



'''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()

snake_case_			:     int  	=  logging.get_logger(__name__)


snake_case_			:     List[Any]  	=  {name: getattr(transformers, name + 'Fast') for name in SLOW_TO_FAST_CONVERTERS}






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						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:
												_UpperCamelCase :		str			    =       TOKENIZER_CLASSES
						else:
												_UpperCamelCase :		List[Any]			    =       {tokenizer_name: getattr(UpperCAmelCase_  ,  tokenizer_name + 'Fast'							)}

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

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

												_UpperCamelCase :		str			    =       True
												if checkpoint_name is None:
																		_UpperCamelCase :		Union[str, Any]			    =       list(tokenizer_class.max_model_input_sizes.keys()							)
												else:
																		_UpperCamelCase :		Dict			    =       [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
																		_UpperCamelCase :		str			    =       tokenizer_class.from_pretrained(UpperCAmelCase_  ,  force_download=UpperCAmelCase_							)

																		# 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:
																								_UpperCamelCase    ,      _UpperCamelCase :		Optional[Any]			    =       checkpoint.split('/'							)
																								_UpperCamelCase :		List[Any]			    =       os.path.join(UpperCAmelCase_  ,  UpperCAmelCase_							)
																		elif add_prefix:
																								_UpperCamelCase :		Union[str, Any]			    =       checkpoint
																								_UpperCamelCase :		Tuple			    =       dump_path
																		else:
																								_UpperCamelCase :		List[Any]			    =       None
																								_UpperCamelCase :		List[str]			    =       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]:
																								_UpperCamelCase :		Tuple			    =       list(tokenizer.pretrained_vocab_files_map.values()							)[0][checkpoint]
																								_UpperCamelCase :		Dict			    =       file_path.split(UpperCAmelCase_							)[-1][0]
																								if next_char == "/":
																														_UpperCamelCase :		Optional[int]			    =       os.path.join(UpperCAmelCase_  ,  UpperCAmelCase_							)
																														_UpperCamelCase :		Any			    =       None

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

																		_UpperCamelCase :		Tuple			    =       tokenizer.save_pretrained(
																		    UpperCAmelCase_  ,  legacy_format=UpperCAmelCase_  ,  filename_prefix=UpperCAmelCase_							)
																		logger.info(f'=> File names {file_names}'							)

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


if __name__ == "__main__":
					snake_case_			:     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.',
					)
					snake_case_			:     Dict  	=  parser.parse_args()

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







'''simple docstring'''
from torch import nn






def A__   (					UpperCAmelCase_							):
						if act_fn in ["swish", "silu"]:
												return nn.SiLU()
						elif act_fn == "mish":
												return nn.Mish()
						elif act_fn == "gelu":
												return nn.GELU()
						else:
												raise ValueError(f'Unsupported activation function: {act_fn}'							)


