




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


__SCREAMING_SNAKE_CASE 						=logging.getLogger(__name__)







@dataclass
class  __magic_name__						:
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Tuple	      =					42
				SCREAMING_SNAKE_CASE__					:							Tuple	      =					42
				SCREAMING_SNAKE_CASE__					:							Dict	      =					42







@dataclass
class  __magic_name__						:
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Union[str, Any]	      =					42
				SCREAMING_SNAKE_CASE__					:							Tuple	      =					42
				SCREAMING_SNAKE_CASE__					:							Dict	      =					None
				SCREAMING_SNAKE_CASE__					:							int	      =					None







class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Tuple	      =					"train"
				SCREAMING_SNAKE_CASE__					:							Dict	      =					"dev"
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					"test"







class  __magic_name__						:
				'''simple docstring'''







				@staticmethod
				def     _A							(							_lowerCamelCase:   Tuple		,					_lowerCamelCase:   Union[str, Any]		):
											raise NotImplementedError


				@staticmethod
				def     _A							(							_lowerCamelCase:   int		):
											raise NotImplementedError





				@staticmethod
				def     _A							(							_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Optional[int]=False		,					_lowerCamelCase:   Dict="[CLS]"		,					_lowerCamelCase:   Union[str, Any]=1		,					_lowerCamelCase:   Any="[SEP]"		,					_lowerCamelCase:   Union[str, Any]=False		,					_lowerCamelCase:   Tuple=False		,					_lowerCamelCase:   Dict=0		,					_lowerCamelCase:   Dict=0		,					_lowerCamelCase:   List[str]=-1_00		,					_lowerCamelCase:   str=0		,					_lowerCamelCase:   Dict=True		,					):
											SCREAMING_SNAKE_CASE_   	=    {label: i for i, label in enumerate(_lowerCamelCase		)}

											SCREAMING_SNAKE_CASE_   	=    []
											for ex_index, example in enumerate(_lowerCamelCase		):
																		if ex_index % 1_00_00 == 0:
																									logger.info('''Writing example %d of %d'''		,					_lowerCamelCase		,					len(_lowerCamelCase		)		)

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

																									# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
																									if len(_lowerCamelCase		) > 0:
																																tokens.extend(_lowerCamelCase		)
																																# 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(_lowerCamelCase		) - 1)		)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
																		SCREAMING_SNAKE_CASE_   	=    tokenizer.num_special_tokens_to_add()
																		if len(_lowerCamelCase		) > max_seq_length - special_tokens_count:
																									SCREAMING_SNAKE_CASE_   	=    tokens[: (max_seq_length - special_tokens_count)]
																									SCREAMING_SNAKE_CASE_   	=    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]
																		SCREAMING_SNAKE_CASE_   	=    [sequence_a_segment_id] * len(_lowerCamelCase		)

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

																		SCREAMING_SNAKE_CASE_   	=    tokenizer.convert_tokens_to_ids(_lowerCamelCase		)

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

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

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

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

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


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







			class  __magic_name__						(  __UpperCAmelCase):
							'''simple docstring'''







							SCREAMING_SNAKE_CASE__					:							List[str]	      =					42
							SCREAMING_SNAKE_CASE__					:							Dict	      =					nn.CrossEntropyLoss().ignore_index


							def __init__(							self:   Dict		,					_lowerCamelCase:   str		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   List[str]		,					_lowerCamelCase:   Dict		,					_lowerCamelCase:   Dict		,					_lowerCamelCase:   str = None		,					_lowerCamelCase:   Dict=False		,					_lowerCamelCase:   Any = Split.train		,					):
														# Load data features from cache or dataset file
														SCREAMING_SNAKE_CASE_   	=    os.path.join(
														    _lowerCamelCase		,					'''cached_{}_{}_{}'''.format(mode.value		,					tokenizer.__class__.__name__		,					str(_lowerCamelCase		)		)		,					)

