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 dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_utils import (TFMaskedLanguageModelingLoss, TFModelInputType, TFMultipleChoiceLoss, TFPreTrainedModel, TFQuestionAnsweringLoss, TFSequenceClassificationLoss,
TFTokenClassificationLoss, 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)
from .configuration_longformer import LongformerConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "allenai/longformer-base-4096"
_CONFIG_FOR_DOC = "LongformerConfig"
LARGE_NEGATIVE = -1e8
@dataclass
class TFLongformerBaseModelOutput(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 TFLongformerBaseModelOutputWithPooling(ModelOutput):
    """Args:"""
    last_hidden_state: tf.Tensor = None
    pooler_output: 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 TFLongformerMaskedLMOutput(ModelOutput):
    """Args:"""
    loss: tf.Tensor | None = None
    logits: 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 TFLongformerQuestionAnsweringModelOutput(ModelOutput):
    """Args:"""
    loss: tf.Tensor | None = None
    start_logits: tf.Tensor = None
    end_logits: 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 TFLongformerSequenceClassifierOutput(ModelOutput):
    """Args:"""
    loss: tf.Tensor | None = None
    logits: 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 TFLongformerMultipleChoiceModelOutput(ModelOutput):
    """Args:"""
    loss: tf.Tensor | None = None
    logits: 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 TFLongformerTokenClassifierOutput(ModelOutput):
    """Args:"""
    loss: tf.Tensor | None = None
    logits: tf.Tensor = None
    hidden_states: Tuple[tf.Tensor, ...] | None = None
    attentions: Tuple[tf.Tensor, ...] | None = None
    global_attentions: Tuple[tf.Tensor, ...] | None = None
def _compute_global_attention_mask(input_ids_shape, sep_token_indices, before_sep_token=True):
    assert shape_list(sep_token_indices)[1] == 2, "`input_ids` should have two dimensions"
    question_end_index = tf.reshape(sep_token_indices, (input_ids_shape[0], 3, 2))[:, 0, 1][:, None]
    attention_mask = tf.expand_dims(tf.range(input_ids_shape[1], dtype=tf.int64), axis=0)
    attention_mask = tf.tile(attention_mask, (input_ids_shape[0], 1))
    if before_sep_token is True:
        question_end_index = tf.tile(question_end_index, (1, input_ids_shape[1]))
        attention_mask = tf.cast(attention_mask < question_end_index, dtype=question_end_index.dtype)
    else:
        question_end_index = tf.tile(question_end_index + 1, (1, input_ids_shape[1]))
        attention_mask = tf.cast(attention_mask > question_end_index, dtype=question_end_index.dtype) * tf.cast(attention_mask < input_ids_shape[-1], dtype=question_end_index.dtype)
    return attention_mask
class TFLongformerLMHead(keras.layers.Layer):
    def __init__(self, config, input_embeddings, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.hidden_size = config.hidden_size
        self.dense = keras.layers.Dense(config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense")
        self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
        self.act = get_tf_activation("gelu")
        self.decoder = input_embeddings
    def build(self, input_shape=None):
        self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias")
        if self.built: return
        self.built = True
        if getattr(self, "dense", None) is not None:
            with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size])
        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])
    def get_output_embeddings(self): return self.decoder
    def set_output_embeddings(self, value):
        self.decoder.weight = value
        self.decoder.vocab_size = shape_list(value)[0]
    def get_bias(self): return {"bias": self.bias}
    def set_bias(self, value):
        self.bias = value["bias"]
        self.config.vocab_size = shape_list(value["bias"])[0]
    def call(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.layer_norm(hidden_states)
        seq_length = shape_list(tensor=hidden_states)[1]
        hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size])
        hidden_states = tf.matmul(a=hidden_states, b=self.decoder.weight, transpose_b=True)
        hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.config.vocab_size])
        hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)
        return hidden_states
class TFLongformerEmbeddings(keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.padding_idx = 1
        self.config = config
        self.hidden_size = config.hidden_size
        self.max_position_embeddings = config.max_position_embeddings
        self.initializer_range = config.initializer_range
        self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
        self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
    def build(self, input_shape=None):
