from __future__ import annotations
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
import random
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import TFBaseModelOutputWithPastAndCrossAttentions
from ...modeling_tf_utils import (TFModelInputType, TFPreTrainedModel, get_initializer, keras, keras_serializable, unpack_inputs)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import (ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings)
from .configuration_led import LEDConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "allenai/led-base-16384"
_CONFIG_FOR_DOC = "LEDConfig"
LARGE_NEGATIVE = -1e8
def shift_tokens_right(input_ids: tf.Tensor, pad_token_id: int, decoder_start_token_id: int):
    pad_token_id = tf.cast(pad_token_id, input_ids.dtype)
    decoder_start_token_id = tf.cast(decoder_start_token_id, input_ids.dtype)
    start_tokens = tf.fill((shape_list(input_ids)[0], 1), tf.convert_to_tensor(decoder_start_token_id, input_ids.dtype))
    shifted_input_ids = tf.concat([start_tokens, input_ids[:, :-1]], -1)
    shifted_input_ids = tf.where(shifted_input_ids == -100, tf.fill(shape_list(shifted_input_ids), tf.convert_to_tensor(pad_token_id, input_ids.dtype)), shifted_input_ids)
    assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.constant(0, dtype=input_ids.dtype))
    with tf.control_dependencies([assert_gte0]): shifted_input_ids = tf.identity(shifted_input_ids)
    return shifted_input_ids
def _make_causal_mask(input_ids_shape: tf.TensorShape, past_key_values_length: int = 0):
    bsz = input_ids_shape[0]
    tgt_len = input_ids_shape[1]
    mask = tf.ones((tgt_len, tgt_len)) * LARGE_NEGATIVE
    mask_cond = tf.range(shape_list(mask)[-1])
    mask = tf.where(mask_cond < tf.reshape(mask_cond + 1, (shape_list(mask)[-1], 1)), 0.0, mask)
    if past_key_values_length > 0: mask = tf.concat([tf.zeros((tgt_len, past_key_values_length)), mask], axis=-1)
    return tf.tile(mask[None, None, :, :], (bsz, 1, 1, 1))
def _expand_mask(mask: tf.Tensor, tgt_len: Optional[int] = None):
    src_len = shape_list(mask)[1]
    tgt_len = tgt_len if tgt_len is not None else src_len
    one_cst = tf.constant(1.0)
    mask = tf.cast(mask, dtype=one_cst.dtype)
    expanded_mask = tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1))
    return (one_cst - expanded_mask) * LARGE_NEGATIVE
class TFLEDLearnedPositionalEmbedding(keras.layers.Embedding):
    def __init__(self, num_embeddings: int, embedding_dim: int, **kwargs): super().__init__(num_embeddings, embedding_dim, **kwargs)
    def call(self, input_shape: tf.TensorShape, past_key_values_length: int = 0):
        seq_len = input_shape[1]
        position_ids = tf.range(seq_len, delta=1, name="range")
        position_ids += past_key_values_length
        return super().call(tf.cast(position_ids, dtype=tf.int32))
class TFLEDEncoderSelfAttention(keras.layers.Layer):
    def __init__(self, config, layer_id, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        if config.hidden_size % config.num_attention_heads != 0: raise ValueError(f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention heads ({config.num_attention_heads}")
        self.num_heads = config.num_attention_heads
        self.head_dim = int(config.hidden_size / config.num_attention_heads)
        self.embed_dim = config.hidden_size
        self.query = keras.layers.Dense(self.embed_dim, kernel_initializer=get_initializer(config.initializer_range), name="query")
        self.key = keras.layers.Dense(self.embed_dim, kernel_initializer=get_initializer(config.initializer_range), name="key")
        self.value = keras.layers.Dense(self.embed_dim, kernel_initializer=get_initializer(config.initializer_range), name="value")
        self.query_global = keras.layers.Dense(self.embed_dim, kernel_initializer=get_initializer(config.initializer_range), name="query_global")
        self.key_global = keras.layers.Dense(self.embed_dim, kernel_initializer=get_initializer(config.initializer_range), name="key_global")
        self.value_global = keras.layers.Dense(self.embed_dim, kernel_initializer=get_initializer(config.initializer_range), name="value_global")
        self.dropout = keras.layers.Dropout(config.attention_probs_dropout_prob)
        self.global_dropout = keras.layers.Dropout(config.attention_probs_dropout_prob)
        self.layer_id = layer_id
        attention_window = config.attention_window[self.layer_id]
        assert (attention_window % 2 == 0), f"`attention_window` for layer {self.layer_id} has to be an even value. Given {attention_window}"
        assert (attention_window > 0), f"`attention_window` for layer {self.layer_id} has to be positive. Given {attention_window}"
        self.one_sided_attn_window_size = attention_window // 2
    def build(self, input_shape=None):
        if not self.built:
            with tf.name_scope("query_global"): self.query_global.build((self.config.hidden_size,))
            with tf.name_scope("key_global"): self.key_global.build((self.config.hidden_size,))
            with tf.name_scope("value_global"): self.value_global.build((self.config.hidden_size,))
        if self.built: return
        self.built = True
        if getattr(self, "query", None) is not None:
            with tf.name_scope(self.query.name): self.query.build([None, None, self.config.hidden_size])
        if getattr(self, "key", None) is not None:
            with tf.name_scope(self.key.name): self.key.build([None, None, self.config.hidden_size])
        if getattr(self, "value", None) is not None:
            with tf.name_scope(self.value.name): self.value.build([None, None, self.config.hidden_size])
        if getattr(self, "query_global", None) is not None:
            with tf.name_scope(self.query_global.name): self.query_global.build([None, None, self.config.hidden_size])
        if getattr(self, "key_global", None) is not None:
            with tf.name_scope(self.key_global.name): self.key_global.build([None, None, self.config.hidden_size])
        if getattr(self, "value_global", None) is not None:
            with tf.name_scope(self.value_global.name): self.value_global.build([None, None, self.config.hidden_size])
