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 random
from typing import Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation, glu
from ...modeling_tf_outputs import (TFBaseModelOutput, TFBaseModelOutputWithPastAndCrossAttentions, TFSeq2SeqLMOutput, TFSeq2SeqModelOutput)
from ...modeling_tf_utils import (TFCausalLanguageModelingLoss, TFModelInputType, TFPreTrainedModel, TFSharedEmbeddings, 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_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings)
from .configuration_speech_to_text import Speech2TextConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "Speech2TextConfig"
_CHECKPOINT_FOR_DOC = "facebook/s2t-small-librispeech-asr"
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 TFConv1dSubsampler(keras.layers.Layer):
    def __init__(self, config: Speech2TextConfig, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.num_layers = config.num_conv_layers
        self.in_channels = config.input_feat_per_channel * config.input_channels
        self.mid_channels = config.conv_channels
        self.out_channels = config.d_model
        self.kernel_sizes = config.conv_kernel_sizes
        self.conv_layers = [keras.layers.Conv1D(filters=self.mid_channels if i < self.num_layers - 1 else self.out_channels * 2, kernel_size=k,
        strides=2, name=f"conv_layers.{i}") for i, k in enumerate(self.kernel_sizes)]
    def call(self, input_features: tf.Tensor) -> tf.Tensor:
        hidden_states = tf.cast(input_features, tf.float32)
        for i, conv in enumerate(self.conv_layers):
            pad_len = self.kernel_sizes[i] // 2
            hidden_shapes = shape_list(hidden_states)
            hidden_states = tf.concat((tf.zeros((hidden_shapes[0], pad_len, hidden_shapes[2])), hidden_states, tf.zeros((hidden_shapes[0], pad_len, hidden_shapes[2]))), axis=1)
            hidden_states = conv(hidden_states)
            hidden_states = glu(hidden_states, axis=2)
        return hidden_states
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "conv_layers", None) is not None:
            for i, layer in enumerate(self.conv_layers):
                with tf.name_scope(layer.name): layer.build([None, None, self.in_channels] if i == 0 else [None, None, self.mid_channels // 2])
class TFSpeech2TextSinusoidalPositionalEmbedding(keras.layers.Layer):
    def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None, **kwargs):
        super().__init__(**kwargs)
        self.offset = 2
        self.embedding_dim = embedding_dim
        self.padding_idx = padding_idx
        self.embedding_weights = self._get_embedding(num_positions + self.offset, embedding_dim, padding_idx)
    @staticmethod
    def _get_embedding(num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None) -> tf.Tensor:
        half_dim = embedding_dim // 2
        emb = tf.math.log(10000.0) / (half_dim - 1)
        emb = tf.math.exp(tf.range(half_dim, dtype=tf.float32) * -emb)
        emb = tf.expand_dims(tf.range(num_embeddings, dtype=tf.float32), axis=1) * tf.expand_dims(emb, axis=0)
        emb = tf.reshape(tf.concat([tf.math.sin(emb), tf.math.cos(emb)], axis=1), shape=[num_embeddings, -1])
        if embedding_dim % 2 == 1: emb = tf.concat([emb, tf.zeros(num_embeddings, 1)], axis=1)
        if padding_idx is not None: emb = tf.concat([emb[:padding_idx, :], tf.zeros((1, tf.shape(emb)[1])), emb[padding_idx + 1 :, :]], axis=0)
        return emb
    def call(self, input_ids: tf.Tensor, past_key_values_length: int = 0) -> tf.Tensor:
        bsz, seq_len = shape_list(input_ids)
        position_ids = self.create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length)
        embeddings = self._get_embedding(self.padding_idx + 1 + seq_len + self.offset + past_key_values_length, self.embedding_dim, self.padding_idx)
        return tf.reshape(tf.gather(embeddings, tf.reshape(position_ids, (-1,)), axis=0), (bsz, seq_len, -1))