        with tf.name_scope("word_embeddings"): self.weight = self.add_weight(name="weight", shape=[self.config.vocab_size, self.hidden_size], initializer=get_initializer(self.initializer_range))
        with tf.name_scope("token_type_embeddings"): self.token_type_embeddings = self.add_weight(name="embeddings", shape=[self.config.type_vocab_size, self.hidden_size], initializer=get_initializer(self.initializer_range))
        with tf.name_scope("position_embeddings"): self.position_embeddings = self.add_weight(name="embeddings", shape=[self.max_position_embeddings, self.hidden_size], initializer=get_initializer(self.initializer_range))
        if self.built: return
        self.built = True
        if getattr(self, "LayerNorm", None) is not None:
            with tf.name_scope(self.LayerNorm.name): self.LayerNorm.build([None, None, self.config.hidden_size])
    def create_position_ids_from_input_ids(self, input_ids, past_key_values_length=0):
        mask = tf.cast(tf.math.not_equal(input_ids, self.padding_idx), dtype=input_ids.dtype)
        incremental_indices = (tf.math.cumsum(mask, axis=1) + past_key_values_length) * mask
        return incremental_indices + self.padding_idx
    def call(self, input_ids=None, position_ids=None, token_type_ids=None, inputs_embeds=None, past_key_values_length=0, training=False):
        assert not (input_ids is None and inputs_embeds is None)
        if input_ids is not None:
            check_embeddings_within_bounds(input_ids, self.config.vocab_size)
            inputs_embeds = tf.gather(params=self.weight, indices=input_ids)
        input_shape = shape_list(inputs_embeds)[:-1]
        if token_type_ids is None: token_type_ids = tf.cast(tf.fill(dims=input_shape, value=0), tf.int64)
        if position_ids is None:
            if input_ids is not None: position_ids = self.create_position_ids_from_input_ids(input_ids=input_ids, past_key_values_length=past_key_values_length)
            else: position_ids = tf.expand_dims(tf.range(start=self.padding_idx + 1, limit=input_shape[-1] + self.padding_idx + 1, dtype=tf.int64), axis=0)
        position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
        token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
        final_embeddings = inputs_embeds + position_embeds + token_type_embeds
        final_embeddings = self.LayerNorm(inputs=final_embeddings)
        final_embeddings = self.dropout(inputs=final_embeddings, training=training)
        return final_embeddings
class TFLongformerIntermediate(keras.layers.Layer):
    def __init__(self, config: LongformerConfig, **kwargs):
        super().__init__(**kwargs)
        self.dense = keras.layers.Dense(units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense")
        if isinstance(config.hidden_act, str): self.intermediate_act_fn = get_tf_activation(config.hidden_act)
        else: self.intermediate_act_fn = config.hidden_act
        self.config = config
    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
        hidden_states = self.dense(inputs=hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "dense", None) is not None:
            with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size])
class TFLongformerOutput(keras.layers.Layer):
    def __init__(self, config: LongformerConfig, **kwargs):
        super().__init__(**kwargs)
        self.dense = keras.layers.Dense(units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense")
        self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
        self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
        self.config = config
    def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
        hidden_states = self.dense(inputs=hidden_states)
        hidden_states = self.dropout(inputs=hidden_states, training=training)
        hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)
        return hidden_states
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "dense", None) is not None:
            with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.intermediate_size])
        if getattr(self, "LayerNorm", None) is not None:
            with tf.name_scope(self.LayerNorm.name): self.LayerNorm.build([None, None, self.config.hidden_size])
class TFLongformerPooler(keras.layers.Layer):
    def __init__(self, config: LongformerConfig, **kwargs):
        super().__init__(**kwargs)
        self.dense = keras.layers.Dense(units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), activation="tanh", name="dense")
        self.config = config
    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(inputs=first_token_tensor)
        return pooled_output
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "dense", None) is not None:
            with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size])
class TFLongformerSelfOutput(keras.layers.Layer):
    def __init__(self, config: LongformerConfig, **kwargs):
        super().__init__(**kwargs)
        self.dense = keras.layers.Dense(units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense")