    def call(self, inputs, training=False):
        (hidden_states, attention_mask, layer_head_mask, is_index_masked, is_index_global_attn, is_global_attn) = inputs
        query_vectors = self.query(hidden_states)
        key_vectors = self.key(hidden_states)
        value_vectors = self.value(hidden_states)
        batch_size, seq_len, embed_dim = shape_list(hidden_states)
        tf.debugging.assert_equal(embed_dim, self.embed_dim, message=f"hidden_states should have embed_dim = {self.embed_dim}, but has {embed_dim}")
        query_vectors /= tf.math.sqrt(tf.cast(self.head_dim, dtype=query_vectors.dtype))
        query_vectors = tf.reshape(query_vectors, (batch_size, seq_len, self.num_heads, self.head_dim))
        key_vectors = tf.reshape(key_vectors, (batch_size, seq_len, self.num_heads, self.head_dim))
        attn_scores = self._sliding_chunks_query_key_matmul(query_vectors, key_vectors, self.one_sided_attn_window_size)
        remove_from_windowed_attention_mask = attention_mask != 0
        float_mask = tf.cast(remove_from_windowed_attention_mask, dtype=query_vectors.dtype) * LARGE_NEGATIVE
        diagonal_mask = self._sliding_chunks_query_key_matmul(tf.ones(shape_list(attention_mask)), float_mask, self.one_sided_attn_window_size)
        attn_scores += diagonal_mask
        tf.debugging.assert_equal(shape_list(attn_scores), [batch_size, seq_len, self.num_heads, self.one_sided_attn_window_size * 2 + 1], message=(f"attn_probs should be of size ({batch_size}, {seq_len}, {self.num_heads}, {self.one_sided_attn_window_size * 2 + 1}), but is of size {shape_list(attn_scores)}"))
        (max_num_global_attn_indices, is_index_global_attn_nonzero, is_local_index_global_attn_nonzero, is_local_index_no_global_attn_nonzero) = self._get_global_attn_indices(is_index_global_attn)
        if is_global_attn: attn_scores = self._concat_with_global_key_attn_probs(attn_scores=attn_scores, query_vectors=query_vectors, key_vectors=key_vectors,
        max_num_global_attn_indices=max_num_global_attn_indices, is_index_global_attn_nonzero=is_index_global_attn_nonzero, is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero,
        is_local_index_no_global_attn_nonzero=is_local_index_no_global_attn_nonzero)
        attn_probs = stable_softmax(attn_scores, axis=-1)
        if is_global_attn: masked_index = tf.tile(is_index_masked[:, :, None, None], (1, 1, self.num_heads, self.one_sided_attn_window_size * 2 + max_num_global_attn_indices + 1))
        else: masked_index = tf.tile(is_index_masked[:, :, None, None], (1, 1, self.num_heads, self.one_sided_attn_window_size * 2 + 1))
        attn_probs = tf.where(masked_index, tf.zeros(shape_list(masked_index), dtype=attn_probs.dtype), attn_probs)
        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_probs = tf.reshape(layer_head_mask, (1, 1, -1, 1)) * attn_probs
        attn_probs = self.dropout(attn_probs, training=training)
        value_vectors = tf.reshape(value_vectors, (batch_size, seq_len, self.num_heads, self.head_dim))
        if is_global_attn: attn_output = self._compute_attn_output_with_global_indices(value_vectors=value_vectors, attn_probs=attn_probs, max_num_global_attn_indices=max_num_global_attn_indices,
        is_index_global_attn_nonzero=is_index_global_attn_nonzero, is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero)
        else: attn_output = self._sliding_chunks_matmul_attn_probs_value(attn_probs, value_vectors, self.one_sided_attn_window_size)
        tf.debugging.assert_equal(shape_list(attn_output), [batch_size, seq_len, self.num_heads, self.head_dim], message="Unexpected size")
        attn_output = tf.reshape(attn_output, (batch_size, seq_len, embed_dim))
        if is_global_attn: attn_output, global_attn_probs = self._compute_global_attn_output_from_hidden(attn_output=attn_output, hidden_states=hidden_states,
        max_num_global_attn_indices=max_num_global_attn_indices, layer_head_mask=layer_head_mask, is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero,
        is_index_global_attn_nonzero=is_index_global_attn_nonzero, is_local_index_no_global_attn_nonzero=is_local_index_no_global_attn_nonzero, is_index_masked=is_index_masked, training=training)
        else: global_attn_probs = tf.zeros((batch_size, self.num_heads, max_num_global_attn_indices, seq_len))
        if is_global_attn: masked_global_attn_index = tf.tile(is_index_global_attn[:, :, None, None], (1, 1, self.num_heads, self.one_sided_attn_window_size * 2 + max_num_global_attn_indices + 1))
        else: masked_global_attn_index = tf.tile(is_index_global_attn[:, :, None, None], (1, 1, self.num_heads, self.one_sided_attn_window_size * 2 + 1))
        attn_probs = tf.where(masked_global_attn_index, tf.zeros(shape_list(masked_global_attn_index), dtype=attn_probs.dtype), attn_probs)
        outputs = (attn_output, attn_probs, global_attn_probs)
        return outputs
    def _sliding_chunks_query_key_matmul(self, query, key, window_overlap):
        batch_size, seq_len, num_heads, head_dim = shape_list(query)
        tf.debugging.assert_equal(seq_len % (window_overlap * 2), 0, message=f"Sequence length should be multiple of {window_overlap * 2}. Given {seq_len}")
        tf.debugging.assert_equal(shape_list(query), shape_list(key), message=(f"Shape of query and key should be equal, but got query: {shape_list(query)} and key: {shape_list(key)}"))
        chunks_count = seq_len // window_overlap - 1
        query = tf.reshape(tf.transpose(query, (0, 2, 1, 3)), (batch_size * num_heads, seq_len, head_dim))
        key = tf.reshape(tf.transpose(key, (0, 2, 1, 3)), (batch_size * num_heads, seq_len, head_dim))
        chunked_query = self._chunk(query, window_overlap)
        chunked_key = self._chunk(key, window_overlap)
        chunked_query = tf.cast(chunked_query, dtype=chunked_key.dtype)
        chunked_attention_scores = tf.einsum("bcxd,bcyd->bcxy", chunked_query, chunked_key)
        paddings = tf.convert_to_tensor([[0, 0], [0, 0], [0, 1], [0, 0]])
        diagonal_chunked_attention_scores = self._pad_and_transpose_last_two_dims(chunked_attention_scores, paddings)
