


"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A  :     Any    					=    {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A  :     str    					=    ["XLNetTokenizer"]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A  :     List[Any]    					=    ["XLNetTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A  :     Optional[Any]    					=    [
	    "XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "XLNetForMultipleChoice",
	    "XLNetForQuestionAnswering",
	    "XLNetForQuestionAnsweringSimple",
	    "XLNetForSequenceClassification",
	    "XLNetForTokenClassification",
	    "XLNetLMHeadModel",
	    "XLNetModel",
	    "XLNetPreTrainedModel",
	    "load_tf_weights_in_xlnet",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A  :     int    					=    [
	    "TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFXLNetForMultipleChoice",
	    "TFXLNetForQuestionAnsweringSimple",
	    "TFXLNetForSequenceClassification",
	    "TFXLNetForTokenClassification",
	    "TFXLNetLMHeadModel",
	    "TFXLNetMainLayer",
	    "TFXLNetModel",
	    "TFXLNetPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet import XLNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet_fast import XLNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlnet import (
		    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLNetForMultipleChoice,
		    XLNetForQuestionAnswering,
		    XLNetForQuestionAnsweringSimple,
		    XLNetForSequenceClassification,
		    XLNetForTokenClassification,
		    XLNetLMHeadModel,
		    XLNetModel,
		    XLNetPreTrainedModel,
		    load_tf_weights_in_xlnet,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlnet import (
		    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLNetForMultipleChoice,
		    TFXLNetForQuestionAnsweringSimple,
		    TFXLNetForSequenceClassification,
		    TFXLNetForTokenClassification,
		    TFXLNetLMHeadModel,
		    TFXLNetMainLayer,
		    TFXLNetModel,
		    TFXLNetPreTrainedModel,
		)

else:
	import sys

	A  :     List[Any]    					=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








"""simple docstring"""


import tensorflow as tf

from ...tf_utils import shape_list
class 						_UpperCamelCase						(     tf.keras.layers.Layer	):
							'''simple docstring'''







							def __init__(  self					,     __a					,     __a					,     __a					,     __a					,     __a=1					,     __a=False					,     **__a			):
										super().__init__(**__a			)

										__lowerCAmelCase       					=    vocab_size
										__lowerCAmelCase       					=    d_embed
										__lowerCAmelCase       					=    d_proj

										__lowerCAmelCase       					=    cutoffs + [vocab_size]
										__lowerCAmelCase       					=    [0] + self.cutoffs
										__lowerCAmelCase       					=    div_val

										__lowerCAmelCase       					=    self.cutoffs[0]
										__lowerCAmelCase       					=    len(self.cutoffs			) - 1
										__lowerCAmelCase       					=    self.shortlist_size + self.n_clusters
										__lowerCAmelCase       					=    keep_order

										__lowerCAmelCase       					=    []
										__lowerCAmelCase       					=    []

							def     snake_case       (  self					,     __a			):
										if self.n_clusters > 0:
													__lowerCAmelCase       					=    self.add_weight(
													    shape=(self.n_clusters, self.d_embed)					,     initializer="zeros"					,     trainable=__a					,     name="cluster_weight"			)
													__lowerCAmelCase       					=    self.add_weight(
													    shape=(self.n_clusters,)					,     initializer="zeros"					,     trainable=__a					,     name="cluster_bias"			)

										if self.div_val == 1:
													for i in range(len(self.cutoffs			)			):
																if self.d_proj != self.d_embed:
																			__lowerCAmelCase       					=    self.add_weight(
																			    shape=(self.d_embed, self.d_proj)					,     initializer="zeros"					,     trainable=__a					,     name=f"out_projs_._{i}"					,     )
																			self.out_projs.append(__a			)
																else:
																			self.out_projs.append(__a			)
																__lowerCAmelCase       					=    self.add_weight(
																    shape=(self.vocab_size, self.d_embed)					,     initializer="zeros"					,     trainable=__a					,     name=f"out_layers_._{i}_._weight"					,     )
																__lowerCAmelCase       					=    self.add_weight(
																    shape=(self.vocab_size,)					,     initializer="zeros"					,     trainable=__a					,     name=f"out_layers_._{i}_._bias"					,     )
																self.out_layers.append((weight, bias)			)
										else:
													for i in range(len(self.cutoffs			)			):
																__lowerCAmelCase					,			__lowerCAmelCase       					=    self.cutoff_ends[i], self.cutoff_ends[i + 1]
																__lowerCAmelCase       					=    self.d_embed // (self.div_val**i)