        self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
        self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
        self.config = config
    def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
        hidden_states = self.dense(inputs=hidden_states)
        hidden_states = self.dropout(inputs=hidden_states, training=training)
        hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)
        return hidden_states
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "dense", None) is not None:
            with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size])
        if getattr(self, "LayerNorm", None) is not None:
            with tf.name_scope(self.LayerNorm.name): self.LayerNorm.build([None, None, self.config.hidden_size])
class TFLongformerSelfAttention(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 TFLongformerAttention(keras.layers.Layer):
    def __init__(self, config, layer_id=0, **kwargs):
        super().__init__(**kwargs)
        self.self_attention = TFLongformerSelfAttention(config, layer_id, name="self")
        self.dense_output = TFLongformerSelfOutput(config, name="output")
    def prune_heads(self, heads): raise NotImplementedError
    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.self_attention([hidden_states, attention_mask, layer_head_mask, is_index_masked, is_index_global_attn, is_global_attn], training=training)
        attention_output = self.dense_output(self_outputs[0], hidden_states, 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, "self_attention", None) is not None:
            with tf.name_scope(self.self_attention.name): self.self_attention.build(None)
        if getattr(self, "dense_output", None) is not None:
            with tf.name_scope(self.dense_output.name): self.dense_output.build(None)
class TFLongformerLayer(keras.layers.Layer):
    def __init__(self, config, layer_id=0, **kwargs):
        super().__init__(**kwargs)
        self.attention = TFLongformerAttention(config, layer_id, name="attention")
        self.intermediate = TFLongformerIntermediate(config, name="intermediate")
        self.longformer_output = TFLongformerOutput(config, name="output")
    def call(self, inputs, training=False):
        (hidden_states, attention_mask, layer_head_mask, is_index_masked, is_index_global_attn, is_global_attn) = inputs
        attention_outputs = self.attention([hidden_states, attention_mask, layer_head_mask, is_index_masked, is_index_global_attn, is_global_attn], training=training)
        attention_output = attention_outputs[0]
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.longformer_output(intermediate_output, attention_output, training=training)
        outputs = (layer_output,) + attention_outputs[1:]
        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, "intermediate", None) is not None:
            with tf.name_scope(self.intermediate.name): self.intermediate.build(None)
        if getattr(self, "longformer_output", None) is not None:
            with tf.name_scope(self.longformer_output.name): self.longformer_output.build(None)
class TFLongformerEncoder(keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.output_hidden_states = config.output_hidden_states
        self.output_attentions = config.output_attentions
        self.layer = [TFLongformerLayer(config, i, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
    def call(self, hidden_states, attention_mask=None, head_mask=None, padding_len=0, is_index_masked=None, is_index_global_attn=None, is_global_attn=None,
    output_attentions=None, output_hidden_states=None, return_dict=None, training=False):
        all_hidden_states = () if output_hidden_states else None
        all_attentions = all_global_attentions = () if output_attentions else None
        for idx, layer_module in enumerate(self.layer):
            if output_hidden_states:
                hidden_states_to_add = hidden_states[:, :-padding_len] if padding_len > 0 else hidden_states
                all_hidden_states = all_hidden_states + (hidden_states_to_add,)
            layer_outputs = layer_module([hidden_states, attention_mask, head_mask[idx] if head_mask is not None else None, is_index_masked, is_index_global_attn, is_global_attn], training=training)
            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)),)
        if output_hidden_states:
            hidden_states_to_add = hidden_states[:, :-padding_len] if padding_len > 0 else hidden_states
            all_hidden_states = all_hidden_states + (hidden_states_to_add,)
        hidden_states = hidden_states[:, :-padding_len] if padding_len > 0 else hidden_states
        if output_attentions: all_attentions = (tuple([state[:, :, :-padding_len, :] for state in all_attentions]) if padding_len > 0 else all_attentions)
        if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_attentions, all_global_attentions] if v is not None)