        diagonal_attn_scores_up_triang = tf.concat([diagonal_chunked_attention_scores[:, :, :window_overlap, : window_overlap + 1], diagonal_chunked_attention_scores[:, -1:, window_overlap:, : window_overlap + 1]], axis=1)
        diagonal_attn_scores_low_triang = tf.concat([tf.zeros((batch_size * num_heads, 1, window_overlap, window_overlap), dtype=diagonal_chunked_attention_scores.dtype), diagonal_chunked_attention_scores[:, :, -(window_overlap + 1) : -1, window_overlap + 1 :]], axis=1)
        diagonal_attn_scores_first_chunk = tf.concat([tf.roll(diagonal_chunked_attention_scores, shift=[1, window_overlap], axis=[2, 3])[:, :, :window_overlap, :window_overlap], tf.zeros((batch_size * num_heads, 1, window_overlap, window_overlap),
        dtype=diagonal_chunked_attention_scores.dtype)], axis=1)
        first_chunk_mask = (tf.tile(tf.range(chunks_count + 1, dtype=tf.int64)[None, :, None, None], (batch_size * num_heads, 1, window_overlap, window_overlap)) < 1)
        diagonal_attn_scores_low_triang = tf.where(first_chunk_mask, diagonal_attn_scores_first_chunk, diagonal_attn_scores_low_triang)
        diagonal_attention_scores = tf.concat([diagonal_attn_scores_low_triang, diagonal_attn_scores_up_triang], axis=-1)
        diagonal_attention_scores = tf.transpose(tf.reshape(diagonal_attention_scores, (batch_size, num_heads, seq_len, 2 * window_overlap + 1)), (0, 2, 1, 3))
        diagonal_attention_scores = self._mask_invalid_locations(diagonal_attention_scores, window_overlap)
        return diagonal_attention_scores
    @staticmethod
    def _mask_invalid_locations(input_tensor, window_overlap):
        mask_2d_upper = tf.reverse(tf.linalg.band_part(tf.ones(shape=(window_overlap, window_overlap + 1)), -1, 0), axis=[0])
        padding = tf.convert_to_tensor([[0, shape_list(input_tensor)[1] - window_overlap], [0, shape_list(input_tensor)[3] - window_overlap - 1]])
        mask_2d = tf.pad(mask_2d_upper, padding)
        mask_2d = mask_2d + tf.reverse(mask_2d, axis=[0, 1])
        mask_4d = tf.tile(mask_2d[None, :, None, :], (shape_list(input_tensor)[0], 1, 1, 1))
        inf_tensor = -float("inf") * tf.ones_like(input_tensor)
        input_tensor = tf.where(tf.math.greater(mask_4d, 0), inf_tensor, input_tensor)
        return input_tensor
    def _sliding_chunks_matmul_attn_probs_value(self, attn_probs, value, window_overlap):
        batch_size, seq_len, num_heads, head_dim = shape_list(value)
        tf.debugging.assert_equal(seq_len % (window_overlap * 2), 0, message="Seq_len has to be multiple of 2 * window_overlap")
        tf.debugging.assert_equal(shape_list(attn_probs)[:3], shape_list(value)[:3], message="value and attn_probs must have same dims (except head_dim)")
        tf.debugging.assert_equal(shape_list(attn_probs)[3], 2 * window_overlap + 1, message="attn_probs last dim has to be 2 * window_overlap + 1")
        chunks_count = seq_len // window_overlap - 1
        chunked_attn_probs = tf.reshape(tf.transpose(attn_probs, (0, 2, 1, 3)), (batch_size * num_heads, seq_len // window_overlap, window_overlap, 2 * window_overlap + 1))
        value = tf.reshape(tf.transpose(value, (0, 2, 1, 3)), (batch_size * num_heads, seq_len, head_dim))
        paddings = tf.convert_to_tensor([[0, 0], [window_overlap, window_overlap], [0, 0]])
        padded_value = tf.pad(value, paddings, constant_values=-1)
        frame_size = 3 * window_overlap * head_dim
        frame_hop_size = (shape_list(padded_value)[1] * head_dim - frame_size) // chunks_count
        chunked_value = tf.signal.frame(tf.reshape(padded_value, (batch_size * num_heads, -1)), frame_size, frame_hop_size)
        chunked_value = tf.reshape(chunked_value, (batch_size * num_heads, chunks_count + 1, 3 * window_overlap, head_dim))
        tf.debugging.assert_equal(shape_list(chunked_value), [batch_size * num_heads, chunks_count + 1, 3 * window_overlap, head_dim], message="Chunked value has the wrong shape")
        chunked_attn_probs = self._pad_and_diagonalize(chunked_attn_probs)
        context = tf.einsum("bcwd,bcdh->bcwh", chunked_attn_probs, chunked_value)
        context = tf.transpose(tf.reshape(context, (batch_size, num_heads, seq_len, head_dim)), (0, 2, 1, 3))
        return context
    @staticmethod
    def _pad_and_transpose_last_two_dims(hidden_states_padded, paddings):
        hidden_states_padded = tf.pad(hidden_states_padded, paddings)
        batch_size, chunk_size, seq_length, hidden_dim = shape_list(hidden_states_padded)
        hidden_states_padded = tf.reshape(hidden_states_padded, (batch_size, chunk_size, hidden_dim, seq_length))
        return hidden_states_padded
    @staticmethod
    def _pad_and_diagonalize(chunked_hidden_states):
        total_num_heads, num_chunks, window_overlap, hidden_dim = shape_list(chunked_hidden_states)
        paddings = tf.convert_to_tensor([[0, 0], [0, 0], [0, 0], [0, window_overlap + 1]])
        chunked_hidden_states = tf.pad(chunked_hidden_states, paddings)
        chunked_hidden_states = tf.reshape(chunked_hidden_states, (total_num_heads, num_chunks, -1))
        chunked_hidden_states = chunked_hidden_states[:, :, :-window_overlap]
        chunked_hidden_states = tf.reshape(chunked_hidden_states, (total_num_heads, num_chunks, window_overlap, window_overlap + hidden_dim))
        chunked_hidden_states = chunked_hidden_states[:, :, :, :-1]
        return chunked_hidden_states
    @staticmethod
    def _chunk(hidden_states, window_overlap):
        batch_size, seq_length, hidden_dim = shape_list(hidden_states)
        num_output_chunks = 2 * (seq_length // (2 * window_overlap)) - 1
        frame_hop_size = window_overlap * hidden_dim
        frame_size = 2 * frame_hop_size
        hidden_states = tf.reshape(hidden_states, (batch_size, seq_length * hidden_dim))
        chunked_hidden_states = tf.signal.frame(hidden_states, frame_size, frame_hop_size)
        tf.debugging.assert_equal(shape_list(chunked_hidden_states), [batch_size, num_output_chunks, frame_size], message=(f"Make sure chunking is correctly applied. `Chunked hidden states should have output  dimension {[batch_size, frame_size, num_output_chunks]}, but got {shape_list(chunked_hidden_states)}."))
