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 warnings
from typing import Any, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import TFBaseModelOutput, TFCausalLMOutput
from ...modeling_tf_utils import (TFPreTrainedModel, get_initializer, keras, keras_serializable, unpack_inputs)
from ...tf_utils import shape_list, stable_softmax
from ...utils import (add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings)
from .configuration_hubert import HubertConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "HubertConfig"
LARGE_NEGATIVE = -1e8
def _sample_without_replacement(distribution, num_samples):
    z = -tf.math.log(tf.random.uniform(shape_list(distribution), 0, 1))
    _, indices = tf.nn.top_k(distribution + z, num_samples)
    return indices
def _scatter_values_on_batch_indices(values, batch_indices, output_shape):
    indices_shape = shape_list(batch_indices)
    broad_casted_batch_dims = tf.reshape(tf.broadcast_to(tf.expand_dims(tf.range(indices_shape[0]), axis=-1), indices_shape), [1, -1])
    pair_indices = tf.transpose(tf.concat([broad_casted_batch_dims, tf.reshape(batch_indices, [1, -1])], 0))
    return tf.scatter_nd(pair_indices, tf.reshape(values, [-1]), output_shape)
def _compute_mask_indices(shape: Tuple[int, int], mask_prob: float, mask_length: int, min_masks: int = 0) -> tf.Tensor:
    batch_size, sequence_length = shape
    if mask_length < 1: raise ValueError("`mask_length` has to be bigger than 0.")
    tf.debugging.assert_less(mask_length, sequence_length, message=(f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"))
    num_masked_spans = mask_prob * tf.cast(sequence_length, tf.float32) / mask_length + tf.random.uniform((1,))
    num_masked_spans = tf.maximum(num_masked_spans, min_masks)
    num_masked_spans = tf.cast(num_masked_spans, tf.int32)
    num_masked_spans = tf.math.minimum(sequence_length // mask_length, num_masked_spans)
    num_masked_spans = tf.squeeze(num_masked_spans)
    spec_aug_mask = tf.zeros((batch_size, sequence_length), dtype=tf.int32)
    uniform_dist = tf.ones((batch_size, sequence_length - (mask_length - 1)))
    spec_aug_mask_idxs = _sample_without_replacement(uniform_dist, num_masked_spans)
    spec_aug_mask_idxs = tf.expand_dims(spec_aug_mask_idxs, -1)
    spec_aug_mask_idxs = tf.tile(spec_aug_mask_idxs, (1, 1, mask_length))
    spec_aug_mask_idxs = tf.reshape(spec_aug_mask_idxs, (batch_size, num_masked_spans * mask_length))
    offsets = tf.range(mask_length)[tf.newaxis, tf.newaxis, :]
    offsets = tf.tile(offsets, (batch_size, num_masked_spans, 1))
    offsets = tf.reshape(offsets, (batch_size, num_masked_spans * mask_length))
    spec_aug_mask_idxs = spec_aug_mask_idxs + offsets
    spec_aug_mask = _scatter_values_on_batch_indices(tf.ones_like(spec_aug_mask_idxs), spec_aug_mask_idxs, tf.shape(spec_aug_mask))
    return spec_aug_mask
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 TFHubertGroupNorm(keras.layers.Layer):
    def __init__(self, groups: int = 32, axis: int = -1, epsilon: float = 1e-3, center: bool = True, scale: bool = True, beta_initializer: keras.initializers.Initializer = "zeros",
    gamma_initializer: keras.initializers.Initializer = "ones", beta_regularizer: keras.regularizers.Regularizer = None, gamma_regularizer: keras.regularizers.Regularizer = None,
    beta_constraint: keras.constraints.Constraint = None, gamma_constraint: keras.constraints.Constraint = None, **kwargs):
        super().__init__(**kwargs)
        self.supports_masking = True
        self.groups = groups
        self.axis = axis
        self.epsilon = epsilon
        self.center = center
        self.scale = scale
        self.beta_initializer = keras.initializers.get(beta_initializer)
        self.gamma_initializer = keras.initializers.get(gamma_initializer)
        self.beta_regularizer = keras.regularizers.get(beta_regularizer)
        self.gamma_regularizer = keras.regularizers.get(gamma_regularizer)
        self.beta_constraint = keras.constraints.get(beta_constraint)
        self.gamma_constraint = keras.constraints.get(gamma_constraint)
        self._check_axis()
    def build(self, input_shape):
        self._check_if_input_shape_is_none(input_shape)
