





'''simple docstring'''





import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

lowercase_		:				Optional[int]				=						logging.get_logger(__name__)


lowercase_		:				Any				=						{name: getattr(transformers, name + '''Fast''') for name in SLOW_TO_FAST_CONVERTERS}

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
								raise ValueError(F'''Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys() )}.''' )

				if tokenizer_name is None:
								_snake_case :			Optional[int]  						=   TOKENIZER_CLASSES
				else:
								_snake_case :			Optional[int]  						=   {tokenizer_name: getattr(_A	,			tokenizer_name + 'Fast' )}

				logger.info(F'''Loading tokenizer classes: {tokenizer_names}''' )

				for tokenizer_name in tokenizer_names:
								_snake_case :			Optional[int]  						=   TOKENIZER_CLASSES[tokenizer_name]

								_snake_case :			List[Any]  						=   True
								if checkpoint_name is None:
												_snake_case :			Dict  						=   list(tokenizer_class.max_model_input_sizes.keys() )
								else:
												_snake_case :			Optional[int]  						=   [checkpoint_name]

								logger.info(F'''For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}''' )

								for checkpoint in checkpoint_names:
												logger.info(F'''Loading {tokenizer_class.__class__.__name__} {checkpoint}''' )

												# Load tokenizer
												_snake_case :			List[str]  						=   tokenizer_class.from_pretrained(_A	,			force_download=_A )

												# Save fast tokenizer
												logger.info(F'''Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}''' )

												# For organization names we create sub-directories
												if "/" in checkpoint:
																_snake_case						,			_snake_case :			Union[str, Any]  						=   checkpoint.split('/' )
																_snake_case :			Optional[Any]  						=   os.path.join(_A	,			_A )
												elif add_prefix:
																_snake_case :			List[Any]  						=   checkpoint
																_snake_case :			List[Any]  						=   dump_path
												else:
																_snake_case :			Optional[Any]  						=   None
																_snake_case :			Any  						=   dump_path

												logger.info(F'''=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}''' )

												if checkpoint in list(tokenizer.pretrained_vocab_files_map.values() )[0]:
																_snake_case :			Tuple  						=   list(tokenizer.pretrained_vocab_files_map.values() )[0][checkpoint]
																_snake_case :			Any  						=   file_path.split(_A )[-1][0]
																if next_char == "/":
																				_snake_case :			str  						=   os.path.join(_A	,			_A )
																				_snake_case :			List[str]  						=   None

																logger.info(F'''=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}''' )

												_snake_case :			Tuple  						=   tokenizer.save_pretrained(
												    _A	,			legacy_format=_A	,			filename_prefix=_A )
												logger.info(F'''=> File names {file_names}''' )

												for file_name in file_names:
																if not file_name.endswith('tokenizer.json' ):
																				os.remove(_A )
																				logger.info(F'''=> removing {file_name}''' )


if __name__ == "__main__":
		lowercase_		:				Union[str, Any]				=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--dump_path''', default=None, type=str, required=True, help='''Path to output generated fast tokenizer files.'''
		)
		parser.add_argument(
		    '''--tokenizer_name''',
		    default=None,
		    type=str,
		    help=(
		        F'''Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will '''
		        '''download and convert all the checkpoints from AWS.'''
		    ),
		)
		parser.add_argument(
		    '''--checkpoint_name''',
		    default=None,
		    type=str,
		    help='''Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.''',
		)
		parser.add_argument(
		    '''--force_download''',
		    action='''store_true''',
		    help='''Re-download checkpoints.''',
		)
		lowercase_		:				str				=						parser.parse_args()

		convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)













import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				# Initialise PyTorch model
				_snake_case :			Optional[int]  						=   BertConfig.from_json_file(__lowerCAmelCase )
				print(F'''Building PyTorch model from configuration: {config}''' )
				_snake_case :			List[str]  						=   BertForPreTraining(__lowerCAmelCase )

				# Load weights from tf checkpoint
				load_tf_weights_in_bert(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase )

				# Save pytorch-model
				print(F'''Save PyTorch model to {pytorch_dump_path}''' )
				torch.save(model.state_dict()	,			__lowerCAmelCase )


if __name__ == "__main__":
		lowercase_		:				Any				=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
		)
		parser.add_argument(
		    '''--bert_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained BERT model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		lowercase_		:				List[str]				=						parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






