import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


lowerCamelCase_    :		Optional[Any]							=	logging.getLogger(__name__)


@dataclass
class 						a__							:
			A__    :       str
			A__    :       List[str]
			A__    :       Optional[List[str]]






@dataclass
class 						a__							:
			A__    :       List[int]
			A__    :       List[int]
			A__    :       Optional[List[int]]        =      None
			A__    :       Optional[List[int]]        =      None






class 						a__							(       lowercase_		):
			A__    :       str        =      "train"
			A__    :       Optional[int]        =      "dev"
			A__    :       Union[str, Any]        =      "test"






class 						a__							:



			@staticmethod
			def __SCREAMING_SNAKE_CASE							(						UpperCAmelCase				,						UpperCAmelCase   )			->   List[InputExample]:
								raise NotImplementedError



			@staticmethod
			def __SCREAMING_SNAKE_CASE							(						UpperCAmelCase   )			->   List[str]:
								raise NotImplementedError







			@staticmethod
			def __SCREAMING_SNAKE_CASE							(						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase=False				,						UpperCAmelCase="[CLS]"				,						UpperCAmelCase=1				,						UpperCAmelCase="[SEP]"				,						UpperCAmelCase=False				,						UpperCAmelCase=False				,						UpperCAmelCase=0				,						UpperCAmelCase=0				,						UpperCAmelCase=-1_0_0				,						UpperCAmelCase=0				,						UpperCAmelCase=True				,						)			->   List[InputFeatures]:
								__a		 =       {label: i for i, label in enumerate(a__   )}

								__a		 =       []
								for ex_index, example in enumerate(a__   ):
													if ex_index % 1_0_0_0_0 == 0:
																		logger.info('Writing example %d of %d'				,						a__				,						len(a__   )   )

													__a		 =       []
													__a		 =       []
													for word, label in zip(example.words				,						example.labels   ):
																		__a		 =       tokenizer.tokenize(a__   )

																		# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
																		if len(a__   ) > 0:
																							tokens.extend(a__   )
																							# Use the real label id for the first token of the word, and padding ids for the remaining tokens
																							label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(a__   ) - 1)   )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
													__a		 =       tokenizer.num_special_tokens_to_add()
													if len(a__   ) > max_seq_length - special_tokens_count:
																		__a		 =       tokens[: (max_seq_length - special_tokens_count)]
																		__a		 =       label_ids[: (max_seq_length - special_tokens_count)]

													# The convention in BERT is:
													# (a) For sequence pairs:
													#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
													#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
													# (b) For single sequences:
													#  tokens:   [CLS] the dog is hairy . [SEP]
													#  type_ids:   0   0   0   0  0     0   0
													#
													# Where "type_ids" are used to indicate whether this is the first
													# sequence or the second sequence. The embedding vectors for `type=0` and
													# `type=1` were learned during pre-training and are added to the wordpiece
													# embedding vector (and position vector). This is not *strictly* necessary
													# since the [SEP] token unambiguously separates the sequences, but it makes
													# it easier for the model to learn the concept of sequences.
													#
													# For classification tasks, the first vector (corresponding to [CLS]) is
													# used as the "sentence vector". Note that this only makes sense because
													# the entire model is fine-tuned.
													tokens += [sep_token]
													label_ids += [pad_token_label_id]
													if sep_token_extra:
																		# roberta uses an extra separator b/w pairs of sentences
																		tokens += [sep_token]
																		label_ids += [pad_token_label_id]
													__a		 =       [sequence_a_segment_id] * len(a__   )

													if cls_token_at_end:
																		tokens += [cls_token]
																		label_ids += [pad_token_label_id]
																		segment_ids += [cls_token_segment_id]
													else:
																		__a		 =       [cls_token] + tokens
																		__a		 =       [pad_token_label_id] + label_ids
																		__a		 =       [cls_token_segment_id] + segment_ids

													__a		 =       tokenizer.convert_tokens_to_ids(a__   )

													# The mask has 1 for real tokens and 0 for padding tokens. Only real
													# tokens are attended to.
													__a		 =       [1 if mask_padding_with_zero else 0] * len(a__   )

													# Zero-pad up to the sequence length.
													__a		 =       max_seq_length - len(a__   )
													if pad_on_left:
																		__a		 =       ([pad_token] * padding_length) + input_ids
																		__a		 =       ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																		__a		 =       ([pad_token_segment_id] * padding_length) + segment_ids
																		__a		 =       ([pad_token_label_id] * padding_length) + label_ids
													else:
																		input_ids += [pad_token] * padding_length
																		input_mask += [0 if mask_padding_with_zero else 1] * padding_length
																		segment_ids += [pad_token_segment_id] * padding_length
																		label_ids += [pad_token_label_id] * padding_length

													assert len(a__   ) == max_seq_length
													assert len(a__   ) == max_seq_length
													assert len(a__   ) == max_seq_length
													assert len(a__   ) == max_seq_length

													if ex_index < 5:
																		logger.info('*** Example ***'   )
																		logger.info('guid: %s'				,						example.guid   )
																		logger.info('tokens: %s'				,						' '.join([str(a__   ) for x in tokens]   )   )
																		logger.info('input_ids: %s'				,						' '.join([str(a__   ) for x in input_ids]   )   )
																		logger.info('input_mask: %s'				,						' '.join([str(a__   ) for x in input_mask]   )   )
																		logger.info('segment_ids: %s'				,						' '.join([str(a__   ) for x in segment_ids]   )   )
																		logger.info('label_ids: %s'				,						' '.join([str(a__   ) for x in label_ids]   )   )