    @staticmethod
    def create_position_ids_from_input_ids(input_ids: tf.Tensor, padding_idx: int, past_key_values_length: Optional[int] = 0) -> tf.Tensor:
        mask = tf.cast(tf.math.not_equal(input_ids, padding_idx), dtype=tf.int32)
        incremental_indices = (tf.math.cumsum(mask, axis=1) + past_key_values_length) * mask
        return tf.cast(incremental_indices, dtype=tf.int64) + padding_idx
class TFSpeech2TextAttention(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 TFSpeech2TextEncoderLayer(keras.layers.Layer):
    def __init__(self, config: Speech2TextConfig, **kwargs):
        super().__init__(**kwargs)
        self.embed_dim = config.d_model
        self.self_attn = TFSpeech2TextAttention(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: 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, training=training)
        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 TFSpeech2TextDecoderLayer(keras.layers.Layer):
    def __init__(self, config: Speech2TextConfig, **kwargs):
        super().__init__(**kwargs)
        self.embed_dim = config.d_model
        self.self_attn = TFSpeech2TextAttention(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 = TFSpeech2TextAttention(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, 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=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, training=training)
        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, training=training)
            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 TFSpeech2TextPreTrainedModel(TFPreTrainedModel):
    config_class = Speech2TextConfig
    base_model_prefix = "model"
    main_input_name = "input_features"
    _keys_to_ignore_on_load_unexpected = [r"encoder.embed_positions.weights"]
    def _get_feat_extract_output_lengths(self, input_lengths: tf.Tensor):
        for _ in range(self.config.num_conv_layers): input_lengths = (input_lengths - 1) // 2 + 1
        return input_lengths
    @property
    def input_signature(self):
        return {"input_features": tf.TensorSpec((None, None, self.config.input_feat_per_channel * self.config.input_channels), tf.float32, name="input_features"),
        "attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"), "decoder_input_ids": tf.TensorSpec((None, None), tf.int32, name="decoder_input_ids"),
        "decoder_attention_mask": tf.TensorSpec((None, None), tf.int32, name="decoder_attention_mask")}
SPEECH_TO_TEXT_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>
    Parameters:
        config ([`Speech2TextConfig`]):
            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.
"""
SPEECH_TO_TEXT_INPUTS_DOCSTRING = r"""
    Args:
        input_features (`tf.Tensor` of shape `(batch_size, sequence_length, feature_size)`):
            Float values of fbank features extracted from the raw speech waveform. Raw speech waveform can be obtained
            by loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.*
            via the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the
            [`AutoFeatureExtractor`] should be used for extracting the fbank features, padding and conversion into a
            tensor of floats. See [`~Speech2TextFeatureExtractor.__call__`]
        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 [`Speech2TextTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are decoder input IDs?](../glossary#decoder-input-ids)
            SpeechToText uses the `eos_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`).
            For translation and summarization training, `decoder_input_ids` should be provided. If no
            `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
            for denoising pre-training following the paper.
        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.
        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)`.
