = TFBlenderbotAttention(
	self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout, name="self_attn"
	)
	self.self_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
	self.dropout = tf.keras.layers.Dropout(config.dropout)
	self.activation_fn = get_tf_activation(config.activation_function)
	self.activation_dropout = tf.keras.layers.Dropout(config.activation_dropout)
	self.fc1 = tf.keras.layers.Dense(config.encoder_ffn_dim, name="fc1")
	self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
	self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")

	def call(
	self,
	hidden_states: tf.Tensor,
	attention_mask: tf.Tensor,
	layer_head_mask: tf.Tensor,
	training: Optional[bool] = False,
	):
	"""
	Args:
	hidden_states (`tf.Tensor`): input to the layer of shape (batch, seq_len, embed_dim)
	attention_mask (`tf.Tensor`): attention mask of size
	(batch, 1, tgt_len, src_len) where padding elements are indicated by very large negative values.
	layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size
	(encoder_attention_heads,)
	"""
	residual = hidden_states
	hidden_states = self.self_attn_layer_norm(hidden_states)
	hidden_states, self_attn_weights, _ = self.self_attn(
	hidden_states=hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask
	)

	tf.debugging.assert_equal(
	shape_list(hidden_states),
	shape_list(residual),
	message=f"Self attn modified the shape of query {shape_list(residual)} to {shape_list(hidden_states)}",
	)

	hidden_states = self.dropout(hidden_states, training=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 = self.activation_dropout(hidden_states, training=training)
	hidden_states = self.fc2(hidden_states)
	hidden_states = self.dropout(hidden_states, training=training)
	hidden_states = residual + hidden_states

	return hidden_states, self_attn_weights


	# Copied from transformers.models.mbart.modeling_tf_mbart.TFMBartDecoderLayer with MBart->Blenderbot
	class TFBlenderbotDecoderLayer(tf.keras.layers.Layer):
	def __init__(self, config: BlenderbotConfig, **kwargs):
	super().__init__(**kwargs)
	self.embed_dim = config.d_model
	self.self_attn = TFBlenderbotAttention(
	embed_dim=self.embed_dim,
	num_heads=config.decoder_attention_heads,
	dropout=config.attention_dropout,
	name="self_attn",
	is_decoder=True,
	)
	self.dropout = tf.keras.layers.Dropout(config.dropout)
	self.activation_fn = get_tf_activation(config.activation_function)
	self.activation_dropout = tf.keras.layers.Dropout(config.activation_dropout)

	self.self_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
	self.encoder_attn = TFBlenderbotAttention(
	self.embed_dim,
	config.decoder_attention_heads,
	dropout=config.attention_dropout,
	name="encoder_attn",
	is_decoder=True,
	)
	self.encoder_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="encoder_attn_layer_norm")
	self.fc1 = tf.keras.layers.Dense(config.decoder_ffn_dim, name="fc1")
	self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
	self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")

	def call(
	self,
	hidden_states: tf.Tensor,
	attention_mask: tf.Tensor \| None = None,
	encoder_hidden_states: tf.Tensor \| None = None,
	encoder_attention_mask: tf.Tensor \| None = None,
	layer_head_mask: tf.Tensor \| None = None,
	cross_attn_layer_head_mask: tf.Tensor \| None = None,
	past_key_value: Tuple[tf.Tensor] \| None = None,
	training: Optional[bool] = False,
	) -> Tuple[tf.Tensor, tf.Tensor, Tuple[Tuple[tf.Tensor]]]:
	"""
	Args:
	hidden_states (`tf.Tensor`): input to the layer of shape (batch, seq_len, embed_dim)
	attention_mask (`tf.Tensor`): attention mask of size
	(batch, 1, tgt_len, src_len) where padding elements are indicated by very large negative values.
	encoder_hidden_states (`tf.Tensor`):
	cross attention input to the layer of shape (batch, seq_len, embed_dim)
	encoder_attention_mask (`tf.Tensor`): encoder attention mask of size
	(batch, 1, tgt_len, src_len) where padding elements are indicated by very large negative values.
	layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size
	(decoder_attention_heads,)
	cross_attn_layer_head_mask (`tf.Tensor`): mask for heads of the cross-attention module.
	(decoder_attention_heads,)
	past_key_value (`Tuple(tf.Tensor)`): cached past key and value projection states
	"""
	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,
	)
	hidden_states = self.dropout(hidden_states, training=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,
	)
	hidden_states = self.dropout(hidden_states, training=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 = self.activation_dropout(hidden_states, training=training)
	hidden_states = self.fc2(hidden_states)
	hidden_states = self.dropout(hidden_states, training=training)
	hidden_states = residual + hidden_states