													if "token_type_ids" not in tokenizer.model_input_names:
																		__a		 =       None

													features.append(
													    InputFeatures(
													        input_ids=a__				,						attention_mask=a__				,						token_type_ids=a__				,						label_ids=a__   )   )
								return features


if is_torch_available():
	import torch
	from torch import nn
	from torch.utils.data import Dataset


	class 						a__							(       lowercase_		):
				A__    :       List[InputFeatures]
				A__    :       int        =      nn.CrossEntropyLoss().ignore_index



				def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = None				,						UpperCAmelCase=False				,						UpperCAmelCase = Split.train				,						)			->   str:
									__a		 =       os.path.join(
									    a__				,						'cached_{}_{}_{}'.format(mode.value				,						tokenizer.__class__.__name__				,						str(a__   )   )				,						)

									# Make sure only the first process in distributed training processes the dataset,
									# and the others will use the cache.
									__a		 =       cached_features_file + '.lock'
									with FileLock(a__   ):
														if os.path.exists(a__   ) and not overwrite_cache:
																			logger.info(f'''Loading features from cached file {cached_features_file}'''   )
																			__a		 =       torch.load(a__   )
														else:
																			logger.info(f'''Creating features from dataset file at {data_dir}'''   )
																			__a		 =       token_classification_task.read_examples_from_file(a__				,						a__   )
																			# TODO clean up all this to leverage built-in features of tokenizers
																			__a		 =       token_classification_task.convert_examples_to_features(
																			    a__				,						a__				,						a__				,						a__				,						cls_token_at_end=bool(model_type in ['xlnet']   )				,						cls_token=tokenizer.cls_token				,						cls_token_segment_id=2 if model_type in ['xlnet'] else 0				,						sep_token=tokenizer.sep_token				,						sep_token_extra=a__				,						pad_on_left=bool(tokenizer.padding_side == 'left'   )				,						pad_token=tokenizer.pad_token_id				,						pad_token_segment_id=tokenizer.pad_token_type_id				,						pad_token_label_id=self.pad_token_label_id				,						)
																			logger.info(f'''Saving features into cached file {cached_features_file}'''   )
																			torch.save(self.features				,						a__   )



				def __len__(						self   )			->   List[Any]:
									return len(self.features   )







				def __getitem__(						self				,						UpperCAmelCase   )			->   InputFeatures:
									return self.features[i]


if is_tf_available():
	import tensorflow as tf


	class 						a__							:
				A__    :       List[InputFeatures]
				A__    :       int        =      -1_00



				def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = None				,						UpperCAmelCase=False				,						UpperCAmelCase = Split.train				,						)			->   Optional[int]:
									__a		 =       token_classification_task.read_examples_from_file(a__				,						a__   )
									# TODO clean up all this to leverage built-in features of tokenizers
									__a		 =       token_classification_task.convert_examples_to_features(
									    a__				,						a__				,						a__				,						a__				,						cls_token_at_end=bool(model_type in ['xlnet']   )				,						cls_token=tokenizer.cls_token				,						cls_token_segment_id=2 if model_type in ['xlnet'] else 0				,						sep_token=tokenizer.sep_token				,						sep_token_extra=a__				,						pad_on_left=bool(tokenizer.padding_side == 'left'   )				,						pad_token=tokenizer.pad_token_id				,						pad_token_segment_id=tokenizer.pad_token_type_id				,						pad_token_label_id=self.pad_token_label_id				,						)

									def gen():
														for ex in self.features:
																			if ex.token_type_ids is None:
																								yield (
																								    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																								    ex.label_ids,
																								)
																			else:
																								yield (
																								    {
																								        "input_ids": ex.input_ids,
																								        "attention_mask": ex.attention_mask,
																								        "token_type_ids": ex.token_type_ids,
																								    },
																								    ex.label_ids,
																								)

									if "token_type_ids" not in tokenizer.model_input_names:
														__a		 =       tf.data.Dataset.from_generator(
														    a__				,						({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)				,						(
														        {'input_ids': tf.TensorShape([None]   ), 'attention_mask': tf.TensorShape([None]   )},
														        tf.TensorShape([None]   ),
														    )				,						)
									else:
														__a		 =       tf.data.Dataset.from_generator(
														    a__				,						({'input_ids': tf.intaa, 'attention_mask': tf.intaa, 'token_type_ids': tf.intaa}, tf.intaa)				,						(
														        {
														            'input_ids': tf.TensorShape([None]   ),
														            'attention_mask': tf.TensorShape([None]   ),
														            'token_type_ids': tf.TensorShape([None]   ),
														        },
														        tf.TensorShape([None]   ),
														    )				,						)



				def __SCREAMING_SNAKE_CASE							(						self   )			->   str:
									__a		 =       self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features   )   )   )

									return self.dataset



				def __len__(						self   )			->   List[Any]:
									return len(self.features   )







				def __getitem__(						self				,						UpperCAmelCase   )			->   InputFeatures:
									return self.features[i]





from typing import TYPE_CHECKING

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


lowerCamelCase_    :		Optional[int]							=	{"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

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

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Union[str, 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:
	lowerCamelCase_    :		Optional[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

	lowerCamelCase_    :		Tuple							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