        decoder_inputs_embeds (`tf.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
            representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
            input (see `past_key_values`). This is useful if you want more control over how to convert
            `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail. 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 TFSpeech2TextEncoder(keras.layers.Layer):
    config_class = Speech2TextConfig
    def __init__(self, config: Speech2TextConfig, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.dropout = keras.layers.Dropout(config.dropout)
        self.layerdrop = config.encoder_layerdrop
        embed_dim = config.d_model
        self.padding_idx = config.pad_token_id
        self.max_source_positions = config.max_source_positions
        self.embed_scale = tf.math.sqrt(float(embed_dim)) if config.scale_embedding else 1.0
        self.conv = TFConv1dSubsampler(config, name="conv")
        self.embed_positions = TFSpeech2TextSinusoidalPositionalEmbedding(num_positions=config.max_source_positions, embedding_dim=embed_dim, padding_idx=self.padding_idx, name="embed_positions")
        self.layers = [TFSpeech2TextEncoderLayer(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_feat_extract_output_lengths(self, input_lengths: tf.Tensor):
        for _ in range(self.config.num_conv_layers): input_lengths = (input_lengths - 1) // 2 + 1
        return input_lengths
    def _get_feature_vector_attention_mask(self, feature_vector_length, attention_mask):
        if len(attention_mask.shape) > 2: attention_mask = attention_mask[:, :, -1]
        subsampled_lengths = self._get_feat_extract_output_lengths(tf.math.reduce_sum(attention_mask, -1))
        bsz = shape_list(attention_mask)[0]
        indices = tf.concat((tf.expand_dims(tf.range(bsz, dtype=attention_mask.dtype), -1), tf.expand_dims(subsampled_lengths - 1, -1)), axis=-1)
        attention_mask = tf.scatter_nd(indices=indices, updates=tf.ones(bsz), shape=[bsz, feature_vector_length])
        attention_mask = tf.cast(tf.reverse(tf.math.cumsum(tf.reverse(attention_mask, [-1]), -1), [-1]), tf.int64)
        return attention_mask
    @unpack_inputs
    def call(self, input_features=None, attention_mask=None, head_mask=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False):
        if input_features is None: raise ValueError("You have to specify input_features")
        inputs_embeds = self.conv(input_features)
        inputs_embeds = self.embed_scale * inputs_embeds
        if attention_mask is not None:
            attention_mask = self._get_feature_vector_attention_mask(tf.shape(inputs_embeds)[1], attention_mask)
            padding_mask = tf.cast(tf.math.not_equal(attention_mask, 1), tf.int64)
        else: padding_mask = tf.zeros(tf.shape(inputs_embeds)[:-1], dtype=tf.int64)
        embed_pos = self.embed_positions(padding_mask)
        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)
        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, training=training)
            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, "conv", None) is not None:
            with tf.name_scope(self.conv.name): self.conv.build(None)
        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 TFSpeech2TextDecoder(keras.layers.Layer):
    config_class = Speech2TextConfig
    def __init__(self, config: Speech2TextConfig, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.layerdrop = config.decoder_layerdrop
        self.padding_idx = config.pad_token_id
        self.max_target_positions = config.max_target_positions
        self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0
        self.embed_tokens = TFSharedEmbeddings(config.vocab_size, config.d_model, name="embed_tokens")
        self.embed_positions = TFSpeech2TextSinusoidalPositionalEmbedding(num_positions=config.max_target_positions, embedding_dim=config.d_model, padding_idx=self.padding_idx, name="embed_positions")
        self.layers = [TFSpeech2TextDecoderLayer(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, 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 inputs_embeds is None:
            check_embeddings_within_bounds(input_ids, self.embed_tokens.vocab_size)
            inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
        else: inputs_embeds = 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])
        positions = self.embed_positions(input_ids, past_key_values_length=past_key_values_length)
        hidden_states = inputs_embeds + 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
        next_decoder_cache = () 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
            cross_attn_layer_head_mask = cross_attn_head_mask[idx] if cross_attn_head_mask 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_layer_head_mask, past_key_value=past_key_value)
            if use_cache: next_decoder_cache += (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,)
        next_cache = next_decoder_cache if use_cache else None
        if not return_dict: return hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attns
        else: return TFBaseModelOutputWithPastAndCrossAttentions(last_hidden_state=hidden_states, past_key_values=next_cache, 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_tokens", None) is not None:
            with tf.name_scope(self.embed_tokens.name): self.embed_tokens.build(None)
        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 TFSpeech2TextMainLayer(keras.layers.Layer):
    config_class = Speech2TextConfig
    def __init__(self, config: Speech2TextConfig, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.encoder = TFSpeech2TextEncoder(config, name="encoder")