	return (
	hidden_states,
	self_attn_weights,
	cross_attn_weights,
	present_key_value,
	)


	class TFBlenderbotPreTrainedModel(TFPreTrainedModel):
	config_class = BlenderbotConfig
	base_model_prefix = "model"


	BLENDERBOT_START_DOCSTRING = r"""
	This model inherits from [`TFPreTrainedModel`]. 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 [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
	as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
	behavior.

	<Tip>

	TensorFlow models and layers in `transformers` accept two formats as input:

	- having all inputs as keyword arguments (like PyTorch models), or
	- having all inputs as a list, tuple or dict in the first positional argument.

	The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
	and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
	pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
	format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
	the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
	positional argument:

	- a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
	- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
	`model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
	- a dictionary with one or several input Tensors associated to the input names given in the docstring:
	`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`

	Note that when creating models and layers with
	[subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
	about any of this, as you can just pass inputs like you would to any other Python function!

	</Tip>

	Args:
	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 [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
	"""

	BLENDERBOT_GENERATION_EXAMPLE = r"""
	Conversation example::

	```py
	>>> from transformers import AutoTokenizer, TFBlenderbotForConditionalGeneration

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

	>>> inputs = tokenizer([UTTERANCE], return_tensors="tf")
	>>> reply_ids = model.generate(**inputs)
	>>> print("Bot: ", tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0])

	>>> REPLY = "I'm not sure"
	>>> print("Human: ", REPLY)
	>>> 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="tf")
	>>> next_reply_ids = model.generate(**inputs)
	>>> print("Bot: ", tokenizer.batch_decode(next_reply_ids, skip_special_tokens=True)[0])
	```
	"""

	BLENDERBOT_INPUTS_DOCSTRING = r"""
	Args:
	input_ids (`tf.Tensor` of shape `({0})`):
	Indices of input sequence tokens in the vocabulary.

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

	[What are input IDs?](../glossary#input-ids)
	attention_mask (`tf.Tensor` of shape `({0})`, 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 (`tf.Tensor` 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 (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, optional):
	will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.
	decoder_position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, optional):
	Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
	range `[0, config.max_position_embeddings - 1]`.
	head_mask (`tf.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 (`tf.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 (`tf.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.

	encoder_outputs (`tf.FloatTensor`, optional):
	hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
	of shape `(batch_size, sequence_length, hidden_size)` is a sequence of
	past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`)
	contains precomputed key and value hidden states of the attention blocks. Can be used to speed up 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)`.
	use_cache (`bool`, optional, defaults to `True`):
	If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
	`past_key_values`). Set to `False` during training, `True` during generation
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
	tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
	config will be used instead.
	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. This argument can be used only in eager mode, in graph mode the value in the config will be
	used instead.
	return_dict (`bool`, optional):
	Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
	eager mode, in graph mode the value will always be set to True.
	training (`bool`, optional, defaults to `False`):
	Whether or not to use the model in training mode (some modules like dropout modules have different
	behaviors between training and evaluation).
	"""


	@keras_serializable
	class TFBlenderbotEncoder(tf.keras.layers.Layer):
	config_class = BlenderbotConfig
	"""
	Transformer encoder consisting of config.encoder_layers self attention layers. Each layer is a
	[`TFBlenderbotEncoderLayer`].