        return TFLongformerBaseModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions, global_attentions=all_global_attentions)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        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 TFLongformerMainLayer(keras.layers.Layer):
    config_class = LongformerConfig
    def __init__(self, config, add_pooling_layer=True, **kwargs):
        super().__init__(**kwargs)
        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.config = config
        self.num_hidden_layers = config.num_hidden_layers
        self.initializer_range = config.initializer_range
        self.output_attentions = config.output_attentions
        self.output_hidden_states = config.output_hidden_states
        self.return_dict = config.use_return_dict
        self.pad_token_id = config.pad_token_id
        self.attention_window = config.attention_window
        self.embeddings = TFLongformerEmbeddings(config, name="embeddings")
        self.encoder = TFLongformerEncoder(config, name="encoder")
        self.pooler = TFLongformerPooler(config, name="pooler") if add_pooling_layer else None
    def get_input_embeddings(self): return self.embeddings
    def set_input_embeddings(self, value):
        self.embeddings.weight = value
        self.embeddings.vocab_size = shape_list(value)[0]
    def _prune_heads(self, heads_to_prune): raise NotImplementedError
    @unpack_inputs
    def call(self, input_ids=None, attention_mask=None, head_mask=None, global_attention_mask=None, token_type_ids=None, position_ids=None, inputs_embeds=None,
    output_attentions=None, output_hidden_states=None, return_dict=None, training=False):
        if input_ids is not None and not isinstance(input_ids, tf.Tensor): input_ids = tf.convert_to_tensor(input_ids, dtype=tf.int64)
        elif input_ids is not None: input_ids = tf.cast(input_ids, tf.int64)
        if attention_mask is not None and not isinstance(attention_mask, tf.Tensor): attention_mask = tf.convert_to_tensor(attention_mask, dtype=tf.int64)
        elif attention_mask is not None: attention_mask = tf.cast(attention_mask, tf.int64)
        if global_attention_mask is not None and not isinstance(global_attention_mask, tf.Tensor): global_attention_mask = tf.convert_to_tensor(global_attention_mask, dtype=tf.int64)
        elif global_attention_mask is not None: global_attention_mask = tf.cast(global_attention_mask, tf.int64)
        if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None: input_shape = shape_list(input_ids)
        elif inputs_embeds is not None: input_shape = shape_list(inputs_embeds)[:-1]
        else: raise ValueError("You have to specify either input_ids or inputs_embeds")
        if attention_mask is None: attention_mask = tf.cast(tf.fill(input_shape, 1), tf.int64)
        if token_type_ids is None: token_type_ids = tf.cast(tf.fill(input_shape, 0), tf.int64)
        if global_attention_mask is not None: attention_mask = self._merge_to_attention_mask(attention_mask, global_attention_mask)
        (padding_len, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds) = self._pad_to_window_size(input_ids=input_ids, attention_mask=attention_mask,
        token_type_ids=token_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, pad_token_id=self.pad_token_id)
        is_index_masked = tf.math.less(attention_mask, 1)
        is_index_global_attn = tf.math.greater(attention_mask, 1)
        is_global_attn = tf.math.reduce_any(is_index_global_attn)
        attention_mask_shape = shape_list(attention_mask)
        extended_attention_mask = tf.reshape(attention_mask, (attention_mask_shape[0], attention_mask_shape[1], 1, 1))
        extended_attention_mask = tf.cast(tf.math.abs(1 - extended_attention_mask), tf.dtypes.float32) * -10000.0
        embedding_output = self.embeddings(input_ids, position_ids, token_type_ids, inputs_embeds, training=training)
        encoder_outputs = self.encoder(embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, padding_len=padding_len, is_index_masked=is_index_masked,
        is_index_global_attn=is_index_global_attn, is_global_attn=is_global_attn, output_attentions=output_attentions, output_hidden_states=output_hidden_states,
        return_dict=return_dict, training=training)
        sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
        if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:]
        return TFLongformerBaseModelOutputWithPooling(last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states,
        attentions=encoder_outputs.attentions, global_attentions=encoder_outputs.global_attentions)
    def _pad_to_window_size(self, input_ids, attention_mask, token_type_ids, position_ids, 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
        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 position_ids is not None: position_ids = tf.pad(position_ids, paddings, constant_values=pad_token_id)
        if inputs_embeds is not None:
            if padding_len > 0:
                input_ids_padding = tf.cast(tf.fill((batch_size, padding_len), self.pad_token_id), tf.int64)
