from __future__ import annotations
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
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)
from ...modeling_tf_utils import (TFCausalLanguageModelingLoss, TFPreTrainedModel, keras, keras_serializable, unpack_inputs)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import (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
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)
    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)
    assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.constant(0, dtype=input_ids.dtype))
    with tf.control_dependencies([assert_gte0]): shifted_input_ids = tf.identity(shifted_input_ids)
    return shifted_input_ids
def _make_causal_mask(input_ids_shape: tf.TensorShape, past_key_values_length: int = 0):
    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))
def _expand_mask(mask: tf.Tensor, tgt_len: Optional[int] = None):
    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(keras.layers.Embedding):
    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):
        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))
class TFBlenderbotAttention(keras.layers.Layer):
    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 = 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} and `num_heads`: {num_heads}).")
        self.scaling = self.head_dim**-0.5
        self.is_decoder = is_decoder
        self.k_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="k_proj")
        self.q_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj")
        self.v_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj")
        self.out_proj = 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]:
        is_cross_attention = key_value_states is not None
        bsz, tgt_len, embed_dim = shape_list(hidden_states)
        query_states = self.q_proj(hidden_states) * self.scaling
        if is_cross_attention and past_key_value is not None:
            key_states = past_key_value[0]
            value_states = past_key_value[1]
        elif is_cross_attention:
            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:
            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:
            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: 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 {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 {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 {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 {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
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "k_proj", None) is not None:
            with tf.name_scope(self.k_proj.name): self.k_proj.build([None, None, self.embed_dim])
        if getattr(self, "q_proj", None) is not None:
            with tf.name_scope(self.q_proj.name): self.q_proj.build([None, None, self.embed_dim])
        if getattr(self, "v_proj", None) is not None:
            with tf.name_scope(self.v_proj.name): self.v_proj.build([None, None, self.embed_dim])
        if getattr(self, "out_proj", None) is not None:
            with tf.name_scope(self.out_proj.name): self.out_proj.build([None, None, self.embed_dim])
class TFBlenderbotEncoderLayer(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 = keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
        self.dropout = keras.layers.Dropout(config.dropout)
        self.activation_fn = get_tf_activation(config.activation_function)
        self.activation_dropout = keras.layers.Dropout(config.activation_dropout)
        self.fc1 = keras.layers.Dense(config.encoder_ffn_dim, name="fc1")
        self.fc2 = keras.layers.Dense(self.embed_dim, name="fc2")
        self.final_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
        self.config = config
    def call(self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, layer_head_mask: tf.Tensor, training: Optional[bool] = False):
        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
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "self_attn", None) is not None:
            with tf.name_scope(self.self_attn.name): self.self_attn.build(None)
        if getattr(self, "self_attn_layer_norm", None) is not None:
            with tf.name_scope(self.self_attn_layer_norm.name): self.self_attn_layer_norm.build([None, None, self.embed_dim])
        if getattr(self, "fc1", None) is not None:
            with tf.name_scope(self.fc1.name): self.fc1.build([None, None, self.embed_dim])
        if getattr(self, "fc2", None) is not None:
            with tf.name_scope(self.fc2.name): self.fc2.build([None, None, self.config.encoder_ffn_dim])
        if getattr(self, "final_layer_norm", None) is not None:
            with tf.name_scope(self.final_layer_norm.name): self.final_layer_norm.build([None, None, self.embed_dim])
class TFBlenderbotDecoderLayer(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 = keras.layers.Dropout(config.dropout)
        self.activation_fn = get_tf_activation(config.activation_function)
        self.activation_dropout = keras.layers.Dropout(config.activation_dropout)
        self.self_attn_layer_norm = 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 = keras.layers.LayerNormalization(epsilon=1e-5, name="encoder_attn_layer_norm")
        self.fc1 = keras.layers.Dense(config.decoder_ffn_dim, name="fc1")
        self.fc2 = keras.layers.Dense(self.embed_dim, name="fc2")
        self.final_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
        self.config = config
    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]]]:
        residual = hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)
        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
        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_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_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
            present_key_value = present_key_value + cross_attn_present_key_value
        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)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "self_attn", None) is not None:
            with tf.name_scope(self.self_attn.name): self.self_attn.build(None)
        if getattr(self, "self_attn_layer_norm", None) is not None:
            with tf.name_scope(self.self_attn_layer_norm.name): self.self_attn_layer_norm.build([None, None, self.embed_dim])
        if getattr(self, "encoder_attn", None) is not None:
            with tf.name_scope(self.encoder_attn.name): self.encoder_attn.build(None)
        if getattr(self, "encoder_attn_layer_norm", None) is not None:
            with tf.name_scope(self.encoder_attn_layer_norm.name): self.encoder_attn_layer_norm.build([None, None, self.embed_dim])
        if getattr(self, "fc1", None) is not None:
            with tf.name_scope(self.fc1.name): self.fc1.build([None, None, self.embed_dim])
        if getattr(self, "fc2", None) is not None:
            with tf.name_scope(self.fc2.name): self.fc2.build([None, None, self.config.decoder_ffn_dim])
        if getattr(self, "final_layer_norm", None) is not None:
            with tf.name_scope(self.final_layer_norm.name): self.final_layer_norm.build([None, None, self.embed_dim])
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 [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.