	Args:
	config: BlenderbotConfig
	"""

	def __init__(self, config: BlenderbotConfig, embed_tokens: Optional[tf.keras.layers.Embedding] = None, **kwargs):
	super().__init__(**kwargs)
	self.config = config
	self.dropout = tf.keras.layers.Dropout(config.dropout)
	self.layerdrop = config.encoder_layerdrop
	self.padding_idx = config.pad_token_id
	self.max_source_positions = config.max_position_embeddings
	self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0

	self.embed_tokens = embed_tokens
	self.embed_positions = TFBlenderbotLearnedPositionalEmbedding(
	config.max_position_embeddings,
	config.d_model,
	name="embed_positions",
	)
	self.layers = [TFBlenderbotEncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)]
	self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layer_norm")

	def get_embed_tokens(self):
	return self.embed_tokens

	def set_embed_tokens(self, embed_tokens):
	self.embed_tokens = embed_tokens

	@unpack_inputs
	def call(
	self,
	input_ids=None,
	inputs_embeds=None,
	attention_mask=None,
	head_mask=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	training=False,
	):
	"""
	Args:
	input_ids (`tf.Tensor` 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 (`tf.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 (`tf.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 (`tf.Tensor` 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. This argument can be used only in eager mode, in graph mode the value
	in the config will be used instead.
	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. This argument can be used only in eager mode, in graph mode the value in the config
	will be used instead.
	return_dict (`bool`, optional):
	Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used
	in eager mode, in graph mode the value will always be set to True.
	training (`bool`, optional, defaults to `False`):
	Whether or not to use the model in training mode (some modules like dropout modules have different
	behaviors between training and evaluation).
	"""
	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:
	input_shape = shape_list(input_ids)
	elif inputs_embeds is not None:
	input_shape = shape_list(inputs_embeds)[:-1]
	else:
	raise ValueError("You have to specify either input_ids or inputs_embeds")

	if inputs_embeds is None:
	# if `self.embed_tokens.load_weight_prefix` is set, runs the embedding operation with the correct name
	# scope, so that its weights are registered with the desired name for loading/storing. When `tf.name_scope`
	# is used with a name ending in `/`, that name replaces the current name scope.
	# (embeddings with tf.name_scope: self.embed_tokens.load_weight_prefix/self.embed_tokens.name/embeddings:0)
	context = []
	if hasattr(self.embed_tokens, "load_weight_prefix"):
	context.append(tf.name_scope(self.embed_tokens.load_weight_prefix + "/"))
	with ContextManagers(context):
	check_embeddings_within_bounds(input_ids, self.embed_tokens.input_dim)
	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 = self.dropout(hidden_states, training=training)

	# check attention mask and invert
	if attention_mask is not None:
	# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
	attention_mask = _expand_mask(attention_mask)
	else:
	attention_mask = None

	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:
	tf.debugging.assert_equal(
	shape_list(head_mask)[0],
	len(self.layers),
	message=(
	f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
	f" {shape_list(head_mask)[0]}."
	),
	)

	# encoder layers
	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)
	dropout_probability = random.uniform(0, 1)
	if training and (dropout_probability < self.layerdrop): # skip the layer
	continue

	hidden_states, attn = encoder_layer(
	hidden_states,
	attention_mask,
	head_mask[idx] if head_mask is not None else None,
	)

	if output_attentions:
	all_attentions += (attn,)

	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 TFBaseModelOutput(
	last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
	)


	@keras_serializable
	class TFBlenderbotDecoder(tf.keras.layers.Layer):
	config_class = BlenderbotConfig
	"""
	Transformer decoder consisting of config.decoder_layers layers. Each layer is a [`TFBlenderbotDecoderLayer`]

	Args:
	config: BlenderbotConfig
	embed_tokens: output embedding
	"""

	def __init__(self, config: BlenderbotConfig, embed_tokens: Optional[tf.keras.layers.Embedding] = None, **kwargs):
	super().__init__(**kwargs)
	self.config = config
	self.padding_idx = config.pad_token_id
	self.embed_tokens = embed_tokens
	self.layerdrop = config.decoder_layerdrop
	self.embed_positions = TFBlenderbotLearnedPositionalEmbedding(
	config.max_position_embeddings,
	config.d_model,
	name="embed_positions",
	)
	self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0
	self.layers = [TFBlenderbotDecoderLayer(config, name=f"layers.{i}") for i in range(config.decoder_layers)]
	self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layer_norm")