                inputs_embeds_padding = self.embeddings(input_ids_padding)
                inputs_embeds = tf.concat([inputs_embeds, inputs_embeds_padding], axis=-2)
        attention_mask = tf.pad(attention_mask, paddings, constant_values=False)
        token_type_ids = tf.pad(token_type_ids, paddings, constant_values=0)
        return (padding_len, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds)
    @staticmethod
    def _merge_to_attention_mask(attention_mask: tf.Tensor, global_attention_mask: tf.Tensor):
        if attention_mask is not None: attention_mask = attention_mask * (global_attention_mask + 1)
        else: attention_mask = global_attention_mask + 1
        return attention_mask
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "embeddings", None) is not None:
            with tf.name_scope(self.embeddings.name): self.embeddings.build(None)
        if getattr(self, "encoder", None) is not None:
            with tf.name_scope(self.encoder.name): self.encoder.build(None)
        if getattr(self, "pooler", None) is not None:
            with tf.name_scope(self.pooler.name): self.pooler.build(None)
class TFLongformerPreTrainedModel(TFPreTrainedModel):
    config_class = LongformerConfig
    base_model_prefix = "longformer"
    @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
LONGFORMER_START_DOCSTRING = r"""
    This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)
    This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
    as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
    behavior.
    <Tip>
    TensorFlow models and layers in `transformers` accept two formats as input:
    - having all inputs as keyword arguments (like PyTorch models), or
    - having all inputs as a list, tuple or dict in the first positional argument.
    The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
    and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
    pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
    format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
    the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
    positional argument:
    - a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
    - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
    `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
    - a dictionary with one or several input Tensors associated to the input names given in the docstring:
    `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
    Note that when creating models and layers with
    [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
    about any of this, as you can just pass inputs like you would to any other Python function!
    </Tip>
    Parameters:
        config ([`LongformerConfig`]): 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.
"""
LONGFORMER_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`np.ndarray` or `tf.Tensor` of 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)
        head_mask (`np.ndarray` or `tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
            Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        global_attention_mask (`np.ndarray` or `tf.Tensor` of shape `(0)`, *optional*):
            Mask to decide the attention given on each token, local attention or global attention. Tokens with global
            attention attends to all other tokens, and all other tokens attend to them. This is important for
            task-specific finetuning because it makes the model more flexible at representing the task. For example,
            for classification, the <s> token should be given global attention. For QA, all question tokens should also
            have global attention. Please refer to the [Longformer paper](https://arxiv.org/abs/2004.05150) for more
            details. Mask values selected in `[0, 1]`:
            - 0 for local attention (a sliding window attention),
            - 1 for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
        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)
        inputs_embeds (`np.ndarray` or `tf.Tensor` of shape `(0, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` 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 Longformer Model outputting raw hidden-states without any specific head on top.", LONGFORMER_START_DOCSTRING)
class TFLongformerModel(TFLongformerPreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.longformer = TFLongformerMainLayer(config, name="longformer")
    @unpack_inputs
    @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def call(self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None,
    global_attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None,
    inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None, training: Optional[bool] = False) -> Union[TFLongformerBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
