"""simple docstring"""
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





def    lowerCAmelCase__    (			_UpperCamelCase       :				Union[str, Any] ,			_UpperCamelCase       :				List[Any] ,			_UpperCamelCase       :				List[Any] ,			_UpperCamelCase       :				Tuple					)					->				List[str]:





						"""simple docstring"""






						if isinstance(_UpperCamelCase ,			_UpperCamelCase					):
												snake_case								=			np.full((len(_UpperCamelCase					), sequence_length, 2) ,			_UpperCamelCase					)
						else:
												snake_case								=			np.full((len(_UpperCamelCase					), sequence_length) ,			_UpperCamelCase					)

						for i, tensor in enumerate(_UpperCamelCase					):
												if padding_side == "right":
																		if isinstance(_UpperCamelCase ,			_UpperCamelCase					):
																								snake_case								=			tensor[:sequence_length]
																		else:
																								snake_case								=			tensor[:sequence_length]
												else:
																		if isinstance(_UpperCamelCase ,			_UpperCamelCase					):
																								snake_case								=			tensor[:sequence_length]
																		else:
																								snake_case								=			tensor[:sequence_length]

						return out_tensor.tolist()





def    lowerCAmelCase__    (			_UpperCamelCase       :				int					)					->				Optional[int]:





						"""simple docstring"""






						snake_case								=			ord(_UpperCamelCase					)
						if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
												return True
						snake_case								=			unicodedata.category(_UpperCamelCase					)
						if cat.startswith('P'					):
												return True
						return False



@dataclass
class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    PreTrainedTokenizerBase
				_lowerCAmelCase     :    Union[bool, str, PaddingStrategy]			  =						True
				_lowerCAmelCase     :    Optional[int]			  =						None
				_lowerCAmelCase     :    Optional[int]			  =						None
				_lowerCAmelCase     :    int			  =						-100
				_lowerCAmelCase     :    str			  =						"pt"

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										import torch

										snake_case								=			'label' if 'label' in features[0].keys() else 'labels'
										snake_case								=			[feature[label_name] for feature in features] if label_name in features[0].keys() else None
										snake_case								=			self.tokenizer.pad(
										    lowerCAmelCase		,    padding=self.padding		,    max_length=self.max_length		,    pad_to_multiple_of=self.pad_to_multiple_of		,    return_tensors='pt' if labels is None else None		,    )

										if labels is None:
																return batch

										snake_case								=			torch.tensor(batch['entity_ids']    ).shape[1]
										snake_case								=			self.tokenizer.padding_side
										if padding_side == "right":
																snake_case								=			[
																    list(lowerCAmelCase    ) + [self.label_pad_token_id] * (sequence_length - len(lowerCAmelCase    )) for label in labels
																]
										else:
																snake_case								=			[
																    [self.label_pad_token_id] * (sequence_length - len(lowerCAmelCase    )) + list(lowerCAmelCase    ) for label in labels
																]

										snake_case								=			[feature['ner_tags'] for feature in features]
										snake_case								=			padding_tensor(lowerCAmelCase		,    -1		,    lowerCAmelCase		,    lowerCAmelCase    )
										snake_case								=			[feature['original_entity_spans'] for feature in features]
										snake_case								=			padding_tensor(lowerCAmelCase		,    (-1, -1)		,    lowerCAmelCase		,    lowerCAmelCase    )
										snake_case								=			{k: torch.tensor(lowerCAmelCase		,    dtype=torch.intaa    ) for k, v in batch.items()}

										return batch



"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


SCREAMING_SNAKE_CASE__      						=  get_logger(__name__)



class   lowerCAmelCase_    (			enum.Enum					):



				"""simple docstring"""
				_lowerCAmelCase     :    Union[str, Any]			  =						"""all_checks"""
				_lowerCAmelCase     :    Optional[Any]			  =						"""basic_checks"""
				_lowerCAmelCase     :    Optional[int]			  =						"""no_checks"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[dict] ,			_UpperCamelCase       :				dict ,			_UpperCamelCase       :				str=None					)					->				Dict:





						"""simple docstring"""






						if expected_checksums is None:
												logger.info('Unable to verify checksums.'					)
												return
						if len(set(_UpperCamelCase					) - set(_UpperCamelCase					)					) > 0:
												raise ExpectedMoreDownloadedFiles(str(set(_UpperCamelCase					) - set(_UpperCamelCase					)					)					)
						if len(set(_UpperCamelCase					) - set(_UpperCamelCase					)					) > 0:
												raise UnexpectedDownloadedFile(str(set(_UpperCamelCase					) - set(_UpperCamelCase					)					)					)
						snake_case								=			[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
						snake_case								=			' for ' + verification_name if verification_name is not None else ''
						if len(_UpperCamelCase					) > 0:
												raise NonMatchingChecksumError(
												    f"""Checksums didn't match{for_verification_name}:\n"""
												    f"""{bad_urls}\n"""
												    'Set `verification_mode=\'no_checks\'` to skip checksums verification and ignore this error'					)
						logger.info('All the checksums matched successfully' + for_verification_name					)



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[dict] ,			_UpperCamelCase       :				dict					)					->				Tuple:





						"""simple docstring"""






						if expected_splits is None:
												logger.info('Unable to verify splits sizes.'					)
												return
						if len(set(_UpperCamelCase					) - set(_UpperCamelCase					)					) > 0:
												raise ExpectedMoreSplits(str(set(_UpperCamelCase					) - set(_UpperCamelCase					)					)					)
						if len(set(_UpperCamelCase					) - set(_UpperCamelCase					)					) > 0:
												raise UnexpectedSplits(str(set(_UpperCamelCase					) - set(_UpperCamelCase					)					)					)
						snake_case								=			[
						    {'expected': expected_splits[name], 'recorded': recorded_splits[name]}
						    for name in expected_splits
						    if expected_splits[name].num_examples != recorded_splits[name].num_examples
						]
						if len(_UpperCamelCase					) > 0:
												raise NonMatchingSplitsSizesError(str(_UpperCamelCase					)					)
						logger.info('All the splits matched successfully.'					)





def    lowerCAmelCase__    (			_UpperCamelCase       :				str ,			_UpperCamelCase       :				bool = True					)					->				dict:





						"""simple docstring"""






						if record_checksum:
												snake_case								=			shaaaa()
												with open(_UpperCamelCase ,			'rb'					) as f:
																		for chunk in iter(lambda: f.read(1 << 2_0					) ,			B''					):
																								m.update(_UpperCamelCase					)
																		snake_case								=			m.hexdigest()
						else:
												snake_case								=			None
						return {"num_bytes": os.path.getsize(_UpperCamelCase					), "checksum": checksum}





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[int]					)					->				int:





						"""simple docstring"""






						if dataset_size and config.IN_MEMORY_MAX_SIZE:
												return dataset_size < config.IN_MEMORY_MAX_SIZE
						else:
												return False