        chunked_hidden_states = tf.reshape(chunked_hidden_states, (batch_size, num_output_chunks, 2 * window_overlap, hidden_dim))
        return chunked_hidden_states
    @staticmethod
    def _get_global_attn_indices(is_index_global_attn):
        num_global_attn_indices = tf.math.count_nonzero(is_index_global_attn, axis=1)
        num_global_attn_indices = tf.cast(num_global_attn_indices, dtype=tf.constant(1).dtype)
        max_num_global_attn_indices = tf.reduce_max(num_global_attn_indices)
        is_index_global_attn_nonzero = tf.where(is_index_global_attn)
        is_local_index_global_attn = tf.range(max_num_global_attn_indices) < tf.expand_dims(num_global_attn_indices, axis=-1)
        is_local_index_global_attn_nonzero = tf.where(is_local_index_global_attn)
        is_local_index_no_global_attn_nonzero = tf.where(tf.math.logical_not(is_local_index_global_attn))
        return (max_num_global_attn_indices, is_index_global_attn_nonzero, is_local_index_global_attn_nonzero, is_local_index_no_global_attn_nonzero)
    def _concat_with_global_key_attn_probs(self, attn_scores, key_vectors, query_vectors, max_num_global_attn_indices, is_index_global_attn_nonzero,
    is_local_index_global_attn_nonzero, is_local_index_no_global_attn_nonzero):
        batch_size = shape_list(key_vectors)[0]
        global_key_vectors = tf.gather_nd(key_vectors, is_index_global_attn_nonzero)
        key_vectors_only_global = tf.scatter_nd(is_local_index_global_attn_nonzero, global_key_vectors, shape=(batch_size, max_num_global_attn_indices, self.num_heads, self.head_dim))
        attn_probs_from_global_key = tf.einsum("blhd,bshd->blhs", query_vectors, key_vectors_only_global)
        attn_probs_from_global_key_trans = tf.transpose(attn_probs_from_global_key, (0, 3, 1, 2))
        mask_shape = (shape_list(is_local_index_no_global_attn_nonzero)[0],) + tuple(shape_list(attn_probs_from_global_key_trans)[-2:])
        mask = tf.ones(mask_shape) * -10000.0
        mask = tf.cast(mask, dtype=attn_probs_from_global_key_trans.dtype)
        attn_probs_from_global_key_trans = tf.tensor_scatter_nd_update(attn_probs_from_global_key_trans, is_local_index_no_global_attn_nonzero, mask)
        attn_probs_from_global_key = tf.transpose(attn_probs_from_global_key_trans, (0, 2, 3, 1))
        attn_scores = tf.concat((attn_probs_from_global_key, attn_scores), axis=-1)
        return attn_scores
    def _compute_attn_output_with_global_indices(self, value_vectors, attn_probs, max_num_global_attn_indices, is_index_global_attn_nonzero, is_local_index_global_attn_nonzero):
        batch_size = shape_list(attn_probs)[0]
        attn_probs_only_global = attn_probs[:, :, :, :max_num_global_attn_indices]
        global_value_vectors = tf.gather_nd(value_vectors, is_index_global_attn_nonzero)
        value_vectors_only_global = tf.scatter_nd(is_local_index_global_attn_nonzero, global_value_vectors, shape=(batch_size, max_num_global_attn_indices, self.num_heads, self.head_dim))
        attn_output_only_global = tf.einsum("blhs,bshd->blhd", attn_probs_only_global, value_vectors_only_global)
        attn_probs_without_global = attn_probs[:, :, :, max_num_global_attn_indices:]
        attn_output_without_global = self._sliding_chunks_matmul_attn_probs_value(attn_probs_without_global, value_vectors, self.one_sided_attn_window_size)
        return attn_output_only_global + attn_output_without_global
    def _compute_global_attn_output_from_hidden(self, attn_output, hidden_states, max_num_global_attn_indices, layer_head_mask, is_local_index_global_attn_nonzero,
    is_index_global_attn_nonzero, is_local_index_no_global_attn_nonzero, is_index_masked, training):
        batch_size, seq_len = shape_list(hidden_states)[:2]
        global_attn_hidden_states = tf.gather_nd(hidden_states, is_index_global_attn_nonzero)
        global_attn_hidden_states = tf.scatter_nd(is_local_index_global_attn_nonzero, global_attn_hidden_states, shape=(batch_size, max_num_global_attn_indices, self.embed_dim))
        global_query_vectors_only_global = self.query_global(global_attn_hidden_states)
        global_key_vectors = self.key_global(hidden_states)
        global_value_vectors = self.value_global(hidden_states)
        global_query_vectors_only_global /= tf.math.sqrt(tf.cast(self.head_dim, dtype=global_query_vectors_only_global.dtype))
        global_query_vectors_only_global = self.reshape_and_transpose(global_query_vectors_only_global, batch_size)
        global_key_vectors = self.reshape_and_transpose(global_key_vectors, batch_size)
        global_value_vectors = self.reshape_and_transpose(global_value_vectors, batch_size)
        global_attn_scores = tf.matmul(global_query_vectors_only_global, global_key_vectors, transpose_b=True)
        tf.debugging.assert_equal(shape_list(global_attn_scores), [batch_size * self.num_heads, max_num_global_attn_indices, seq_len], message=(f"global_attn_scores have the wrong size. Size should be {(batch_size * self.num_heads, max_num_global_attn_indices, seq_len)}, but is {shape_list(global_attn_scores)}."))
        global_attn_scores = tf.reshape(global_attn_scores, (batch_size, self.num_heads, max_num_global_attn_indices, seq_len))
        global_attn_scores_trans = tf.transpose(global_attn_scores, (0, 2, 1, 3))
        mask_shape = (shape_list(is_local_index_no_global_attn_nonzero)[0],) + tuple(shape_list(global_attn_scores_trans)[-2:])
        global_attn_mask = tf.ones(mask_shape) * -10000.0
        global_attn_mask = tf.cast(global_attn_mask, dtype=global_attn_scores_trans.dtype)
        global_attn_scores_trans = tf.tensor_scatter_nd_update(global_attn_scores_trans, is_local_index_no_global_attn_nonzero, global_attn_mask)
        global_attn_scores = tf.transpose(global_attn_scores_trans, (0, 2, 1, 3))
        attn_mask = tf.tile(is_index_masked[:, None, None, :], (1, shape_list(global_attn_scores)[1], 1, 1))
        global_attn_scores = tf.where(attn_mask, -10000.0, global_attn_scores)
        global_attn_scores = tf.reshape(global_attn_scores, (batch_size * self.num_heads, max_num_global_attn_indices, seq_len))
        global_attn_probs_float = stable_softmax(global_attn_scores, 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)}"))
            global_attn_probs_float = tf.reshape(layer_head_mask, (1, -1, 1, 1)) * tf.reshape(global_attn_probs_float, (batch_size, self.num_heads, max_num_global_attn_indices, seq_len))
            global_attn_probs_float = tf.reshape(global_attn_probs_float, (batch_size * self.num_heads, max_num_global_attn_indices, seq_len))
        global_attn_probs = self.global_dropout(global_attn_probs_float, training=training)
        global_attn_output = tf.matmul(global_attn_probs, global_value_vectors)
        tf.debugging.assert_equal(shape_list(global_attn_output), [batch_size * self.num_heads, max_num_global_attn_indices, self.head_dim], message=(f"global_attn_output tensor has the wrong size. Size should be {(batch_size * self.num_heads, max_num_global_attn_indices, self.head_dim)}, but is {shape_list(global_attn_output)}."))
