




'''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()

__lowerCamelCase   :					Dict   							=					logging.get_logger(__name__)


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

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__						)->    Any:



			"""simple docstring"""



			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_				:					Dict   =       TOKENIZER_CLASSES
			else:
						snake_case_				:					List[str]   =       {tokenizer_name: getattr(_SCREAMING_SNAKE_CASE     ,tokenizer_name + "Fast"						)}

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

			for tokenizer_name in tokenizer_names:
						snake_case_				:					Dict   =       TOKENIZER_CLASSES[tokenizer_name]

						snake_case_				:					int   =       True
						if checkpoint_name is None:
									snake_case_				:					str   =       list(tokenizer_class.max_model_input_sizes.keys()						)
						else:
									snake_case_				:					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_				:					Dict   =       tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE     ,force_download=_SCREAMING_SNAKE_CASE						)

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

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

									snake_case_				:					Union[str, Any]   =       tokenizer.save_pretrained(
									    _SCREAMING_SNAKE_CASE     ,legacy_format=_SCREAMING_SNAKE_CASE     ,filename_prefix=_SCREAMING_SNAKE_CASE						)
									logger.info(F'''=> File names {file_names}'''						)

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


if __name__ == "__main__":
		__lowerCamelCase   :					List[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.''',
		)
		__lowerCamelCase   :					Any   							=					parser.parse_args()

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












'''simple docstring'''




from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters

def 	__UpperCAmelCase       ( )->    int:



			"""simple docstring"""



			snake_case_				:					Any   =       {
			    "repo_name": ["test_repo1", "test_repo2", "test_repo3"],
			    "path": ["test_1.py", "test_2.py", "unit_test.py"],
			    "content": ["a " * 20, "a " * 30, "b " * 7],
			}
			snake_case_				:					int   =       Dataset.from_dict(__magic_name__						)
			return dataset







class    A_	(a_							):
							"""simple docstring"""




							def 							_A	(		self			:List[str]				)				->  str:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       get_dataset()
										snake_case_				:					Optional[int]   =       make_duplicate_clusters(lowerCAmelCase__       ,							0.8_5				)
										self.assertEqual(len(duplicate_clusters[0]				)       ,							2				)






							def 							_A	(		self			:Union[str, Any]				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       get_dataset()
										snake_case_,	snake_case_				:					List[Any]   =       deduplicate_dataset(lowerCAmelCase__				)
										self.assertEqual(len(lowerCAmelCase__				)       ,							2				)
										print(lowerCAmelCase__				)
										self.assertEqual(duplicate_clusters[0][0]["copies"]       ,							2				)
										self.assertEqual(duplicate_clusters[0][0]["is_extreme"]       ,							lowerCAmelCase__				)