        outputs = self.longformer(input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, global_attention_mask=global_attention_mask,
        token_type_ids=token_type_ids, position_ids=position_ids, 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, "longformer", None) is not None:
            with tf.name_scope(self.longformer.name): self.longformer.build(None)
@add_start_docstrings("Longformer Model with a `language modeling` head on top.", LONGFORMER_START_DOCSTRING)
class TFLongformerForMaskedLM(TFLongformerPreTrainedModel, TFMaskedLanguageModelingLoss):
    _keys_to_ignore_on_load_unexpected = [r"pooler"]
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.longformer = TFLongformerMainLayer(config, add_pooling_layer=False, name="longformer")
        self.lm_head = TFLongformerLMHead(config, self.longformer.embeddings, name="lm_head")
    def get_lm_head(self): return self.lm_head
    def get_prefix_bias_name(self):
        warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
        return self.name + "/" + self.lm_head.name
    @unpack_inputs
    @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def call(self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None,
    global_attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None,
    inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False) -> Union[TFLongformerMaskedLMOutput, Tuple[tf.Tensor]]:
        outputs = self.longformer(input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, global_attention_mask=global_attention_mask,
        token_type_ids=token_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states,
        return_dict=return_dict, training=training)
        sequence_output = outputs[0]
        prediction_scores = self.lm_head(sequence_output, training=training)
        loss = None if labels is None else self.hf_compute_loss(labels, prediction_scores)
        if not return_dict:
            output = (prediction_scores,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return TFLongformerMaskedLMOutput(loss=loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, global_attentions=outputs.global_attentions)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "longformer", None) is not None:
            with tf.name_scope(self.longformer.name): self.longformer.build(None)
        if getattr(self, "lm_head", None) is not None:
            with tf.name_scope(self.lm_head.name): self.lm_head.build(None)
@add_start_docstrings("""
    Longformer Model with a span classification head on top for extractive question-answering tasks like SQuAD /
    TriviaQA (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
    """, LONGFORMER_START_DOCSTRING)
class TFLongformerForQuestionAnswering(TFLongformerPreTrainedModel, TFQuestionAnsweringLoss):
    _keys_to_ignore_on_load_unexpected = [r"pooler"]
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.num_labels = config.num_labels
        self.longformer = TFLongformerMainLayer(config, add_pooling_layer=False, name="longformer")
        self.qa_outputs = keras.layers.Dense(config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs")
        self.config = config
    @unpack_inputs
    @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def call(self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, global_attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, start_positions: np.ndarray | tf.Tensor | None = None, end_positions: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False) -> Union[TFLongformerQuestionAnsweringModelOutput, Tuple[tf.Tensor]]:
        if input_ids is not None and not isinstance(input_ids, tf.Tensor): input_ids = tf.convert_to_tensor(input_ids, dtype=tf.int64)
        elif input_ids is not None: input_ids = tf.cast(input_ids, tf.int64)
        if attention_mask is not None and not isinstance(attention_mask, tf.Tensor): attention_mask = tf.convert_to_tensor(attention_mask, dtype=tf.int64)
        elif attention_mask is not None: attention_mask = tf.cast(attention_mask, tf.int64)
        if global_attention_mask is not None and not isinstance(global_attention_mask, tf.Tensor): global_attention_mask = tf.convert_to_tensor(global_attention_mask, dtype=tf.int64)
        elif global_attention_mask is not None: global_attention_mask = tf.cast(global_attention_mask, tf.int64)
        if global_attention_mask is None and input_ids is not None:
            if shape_list(tf.where(input_ids == self.config.sep_token_id))[0] != 3 * shape_list(input_ids)[0]:
                logger.warning(f"There should be exactly three separator tokens: {self.config.sep_token_id} in every sample for questions answering. You might also consider to set `global_attention_mask` manually in the forward function to avoid this. This is most likely an error. The global attention is disabled for this forward pass.")
                global_attention_mask = tf.cast(tf.fill(shape_list(input_ids), value=0), tf.int64)
            else:
                logger.warning_once("Initializing global attention on question tokens...")
