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 math
import random
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...generation.configuration_utils import GenerationConfig
from ...generation.tf_logits_process import TFLogitsProcessorList
from ...modeling_tf_outputs import (TFBaseModelOutput, TFBaseModelOutputWithPastAndCrossAttentions, TFSeq2SeqLMOutput, TFSeq2SeqModelOutput)
from ...modeling_tf_utils import (TFCausalLanguageModelingLoss, TFModelInputType, TFPreTrainedModel, keras, keras_serializable, unpack_inputs)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from .configuration_whisper import WhisperConfig
from .tokenization_whisper import TASK_IDS, TO_LANGUAGE_CODE
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "WhisperConfig"
LARGE_NEGATIVE = -1e8
def sinusoidal_embedding_init(shape, dtype=tf.float32) -> tf.Tensor:
    length, channels = shape
    if channels % 2 != 0: raise ValueError(f"Number of channels has to be divisible by 2 for sinusoidal positional embeddings, got {channels} channels.")
    log_timescale_increment = math.log(10000) / (channels // 2 - 1)
    inv_timescales = tf.exp(-log_timescale_increment * tf.range(channels // 2, dtype=tf.float32))
    scaled_time = tf.reshape(tf.range(length, dtype=tf.float32), (-1, 1)) * tf.reshape(inv_timescales, (1, -1))
    return tf.cast(tf.concat([tf.sin(scaled_time), tf.cos(scaled_time)], axis=1), dtype)
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 TFWhisperPositionalEmbedding(keras.layers.Layer):
    def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None, embedding_initializer=None, **kwargs):
        super().__init__(**kwargs)
        self.num_positions = num_positions
        self.embedding_dim = embedding_dim
        self.padding_idx = padding_idx
        self.embedding_initializer = keras.initializers.get(embedding_initializer)
    def build(self, input_shape):
        self.weight = self.add_weight(name="weight", shape=[self.num_positions, self.embedding_dim], initializer=self.embedding_initializer, trainable=True)
        super().build(input_shape)
    def call(self, input_ids, past_key_values_length=0):
        past_key_values_length = tf.cast(past_key_values_length, tf.int32)
        gather_indices = tf.range(tf.shape(input_ids)[1], delta=1) + past_key_values_length
        return tf.gather(self.weight, gather_indices)
class TFWhisperAttention(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=False, name="k_proj")
        self.v_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj")
        self.q_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="q_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, "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, "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, "out_proj", None) is not None:
            with tf.name_scope(self.out_proj.name): self.out_proj.build([None, None, self.embed_dim])
class TFWhisperEncoderLayer(keras.layers.Layer):
    def __init__(self, config: WhisperConfig, **kwargs):
        super().__init__(**kwargs)
        self.embed_dim = config.d_model
        self.self_attn = TFWhisperAttention(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 TFWhisperDecoderLayer(keras.layers.Layer):
    def __init__(self, config: WhisperConfig, **kwargs):
        super().__init__(**kwargs)
        self.embed_dim = config.d_model
        self.self_attn = TFWhisperAttention(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 = TFWhisperAttention(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 TFWhisperPreTrainedModel(TFPreTrainedModel):
    config_class = WhisperConfig
    base_model_prefix = "model"
    main_input_name = "input_features"
    def _get_feat_extract_output_lengths(self, input_lengths: tf.Tensor) -> int:
        input_lengths = (input_lengths - 1) // 2 + 1
        return input_lengths
    @property
    def dummy_inputs(self) -> Dict[str, tf.Tensor]: return {self.main_input_name: tf.random.uniform([1, self.config.num_mel_bins, self.config.max_source_positions * 2 - 1], dtype=tf.float32), "decoder_input_ids": tf.constant([[1, 3]], dtype=tf.int32)}
    @property
    def input_signature(self): return {"input_features": tf.TensorSpec((None, self.config.num_mel_bins, None), tf.float32, name="input_features"), "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")}
WHISPER_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.
    Parameters:
        config ([`WhisperConfig`]):
            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.
"""
WHISPER_INPUTS_DOCSTRING = r"""
    Args:
        input_features (`tf.Tensor` of shape `(batch_size, feature_size, sequence_length)`):
            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 type `tf.Tensor`. See [`~WhisperFeatureExtractor.__call__`]
        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 [`SpeechToTextTokenizer`]. 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`).
        decoder_attention_mask (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
            be used by default.