        self._set_number_of_groups_for_instance_norm(input_shape)
        self._check_size_of_dimensions(input_shape)
        self._create_input_spec(input_shape)
        self._add_gamma_weight(input_shape)
        self._add_beta_weight(input_shape)
        self.built = True
        super().build(input_shape)
    def call(self, inputs):
        input_shape = keras.backend.int_shape(inputs)
        tensor_input_shape = tf.shape(inputs)
        reshaped_inputs, group_shape = self._reshape_into_groups(inputs, input_shape, tensor_input_shape)
        normalized_inputs = self._apply_normalization(reshaped_inputs, input_shape)
        is_instance_norm = (input_shape[self.axis] // self.groups) == 1
        if not is_instance_norm: outputs = tf.reshape(normalized_inputs, tensor_input_shape)
        else: outputs = normalized_inputs
        return outputs
    def get_config(self):
        config = {"groups": self.groups, "axis": self.axis, "epsilon": self.epsilon, "center": self.center, "scale": self.scale, "beta_initializer": keras.initializers.serialize(self.beta_initializer),
        "gamma_initializer": keras.initializers.serialize(self.gamma_initializer), "beta_regularizer": keras.regularizers.serialize(self.beta_regularizer), "gamma_regularizer": keras.regularizers.serialize(self.gamma_regularizer),
        "beta_constraint": keras.constraints.serialize(self.beta_constraint), "gamma_constraint": keras.constraints.serialize(self.gamma_constraint)}
        base_config = super().get_config()
        return {**base_config, **config}
    def compute_output_shape(self, input_shape): return input_shape
    def _reshape_into_groups(self, inputs, input_shape, tensor_input_shape):
        group_shape = [tensor_input_shape[i] for i in range(len(input_shape))]
        is_instance_norm = (input_shape[self.axis] // self.groups) == 1
        if not is_instance_norm:
            group_shape[self.axis] = input_shape[self.axis] // self.groups
            group_shape.insert(self.axis, self.groups)
            group_shape = tf.stack(group_shape)
            reshaped_inputs = tf.reshape(inputs, group_shape)
            return reshaped_inputs, group_shape
        else: return inputs, group_shape
    def _apply_normalization(self, reshaped_inputs, input_shape):
        group_shape = keras.backend.int_shape(reshaped_inputs)
        group_reduction_axes = list(range(1, len(group_shape)))
        is_instance_norm = (input_shape[self.axis] // self.groups) == 1
        if not is_instance_norm: axis = -2 if self.axis == -1 else self.axis - 1
        else: axis = -1 if self.axis == -1 else self.axis - 1
        group_reduction_axes.pop(axis)
        mean, variance = tf.nn.moments(reshaped_inputs, group_reduction_axes, keepdims=True)
        gamma, beta = self._get_reshaped_weights(input_shape)
        normalized_inputs = tf.nn.batch_normalization(reshaped_inputs, mean=mean, variance=variance, scale=gamma, offset=beta, variance_epsilon=self.epsilon)
        return normalized_inputs
    def _get_reshaped_weights(self, input_shape):
        broadcast_shape = self._create_broadcast_shape(input_shape)
        gamma = None
        beta = None
        if self.scale: gamma = tf.reshape(self.gamma, broadcast_shape)
        if self.center: beta = tf.reshape(self.beta, broadcast_shape)
        return gamma, beta
    def _check_if_input_shape_is_none(self, input_shape):
        dim = input_shape[self.axis]
        if dim is None: raise ValueError("Axis " + str(self.axis) + " of input tensor should have a defined dimension but the layer received an input with shape " + str(input_shape) + ".")
    def _set_number_of_groups_for_instance_norm(self, input_shape):
        dim = input_shape[self.axis]
        if self.groups == -1: self.groups = dim
    def _check_size_of_dimensions(self, input_shape):
        dim = input_shape[self.axis]
        if dim < self.groups: raise ValueError("Number of groups (" + str(self.groups) + ") cannot be more than the number of channels (" + str(dim) + ").")
        if dim % self.groups != 0: raise ValueError("Number of groups (" + str(self.groups) + ") must be a multiple of the number of channels (" + str(dim) + ").")