																__lowerCAmelCase       					=    self.add_weight(
																    shape=(d_emb_i, self.d_proj)					,     initializer="zeros"					,     trainable=__a					,     name=f"out_projs_._{i}"			)
																self.out_projs.append(__a			)
																__lowerCAmelCase       					=    self.add_weight(
																    shape=(r_idx - l_idx, d_emb_i)					,     initializer="zeros"					,     trainable=__a					,     name=f"out_layers_._{i}_._weight"					,     )
																__lowerCAmelCase       					=    self.add_weight(
																    shape=(r_idx - l_idx,)					,     initializer="zeros"					,     trainable=__a					,     name=f"out_layers_._{i}_._bias"					,     )
																self.out_layers.append((weight, bias)			)
										super().build(__a			)

							@staticmethod
							def     snake_case       (  __a					,     __a					,     __a					,     __a=None			):
										__lowerCAmelCase       					=    x
										if proj is not None:
													__lowerCAmelCase       					=    tf.einsum("ibd,ed->ibe"					,     __a					,     __a			)
										return tf.einsum("ibd,nd->ibn"					,     __a					,     __a			) + b

							@staticmethod
							def     snake_case       (  __a					,     __a			):
										__lowerCAmelCase       					=    shape_list(__a			)
										__lowerCAmelCase       					=    tf.range(lp_size[0]					,     dtype=target.dtype			)
										__lowerCAmelCase       					=    tf.stack([r, target]					,     1			)
										return tf.gather_nd(__a					,     __a			)



							def     snake_case       (  self					,     __a					,     __a					,     __a=True					,     __a=False			):
										__lowerCAmelCase       					=    0
										if self.n_clusters == 0:
													__lowerCAmelCase       					=    self._logit(__a					,     self.out_layers[0][0]					,     self.out_layers[0][1]					,     self.out_projs[0]			)
													if target is not None:
																__lowerCAmelCase       					=    tf.nn.sparse_softmax_cross_entropy_with_logits(labels=__a					,     logits=__a			)
													__lowerCAmelCase       					=    tf.nn.log_softmax(__a					,     axis=-1			)
										else:
													__lowerCAmelCase       					=    shape_list(__a			)
													__lowerCAmelCase       					=    []
													__lowerCAmelCase       					=    tf.zeros(hidden_sizes[:2]			)
													for i in range(len(self.cutoffs			)			):
																__lowerCAmelCase					,			__lowerCAmelCase       					=    self.cutoff_ends[i], self.cutoff_ends[i + 1]
																if target is not None:
																			__lowerCAmelCase       					=    (target >= l_idx) & (target < r_idx)
																			__lowerCAmelCase       					=    tf.where(__a			)
																			__lowerCAmelCase       					=    tf.boolean_mask(__a					,     __a			) - l_idx

																if self.div_val == 1:
																			__lowerCAmelCase       					=    self.out_layers[0][0][l_idx:r_idx]
																			__lowerCAmelCase       					=    self.out_layers[0][1][l_idx:r_idx]
																else:
																			__lowerCAmelCase       					=    self.out_layers[i][0]
																			__lowerCAmelCase       					=    self.out_layers[i][1]

																if i == 0:
																			__lowerCAmelCase       					=    tf.concat([cur_W, self.cluster_weight]					,     0			)
																			__lowerCAmelCase       					=    tf.concat([cur_b, self.cluster_bias]					,     0			)

																			__lowerCAmelCase       					=    self._logit(__a					,     __a					,     __a					,     self.out_projs[0]			)
																			__lowerCAmelCase       					=    tf.nn.log_softmax(__a			)
																			out.append(head_logprob[..., : self.cutoffs[0]]			)
																			if target is not None:
																						__lowerCAmelCase       					=    tf.boolean_mask(__a					,     __a			)
																						__lowerCAmelCase       					=    self._gather_logprob(__a					,     __a			)
																else:
																			__lowerCAmelCase       					=    self._logit(__a					,     __a					,     __a					,     self.out_projs[i]			)
																			__lowerCAmelCase       					=    tf.nn.log_softmax(__a			)
																			__lowerCAmelCase       					=    self.cutoffs[0] + i - 1  # No probability for the head cluster
																			__lowerCAmelCase       					=    head_logprob[..., cluster_prob_idx, None] + tail_logprob
																			out.append(__a			)
																			if target is not None:
																						__lowerCAmelCase       					=    tf.boolean_mask(__a					,     __a			)
																						__lowerCAmelCase       					=    tf.boolean_mask(__a					,     __a			)
																						__lowerCAmelCase       					=    self._gather_logprob(__a					,     __a			)
																						cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
																if target is not None:
																			loss += tf.scatter_nd(__a					,     -cur_logprob					,     shape_list(__a			)			)
													__lowerCAmelCase       					=    tf.concat(__a					,     axis=-1			)

										if target is not None:
													if return_mean:
																__lowerCAmelCase       					=    tf.reduce_mean(__a			)
													# Add the training-time loss value to the layer using `self.add_loss()`.
													self.add_loss(__a			)

													# Log the loss as a metric (we could log arbitrary metrics,
													# including different metrics for training and inference.
													self.add_metric(__a					,     name=self.name					,     aggregation="mean" if return_mean else ""			)

										return out