            If you want to change padding behavior, you should read
            [`modeling_whisper._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in [the
            paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
        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 (`tuple(tuple(tf.Tensor)`, *optional*):
            Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
            hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
        past_key_values (`tuple(tuple(tf.Tensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            Tuple of `tuple(tf.Tensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
            `(batch_size, num_heads, sequence_length, embed_size_per_head)` and 2 additional tensors of shape
            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        decoder_inputs_embeds (`tf.Tensor` 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.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@keras_serializable
class TFWhisperEncoder(keras.layers.Layer):
    config_class = WhisperConfig
    def __init__(self, config: WhisperConfig, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.layerdrop = config.encoder_layerdrop
        self.embed_dim = config.d_model
        self.num_mel_bins = config.num_mel_bins
        self.padding_idx = config.pad_token_id
        self.max_source_positions = config.max_source_positions
        self.embed_scale = math.sqrt(self.embed_dim) if config.scale_embedding else 1.0
        self.conv1 = keras.layers.Conv1D(self.embed_dim, kernel_size=3, strides=1, padding="valid", name="conv1")
        self.conv2 = keras.layers.Conv1D(self.embed_dim, kernel_size=3, strides=2, padding="valid", name="conv2")
        self.embed_positions = TFWhisperPositionalEmbedding(num_positions=self.max_source_positions, embedding_dim=self.embed_dim, embedding_initializer=sinusoidal_embedding_init, name="embed_positions")
        self.embed_positions.trainable = False
        self.encoder_layers = [TFWhisperEncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)]
        self.layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="layer_norm")
        self.dropout = keras.layers.Dropout(config.dropout)
    @unpack_inputs
    def call(self, input_features=None, head_mask=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        input_features = tf.transpose(input_features, perm=(0, 2, 1))
        input_features = tf.pad(input_features, [[0, 0], [1, 1], [0, 0]])
        inputs_embeds = keras.activations.gelu(self.conv1(input_features))
        inputs_embeds = tf.pad(inputs_embeds, [[0, 0], [1, 1], [0, 0]])
        inputs_embeds = keras.activations.gelu(self.conv2(inputs_embeds))
        inputs_embeds = tf.transpose(inputs_embeds, perm=(0, 1, 2))
        embed_pos = self.embed_positions(input_ids=tf.zeros((1, self.max_source_positions), dtype=tf.int32))
        hidden_states = inputs_embeds + embed_pos
        hidden_states = self.dropout(hidden_states, training=training)
        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.encoder_layers), message=(f"The head_mask should be specified for {len(self.encoder_layers)} layers, but it is for {shape_list(head_mask)[0]}."))
        for idx, encoder_layer in enumerate(self.encoder_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, None, layer_head_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, "conv1", None) is not None:
            with tf.name_scope(self.conv1.name): self.conv1.build([None, None, self.num_mel_bins])
        if getattr(self, "conv2", None) is not None:
            with tf.name_scope(self.conv2.name): self.conv2.build([None, None, self.embed_dim])
        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, "encoder_layers", None) is not None:
            for layer in self.encoder_layers:
                with tf.name_scope(layer.name): layer.build(None)
@keras_serializable
class TFWhisperDecoder(keras.layers.Layer):
    config_class = WhisperConfig
    def __init__(self, config: WhisperConfig, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.dropout = keras.layers.Dropout(config.dropout)
        self.layerdrop = config.decoder_layerdrop
        self.padding_idx = config.pad_token_id
        self.max_target_positions = config.max_target_positions
        self.max_source_positions = config.max_source_positions
        self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
        self.embed_tokens = keras.layers.Embedding(input_dim=config.vocab_size, output_dim=config.d_model, embeddings_initializer=keras.initializers.TruncatedNormal(stddev=self.config.init_std), name="embed_tokens")
        self.embed_positions = TFWhisperPositionalEmbedding(self.max_target_positions, config.d_model, name="embed_positions")
        self.decoder_layers = [TFWhisperDecoderLayer(config, name=f"layers.{i}") for i in range(config.decoder_layers)]
        self.layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="layer_norm")
    def get_input_embeddings(self): return self.embed_tokens
    def set_input_embeddings(self, value): self.embed_tokens = value
    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, past_key_values_length):
        batch_size, seq_len = input_shape[0], input_shape[1]