	self.dropout = tf.keras.layers.Dropout(config.dropout)

	def get_embed_tokens(self):
	return self.embed_tokens

	def set_embed_tokens(self, embed_tokens):
	self.embed_tokens = embed_tokens

	@unpack_inputs
	def call(
	self,
	input_ids=None,
	inputs_embeds=None,
	attention_mask=None,
	position_ids=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	head_mask=None,
	cross_attn_head_mask=None,
	past_key_values=None,
	use_cache=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	training=False,
	):
	r"""
	Args:
	input_ids (`tf.Tensor` 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 (`tf.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)
	position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, optional):
	Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
	range `[0, config.max_position_embeddings - 1]`.
	encoder_hidden_states (`tf.Tensor` 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 (`tf.Tensor` 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 (`tf.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 (`tf.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[tf.Tensor]]` of length `config.n_layers` with each tuple having 2 tuples each of which has 2 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
	Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up
	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 (`tf.Tensor` 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. This argument can be used only in eager mode, in graph mode the value
	in the config will be used instead.
	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. This argument can be used only in eager mode, in graph mode the value in the config
	will be used instead.
	return_dict (`bool`, optional):
	Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used
	in eager mode, in graph mode the value will always be set to True.
	training (`bool`, optional, defaults to `False`):
	Whether or not to use the model in training mode (some modules like dropout modules have different
	behaviors between training and evaluation).
	"""
	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 = shape_list(input_ids)
	elif inputs_embeds is not None:
	input_shape = shape_list(inputs_embeds)[:-1]
	else:
	raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")

	past_key_values_length = shape_list(past_key_values[0][0])[2] if past_key_values is not None else 0

	# embed positions
	if position_ids is None:
	positions = self.embed_positions(input_shape, past_key_values_length)
	else:
	positions = self.embed_positions(input_shape, position_ids=position_ids)

	if inputs_embeds is None:
	context = []
	if hasattr(self.embed_tokens, "load_weight_prefix"):
	context.append(tf.name_scope(self.embed_tokens.load_weight_prefix + "/"))
	with ContextManagers(context):
	check_embeddings_within_bounds(input_ids, self.embed_tokens.input_dim)
	inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale

	hidden_states = inputs_embeds

	# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
	if input_shape[-1] > 1:
	combined_attention_mask = _make_causal_mask(input_shape, past_key_values_length=past_key_values_length)
	else:
	combined_attention_mask = _expand_mask(
	tf.ones((input_shape[0], input_shape[1] + past_key_values_length)), tgt_len=input_shape[-1]
	)

	if attention_mask is not None:
	combined_attention_mask = combined_attention_mask + _expand_mask(attention_mask, tgt_len=input_shape[-1])

	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, tgt_len=input_shape[-1])

	hidden_states = hidden_states + positions
	hidden_states = self.dropout(hidden_states, training=training)

	# decoder layers
	all_hidden_states = () if output_hidden_states else None
	all_self_attns = () if output_attentions else None
	all_cross_attns = () if (output_attentions and encoder_hidden_states is not None) else None
	present_key_values = () if use_cache else None

	# check if head_mask and cross_attn_head_mask have a correct number of layers specified if desired
	for attn_mask_name, attn_mask in [("head_mask", head_mask), ("cross_attn_head_mask", cross_attn_head_mask)]:
	if attn_mask is not None:
	tf.debugging.assert_equal(
	shape_list(attn_mask)[0],
	len(self.layers),
	message=(
	f"The {attn_mask_name} should be specified for {len(self.layers)} layers, but it is for"
	f" {shape_list(attn_mask)[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,)
	dropout_probability = random.uniform(0, 1)

	if training and (dropout_probability < self.layerdrop):
	continue

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

	hidden_states, layer_self_attn, layer_cross_attn, present_key_value = decoder_layer(
	hidden_states,
	attention_mask=combined_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,
	)

	if use_cache:
	present_key_values += (present_key_value,)

	if output_attentions:
	all_self_attns += (layer_self_attn,)

	if encoder_hidden_states is not None:
	all_cross_attns += (layer_cross_attn,)

	hidden_states = self.layer_norm(hidden_states)

	if output_hidden_states:
	all_hidden_states += (hidden_states,)

	if not return_dict:
	return hidden_states, present_key_values, all_hidden_states, all_self_attns, all_cross_attns
	else:
	return TFBaseModelOutputWithPastAndCrossAttentions(
	last_hidden_state=hidden_states,
	past_key_values=present_key_values,
	hidden_states=all_hidden_states,
	attentions=all_self_attns,
	cross_attentions=all_cross_attns,
	)


