


import argparse
import os
import re


snake_case							:       Optional[int]     			=      '''src/transformers/models/auto'''


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






def 							__lowercase   ( __lowerCAmelCase							:				Union[str, Any]					,    __lowerCAmelCase							:				Optional[int] = False   ):




							with open(__lowerCAmelCase					,    'r'					,    encoding='utf-8'   ) as f:
														a__             =     f.read()

							a__             =     content.split('\n'   )
							a__             =     []
							a__             =     0
							while line_idx < len(__lowerCAmelCase   ):
														if _re_intro_mapping.search(lines[line_idx]   ) is not None:
																					a__             =     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

																					a__             =     []
																					while lines[line_idx].strip() != "]":
																												# Blocks either fit in one line or not
																												if lines[line_idx].strip() == "(":
																																			a__             =     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
																					a__             =     sorted(__lowerCAmelCase					,    key=lambda __lowerCAmelCase   : _re_identifier.search(__lowerCAmelCase   ).groups()[0]   )
																					new_lines += blocks
														else:
																					new_lines.append(lines[line_idx]   )
																					line_idx += 1

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






def 							__lowercase   ( __lowerCAmelCase							:				Union[str, Any] = False   ):
							a__             =     [os.path.join(__lowerCAmelCase					,    __lowerCAmelCase   ) for f in os.listdir(__lowerCAmelCase   ) if f.endswith('.py'   )]
							a__             =     [sort_auto_mapping(__lowerCAmelCase					,    overwrite=__lowerCAmelCase   ) for fname in fnames]

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


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

						sort_all_auto_mappings(not args.check_only)








import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase       =							logging.get_logger(__name__)
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      RobertaPreLayerNormConfig.from_pretrained(
		    SCREAMING_SNAKE_CASE						,				architectures=['''RobertaPreLayerNormForMaskedLM''']     )

		# convert state_dict
		lowercase__            =      torch.load(hf_hub_download(repo_id=SCREAMING_SNAKE_CASE						,				filename='''pytorch_model.bin'''     )     )
		lowercase__            =      {}
		for tensor_key, tensor_value in original_state_dict.items():
				# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
				if tensor_key.startswith('''roberta.'''     ):
						lowercase__            =      '''roberta_prelayernorm.''' + tensor_key[len('''roberta.'''     ) :]

				# The original implementation contains weights which are not used, remove them from the state_dict
				if tensor_key.endswith('''.self.LayerNorm.weight'''     ) or tensor_key.endswith('''.self.LayerNorm.bias'''     ):
						continue

				lowercase__            =      tensor_value

		lowercase__            =      RobertaPreLayerNormForMaskedLM.from_pretrained(
		    pretrained_model_name_or_path=SCREAMING_SNAKE_CASE						,				config=SCREAMING_SNAKE_CASE						,				state_dict=SCREAMING_SNAKE_CASE     )
		model.save_pretrained(SCREAMING_SNAKE_CASE     )

		# convert tokenizer
		lowercase__            =      AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE     )
		tokenizer.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
			lowerCAmelCase       =							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--checkpoint-repo',
			    default=None,
			    type=str,
			    required=True,
			    help='Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.',
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			lowerCAmelCase       =							parser.parse_args()
			convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)