        combined_attention_mask = tf.cond(tf.math.greater(seq_len, 1), lambda: _make_causal_mask(input_shape, past_key_values_length=past_key_values_length),
        lambda: _expand_mask(tf.ones((batch_size, seq_len + past_key_values_length)), tgt_len=seq_len))
        if attention_mask is not None:
            expanded_attn_mask = _expand_mask(attention_mask, tgt_len=input_shape[-1])
            combined_attention_mask = (expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask)
        return combined_attention_mask
    @unpack_inputs
    def call(self, input_ids=None, attention_mask=None, position_ids=None, encoder_hidden_states=None, head_mask=None, cross_attn_head_mask=None, past_key_values=None,
    inputs_embeds=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False):
        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 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 = tf.shape(input_ids)
            input_ids = tf.reshape(input_ids, (-1, input_shape[-1]))
        elif inputs_embeds is not None: input_shape = tf.shape(inputs_embeds)[:-1]
        else: raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
        past_key_values_length = tf.shape(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.input_dim)
            inputs_embeds = self.embed_tokens(input_ids)
        attention_mask = self._prepare_decoder_attention_mask(attention_mask, input_shape, past_key_values_length)
        filled_past_positions = past_key_values_length if position_ids is None else position_ids[0, -1]
        positions = self.embed_positions(input_ids, past_key_values_length=filled_past_positions)
        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_attentions = () 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.decoder_layers), message=(f"The {attn_mask_name} should be specified for {len(self.decoder_layers)} layers, but it is for {shape_list(attn_mask)[0]}."))
        for idx, decoder_layer in enumerate(self.decoder_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
            layer_outputs = decoder_layer(hidden_states, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, 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, training=training)
            hidden_states = layer_outputs[0]
            if use_cache: next_decoder_cache += (layer_outputs[3],)
            if output_attentions:
                all_self_attns += (layer_outputs[1],)
                if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],)
        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 tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions] if v is not None)
        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_attentions)
    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, "decoder_layers", None) is not None:
            for layer in self.decoder_layers:
                with tf.name_scope(layer.name): layer.build(None)
@add_start_docstrings("The bare Whisper Model outputting raw hidden-states without any specific head on top.", WHISPER_START_DOCSTRING)
@keras_serializable
class TFWhisperMainLayer(keras.layers.Layer):
    config_class = WhisperConfig
    def __init__(self, config: WhisperConfig, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.encoder = TFWhisperEncoder(config, name="encoder")
        self.decoder = TFWhisperDecoder(config, name="decoder")
    def get_input_embeddings(self): return self.decoder.embed_tokens
    def set_input_embeddings(self, value): self.decoder.embed_tokens = value
    def get_encoder(self): return self.encoder
    def get_decoder(self): return self.decoder
    @add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING)
    @unpack_inputs
    def call(self, input_features=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=None, past_key_values=None, decoder_inputs_embeds=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False):
        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, 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)
        decoder_outputs = self.decoder(input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, position_ids=decoder_position_ids, encoder_hidden_states=encoder_outputs[0],
        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 Whisper Model outputting raw hidden-states without any specific head on top.", WHISPER_START_DOCSTRING)
class TFWhisperModel(TFWhisperPreTrainedModel):
    def __init__(self, config: WhisperConfig, **kwargs):
        super().__init__(config, **kwargs)
        self.model = TFWhisperMainLayer(config, name="model")
    def get_input_embeddings(self): return self.model.decoder.embed_tokens
    def set_input_embeddings(self, value): self.model.decoder.embed_tokens = value
    def get_encoder(self): return self.model.encoder
    def get_decoder(self): return self.model.decoder
    def decoder(self): return self.model.decoder
    def encoder(self): return self.model.encoder
    @add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING)
    @unpack_inputs
    def call(self, input_features: TFModelInputType | None = None, decoder_input_ids: np.ndarray | tf.Tensor | None = None, decoder_attention_mask: np.ndarray | tf.Tensor | None = None,