	@keras_serializable
	class TFBlenderbotMainLayer(tf.keras.layers.Layer):
	config_class = BlenderbotConfig

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

	self.config = config
	self.shared = tf.keras.layers.Embedding(
	input_dim=config.vocab_size,
	output_dim=config.d_model,
	embeddings_initializer=tf.keras.initializers.TruncatedNormal(stddev=self.config.init_std),
	name="model.shared",
	)
	# Additional attribute to specify the expected name scope of the layer (for loading/storing weights)
	self.shared.load_weight_prefix = "model.shared"

	self.encoder = TFBlenderbotEncoder(config, self.shared, name="encoder")
	self.decoder = TFBlenderbotDecoder(config, self.shared, name="decoder")

	def get_input_embeddings(self):
	return self.shared

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

	@unpack_inputs
	def call(
	self,
	input_ids=None,
	attention_mask=None,
	decoder_input_ids=None,
	decoder_attention_mask=None,
	decoder_position_ids=None,
	head_mask=None,
	decoder_head_mask=None,
	cross_attn_head_mask=None,
	encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
	past_key_values=None,
	inputs_embeds=None,
	decoder_inputs_embeds=None,
	use_cache=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	training=False,
	**kwargs,
	):
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)

	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,
	training=training,
	)
	# If the user passed a tuple for encoder_outputs, we wrap it in a TFBaseModelOutput when return_dict=True
	elif return_dict and not isinstance(encoder_outputs, TFBaseModelOutput):
	encoder_outputs = TFBaseModelOutput(
	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,
	)
	# If the user passed a TFBaseModelOutput for encoder_outputs, we wrap it in a tuple when return_dict=False
	elif not return_dict and not isinstance(encoder_outputs, tuple):
	encoder_outputs = encoder_outputs.to_tuple()

	decoder_outputs = self.decoder(
	decoder_input_ids,
	attention_mask=decoder_attention_mask,
	position_ids=decoder_position_ids,
	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,
	training=training,
	)

	if not return_dict:
	return decoder_outputs + encoder_outputs

	return TFSeq2SeqModelOutput(
	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 bare BLENDERBOT Model outputting raw hidden-states without any specific head on top.",
	BLENDERBOT_START_DOCSTRING,
	)
	class TFBlenderbotModel(TFBlenderbotPreTrainedModel):
	def __init__(self, config: BlenderbotConfig, inputs, *kwargs):
	super().__init__(config, inputs, *kwargs)

	self.model = TFBlenderbotMainLayer(config, name="model")

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

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

	@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":
	from ..blenderbot_small import TFBlenderbotSmallModel

	warnings.warn(
	"The checkpoint `facebook/blenderbot-90M` is deprecated. In the future, please use the identical"
	" checkpoint `facebook/small_blenderbot-90M` with"
	" `TFBlenderbotSmallForConditionalGeneration.from_pretrained('facebook/small_blenderbot-90M')`"
	" instead.",
	FutureWarning,
	)
	return TFBlenderbotSmallModel.from_pretrained(pretrained_model_name_or_path)