        global_attn_output = tf.reshape(global_attn_output, (batch_size, self.num_heads, max_num_global_attn_indices, self.head_dim))
        nonzero_global_attn_output = tf.gather_nd(tf.transpose(global_attn_output, (0, 2, 1, 3)), is_local_index_global_attn_nonzero)
        nonzero_global_attn_output = tf.reshape(nonzero_global_attn_output, (shape_list(is_local_index_global_attn_nonzero)[0], -1))
        attn_output = tf.tensor_scatter_nd_update(attn_output, is_index_global_attn_nonzero, nonzero_global_attn_output)
        global_attn_probs = tf.reshape(global_attn_probs, (batch_size, self.num_heads, max_num_global_attn_indices, seq_len))
        return attn_output, global_attn_probs
    def reshape_and_transpose(self, vector, batch_size): return tf.reshape(tf.transpose(tf.reshape(vector, (batch_size, -1, self.num_heads, self.head_dim)), (0, 2, 1, 3)), (batch_size * self.num_heads, -1, self.head_dim))
class TFLEDEncoderAttention(keras.layers.Layer):
    def __init__(self, config, layer_id, **kwargs):
        super().__init__(**kwargs)
        self.longformer_self_attn = TFLEDEncoderSelfAttention(config, layer_id=layer_id, name="longformer_self_attn")
        self.output_dense = keras.layers.Dense(config.d_model, use_bias=True, name="output")
        self.config = config
    def call(self, inputs, training=False):
        (hidden_states, attention_mask, layer_head_mask, is_index_masked, is_index_global_attn, is_global_attn) = inputs
        self_outputs = self.longformer_self_attn([hidden_states, attention_mask, layer_head_mask, is_index_masked, is_index_global_attn, is_global_attn], training=training)
        attention_output = self.output_dense(self_outputs[0], training=training)
        outputs = (attention_output,) + self_outputs[1:]
        return outputs
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "longformer_self_attn", None) is not None:
            with tf.name_scope(self.longformer_self_attn.name): self.longformer_self_attn.build(None)
        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.d_model])
class TFLEDDecoderAttention(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
        assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by 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=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)}"))
            attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + tf.cast(attention_mask, dtype=attn_weights.dtype)
            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 TFLEDEncoderLayer(keras.layers.Layer):
    def __init__(self, config: LEDConfig, layer_id: int, **kwargs):
        super().__init__(**kwargs)
        self.embed_dim = config.d_model
        self.self_attn = TFLEDEncoderAttention(config, layer_id, 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, is_index_masked: tf.Tensor, is_index_global_attn: tf.Tensor, is_global_attn: bool, training=False):
        residual = hidden_states
        layer_outputs = self.self_attn([hidden_states, attention_mask, layer_head_mask, is_index_masked, is_index_global_attn, is_global_attn], training=training)
        hidden_states = layer_outputs[0]
        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
        hidden_states = self.self_attn_layer_norm(hidden_states)
        residual = 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
        hidden_states = self.final_layer_norm(hidden_states)
        return (hidden_states,) + layer_outputs[1:]
    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 TFLEDDecoderLayer(keras.layers.Layer):
    def __init__(self, config: LEDConfig, **kwargs):
        super().__init__(**kwargs)
        self.embed_dim = config.d_model
        self.self_attn = TFLEDDecoderAttention(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 = TFLEDDecoderAttention(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, encoder_layer_head_mask: tf.Tensor | None = None, past_key_value: Tuple[tf.Tensor] | None = None, training=False) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor, Tuple[Tuple[tf.Tensor]]]:
        residual = hidden_states
        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
        hidden_states, self_attn_weights, present_key_value = self.self_attn(hidden_states=hidden_states, past_key_value=self_attn_past_key_value, attention_mask=attention_mask, layer_head_mask=layer_head_mask)
        hidden_states = self.dropout(hidden_states, training=training)
        hidden_states = residual + hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)
        cross_attn_present_key_value = None
        cross_attn_weights = None
        if encoder_hidden_states is not None:
            residual = 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=encoder_layer_head_mask, past_key_value=cross_attn_past_key_value)
            hidden_states = self.dropout(hidden_states, training=training)
            hidden_states = residual + hidden_states
            hidden_states = self.encoder_attn_layer_norm(hidden_states)
            present_key_value = present_key_value + cross_attn_present_key_value
        residual = 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
        hidden_states = self.final_layer_norm(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 TFLEDPreTrainedModel(TFPreTrainedModel):
    config_class = LEDConfig
    base_model_prefix = "led"
    @property
    def input_signature(self):
        sig = super().input_signature
        sig["global_attention_mask"] = tf.TensorSpec((None, None), tf.int32, name="global_attention_mask")
        return sig
@dataclass
class TFLEDEncoderBaseModelOutput(ModelOutput):
    """Args:"""
    last_hidden_state: tf.Tensor = None
    hidden_states: Tuple[tf.Tensor, ...] | None = None
    attentions: Tuple[tf.Tensor, ...] | None = None
    global_attentions: Tuple[tf.Tensor, ...] | None = None
@dataclass
class TFLEDSeq2SeqModelOutput(ModelOutput):
    """Args:"""
    last_hidden_state: tf.Tensor = None
    past_key_values: List[tf.Tensor] | None = None
    decoder_hidden_states: Tuple[tf.Tensor, ...] | None = None
    decoder_attentions: Tuple[tf.Tensor, ...] | None = None
    cross_attentions: Tuple[tf.Tensor, ...] | None = None
    encoder_last_hidden_state: tf.Tensor | None = None
    encoder_hidden_states: Tuple[tf.Tensor, ...] | None = None
    encoder_attentions: Tuple[tf.Tensor, ...] | None = None
    encoder_global_attentions: Tuple[tf.Tensor, ...] | None = None
@dataclass
class TFLEDSeq2SeqLMOutput(ModelOutput):
    """Args:"""
    loss: tf.Tensor | None = None
    logits: tf.Tensor = None
    past_key_values: List[tf.Tensor] | None = None
    decoder_hidden_states: Tuple[tf.Tensor, ...] | None = None
    decoder_attentions: Tuple[tf.Tensor, ...] | None = None
    cross_attentions: Tuple[tf.Tensor, ...] | None = None
    encoder_last_hidden_state: tf.Tensor | None = None
    encoder_hidden_states: Tuple[tf.Tensor, ...] | None = None
    encoder_attentions: Tuple[tf.Tensor, ...] | None = None
    encoder_global_attentions: Tuple[tf.Tensor, ...] | None = None
LED_START_DOCSTRING = r"""
    This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)
    This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
    as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
    behavior.
    <Tip>
    TensorFlow models and layers in `transformers` accept two formats as input:
    - having all inputs as keyword arguments (like PyTorch models), or
    - having all inputs as a list, tuple or dict in the first positional argument.
    The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
    and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
    pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
    format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
    the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
    positional argument:
    - a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
    - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
    `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
    - a dictionary with one or several input Tensors associated to the input names given in the docstring:
    `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
    Note that when creating models and layers with
    [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
    about any of this, as you can just pass inputs like you would to any other Python function!
    </Tip>
    Args:
        config ([`LEDConfig`]): 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.
"""
LED_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`tf.Tensor` of shape `(0)`):
            Indices of input sequence tokens in the vocabulary.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are input IDs?](../glossary#input-ids)
        attention_mask (`tf.Tensor` of shape `(0)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
            - 1 for tokens that are *not masked*,
            - 0 for tokens that are *masked*.
            [What are attention masks?](../glossary#attention-mask)
        decoder_input_ids (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Indices of decoder input sequence tokens in the vocabulary.
            Indices can be obtained using [`LedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are input IDs?](../glossary#input-ids)
            LED 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*):
            will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.
        head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
            Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        decoder_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
            Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        encoder_outputs (`tf.Tensor`, *optional*):
            hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
            of shape `(batch_size, sequence_length, hidden_size)` is a sequence of
        past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`)
            contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        use_cache (`bool`, *optional*, defaults to `True`):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`). Set to `False` during training, `True` during generation
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
            config will be used instead.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
            used instead.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
            eager mode, in graph mode the value will always be set to True.
        training (`bool`, *optional*, defaults to `False`):
            Whether or not to use the model in training mode (some modules like dropout modules have different
            behaviors between training and evaluation).
