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

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

							return out_tensor.tolist()






def _lowerCamelCase  (		snake_case  ):
							_lowerCAmelCase							=							ord(snake_case  )
							if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
														return True
							_lowerCAmelCase							=							unicodedata.category(snake_case  )
							if cat.startswith('P'  ):
														return True
							return False



@dataclass
class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							=42
			UpperCamelCase__    							=True
			UpperCamelCase__    							=None
			UpperCamelCase__    							=None
			UpperCamelCase__    							=-1_0_0
			UpperCamelCase__    							="pt"





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]  ,  lowercase__	:	Any						):
										import torch

										_lowerCAmelCase							=							'label' if 'label' in features[0].keys() else 'labels'
										_lowerCAmelCase							=							[feature[label_name] for feature in features] if label_name in features[0].keys() else None
										_lowerCAmelCase							=							self.tokenizer.pad(
										    lowercase__  ,  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

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

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

										return batch







import argparse
import os
import re


_lowercase:	Optional[Any]       			=		'''src/transformers/models/auto'''


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_lowercase:	Union[str, Any]       			=		re.compile(R'''[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict''')
# re pattern that matches identifiers in mappings
_lowercase:	Optional[Any]       			=		re.compile(R'''\s*\(\s*"(\S[^"]+)"''')






def _lowerCamelCase  (		snake_case     , snake_case = False  ):
							with open(snake_case     , 'r'     , encoding='utf-8'  ) as f:
														_lowerCAmelCase							=							f.read()

							_lowerCAmelCase							=							content.split('\n'  )
							_lowerCAmelCase							=							[]
							_lowerCAmelCase							=							0
							while line_idx < len(snake_case  ):
														if _re_intro_mapping.search(lines[line_idx]  ) is not None:
																					_lowerCAmelCase							=							len(re.search(R'^(\s*)\S'     , lines[line_idx]  ).groups()[0]  ) + 8
																					# Start of a new mapping!
																					while not lines[line_idx].startswith(' ' * indent + '('  ):
																												new_lines.append(lines[line_idx]  )
																												line_idx += 1

																					_lowerCAmelCase							=							[]
																					while lines[line_idx].strip() != "]":
																												# Blocks either fit in one line or not
																												if lines[line_idx].strip() == "(":
																																			_lowerCAmelCase							=							line_idx
																																			while not lines[line_idx].startswith(' ' * indent + ')'  ):
																																										line_idx += 1
																																			blocks.append('\n'.join(lines[start_idx : line_idx + 1]  )  )
																												else:
																																			blocks.append(lines[line_idx]  )
																												line_idx += 1

																					# Sort blocks by their identifiers
																					_lowerCAmelCase							=							sorted(snake_case     , key=lambda snake_case  : _re_identifier.search(snake_case  ).groups()[0]  )
																					new_lines += blocks
														else:
																					new_lines.append(lines[line_idx]  )
																					line_idx += 1

							if overwrite:
														with open(snake_case     , 'w'     , encoding='utf-8'  ) as f:
																					f.write('\n'.join(snake_case  )  )
							elif "\n".join(snake_case  ) != content:
														return True






def _lowerCamelCase  (		snake_case = False  ):
							_lowerCAmelCase							=							[os.path.join(snake_case     , snake_case  ) for f in os.listdir(snake_case  ) if f.endswith('.py'  )]
							_lowerCAmelCase							=							[sort_auto_mapping(snake_case     , overwrite=snake_case  ) for fname in fnames]

							if not overwrite and any(snake_case  ):
														_lowerCAmelCase							=							[f for f, d in zip(snake_case     , snake_case  ) if d]
														raise ValueError(
														    F'The following files have auto mappings that need sorting: {", ".join(snake_case  )}. Run `make style` to fix'
														    ' this.'  )


if __name__ == "__main__":
				_lowercase:	Any       			=		argparse.ArgumentParser()
				parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
				_lowercase:	Tuple       			=		parser.parse_args()

				sort_all_auto_mappings(not args.check_only)