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

	@unpack_inputs
	@add_start_docstrings_to_model_forward(BLENDERBOT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@add_code_sample_docstrings(
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=TFSeq2SeqModelOutput,
	config_class=_CONFIG_FOR_DOC,
	)
	def call(
	self,
	input_ids: tf.Tensor \| None = None,
	attention_mask: tf.Tensor \| None = None,
	decoder_input_ids: tf.Tensor \| None = None,
	decoder_attention_mask: tf.Tensor \| None = None,
	decoder_position_ids: tf.Tensor \| None = None,
	head_mask: tf.Tensor \| None = None,
	decoder_head_mask: tf.Tensor \| None = None,
	cross_attn_head_mask: tf.Tensor \| None = None,
	encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
	past_key_values: List[tf.Tensor] \| None = None,
	inputs_embeds: tf.Tensor \| None = None,
	decoder_inputs_embeds: tf.Tensor \| None = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	training: Optional[bool] = False,
	**kwargs,
	) -> Union[Tuple[tf.Tensor], TFSeq2SeqModelOutput]:
	outputs = self.model(
	input_ids=input_ids,
	attention_mask=attention_mask,
	decoder_input_ids=decoder_input_ids,
	decoder_attention_mask=decoder_attention_mask,
	decoder_position_ids=decoder_position_ids,
	head_mask=head_mask,
	decoder_head_mask=decoder_head_mask,
	cross_attn_head_mask=cross_attn_head_mask,
	encoder_outputs=encoder_outputs,
	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,
	training=training,
	)

	return outputs

	# Copied from transformers.models.bart.modeling_tf_bart.TFBartModel.serving_output
	def serving_output(self, output):
	pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
	dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
	dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
	cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None
	enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
	enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None

	return TFSeq2SeqModelOutput(
	last_hidden_state=output.last_hidden_state,
	past_key_values=pkv,
	decoder_hidden_states=dec_hs,
	decoder_attentions=dec_attns,
	cross_attentions=cross_attns,
	encoder_last_hidden_state=output.encoder_last_hidden_state,
	encoder_hidden_states=enc_hs,
	encoder_attentions=enc_attns,
	)


	# Copied from transformers.models.bart.modeling_tf_bart.BiasLayer
	class BiasLayer(tf.keras.layers.Layer):
	"""
	Bias as a layer. It is used for serialization purposes: `tf.keras.Model.save_weights` stores on a per-layer basis,
	so all weights have to be registered in a layer.
	"""

	def __init__(self, shape, initializer, trainable, name, **kwargs):
	super().__init__(name=name, **kwargs)
	# Note: the name of this variable will NOT be scoped when serialized, i.e. it will not be in the format of
	# "outer_layer/inner_layer/.../name:0". Instead, it will be "name:0". For further details, see:
	# https://github.com/huggingface/transformers/pull/18833#issuecomment-1233090214
	self.bias = self.add_weight(name=name, shape=shape, initializer=initializer, trainable=trainable)

	def call(self, x):
	return x + self.bias


	@add_start_docstrings(
	"The BLENDERBOT Model with a language modeling head. Can be used for summarization.",
	BLENDERBOT_START_DOCSTRING,
	)
	class TFBlenderbotForConditionalGeneration(TFBlenderbotPreTrainedModel, TFCausalLanguageModelingLoss):
	_keys_to_ignore_on_load_unexpected = [
	r"model.encoder.embed_tokens.weight",
	r"model.decoder.embed_tokens.weight",
	]

	def __init__(self, config, inputs, *kwargs):
	super().__init__(config, inputs, *kwargs)
	self.model = TFBlenderbotMainLayer(config, name="model")
	self.use_cache = config.use_cache
	# final_bias_logits is registered as a buffer in pytorch, so not trainable for the sake of consistency.
	self.bias_layer = BiasLayer(
	name="final_logits_bias", shape=[1, config.vocab_size], initializer="zeros", trainable=False
	)

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

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

	def get_output_embeddings(self):
	return self.get_input_embeddings()

	def set_output_embeddings(self, value):
	self.set_input_embeddings(value)

	def get_bias(self):
	return {"final_logits_bias": self.bias_layer.bias}

	def set_bias(self, value):
	# Replaces the existing layers containing bias for correct (de)serialization.
	vocab_size = value["final_logits_bias"].shape[-1]
	self.bias_layer = BiasLayer(
	name="final_logits_bias", shape=[1, vocab_size], initializer="zeros", trainable=False
	)
	self.bias_layer.bias.assign(value["final_logits_bias"])