    def _check_axis(self):
        if self.axis == 0: raise ValueError("You are trying to normalize your batch axis. Do you want to use tf.layer.batch_normalization instead")
    def _create_input_spec(self, input_shape):
        dim = input_shape[self.axis]
        self.input_spec = keras.layers.InputSpec(ndim=len(input_shape), axes={self.axis: dim})
    def _add_gamma_weight(self, input_shape):
        dim = input_shape[self.axis]
        shape = (dim,)
        if self.scale: self.gamma = self.add_weight(shape=shape, name="gamma", initializer=self.gamma_initializer, regularizer=self.gamma_regularizer, constraint=self.gamma_constraint)
        else: self.gamma = None
    def _add_beta_weight(self, input_shape):
        dim = input_shape[self.axis]
        shape = (dim,)
        if self.center: self.beta = self.add_weight(shape=shape, name="beta", initializer=self.beta_initializer, regularizer=self.beta_regularizer, constraint=self.beta_constraint)
        else: self.beta = None
    def _create_broadcast_shape(self, input_shape):
        broadcast_shape = [1] * len(input_shape)
        is_instance_norm = (input_shape[self.axis] // self.groups) == 1
        if not is_instance_norm:
            broadcast_shape[self.axis] = input_shape[self.axis] // self.groups
            broadcast_shape.insert(self.axis, self.groups)
        else: broadcast_shape[self.axis] = self.groups
        return broadcast_shape
class TFHubertWeightNormConv1D(keras.layers.Conv1D):
    def __init__(self, filters, kernel_size, groups, explicit_padding, **kwargs):
        super().__init__(filters=filters, kernel_size=kernel_size, groups=groups, padding="valid", use_bias=True, bias_initializer="he_normal", **kwargs)
        self.explicit_padding = explicit_padding
        self.filter_axis = 2
        self.kernel_norm_axes = tf.constant([0, 1])
    def _init_norm(self):
        kernel_norm = tf.sqrt(tf.reduce_sum(tf.square(self.weight_v), axis=self.kernel_norm_axes))
        self.weight_g.assign(kernel_norm[:, tf.newaxis, tf.newaxis])
    def _normalize_kernel(self):
        kernel = tf.nn.l2_normalize(self.weight_v, axis=self.kernel_norm_axes) * tf.transpose(self.weight_g)
        self.kernel = tf.transpose(kernel)
    def build(self, input_shape):
        if not self.built:
            super().build(input_shape)
            self.kernel = tf.Variable(tf.transpose(self.kernel), name="weight_v", trainable=True)
            self.weight_v = self.kernel
            self.weight_g = self.add_weight(name="weight_g", shape=(int(self.weight_v.shape[self.filter_axis]), 1, 1), initializer="ones", dtype=self.weight_v.dtype, trainable=True)
            self._init_norm()
            self.bias = self.add_weight(name="bias", shape=(self.filters,), initializer="zeros", trainable=True)
    def call(self, inputs):
        self._normalize_kernel()
        padded_inputs = tf.pad(inputs, ((0, 0), (self.explicit_padding, self.explicit_padding), (0, 0)))
        output = super().call(padded_inputs)
        return output
class TFHubertNoLayerNormConvLayer(keras.layers.Layer):
    def __init__(self, config: HubertConfig, layer_id: int = 0, **kwargs: Any) -> None:
        super().__init__(**kwargs)
        self.in_conv_dim = config.conv_dim[layer_id] if layer_id > 0 else 1
        self.out_conv_dim = config.conv_dim[layer_id]
        self.conv = keras.layers.Conv1D(filters=self.out_conv_dim, kernel_size=config.conv_kernel[layer_id], strides=config.conv_stride[layer_id], use_bias=config.conv_bias, name="conv")
        self.activation = get_tf_activation(config.feat_extract_activation)
    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
        hidden_states = self.conv(hidden_states)
        hidden_states = self.activation(hidden_states)
        return hidden_states
    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, None, self.in_conv_dim])
class TFHubertLayerNormConvLayer(keras.layers.Layer):
    def __init__(self, config: HubertConfig, layer_id: int = 0, **kwargs: Any) -> None:
        super().__init__(**kwargs)
        self.in_conv_dim = config.conv_dim[layer_id] if layer_id > 0 else 1
        self.out_conv_dim = config.conv_dim[layer_id]
        self.conv = keras.layers.Conv1D(filters=self.out_conv_dim, kernel_size=config.conv_kernel[layer_id], strides=config.conv_stride[layer_id], use_bias=config.conv_bias, name="conv")
        self.layer_norm = keras.layers.LayerNormalization(name="layer_norm", epsilon=config.layer_norm_eps)
        self.activation = get_tf_activation(config.feat_extract_activation)
    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
        hidden_states = self.conv(hidden_states)
        hidden_states = self.layer_norm(hidden_states)
        hidden_states = self.activation(hidden_states)
        return hidden_states
    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, None, self.in_conv_dim])
        if getattr(self, "layer_norm", None) is not None:
            with tf.name_scope(self.layer_norm.name): self.layer_norm.build([None, None, self.out_conv_dim])
class TFHubertGroupNormConvLayer(keras.layers.Layer):
    def __init__(self, config: HubertConfig, layer_id: int = 0, **kwargs: Any) -> None:
        super().__init__(**kwargs)
        self.in_conv_dim = config.conv_dim[layer_id] if layer_id > 0 else 1