"""
@keras_serializable
class TFLEDEncoder(keras.layers.Layer):
    config_class = LEDConfig
    def __init__(self, config: LEDConfig, embed_tokens: Optional[keras.layers.Embedding] = None, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.dropout = keras.layers.Dropout(config.dropout)
        if config.encoder_layerdrop > 0: logger.warning("Layerdrop is currently disabled in TFLED models.")
        self.layerdrop = 0.0
        self.padding_idx = config.pad_token_id
        if isinstance(config.attention_window, int):
            assert config.attention_window % 2 == 0, "`config.attention_window` has to be an even value"
            assert config.attention_window > 0, "`config.attention_window` has to be positive"
            config.attention_window = [config.attention_window] * config.num_hidden_layers
        else: assert len(config.attention_window) == config.num_hidden_layers, (f"`len(config.attention_window)` should equal `config.num_hidden_layers`. Expected {config.num_hidden_layers}, given {len(config.attention_window)}")
        self.attention_window = config.attention_window
        self.embed_tokens = embed_tokens
        self.embed_positions = TFLEDLearnedPositionalEmbedding(config.max_encoder_position_embeddings, config.d_model, name="embed_positions")
        self.layers = [TFLEDEncoderLayer(config, i, name=f"layers.{i}") for i in range(config.encoder_layers)]
        self.layernorm_embedding = keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm_embedding")
        self.embed_dim = config.d_model
    def get_embed_tokens(self): return self.embed_tokens
    def set_embed_tokens(self, embed_tokens): self.embed_tokens = embed_tokens
    @unpack_inputs
    def call(self, input_ids=None, inputs_embeds=None, attention_mask=None, global_attention_mask=None, head_mask=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False):
        if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = shape_list(input_ids)
            check_embeddings_within_bounds(input_ids, self.embed_tokens.input_dim)
            inputs_embeds = self.embed_tokens(input_ids)
        elif inputs_embeds is not None: input_shape = shape_list(inputs_embeds)[:-1]
        else: raise ValueError("You have to specify either input_ids or inputs_embeds")
        if attention_mask is None: attention_mask = tf.fill(input_shape, 1)
        if global_attention_mask is not None: attention_mask = attention_mask * tf.cast((global_attention_mask + 1), dtype=attention_mask.dtype)
        padding_len, input_ids, attention_mask, inputs_embeds = self._pad_to_window_size(input_ids=input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, pad_token_id=self.padding_idx)
        input_shape = shape_list(attention_mask)
        is_index_masked = tf.math.less(tf.cast(attention_mask, tf.int8), 1)
        is_index_global_attn = tf.math.greater(tf.cast(attention_mask, tf.int8), 1)
        is_global_attn = tf.math.reduce_any(is_index_global_attn)
        embed_pos = self.embed_positions(input_shape)
        hidden_states = inputs_embeds + embed_pos
        hidden_states = self.layernorm_embedding(hidden_states)
        hidden_states = self.dropout(hidden_states, training=training)
        if attention_mask is not None:
            attention_mask = _expand_mask(attention_mask)[:, 0, 0, :]
            attention_mask = attention_mask[:, :, None, None]
        encoder_states = () if output_hidden_states else None
        all_attentions = all_global_attentions = () if output_attentions else None
        if head_mask is not None: tf.debugging.assert_equal(shape_list(head_mask)[0], len(self.layers), message=(f"The head_mask should be specified for {len(self.layers)} layers, but it is for {shape_list(head_mask)[0]}."))
        for idx, encoder_layer in enumerate(self.layers):
            if output_hidden_states:
                hidden_states_to_add = self.compute_hidden_states(hidden_states, padding_len)
                encoder_states = encoder_states + (hidden_states_to_add,)
            dropout_probability = random.uniform(0, 1)
            if training and (dropout_probability < self.layerdrop): continue
            layer_outputs = encoder_layer(hidden_states=hidden_states, attention_mask=attention_mask, layer_head_mask=head_mask[idx] if head_mask is not None else None,
            is_index_masked=is_index_masked, is_index_global_attn=is_index_global_attn, is_global_attn=is_global_attn)
            hidden_states = layer_outputs[0]
            if output_attentions:
                all_attentions = all_attentions + (tf.transpose(layer_outputs[1], (0, 2, 1, 3)),)
                all_global_attentions = all_global_attentions + (tf.transpose(layer_outputs[2], (0, 1, 3, 2)),)
        hidden_states = self.compute_hidden_states(hidden_states, padding_len)
        if output_attentions: all_attentions = (tuple([state[:, :, :-padding_len, :] for state in all_attentions]) if padding_len > 0 else all_attentions)
        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 TFLEDEncoderBaseModelOutput(last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions, global_attentions=all_global_attentions)
    @tf.function
    def compute_hidden_states(self, hidden_states, padding_len): return hidden_states[:, :-padding_len] if padding_len > 0 else hidden_states
    def _pad_to_window_size(self, input_ids, attention_mask, inputs_embeds, pad_token_id):
        attention_window = (self.attention_window if isinstance(self.attention_window, int) else max(self.attention_window))
        assert attention_window % 2 == 0, f"`attention_window` should be an even value. Given {attention_window}"
        input_shape = shape_list(input_ids) if input_ids is not None else shape_list(inputs_embeds)
        batch_size, seq_len = input_shape[:2]
        padding_len = (attention_window - seq_len % attention_window) % attention_window
        if padding_len > 0: logger.warning_once(f"Input ids are automatically padded from {seq_len} to {seq_len + padding_len} to be a multiple of `config.attention_window`: {attention_window}")
        paddings = tf.convert_to_tensor([[0, 0], [0, padding_len]])
        if input_ids is not None: input_ids = tf.pad(input_ids, paddings, constant_values=pad_token_id)
        if inputs_embeds is not None:
            if padding_len > 0:
                input_ids_padding = tf.fill((batch_size, padding_len), pad_token_id)
                inputs_embeds_padding = self.embed_tokens(input_ids_padding)
                inputs_embeds = tf.concat([inputs_embeds, inputs_embeds_padding], axis=-2)
        attention_mask = tf.pad(attention_mask, paddings, constant_values=False)
        return (padding_len, input_ids, attention_mask, inputs_embeds)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "embed_positions", None) is not None:
            with tf.name_scope(self.embed_positions.name): self.embed_positions.build(None)
        if getattr(self, "layernorm_embedding", None) is not None:
            with tf.name_scope(self.layernorm_embedding.name): self.layernorm_embedding.build([None, None, self.embed_dim])
        if getattr(self, "layers", None) is not None:
            for layer in self.layers:
                with tf.name_scope(layer.name): layer.build(None)
@keras_serializable
class TFLEDDecoder(keras.layers.Layer):
    config_class = LEDConfig
    def __init__(self, config: LEDConfig, embed_tokens: Optional[keras.layers.Embedding] = None, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.padding_idx = config.pad_token_id
        self.embed_tokens = embed_tokens
        if config.decoder_layerdrop > 0: logger.warning("Layerdrop is currently disabled in TFLED models.")
