


import argparse
import os
import re


lowerCAmelCase       =							'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
lowerCAmelCase       =							re.compile(R'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
lowerCAmelCase       =							re.compile(R'\s*\(\s*"(\S[^"]+)"')
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE = False     ):


		"""simple docstring"""




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

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

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

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


		"""simple docstring"""




		lowercase__            =      [os.path.join(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ) for f in os.listdir(SCREAMING_SNAKE_CASE     ) if f.endswith('''.py'''     )]
		lowercase__            =      [sort_auto_mapping(SCREAMING_SNAKE_CASE						,				overwrite=SCREAMING_SNAKE_CASE     ) for fname in fnames]

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


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