        self.out_conv_dim = config.conv_dim[layer_id]
        self.conv = keras.layers.Conv1D(filters=self.out_conv_dim, kernel_size=config.conv_kernel[layer_id], strides=config.conv_stride[layer_id], use_bias=config.conv_bias, name="conv")
        self.activation = get_tf_activation(config.feat_extract_activation)
        self.layer_norm = TFHubertGroupNorm(groups=self.out_conv_dim, epsilon=config.layer_norm_eps, name="layer_norm")
    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
        hidden_states = self.conv(hidden_states)
        hidden_states = self.layer_norm(hidden_states)
        hidden_states = self.activation(hidden_states)
        return hidden_states
    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, None, self.in_conv_dim])
        if getattr(self, "layer_norm", None) is not None:
            with tf.name_scope(self.layer_norm.name): self.layer_norm.build([None, None, self.out_conv_dim])
class TFHubertPositionalConvEmbedding(keras.layers.Layer):
    def __init__(self, config: HubertConfig, **kwargs: Any) -> None:
        super().__init__(**kwargs)
        self.conv = TFHubertWeightNormConv1D(filters=config.hidden_size, kernel_size=config.num_conv_pos_embeddings, groups=config.num_conv_pos_embedding_groups,
        explicit_padding=config.num_conv_pos_embeddings // 2, name="conv")
        self.padding = TFHubertSamePadLayer(config.num_conv_pos_embeddings)
        self.activation = get_tf_activation(config.feat_extract_activation)
        self.config = config
    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
        hidden_states = self.conv(hidden_states)
        hidden_states = self.padding(hidden_states)
        hidden_states = self.activation(hidden_states)
        return hidden_states
    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, None, self.config.hidden_size])
class TFHubertSamePadLayer(keras.layers.Layer):
    def __init__(self, num_conv_pos_embeddings, **kwargs):
        super().__init__(**kwargs)
        self.num_pad_remove = 1 if num_conv_pos_embeddings % 2 == 0 else 0
    def call(self, hidden_states):
        if self.num_pad_remove > 0: hidden_states = hidden_states[:, : -self.num_pad_remove, :]
        return hidden_states
class TFHubertFeatureEncoder(keras.layers.Layer):
    def __init__(self, config: HubertConfig, **kwargs: Any) -> None:
        super().__init__(**kwargs)
        if config.feat_extract_norm == "group": conv_layers = [TFHubertGroupNormConvLayer(config, layer_id=0, name=f"conv_layers.{0}")] + [TFHubertNoLayerNormConvLayer(config, layer_id=i + 1, name=f"conv_layers.{i+1}") for i in range(config.num_feat_extract_layers - 1)]
        elif config.feat_extract_norm == "layer": conv_layers = [TFHubertLayerNormConvLayer(config, layer_id=i, name=f"conv_layers.{i}") for i in range(config.num_feat_extract_layers)]
        else: raise ValueError(f"`config.feat_extract_norm` is {config.feat_extract_norm}, but has to be one of ['group', 'layer']")
        self.conv_layers = conv_layers
    def call(self, input_values):
        hidden_states = tf.expand_dims(input_values, -1)
        for conv_layer in self.conv_layers: hidden_states = conv_layer(hidden_states)
        return hidden_states
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        for conv_layer in self.conv_layers:
            with tf.name_scope(conv_layer.name): conv_layer.build(None)
class TFHubertFeatureExtractor(TFHubertFeatureEncoder):
    def __init__(self, config, **kwargs):
        super().__init__(config, **kwargs)
        warnings.warn(f"The class `{self.__class__.__name__}` has been depreciated and will be removed in Transformers v5. Use `{self.__class__.__bases__[0].__name__}` instead.", FutureWarning)
class TFHubertFeatureProjection(keras.layers.Layer):
    def __init__(self, config: HubertConfig, **kwargs):
        super().__init__(**kwargs)
        self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
        self.projection = keras.layers.Dense(units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), bias_initializer="zeros", name="projection")
        self.dropout = keras.layers.Dropout(rate=config.feat_proj_dropout)
        self.config = config
    def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
        hidden_states = self.layer_norm(hidden_states)
        hidden_states = self.projection(hidden_states)
        hidden_states = self.dropout(hidden_states, training=training)
        return hidden_states
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        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.conv_dim[-1]])
        if getattr(self, "projection", None) is not None:
            with tf.name_scope(self.projection.name): self.projection.build([None, None, self.config.conv_dim[-1]])
class TFHubertAttention(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 TFHubertFeedForward(keras.layers.Layer):
    def __init__(self, config: HubertConfig, **kwargs):
        super().__init__(**kwargs)
        self.intermediate_dropout = keras.layers.Dropout(config.activation_dropout)
        self.intermediate_dense = keras.layers.Dense(units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), bias_initializer="zeros", name="intermediate_dense")
        self.intermediate_act_fn = get_tf_activation(config.hidden_act)