                sep_token_indices = tf.where(input_ids == self.config.sep_token_id)
                sep_token_indices = tf.cast(sep_token_indices, dtype=tf.int64)
                global_attention_mask = _compute_global_attention_mask(shape_list(input_ids), sep_token_indices)
        outputs = self.longformer(input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, global_attention_mask=global_attention_mask, token_type_ids=token_type_ids,
        position_ids=position_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training)
        sequence_output = outputs[0]
        logits = self.qa_outputs(sequence_output)
        start_logits, end_logits = tf.split(logits, 2, axis=-1)
        start_logits = tf.squeeze(start_logits, axis=-1)
        end_logits = tf.squeeze(end_logits, axis=-1)
        loss = None
        if start_positions is not None and end_positions is not None:
            labels = {"start_position": start_positions}
            labels["end_position"] = end_positions
            loss = self.hf_compute_loss(labels, (start_logits, end_logits))
        if not return_dict:
            output = (start_logits, end_logits) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return TFLongformerQuestionAnsweringModelOutput(loss=loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states,
        attentions=outputs.attentions, global_attentions=outputs.global_attentions)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "longformer", None) is not None:
            with tf.name_scope(self.longformer.name): self.longformer.build(None)
        if getattr(self, "qa_outputs", None) is not None:
            with tf.name_scope(self.qa_outputs.name): self.qa_outputs.build([None, None, self.config.hidden_size])
class TFLongformerClassificationHead(keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.dense = keras.layers.Dense(config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), activation="tanh", name="dense")
        self.dropout = keras.layers.Dropout(config.hidden_dropout_prob)
        self.out_proj = keras.layers.Dense(config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="out_proj")
        self.config = config
    def call(self, hidden_states, training=False):
        hidden_states = hidden_states[:, 0, :]
        hidden_states = self.dropout(hidden_states, training=training)
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states, training=training)
        output = self.out_proj(hidden_states)
        return output
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "dense", None) is not None:
            with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size])
        if getattr(self, "out_proj", None) is not None:
            with tf.name_scope(self.out_proj.name): self.out_proj.build([None, None, self.config.hidden_size])
@add_start_docstrings("Longformer Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.", LONGFORMER_START_DOCSTRING)
class TFLongformerForSequenceClassification(TFLongformerPreTrainedModel, TFSequenceClassificationLoss):
    _keys_to_ignore_on_load_unexpected = [r"pooler"]
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.num_labels = config.num_labels
        self.longformer = TFLongformerMainLayer(config, add_pooling_layer=False, name="longformer")
        self.classifier = TFLongformerClassificationHead(config, name="classifier")
    @unpack_inputs
    @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def call(self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None,
    token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, global_attention_mask: np.ndarray | tf.Tensor | None = None,
    inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False) -> Union[TFLongformerSequenceClassifierOutput, Tuple[tf.Tensor]]:
        if input_ids is not None and not isinstance(input_ids, tf.Tensor): input_ids = tf.convert_to_tensor(input_ids, dtype=tf.int64)
        elif input_ids is not None: input_ids = tf.cast(input_ids, tf.int64)
        if attention_mask is not None and not isinstance(attention_mask, tf.Tensor): attention_mask = tf.convert_to_tensor(attention_mask, dtype=tf.int64)
        elif attention_mask is not None: attention_mask = tf.cast(attention_mask, tf.int64)
        if global_attention_mask is not None and not isinstance(global_attention_mask, tf.Tensor): global_attention_mask = tf.convert_to_tensor(global_attention_mask, dtype=tf.int64)
        elif global_attention_mask is not None: global_attention_mask = tf.cast(global_attention_mask, tf.int64)
        if global_attention_mask is None and input_ids is not None:
            logger.warning_once("Initializing global attention on CLS token...")
            global_attention_mask = tf.zeros_like(input_ids)
            updates = tf.ones(shape_list(input_ids)[0], dtype=tf.int64)
            indices = tf.pad(tensor=tf.expand_dims(tf.range(shape_list(input_ids)[0], dtype=tf.int64), axis=1), paddings=[[0, 0], [0, 1]], constant_values=0)
            global_attention_mask = tf.tensor_scatter_nd_update(global_attention_mask, indices, updates)
        outputs = self.longformer(input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, global_attention_mask=global_attention_mask, token_type_ids=token_type_ids,
        position_ids=position_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training)
        sequence_output = outputs[0]
        logits = self.classifier(sequence_output)
        loss = None if labels is None else self.hf_compute_loss(labels, logits)
        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return TFLongformerSequenceClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, global_attentions=outputs.global_attentions)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "longformer", None) is not None:
            with tf.name_scope(self.longformer.name): self.longformer.build(None)
        if getattr(self, "classifier", None) is not None:
            with tf.name_scope(self.classifier.name): self.classifier.build(None)