    decoder_position_ids: 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: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
    decoder_inputs_embeds: Optional[Tuple[Union[np.ndarray, tf.Tensor]]] = 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) -> Union[Tuple[tf.Tensor], TFSeq2SeqModelOutput]:
        outputs = self.model(input_features=input_features, 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,
        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 Whisper Model with a language modeling head. Can be used for automatic speech recognition.", WHISPER_START_DOCSTRING)
class TFWhisperForConditionalGeneration(TFWhisperPreTrainedModel, TFCausalLanguageModelingLoss):
    base_model_prefix = "model"
    _keys_to_ignore_on_load_missing = [r"encoder.version", r"decoder.version", r"proj_out.weight"]
    _keys_to_ignore_on_save = [r"proj_out.weight"]
    def __init__(self, config: WhisperConfig, **kwargs):
        super().__init__(config, **kwargs)
        self.model = TFWhisperMainLayer(config, name="model")
    def get_encoder(self): return self.model.get_encoder()
    def get_decoder(self): return self.model.get_decoder()
    def get_output_embeddings(self): return self.get_input_embeddings()
    def set_output_embeddings(self, value): self.set_input_embeddings(value)
    def resize_token_embeddings(self, new_num_tokens: int) -> keras.layers.Embedding:
        new_embeddings = super().resize_token_embeddings(new_num_tokens)
        return new_embeddings
    @add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING)
    @unpack_inputs
    def call(self, input_features: TFModelInputType | None = None, decoder_input_ids: np.ndarray | tf.Tensor | None = None, decoder_attention_mask: np.ndarray | tf.Tensor | None = None,
    decoder_position_ids: 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: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
    decoder_inputs_embeds: Optional[Tuple[Union[np.ndarray, tf.Tensor]]] = 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: bool = False) -> Union[Tuple[tf.Tensor], 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, 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, 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)
        decoder_last_hidden_state = outputs[0]
        lm_logits = tf.matmul(decoder_last_hidden_state, self.get_output_embeddings().weights, transpose_b=True)
        loss = None if labels is None else self.hf_compute_loss(labels, lm_logits)
        if not return_dict:
            output = (lm_logits,) + outputs[1:]
            return ((loss,) + output) if loss is not None else output
        return TFSeq2SeqLMOutput(loss=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 generate(self, inputs: Optional[tf.Tensor] = None, generation_config: Optional[GenerationConfig] = None, logits_processor: Optional[TFLogitsProcessorList] = None,
    seed: Optional[List[int]] = None, return_timestamps: Optional[bool] = None, task: Optional[str] = None, language: Optional[str] = None, is_multilingual: Optional[bool] = None,
    prompt_ids: Optional[tf.Tensor] = None, return_token_timestamps=None, **kwargs):
        if generation_config is None: generation_config = self.generation_config
        if return_timestamps is not None:
            if not hasattr(generation_config, "no_timestamps_token_id"): raise ValueError("You are trying to return timestamps, but the generation config is not properly set. Make sure to initialize the generation config with the correct attributes that are needed such as `no_timestamps_token_id`.")
            generation_config.return_timestamps = return_timestamps
        else: generation_config.return_timestamps = False
        if language is not None:
            language = language.lower()
            generation_config.language = language
        if task is not None: generation_config.task = task
        forced_decoder_ids = None
        if hasattr(self.config, "forced_decoder_ids") and self.config.forced_decoder_ids is not None: forced_decoder_ids = self.config.forced_decoder_ids
        elif (hasattr(self.generation_config, "forced_decoder_ids") and self.generation_config.forced_decoder_ids is not None): forced_decoder_ids = self.generation_config.forced_decoder_ids
        else: forced_decoder_ids = kwargs.get("forced_decoder_ids", None)
        if task is not None or language is not None or (forced_decoder_ids is None and prompt_ids is not None):
            forced_decoder_ids = []
            if hasattr(generation_config, "language"):
                if generation_config.language in generation_config.lang_to_id.keys(): language_token = generation_config.language
                elif generation_config.language in TO_LANGUAGE_CODE.keys(): language_token = f"<|{TO_LANGUAGE_CODE[generation_config.language]}|>"
                elif generation_config.language in TO_LANGUAGE_CODE.values(): language_token = f"<|{generation_config.language}|>"
                else:
                    is_language_code = len(generation_config.language) == 2
                    raise ValueError(f"Unsupported language: {generation_config.language}. Language should be one of: {list(TO_LANGUAGE_CODE.values()) if is_language_code else list(TO_LANGUAGE_CODE.keys())}.")