        self.output_dense = keras.layers.Dense(units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), bias_initializer="zeros", name="output_dense")
        self.output_dropout = keras.layers.Dropout(config.hidden_dropout)
        self.config = config
    def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
        hidden_states = self.intermediate_dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        hidden_states = self.intermediate_dropout(hidden_states, training=training)
        hidden_states = self.output_dense(hidden_states)
        hidden_states = self.output_dropout(hidden_states, training=training)
        return hidden_states
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "intermediate_dense", None) is not None:
            with tf.name_scope(self.intermediate_dense.name): self.intermediate_dense.build([None, None, self.config.hidden_size])
        if getattr(self, "output_dense", None) is not None:
            with tf.name_scope(self.output_dense.name): self.output_dense.build([None, None, self.config.intermediate_size])
class TFHubertEncoderLayer(keras.layers.Layer):
    def __init__(self, config: HubertConfig, **kwargs):
        super().__init__(**kwargs)
        self.attention = TFHubertAttention(embed_dim=config.hidden_size, num_heads=config.num_attention_heads, dropout=config.attention_dropout, is_decoder=False, name="attention")
        self.dropout = keras.layers.Dropout(config.hidden_dropout)
        self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
        self.feed_forward = TFHubertFeedForward(config, name="feed_forward")
        self.final_layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="final_layer_norm")
        self.config = config
    def call(self, hidden_states: tf.Tensor, attention_mask: tf.Tensor | None = None, output_attentions: Optional[bool] = False, training: bool = False) -> Tuple[tf.Tensor]:
        attn_residual = hidden_states
        hidden_states, attn_weights, _ = self.attention(hidden_states, attention_mask=attention_mask, training=training)
        hidden_states = self.dropout(hidden_states, training=training)
        hidden_states = attn_residual + hidden_states
        hidden_states = self.layer_norm(hidden_states)
        hidden_states = hidden_states + self.feed_forward(hidden_states)
        hidden_states = self.final_layer_norm(hidden_states)
        outputs = (hidden_states,)
        if output_attentions: outputs += (attn_weights,)
        return outputs
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "attention", None) is not None:
            with tf.name_scope(self.attention.name): self.attention.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.hidden_size])
        if getattr(self, "feed_forward", None) is not None:
            with tf.name_scope(self.feed_forward.name): self.feed_forward.build(None)
        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.config.hidden_size])
class TFHubertEncoderLayerStableLayerNorm(keras.layers.Layer):
    def __init__(self, config: HubertConfig, **kwargs):
        super().__init__(**kwargs)
        self.attention = TFHubertAttention(embed_dim=config.hidden_size, num_heads=config.num_attention_heads, dropout=config.attention_dropout, is_decoder=False, name="attention")
        self.dropout = keras.layers.Dropout(config.hidden_dropout)
        self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
        self.feed_forward = TFHubertFeedForward(config, name="feed_forward")
        self.final_layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="final_layer_norm")
        self.config = config
    def call(self, hidden_states: tf.Tensor, attention_mask: tf.Tensor | None = None, output_attentions: Optional[bool] = False, training: bool = False) -> Tuple[tf.Tensor]:
        attn_residual = hidden_states
        hidden_states = self.layer_norm(hidden_states)
        hidden_states, attn_weights, _ = self.attention(hidden_states, attention_mask=attention_mask, training=training)
        hidden_states = self.dropout(hidden_states, training=training)
        hidden_states = attn_residual + hidden_states
        hidden_states = hidden_states + self.feed_forward(self.final_layer_norm(hidden_states))
        outputs = (hidden_states,)
        if output_attentions: outputs += (attn_weights,)
        return outputs
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "attention", None) is not None:
            with tf.name_scope(self.attention.name): self.attention.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.hidden_size])
        if getattr(self, "feed_forward", None) is not None:
            with tf.name_scope(self.feed_forward.name): self.feed_forward.build(None)
        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.config.hidden_size])
class TFHubertEncoder(keras.layers.Layer):
    def __init__(self, config: HubertConfig, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.pos_conv_embed = TFHubertPositionalConvEmbedding(config, name="pos_conv_embed")
        self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
        self.dropout = keras.layers.Dropout(config.hidden_dropout)
        self.layer = [TFHubertEncoderLayer(config, name=f"layers.{i}") for i in range(config.num_hidden_layers)]