@add_start_docstrings("Longformer Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks.", LONGFORMER_START_DOCSTRING)
class TFLongformerForMultipleChoice(TFLongformerPreTrainedModel, TFMultipleChoiceLoss):
    _keys_to_ignore_on_load_missing = [r"dropout"]
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.longformer = TFLongformerMainLayer(config, name="longformer")
        self.dropout = keras.layers.Dropout(config.hidden_dropout_prob)
        self.classifier = keras.layers.Dense(1, kernel_initializer=get_initializer(config.initializer_range), name="classifier")
        self.config = config
    @property
    def input_signature(self): return {"input_ids": tf.TensorSpec((None, None, None), tf.int32, name="input_ids"), "attention_mask": tf.TensorSpec((None, None, None), tf.int32, name="attention_mask"), "global_attention_mask": tf.TensorSpec((None, None, None), tf.int32, name="global_attention_mask")}
    @unpack_inputs
    @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
    def call(self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None,
    token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, global_attention_mask: np.ndarray | tf.Tensor | None = None,
    inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None,
    labels: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False) -> Union[TFLongformerMultipleChoiceModelOutput, Tuple[tf.Tensor]]:
        if input_ids is not None:
            num_choices = shape_list(input_ids)[1]
            seq_length = shape_list(input_ids)[2]
        else:
            num_choices = shape_list(inputs_embeds)[1]
            seq_length = shape_list(inputs_embeds)[2]
        flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
        flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
        flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
        flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None
        flat_global_attention_mask = (tf.reshape(global_attention_mask, (-1, shape_list(global_attention_mask)[-1])) if global_attention_mask is not None else None)
        flat_inputs_embeds = (tf.reshape(inputs_embeds, (-1, seq_length, shape_list(inputs_embeds)[3])) if inputs_embeds is not None else None)
        outputs = self.longformer(flat_input_ids, position_ids=flat_position_ids, token_type_ids=flat_token_type_ids, attention_mask=flat_attention_mask, head_mask=head_mask,
        global_attention_mask=flat_global_attention_mask, inputs_embeds=flat_inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states,
        return_dict=return_dict, training=training)
        pooled_output = outputs[1]
        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)
        reshaped_logits = tf.reshape(logits, (-1, num_choices))
        loss = None if labels is None else self.hf_compute_loss(labels, reshaped_logits)
        if not return_dict:
            output = (reshaped_logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return TFLongformerMultipleChoiceModelOutput(loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, global_attentions=outputs.global_attentions)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "longformer", None) is not None:
            with tf.name_scope(self.longformer.name): self.longformer.build(None)
        if getattr(self, "classifier", None) is not None:
            with tf.name_scope(self.classifier.name): self.classifier.build([None, None, self.config.hidden_size])
@add_start_docstrings("Longformer Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.", LONGFORMER_START_DOCSTRING)
class TFLongformerForTokenClassification(TFLongformerPreTrainedModel, TFTokenClassificationLoss):
    _keys_to_ignore_on_load_unexpected = [r"pooler"]
    _keys_to_ignore_on_load_missing = [r"dropout"]
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.num_labels = config.num_labels
        self.longformer = TFLongformerMainLayer(config=config, add_pooling_layer=False, name="longformer")
        self.dropout = keras.layers.Dropout(config.hidden_dropout_prob)
        self.classifier = keras.layers.Dense(config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier")
        self.config = config
    @unpack_inputs
    @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def call(self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None,
    token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, global_attention_mask: np.ndarray | tf.Tensor | None = None,
    inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None, labels: Optional[Union[np.array, tf.Tensor]] = None, training: Optional[bool] = False) -> Union[TFLongformerTokenClassifierOutput, Tuple[tf.Tensor]]:
        outputs = self.longformer(input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, global_attention_mask=global_attention_mask, token_type_ids=token_type_ids,
        position_ids=position_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training)
        sequence_output = outputs[0]
        sequence_output = self.dropout(sequence_output)
        logits = self.classifier(sequence_output)
        loss = None if labels is None else self.hf_compute_loss(labels, logits)
        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return TFLongformerTokenClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, global_attentions=outputs.global_attentions)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "longformer", None) is not None:
            with tf.name_scope(self.longformer.name): self.longformer.build(None)
        if getattr(self, "classifier", None) is not None:
            with tf.name_scope(self.classifier.name): self.classifier.build([None, None, self.config.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.                                                                 #
    #################################################################################################################################################################################################
"""