        self.layerdrop = 0.0
        self.embed_positions = TFLEDLearnedPositionalEmbedding(config.max_decoder_position_embeddings, config.d_model, name="embed_positions")
        self.layers = [TFLEDDecoderLayer(config, name=f"layers.{i}") for i in range(config.decoder_layers)]
        self.layernorm_embedding = keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm_embedding")
        self.dropout = keras.layers.Dropout(config.dropout)
    def set_embed_tokens(self, embed_tokens): self.embed_tokens = embed_tokens
    @unpack_inputs
    def call(self, input_ids=None, inputs_embeds=None, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, head_mask=None, encoder_head_mask=None,
    past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False):
        if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
        elif input_ids is not None: input_shape = shape_list(input_ids)
        elif inputs_embeds is not None: input_shape = shape_list(inputs_embeds)[:-1]
        else: raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
        past_key_values_length = shape_list(past_key_values[0][0])[2] if past_key_values is not None else 0
        positions = self.embed_positions(input_shape, past_key_values_length)
        if inputs_embeds is None:
            check_embeddings_within_bounds(input_ids, self.embed_tokens.input_dim)
            inputs_embeds = self.embed_tokens(input_ids)
        hidden_states = inputs_embeds
        if input_shape[-1] > 1: combined_attention_mask = _make_causal_mask(input_shape, past_key_values_length=past_key_values_length)
        else: combined_attention_mask = _expand_mask(tf.ones((input_shape[0], input_shape[1] + past_key_values_length)), tgt_len=input_shape[-1])
        if attention_mask is not None and input_shape[-1] > 1: combined_attention_mask = combined_attention_mask + _expand_mask(attention_mask, tgt_len=input_shape[-1])
        if encoder_hidden_states is not None and encoder_attention_mask is not None: encoder_attention_mask = _expand_mask(encoder_attention_mask, tgt_len=input_shape[-1])
        hidden_states = self.layernorm_embedding(hidden_states + positions)
        hidden_states = self.dropout(hidden_states, training=training)
        all_hidden_states = ()
        all_self_attns = ()
        all_cross_attentions = ()
        present_key_values = ()
        if head_mask is not None: tf.debugging.assert_equal(shape_list(head_mask)[0], len(self.layers), message=(f"The head_mask should be specified for {len(self.layers)} layers, but it is for {shape_list(head_mask)[0]}."))
        for idx, decoder_layer in enumerate(self.layers):
            if output_hidden_states: all_hidden_states += (hidden_states,)
            dropout_probability = random.uniform(0, 1)
            if training and (dropout_probability < self.layerdrop): continue
            past_key_value = past_key_values[idx] if past_key_values is not None else None
            hidden_states, layer_self_attn, layer_cross_attn, present_key_value = decoder_layer(hidden_states, attention_mask=combined_attention_mask, encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask, layer_head_mask=head_mask[idx] if head_mask is not None else None, encoder_layer_head_mask=encoder_head_mask[idx] if encoder_head_mask is not None else None, past_key_value=past_key_value)
            if use_cache: present_key_values += (present_key_value,)
            if output_attentions:
                all_self_attns += (layer_self_attn,)
                all_cross_attentions += (layer_cross_attn,)
        if output_hidden_states: all_hidden_states += (hidden_states,)
        else: all_hidden_states = None
        all_self_attns = all_self_attns if output_attentions else None
        all_cross_attentions = all_cross_attentions if output_attentions else None
        present_key_values = present_key_values if use_cache else None
        if not return_dict: return tuple(v for v in [hidden_states, present_key_values, all_hidden_states, all_self_attns, all_cross_attentions] if v is not None)
        else: return TFBaseModelOutputWithPastAndCrossAttentions(last_hidden_state=hidden_states, past_key_values=present_key_values, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "embed_positions", None) is not None:
            with tf.name_scope(self.embed_positions.name): self.embed_positions.build(None)
        if getattr(self, "layernorm_embedding", None) is not None:
            with tf.name_scope(self.layernorm_embedding.name): self.layernorm_embedding.build([None, None, self.config.d_model])
        if getattr(self, "layers", None) is not None:
            for layer in self.layers:
                with tf.name_scope(layer.name): layer.build(None)
@keras_serializable
class TFLEDMainLayer(keras.layers.Layer):
    config_class = LEDConfig
    def __init__(self, config: LEDConfig, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.shared = keras.layers.Embedding(input_dim=config.vocab_size, output_dim=config.d_model, embeddings_initializer=keras.initializers.TruncatedNormal(stddev=self.config.init_std), name="led.shared")
        self.shared.load_weight_prefix = "led.shared"
        self.encoder = TFLEDEncoder(config, self.shared, name="encoder")
        self.decoder = TFLEDDecoder(config, self.shared, name="decoder")
    def get_input_embeddings(self): return self.shared
    def set_input_embeddings(self, new_embeddings):
        self.shared = new_embeddings
        self.encoder.embed_tokens = self.shared
        self.decoder.embed_tokens = self.shared
    @unpack_inputs
    def call(self, input_ids=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, encoder_outputs: Optional[Union[Tuple, TFLEDEncoderBaseModelOutput]] = None,
    global_attention_mask=None, past_key_values=None, inputs_embeds=None, decoder_inputs_embeds=None, use_cache=None, output_attentions=None, output_hidden_states=None,
    return_dict=None, training=False, **kwargs):
        if decoder_input_ids is None and decoder_inputs_embeds is None: use_cache = False
        if encoder_outputs is None:
            encoder_outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask, global_attention_mask=global_attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds,
            output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training)
        elif return_dict and not isinstance(encoder_outputs, TFLEDEncoderBaseModelOutput):
            encoder_outputs = TFLEDEncoderBaseModelOutput(last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
            attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None)
        elif not return_dict and not isinstance(encoder_outputs, tuple): encoder_outputs = encoder_outputs.to_tuple()
        decoder_outputs = self.decoder(decoder_input_ids, attention_mask=decoder_attention_mask, encoder_hidden_states=encoder_outputs[0], encoder_attention_mask=attention_mask,
        head_mask=decoder_head_mask, encoder_head_mask=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 TFLEDSeq2SeqModelOutput(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, encoder_global_attentions=encoder_outputs.global_attentions)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        with tf.name_scope(self.shared.load_weight_prefix + "/" + self.shared.name + "/"): self.shared.build(None)
        if getattr(self, "encoder", None) is not None:
            with tf.name_scope(self.encoder.name): self.encoder.build(None)
        if getattr(self, "decoder", None) is not None:
            with tf.name_scope(self.decoder.name): self.decoder.build(None)
@add_start_docstrings("The bare LED Model outputting raw hidden-states without any specific head on top.", LED_START_DOCSTRING)
class TFLEDModel(TFLEDPreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.led = TFLEDMainLayer(config, name="led")
    def get_encoder(self): return self.led.encoder
    def get_decoder(self): return self.led.decoder
    @unpack_inputs