    def call(self, hidden_states: tf.Tensor, attention_mask: tf.Tensor | None = None, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False,
    return_dict: Optional[bool] = True, training: Optional[bool] = False) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        if attention_mask is not None:
            hidden_states = hidden_states * tf.expand_dims(attention_mask, -1)
            attention_mask = _expand_mask(attention_mask)
        else: attention_mask = None
        position_embeddings = self.pos_conv_embed(hidden_states)
        hidden_states = hidden_states + position_embeddings
        hidden_states = self.layer_norm(hidden_states)
        hidden_states = self.dropout(hidden_states, training=training)
        for i, layer_module in enumerate(self.layer):
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
            dropout_probability = np.random.uniform(0, 1)
            if training and (dropout_probability < self.config.layerdrop): continue
            layer_outputs = layer_module(hidden_states=hidden_states, attention_mask=attention_mask, output_attentions=output_attentions, training=training)
            hidden_states = layer_outputs[0]
            if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],)
        if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
        if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
        return TFBaseModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "pos_conv_embed", None) is not None:
            with tf.name_scope(self.pos_conv_embed.name): self.pos_conv_embed.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.hidden_size])
        if getattr(self, "layer", None) is not None:
            for layer in self.layer:
                with tf.name_scope(layer.name): layer.build(None)
class TFHubertEncoderStableLayerNorm(keras.layers.Layer):
    def __init__(self, config: HubertConfig, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.pos_conv_embed = TFHubertPositionalConvEmbedding(config, name="pos_conv_embed")
        self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
        self.dropout = keras.layers.Dropout(config.hidden_dropout)
        self.layer = [TFHubertEncoderLayerStableLayerNorm(config, name=f"layers.{i}") for i in range(config.num_hidden_layers)]
    def call(self, hidden_states: tf.Tensor, attention_mask: tf.Tensor | None = None, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False,
    return_dict: Optional[bool] = True, training: Optional[bool] = False) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        if attention_mask is not None:
            hidden_states = hidden_states * tf.expand_dims(attention_mask, -1)
            attention_mask = _expand_mask(attention_mask)
        else: attention_mask = None
        position_embeddings = self.pos_conv_embed(hidden_states)
        hidden_states = hidden_states + position_embeddings
        hidden_states = self.dropout(hidden_states, training=training)
        for i, layer_module in enumerate(self.layer):
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
            dropout_probability = np.random.uniform(0, 1)
            if training and (dropout_probability < self.config.layerdrop): continue
            layer_outputs = layer_module(hidden_states=hidden_states, attention_mask=attention_mask, output_attentions=output_attentions, training=training)
            hidden_states = layer_outputs[0]
            if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],)
        hidden_states = self.layer_norm(hidden_states)
        if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
        if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
        return TFBaseModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "pos_conv_embed", None) is not None:
            with tf.name_scope(self.pos_conv_embed.name): self.pos_conv_embed.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.hidden_size])
        if getattr(self, "layer", None) is not None:
            for layer in self.layer:
                with tf.name_scope(layer.name): layer.build(None)
@keras_serializable
class TFHubertMainLayer(keras.layers.Layer):
    config_class = HubertConfig
    def __init__(self, config: HubertConfig, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.feature_extractor = TFHubertFeatureEncoder(config, name="feature_extractor")
        self.feature_projection = TFHubertFeatureProjection(config, name="feature_projection")
        if config.do_stable_layer_norm: self.encoder = TFHubertEncoderStableLayerNorm(config, name="encoder")
        else: self.encoder = TFHubertEncoder(config, name="encoder")
    def build(self, input_shape=None):
        self.masked_spec_embed = self.add_weight(shape=(self.config.hidden_size,), initializer="uniform", trainable=True, name="masked_spec_embed")
        if self.built: return
        self.built = True
        if getattr(self, "feature_extractor", None) is not None:
            with tf.name_scope(self.feature_extractor.name): self.feature_extractor.build(None)
        if getattr(self, "feature_projection", None) is not None:
            with tf.name_scope(self.feature_projection.name): self.feature_projection.build(None)
        if getattr(self, "encoder", None) is not None:
            with tf.name_scope(self.encoder.name): self.encoder.build(None)