"""
input_pointers = '(**inputs)'
BLENDERBOT_GENERATION_EXAMPLE = r"""
    Conversation example::
    ```py
    >>> from sapiens_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"""+input_pointers+r"""
    >>> 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"""+input_pointers+r"""
    >>> 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(keras.layers.Layer):
    config_class = BlenderbotConfig
    def __init__(self, config: BlenderbotConfig, embed_tokens: Optional[keras.layers.Embedding] = None, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.dropout = 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 = 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):
        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:
            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)
        if attention_mask is not None: 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
        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 {shape_list(head_mask)[0]}."))
        for idx, encoder_layer in enumerate(self.layers):
            if output_hidden_states: encoder_states = encoder_states + (hidden_states,)
            dropout_probability = random.uniform(0, 1)
            if training and (dropout_probability < self.layerdrop): 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)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "embed_positions", None) is not None:
            with tf.name_scope(self.embed_positions.name): self.embed_positions.build(None)
        if getattr(self, "layer_norm", None) is not None:
            with tf.name_scope(self.layer_norm.name): self.layer_norm.build([None, None, self.config.d_model])
        if getattr(self, "layers", None) is not None:
            for layer in self.layers:
                with tf.name_scope(layer.name): layer.build(None)
@keras_serializable
class TFBlenderbotDecoder(keras.layers.Layer):
    config_class = BlenderbotConfig
    def __init__(self, config: BlenderbotConfig, embed_tokens: Optional[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 = keras.layers.LayerNormalization(epsilon=1e-5, name="layer_norm")
        self.dropout = 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):
        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
        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:
            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
        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: 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)
        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
        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 {shape_list(attn_mask)[0]}."))
        for idx, decoder_layer in enumerate(self.layers):
            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)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "embed_positions", None) is not None:
            with tf.name_scope(self.embed_positions.name): self.embed_positions.build(None)
        if getattr(self, "layer_norm", None) is not None:
            with tf.name_scope(self.layer_norm.name): self.layer_norm.build([None, None, self.config.d_model])
        if getattr(self, "layers", None) is not None:
            for layer in self.layers:
                with tf.name_scope(layer.name): layer.build(None)
@keras_serializable
class TFBlenderbotMainLayer(keras.layers.Layer):
    config_class = BlenderbotConfig
    def __init__(self, config: BlenderbotConfig, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.shared = keras.layers.Embedding(input_dim=config.vocab_size, output_dim=config.d_model, embeddings_initializer=keras.initializers.TruncatedNormal(stddev=self.config.init_std), name="model.shared")
        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)
        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)
        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)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        with tf.name_scope(self.shared.load_weight_prefix + "/" + self.shared.name + "/"): self.shared.build(None)
        if getattr(self, "encoder", None) is not None:
            with tf.name_scope(self.encoder.name): self.encoder.build(None)
        if getattr(self, "decoder", None) is not None:
            with tf.name_scope(self.decoder.name): self.decoder.build(None)
@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"))
    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
    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)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "model", None) is not None:
            with tf.name_scope(self.model.name): self.model.build(None)
class BiasLayer(keras.layers.Layer):
    def __init__(self, shape, initializer, trainable, name, **kwargs):
        super().__init__(name=name, **kwargs)
        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
        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):
        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)
    @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]:
        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, 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 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)
    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):
        if past_key_values is not None: decoder_input_ids = decoder_input_ids[:, -1:]
        if decoder_attention_mask is not None: decoder_position_ids = tf.math.cumsum(decoder_attention_mask, axis=-1, exclusive=True)[:, -1:]
        elif past_key_values is not None: decoder_position_ids = past_key_values[0][0].shape[2]
        else: decoder_position_ids = tf.range(decoder_input_ids.shape[1])
        return {"input_ids": None, "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}
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "model", None) is not None:
            with tf.name_scope(self.model.name): self.model.build(None)
        if getattr(self, "bias_layer", None) is not None:
            with tf.name_scope(self.bias_layer.name): self.bias_layer.build(None)
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