                if language_token not in generation_config.lang_to_id: raise ValueError(f"{language_token} is not supported by this specific model as it is not in the `generation_config.lang_to_id`. (You should just add it to the generation config)")
                forced_decoder_ids.append((1, generation_config.lang_to_id[language_token]))
            else: forced_decoder_ids.append((1, None))
            if hasattr(generation_config, "task"):
                if generation_config.task in TASK_IDS: forced_decoder_ids.append((2, generation_config.task_to_id[generation_config.task]))
                else: raise ValueError(f"The `{generation_config.task}`task is not supported. The task should be one of `{TASK_IDS}`")
            elif hasattr(generation_config, "task_to_id"): forced_decoder_ids.append((2, generation_config.task_to_id["transcribe"]))
            if hasattr(generation_config, "no_timestamps_token_id") and not generation_config.return_timestamps:
                idx = forced_decoder_ids[-1][0] + 1 if forced_decoder_ids else 1
                forced_decoder_ids.append((idx, generation_config.no_timestamps_token_id))
        if forced_decoder_ids is not None: generation_config.forced_decoder_ids = forced_decoder_ids
        if prompt_ids is not None:
            if kwargs.get("decoder_start_token_id") is not None: raise ValueError("When specifying `prompt_ids`, you cannot also specify `decoder_start_token_id` as it gets overwritten.")
            prompt_ids = prompt_ids.tolist()
            decoder_start_token_id, *text_prompt_ids = prompt_ids
            text_prompt_ids = text_prompt_ids[-self.config.max_length // 2 - 1 :]
            kwargs.update({"decoder_start_token_id": decoder_start_token_id})
            specified_max_length = kwargs.pop("max_new_tokens", None) or kwargs.pop("max_length", None)
            default_max_length = generation_config.max_new_tokens or generation_config.max_length
            non_prompt_max_length = specified_max_length or default_max_length
            kwargs["max_new_tokens"] = non_prompt_max_length + len(text_prompt_ids)
            non_prompt_forced_decoder_ids = (kwargs.pop("forced_decoder_ids", None) or generation_config.forced_decoder_ids)
            forced_decoder_ids = [*text_prompt_ids, generation_config.decoder_start_token_id, *[token for _rank, token in non_prompt_forced_decoder_ids]]
            forced_decoder_ids = [(rank + 1, token) for rank, token in enumerate(forced_decoder_ids)]
            generation_config.forced_decoder_ids = forced_decoder_ids
        if generation_config.return_timestamps: raise ValueError("`TFWhisperForConditionalGeneration` doesn't support returning the timestamps yet.")
        if return_token_timestamps:
            kwargs["output_attentions"] = True
            kwargs["return_dict_in_generate"] = True
            if getattr(generation_config, "task", None) == "translate": logger.warning("Token-level timestamps may not be reliable for task 'translate'.")
            if not hasattr(generation_config, "alignment_heads"): raise ValueError("Model generation config has no `alignment_heads`, token-level timestamps not available. See https://gist.github.com/hollance/42e32852f24243b748ae6bc1f985b13a on how to add this property to the generation config.")
        outputs = super().generate(inputs, generation_config, logits_processor, **kwargs)
        if return_token_timestamps and hasattr(generation_config, "alignment_heads"): outputs["token_timestamps"] = self._extract_token_timestamps(outputs, generation_config.alignment_heads)
        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 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, use_cache=None, encoder_outputs=None, attention_mask=None, decoder_attention_mask=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])
        decoder_position_ids = tf.broadcast_to(decoder_position_ids, decoder_input_ids.shape)
        return {"input_features": None, "encoder_outputs": encoder_outputs, "past_key_values": past_key_values, "decoder_input_ids": decoder_input_ids,
        "use_cache": use_cache, "decoder_attention_mask": decoder_attention_mask, "decoder_position_ids": decoder_position_ids}
    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)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