    def _get_feat_extract_output_lengths(self, input_lengths: tf.Tensor):
        def _conv_out_length(input_length, kernel_size, stride): return (input_length - kernel_size) // stride + 1
        for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride): input_lengths = _conv_out_length(input_lengths, kernel_size, stride)
        return input_lengths
    def _mask_hidden_states(self, hidden_states: tf.Tensor, mask_time_indices: tf.Tensor | None = None):
        batch_size, sequence_length, hidden_size = shape_list(hidden_states)
        if not getattr(self.config, "apply_spec_augment", True): return hidden_states
        if mask_time_indices is not None: hidden_states = tf.where(tf.cast(mask_time_indices[:, :, tf.newaxis], tf.bool), self.masked_spec_embed[tf.newaxis, tf.newaxis, :], hidden_states)
        elif self.config.mask_time_prob > 0:
            mask_time_indices = _compute_mask_indices((batch_size, sequence_length), mask_prob=self.config.mask_time_prob, mask_length=self.config.mask_time_length, min_masks=2)
            hidden_states = tf.where(tf.cast(mask_time_indices[:, :, tf.newaxis], tf.bool), self.masked_spec_embed[tf.newaxis, tf.newaxis, :], hidden_states)
        if self.config.mask_feature_prob > 0:
            mask_feature_indices = _compute_mask_indices((batch_size, hidden_size), mask_prob=self.config.mask_feature_prob, mask_length=self.config.mask_feature_length)
            hidden_states = tf.where(mask_feature_indices[:, tf.newaxis, :], hidden_states, 0)
        return hidden_states
    @unpack_inputs
    def call(self, input_values: tf.Tensor, attention_mask: tf.Tensor | None = None, token_type_ids: tf.Tensor | None = None, position_ids: tf.Tensor | None = None,
    head_mask: tf.Tensor | None = None, inputs_embeds: tf.Tensor | None = None, output_attentions: tf.Tensor | None = None, output_hidden_states: tf.Tensor | None = None,
    return_dict: Optional[bool] = None, training: bool = False, **kwargs: Any):
        hidden_states = self.feature_extractor(tf.cast(input_values, tf.float32), training=training)
        if attention_mask is not None:
            output_lengths = self._get_feat_extract_output_lengths(tf.reduce_sum(attention_mask, -1))
            attention_mask = tf.sequence_mask(output_lengths, maxlen=shape_list(hidden_states)[1], dtype=hidden_states.dtype)
        hidden_states = self.feature_projection(hidden_states, training=training)
        mask_time_indices = kwargs.get("mask_time_indices", None)
        if training: hidden_states = self._mask_hidden_states(hidden_states, mask_time_indices=mask_time_indices)
        encoder_outputs = self.encoder(hidden_states, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states,
        return_dict=return_dict, training=training)
        hidden_states = encoder_outputs[0]
        if not return_dict: return (hidden_states,) + encoder_outputs[1:]
        return TFBaseModelOutput(last_hidden_state=hidden_states, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions)
class TFHubertPreTrainedModel(TFPreTrainedModel):
    config_class = HubertConfig
    base_model_prefix = "hubert"
    main_input_name = "input_values"
    @property
    def input_signature(self):
        return {"input_values": tf.TensorSpec((None, 16000), tf.float32, name="input_values"), "attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
        "token_type_ids": tf.TensorSpec((None, None), tf.int32, name="token_type_ids")}
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        logger.warning(f"\n{self.__class__.__name__} has backpropagation operations that are NOT supported on CPU. If you wish to train/fine-tune this model, you need a GPU or a TPU")
HUBERT_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_values` only and nothing else: `model(input_values)`
    - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
    `model([input_values, attention_mask])` or `model([input_values, attention_mask, token_type_ids])`
    - a dictionary with one or several input Tensors associated to the input names given in the docstring:
    `model({"input_values": input_values, "token_type_ids": token_type_ids})`
    Note that when creating models and layers with
    [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
    about any of this, as you can just pass inputs like you would to any other Python function!
    </Tip>
    Args:
        config ([`HubertConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
HUBERT_INPUTS_DOCSTRING = r"""
    Args:
        input_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` `Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(0)`):
            Indices of input sequence tokens in the vocabulary.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
            [`PreTrainedTokenizer.encode`] for details.
            [What are input IDs?](../glossary#input-ids)
        attention_mask (`np.ndarray` or `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)
        token_type_ids (`np.ndarray` or `tf.Tensor` of shape `(0)`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:
            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.
            [What are token type IDs?](../glossary#token-type-ids)
        position_ids (`np.ndarray` or `tf.Tensor` of shape `(0)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.