    @add_start_docstrings_to_model_forward(LED_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def call(self, input_ids: TFModelInputType | None = None, attention_mask: tf.Tensor | None = None, decoder_input_ids: tf.Tensor | None = None, decoder_attention_mask: tf.Tensor | None = None,
    head_mask: tf.Tensor | None = None, decoder_head_mask: tf.Tensor | None = None, encoder_outputs: tf.Tensor | None = None, global_attention_mask: tf.Tensor | None = None,
    past_key_values: Tuple[Tuple[tf.Tensor]] | None = None, inputs_embeds: tf.Tensor | None = None, decoder_inputs_embeds: tf.Tensor | None = None, use_cache: bool | None = None,
    output_attentions: bool | None = None, output_hidden_states: bool | None = None, return_dict: bool | None = None, training: bool = False, **kwargs) -> Tuple[tf.Tensor] | TFLEDSeq2SeqModelOutput:
        outputs = self.led(input_ids=input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask,
        encoder_outputs=encoder_outputs, global_attention_mask=global_attention_mask, head_mask=head_mask, decoder_head_mask=decoder_head_mask, past_key_values=past_key_values,
        inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states,
        return_dict=return_dict, training=training)
        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
        enc_g_attns = tf.convert_to_tensor(output.encoder_global_attentions) if self.config.output_attentions else None
        return TFLEDSeq2SeqModelOutput(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, encoder_global_attentions=enc_g_attns)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "led", None) is not None:
            with tf.name_scope(self.led.name): self.led.build(None)
class BiasLayer(keras.layers.Layer):
    def __init__(self, shape, initializer, trainable, name, **kwargs):
        super().__init__(name=name, **kwargs)
        self.bias = self.add_weight(name=name, shape=shape, initializer=initializer, trainable=trainable)
    def call(self, x): return x + self.bias
@add_start_docstrings("The LED Model with a language modeling head. Can be used for summarization.", LED_START_DOCSTRING)
class TFLEDForConditionalGeneration(TFLEDPreTrainedModel):
    _keys_to_ignore_on_load_unexpected = [r"led.encoder.embed_tokens.weight", r"led.decoder.embed_tokens.weight"]
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.led = TFLEDMainLayer(config, name="led")
        self.use_cache = config.use_cache
        self.bias_layer = BiasLayer(name="final_logits_bias", shape=[1, config.vocab_size], initializer="zeros", trainable=False)
        self.supports_xla_generation = False
    def get_decoder(self): return self.led.decoder
    def get_encoder(self): return self.led.encoder
    def get_bias(self): return {"final_logits_bias": self.bias_layer.bias}
    def set_bias(self, value):
        vocab_size = value["final_logits_bias"].shape[-1]
        self.bias_layer = BiasLayer(name="final_logits_bias", shape=[1, vocab_size], initializer="zeros", trainable=False)
        self.bias_layer.bias.assign(value["final_logits_bias"])
    def get_output_embeddings(self): return self.get_input_embeddings()
    def set_output_embeddings(self, value): self.set_input_embeddings(value)
    @unpack_inputs
    @add_start_docstrings_to_model_forward(LED_INPUTS_DOCSTRING)
    def call(self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, decoder_input_ids: np.ndarray | tf.Tensor | None = None,
    decoder_attention_mask: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, decoder_head_mask: np.ndarray | tf.Tensor | None = None,
    encoder_outputs: TFLEDEncoderBaseModelOutput | None = None, global_attention_mask: np.ndarray | tf.Tensor | None = None, past_key_values: Tuple[Tuple[Union[np.ndarray, tf.Tensor]]] | None = None,
    inputs_embeds: np.ndarray | tf.Tensor | None = None, decoder_inputs_embeds: np.ndarray | tf.Tensor | None = None, use_cache: bool | None = None, output_attentions: bool | None = None,
    output_hidden_states: bool | None = None, return_dict: bool | None = None, labels: tf.Tensor | None = None, training: bool = False) -> Tuple[tf.Tensor] | TFLEDSeq2SeqLMOutput:
        if labels is not None:
            use_cache = False
            if decoder_input_ids is None and decoder_inputs_embeds is None: decoder_input_ids = shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
        outputs = self.led(input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, encoder_outputs=encoder_outputs,
        global_attention_mask=global_attention_mask, head_mask=head_mask, decoder_head_mask=decoder_head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds,
        decoder_inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training)
        lm_logits = tf.matmul(outputs[0], self.led.shared.weights, transpose_b=True)
        lm_logits = self.bias_layer(lm_logits)
        masked_lm_loss = None if labels is None else self.hf_compute_loss(labels, lm_logits)
        if not return_dict:
            output = (lm_logits,) + outputs[1:]
            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
        return TFLEDSeq2SeqLMOutput(loss=masked_lm_loss, logits=lm_logits, past_key_values=outputs.past_key_values, decoder_hidden_states=outputs.decoder_hidden_states,
        decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state,
        encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, encoder_global_attentions=outputs.encoder_global_attentions)
    def serving_output(self, output):
        pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
        dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
        dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
        cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None
        enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
        enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None
        enc_g_attns = tf.convert_to_tensor(output.encoder_global_attentions) if self.config.output_attentions else None
        return TFLEDSeq2SeqLMOutput(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, encoder_global_attentions=enc_g_attns)
    def prepare_inputs_for_generation(self, decoder_input_ids, past_key_values=None, attention_mask=None, head_mask=None, decoder_head_mask=None, use_cache=None, encoder_outputs=None, **kwargs):
        if past_key_values is not None: decoder_input_ids = decoder_input_ids[:, -1:]
        return {"input_ids": None, "encoder_outputs": encoder_outputs, "past_key_values": past_key_values, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask,
        "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "use_cache": use_cache}
    def prepare_decoder_input_ids_from_labels(self, labels: tf.Tensor): return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
    def hf_compute_loss(self, labels, logits):
        loss_fn = keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction=keras.losses.Reduction.NONE)
        if self.config.tf_legacy_loss:
            melted_labels = tf.reshape(labels, (-1,))
            active_loss = tf.not_equal(melted_labels, self.config.pad_token_id)
            reduced_logits = tf.boolean_mask(tf.reshape(logits, (-1, shape_list(logits)[2])), active_loss)
            labels = tf.boolean_mask(melted_labels, active_loss)
            return loss_fn(labels, reduced_logits)
        unmasked_loss = loss_fn(tf.nn.relu(labels), logits)
        loss_mask = tf.cast(labels != self.config.pad_token_id, dtype=unmasked_loss.dtype)
        masked_loss = unmasked_loss * loss_mask
        reduced_masked_loss = tf.reduce_sum(masked_loss) / tf.reduce_sum(loss_mask)
        return tf.reshape(reduced_masked_loss, (1,))
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "led", None) is not None:
            with tf.name_scope(self.led.name): self.led.build(None)
        if getattr(self, "bias_layer", None) is not None:
            with tf.name_scope(self.bias_layer.name): self.bias_layer.build(None)
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