	@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":
	from ..blenderbot_small import TFBlenderbotSmallForConditionalGeneration

	warnings.warn(
	"The checkpoint `facebook/blenderbot-90M` is deprecated. In the future, please use the identical"
	" checkpoint `facebook/small_blenderbot-90M` with"
	" `TFBlenderbotSmallForConditionalGeneration.from_pretrained('facebook/small_blenderbot-90M')`"
	" instead.",
	FutureWarning,
	)
	return TFBlenderbotSmallForConditionalGeneration.from_pretrained(pretrained_model_name_or_path)

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

	@unpack_inputs
	@add_start_docstrings_to_model_forward(BLENDERBOT_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
	@add_end_docstrings(BLENDERBOT_GENERATION_EXAMPLE)
	def call(
	self,
	input_ids: tf.Tensor \| None = None,
	attention_mask: tf.Tensor \| None = None,
	decoder_input_ids: tf.Tensor \| None = None,
	decoder_attention_mask: tf.Tensor \| None = None,
	decoder_position_ids: tf.Tensor \| None = None,
	head_mask: tf.Tensor \| None = None,
	decoder_head_mask: tf.Tensor \| None = None,
	cross_attn_head_mask: tf.Tensor \| None = None,
	encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
	past_key_values: List[tf.Tensor] \| None = None,
	inputs_embeds: tf.Tensor \| None = None,
	decoder_inputs_embeds: tf.Tensor \| None = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	labels: tf.Tensor \| None = None,
	training: Optional[bool] = False,
	) -> Union[Tuple[tf.Tensor], TFSeq2SeqLMOutput]:
	r"""
	labels (`tf.tensor` 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:

	"""
	if labels is not None:
	labels = tf.where(
	labels == self.config.pad_token_id,
	tf.cast(tf.fill(shape_list(labels), -100), labels.dtype),
	labels,
	)
	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,
	decoder_position_ids=decoder_position_ids,
	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,
	training=training,
	)
	lm_logits = tf.matmul(outputs[0], self.model.shared.weights, transpose_b=True)
	lm_logits = self.bias_layer(lm_logits)
	masked_lm_loss = None if labels is None else self.hf_compute_loss(labels, lm_logits)

	if not return_dict:
	output = (lm_logits,) + outputs[1:]
	return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
	return TFSeq2SeqLMOutput(
	loss=masked_lm_loss,
	logits=lm_logits,
	past_key_values=outputs.past_key_values, # index 1 of d outputs
	decoder_hidden_states=outputs.decoder_hidden_states, # index 2 of d outputs
	decoder_attentions=outputs.decoder_attentions, # index 3 of d outputs
	cross_attentions=outputs.cross_attentions, # index 4 of d outputs
	encoder_last_hidden_state=outputs.encoder_last_hidden_state, # index 0 of encoder outputs
	encoder_hidden_states=outputs.encoder_hidden_states, # 1 of e out
	encoder_attentions=outputs.encoder_attentions, # 2 of e out
	)

	# Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.serving_output
	def serving_output(self, output):
	pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
	dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
	dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
	cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None
	enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
	enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None

	return TFSeq2SeqLMOutput(
	logits=output.logits,
	past_key_values=pkv,
	decoder_hidden_states=dec_hs,
	decoder_attentions=dec_attns,
	cross_attentions=cross_attns,
	encoder_last_hidden_state=output.encoder_last_hidden_state,
	encoder_hidden_states=enc_hs,
	encoder_attentions=enc_attns,
	)

	# Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.prepare_inputs_for_generation
	def prepare_inputs_for_generation(
	self,
	decoder_input_ids,
	past_key_values=None,
	attention_mask=None,
	decoder_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_key_values is used
	if past_key_values is not None:
	decoder_input_ids = decoder_input_ids[:, -1:]

	if decoder_attention_mask is not None: # xla
	decoder_position_ids = tf.math.cumsum(decoder_attention_mask, axis=-1, exclusive=True)[:, -1:]
	elif past_key_values is not None: # no xla + past_key_values
	decoder_position_ids = past_key_values[0][0].shape[2]
	else: # no xla + no past_key_values
	decoder_position_ids = tf.range(decoder_input_ids.shape[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,
	"decoder_attention_mask": decoder_attention_mask,
	"decoder_position_ids": decoder_position_ids,
	"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)
	}