            [What are position IDs?](../glossary#position-ids)
        head_mask (`np.ndarray` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        inputs_embeds (`np.ndarray` or `tf.Tensor` of shape `(0, hidden_size)`, *optional*):
            Optionally, instead of passing `input_values` you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert `input_values` indices into associated vectors
            than the model's internal embedding lookup matrix.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
            config will be used instead.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
            used instead.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
            eager mode, in graph mode the value will always be set to True.
        training (`bool`, *optional*, defaults to `False``):
            Whether or not to use the model in training mode (some modules like dropout modules have different
            behaviors between training and evaluation).
"""
@add_start_docstrings("The bare TFHubert Model transformer outputing raw hidden-states without any specific head on top.", HUBERT_START_DOCSTRING)
class TFHubertModel(TFHubertPreTrainedModel):
    def __init__(self, config: HubertConfig, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.config = config
        self.hubert = TFHubertMainLayer(config, name="hubert")
    @add_start_docstrings_to_model_forward(HUBERT_INPUTS_DOCSTRING)
    @unpack_inputs
    def call(self, input_values: tf.Tensor, attention_mask: tf.Tensor | None = None, token_type_ids: tf.Tensor | None = None, position_ids: tf.Tensor | None = None,
    head_mask: tf.Tensor | None = None, inputs_embeds: tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None, training: bool = False) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
        output_hidden_states = output_hidden_states if output_hidden_states else self.config.output_hidden_states
        output_attentions = output_attentions if output_attentions else self.config.output_attentions
        return_dict = return_dict if return_dict else self.config.return_dict
        outputs = self.hubert(input_values=input_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask,
        inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training)
        return outputs
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "hubert", None) is not None:
            with tf.name_scope(self.hubert.name): self.hubert.build(None)
@add_start_docstrings("TFHubert Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).", HUBERT_START_DOCSTRING)
class TFHubertForCTC(TFHubertPreTrainedModel):
    def __init__(self, config: HubertConfig, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.hubert = TFHubertMainLayer(config, name="hubert")
        self.dropout = keras.layers.Dropout(config.final_dropout)
        self.lm_head = keras.layers.Dense(config.vocab_size, name="lm_head")
        self.output_hidden_size = (config.output_hidden_size if hasattr(config, "add_adapter") and config.add_adapter else config.hidden_size)
    def freeze_feature_extractor(self):
        warnings.warn("The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5. Please use the equivalent `freeze_feature_encoder` method instead.", FutureWarning)
        self.freeze_feature_encoder()
    def freeze_feature_encoder(self): self.hubert.feature_extractor.trainable = False
    @add_start_docstrings_to_model_forward(HUBERT_INPUTS_DOCSTRING)
    @unpack_inputs
    def call(self, input_values: tf.Tensor, attention_mask: tf.Tensor | None = None, token_type_ids: tf.Tensor | None = None, position_ids: tf.Tensor | None = None,
    head_mask: tf.Tensor | None = None, inputs_embeds: tf.Tensor | None = None, output_attentions: Optional[bool] = None, labels: tf.Tensor | None = None,
    output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False) -> Union[TFCausalLMOutput, Tuple[tf.Tensor]]:
        if labels is not None and tf.reduce_max(labels) >= self.config.vocab_size: raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}")
        outputs = self.hubert(input_values=input_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask,
        inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training)
        hidden_states = outputs[0]
        hidden_states = self.dropout(hidden_states, training=training)
        logits = self.lm_head(hidden_states)
        if labels is not None:
            attention_mask = (attention_mask if attention_mask is not None else tf.ones_like(input_values, dtype=tf.float32))
            input_lengths = self.hubert._get_feat_extract_output_lengths(tf.reduce_sum(attention_mask, axis=-1))
            labels_mask = tf.cast(labels >= 0, tf.int32)
            target_lengths = tf.reduce_sum(labels_mask, axis=-1)
            loss = tf.nn.ctc_loss(logits=logits, labels=labels, logit_length=input_lengths, label_length=target_lengths, blank_index=self.config.pad_token_id, logits_time_major=False)
            if self.config.ctc_loss_reduction == "sum":
                loss = tf.reduce_sum(loss)
                loss = tf.reshape(loss, (1,))
            if self.config.ctc_loss_reduction == "mean":
                loss = tf.reduce_mean(loss)
                loss = tf.reshape(loss, (1,))
        else: loss = None
        if not return_dict:
            output = (logits,) + outputs[1:]
            return ((loss,) + output) if loss is not None else output
        return TFCausalLMOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "hubert", None) is not None:
            with tf.name_scope(self.hubert.name): self.hubert.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.output_hidden_size])
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