														# Make sure only the first process in distributed training processes the dataset,
														# and the others will use the cache.
														SCREAMING_SNAKE_CASE_   	=    cached_features_file + '''.lock'''
														with FileLock(_lowerCamelCase		):
																					if os.path.exists(_lowerCamelCase		) and not overwrite_cache:
																												logger.info(f"Loading features from cached file {cached_features_file}"		)
																												SCREAMING_SNAKE_CASE_   	=    torch.load(_lowerCamelCase		)
																					else:
																												logger.info(f"Creating features from dataset file at {data_dir}"		)
																												SCREAMING_SNAKE_CASE_   	=    token_classification_task.read_examples_from_file(_lowerCamelCase		,					_lowerCamelCase		)
																												# TODO clean up all this to leverage built-in features of tokenizers
																												SCREAMING_SNAKE_CASE_   	=    token_classification_task.convert_examples_to_features(
																												    _lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		,					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=_lowerCamelCase		,					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		,					_lowerCamelCase		)


							def __len__(							self:   str		):
														return len(self.features		)





							def __getitem__(							self:   List[Any]		,					_lowerCamelCase:   Union[str, Any]		):
														return self.features[i]


if is_tf_available():
			import tensorflow as tf







			class  __magic_name__						:
							'''simple docstring'''







							SCREAMING_SNAKE_CASE__					:							int	      =					42
							SCREAMING_SNAKE_CASE__					:							Dict	      =					-100


							def __init__(							self:   str		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Any		,					_lowerCamelCase:   Dict		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   Optional[int] = None		,					_lowerCamelCase:   Optional[Any]=False		,					_lowerCamelCase:   Union[str, Any] = Split.train		,					):
														SCREAMING_SNAKE_CASE_   	=    token_classification_task.read_examples_from_file(_lowerCamelCase		,					_lowerCamelCase		)
														# TODO clean up all this to leverage built-in features of tokenizers
														SCREAMING_SNAKE_CASE_   	=    token_classification_task.convert_examples_to_features(
														    _lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		,					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=_lowerCamelCase		,					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:
																					SCREAMING_SNAKE_CASE_   	=    tf.data.Dataset.from_generator(
																					    _lowerCamelCase		,					({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)		,					(
																					        {'''input_ids''': tf.TensorShape([None]		), '''attention_mask''': tf.TensorShape([None]		)},
																					        tf.TensorShape([None]		),
																					    )		,					)
														else:
																					SCREAMING_SNAKE_CASE_   	=    tf.data.Dataset.from_generator(
																					    _lowerCamelCase		,					({'''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     _A							(							self:   List[Any]		):
														SCREAMING_SNAKE_CASE_   	=    self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features		)		)		)

														return self.dataset


							def __len__(							self:   str		):
														return len(self.features		)





							def __getitem__(							self:   int		,					_lowerCamelCase:   List[Any]		):
														return self.features[i]








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE 						={
    """configuration_xlm_roberta_xl""": [
        """XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaXLConfig""",
        """XLMRobertaXLOnnxConfig""",
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE 						=[
			    """XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """XLMRobertaXLForCausalLM""",
			    """XLMRobertaXLForMaskedLM""",
			    """XLMRobertaXLForMultipleChoice""",
			    """XLMRobertaXLForQuestionAnswering""",
			    """XLMRobertaXLForSequenceClassification""",
			    """XLMRobertaXLForTokenClassification""",
			    """XLMRobertaXLModel""",
			    """XLMRobertaXLPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_xlm_roberta_xl import (
			    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    XLMRobertaXLConfig,
			    XLMRobertaXLOnnxConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm_roberta_xl import (
						    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMRobertaXLForCausalLM,
						    XLMRobertaXLForMaskedLM,
						    XLMRobertaXLForMultipleChoice,
						    XLMRobertaXLForQuestionAnswering,
						    XLMRobertaXLForSequenceClassification,
						    XLMRobertaXLForTokenClassification,
						    XLMRobertaXLModel,
						    XLMRobertaXLPreTrainedModel,
						)

else:
			import sys

			__SCREAMING_SNAKE_CASE 						=_LazyModule(__name__, globals()["""__file__"""], _import_structure)