        self.decoder = TFSpeech2TextDecoder(config, name="decoder")
    def get_input_embeddings(self): return self.decoder.embed_tokens
    def set_input_embeddings(self, new_embeddings): self.decoder.embed_tokens = new_embeddings
    @unpack_inputs
    def call(self, input_features=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None,
    encoder_outputs=None, past_key_values=None, decoder_inputs_embeds=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False, **kwargs):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if encoder_outputs is None: encoder_outputs = self.encoder(input_features=input_features, attention_mask=attention_mask, head_mask=head_mask, 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()
        if attention_mask is not None: encoder_attention_mask = self.encoder._get_feature_vector_attention_mask(tf.shape(encoder_outputs[0])[1], attention_mask)
        else: encoder_attention_mask = None
        decoder_outputs = self.decoder(input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, encoder_hidden_states=encoder_outputs[0],
        encoder_attention_mask=encoder_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
        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 Speech2Text Model outputting raw hidden-states without any specific head on top.", SPEECH_TO_TEXT_START_DOCSTRING)
class TFSpeech2TextModel(TFSpeech2TextPreTrainedModel):
    def __init__(self, config: Speech2TextConfig, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.model = TFSpeech2TextMainLayer(config, name="model")
    def get_encoder(self): return self.model.encoder
    def get_decoder(self): return self.model.decoder
    @unpack_inputs
    @add_start_docstrings_to_model_forward(SPEECH_TO_TEXT_INPUTS_DOCSTRING)
    def call(self, input_features: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, decoder_input_ids: np.ndarray | tf.Tensor | None = None,
    decoder_attention_mask: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, decoder_head_mask: np.ndarray | tf.Tensor | None = None,
    cross_attn_head_mask: np.ndarray | tf.Tensor | None = None, encoder_outputs: np.ndarray | tf.Tensor | None = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
    decoder_inputs_embeds: np.ndarray | 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: bool = False, **kwargs) -> Union[Tuple, TFSeq2SeqModelOutput]:
        outputs = self.model(input_features=input_features, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask,
        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,
        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)
@add_start_docstrings("The Speech2Text Model with a language modeling head. Can be used for summarization.", SPEECH_TO_TEXT_START_DOCSTRING)
class TFSpeech2TextForConditionalGeneration(TFSpeech2TextPreTrainedModel, TFCausalLanguageModelingLoss):
    def __init__(self, config: Speech2TextConfig):
        super().__init__(config)
        self.model = TFSpeech2TextMainLayer(config, name="model")
        self.lm_head = keras.layers.Dense(self.config.vocab_size, use_bias=False, name="lm_head")
        self.supports_xla_generation = False
        self.config = config
    def get_encoder(self): return self.model.encoder
    def get_decoder(self): return self.model.decoder
    def resize_token_embeddings(self, new_num_tokens: int) -> tf.Variable:
        new_embeddings = super().resize_token_embeddings(new_num_tokens)
        return new_embeddings
    def get_output_embeddings(self): return self.lm_head
    def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings
    @unpack_inputs
    @add_start_docstrings_to_model_forward(SPEECH_TO_TEXT_INPUTS_DOCSTRING)
    def call(self, input_features: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, decoder_input_ids: np.ndarray | tf.Tensor | None = None,
    decoder_attention_mask: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, decoder_head_mask: np.ndarray | tf.Tensor | None = None,
    cross_attn_head_mask: np.ndarray | tf.Tensor | None = None, encoder_outputs: np.ndarray | tf.Tensor | None = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
    decoder_inputs_embeds: np.ndarray | tf.Tensor | None = None, labels: np.ndarray | 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, TFSeq2SeqLMOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if labels is not None:
            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_features=input_features, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, encoder_outputs=encoder_outputs,
        decoder_attention_mask=decoder_attention_mask, head_mask=head_mask, decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask,
        past_key_values=past_key_values, 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 = self.lm_head(outputs[0])
        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, 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:]
        return {"input_features": None, "encoder_outputs": encoder_outputs, "past_key_values": past_key_values, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask,
        "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, "use_cache": use_cache}
    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, "lm_head", None) is not None:
            with tf.name_scope(self.lm_head.name): self.lm_head.build([None, None, self.config.d_model])
    def tf_to_pt_weight_rename(self, tf_weight):
        if tf_weight == "lm_head.weight": return tf_weight, "model.decoder.embed_tokens.weight"
        else: return (tf_weight,)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
