





import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

_a							= logging.get_logger(__name__)


_a							= {name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS}





def lowerCAmelCase__(__snake_case			,__snake_case			,__snake_case			,__snake_case				)							-> Tuple:







	'''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:
		lowerCamelCase__														=						TOKENIZER_CLASSES
	else:
		lowerCamelCase__														=						{tokenizer_name: getattr(A__			,tokenizer_name + '''Fast'''				)}

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

	for tokenizer_name in tokenizer_names:
		lowerCamelCase__														=						TOKENIZER_CLASSES[tokenizer_name]

		lowerCamelCase__														=						True
		if checkpoint_name is None:
			lowerCamelCase__														=						list(tokenizer_class.max_model_input_sizes.keys()				)
		else:
			lowerCamelCase__														=						[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
			lowerCamelCase__														=						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:
				lowerCamelCase__														=						checkpoint.split('''/'''				)
				lowerCamelCase__														=						os.path.join(A__			,A__				)
			elif add_prefix:
				lowerCamelCase__														=						checkpoint
				lowerCamelCase__														=						dump_path
			else:
				lowerCamelCase__														=						None
				lowerCamelCase__														=						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]:
				lowerCamelCase__														=						list(tokenizer.pretrained_vocab_files_map.values()				)[0][checkpoint]
				lowerCamelCase__														=						file_path.split(A__				)[-1][0]
				if next_char == "/":
					lowerCamelCase__														=						os.path.join(A__			,A__				)
					lowerCamelCase__														=						None

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

			lowerCamelCase__														=						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__":
			_a							= 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.",
			)
			_a							= parser.parse_args()

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











"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_camembert import CamembertTokenizer
else:
			lowerCamelCase_							      = None


lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase_							      = {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
    },
}

lowerCamelCase_							      = {
    '''camembert-base''': 512,
}

lowerCamelCase_							      = '''▁'''







class      UpperCamelCase_			(__A	):
	__magic_name__       =							VOCAB_FILES_NAMES
	__magic_name__       =							PRETRAINED_VOCAB_FILES_MAP
	__magic_name__       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__       =							['''input_ids''', '''attention_mask''']
	__magic_name__       =							CamembertTokenizer





	def __init__( self   :					Tuple      ,      lowerCAmelCase_   :					List[str]=None      ,      lowerCAmelCase_   :					Optional[int]=None      ,      lowerCAmelCase_   :					Optional[int]="<s>"      ,      lowerCAmelCase_   :					Dict="</s>"      ,      lowerCAmelCase_   :					Any="</s>"      ,      lowerCAmelCase_   :					Union[str, Any]="<s>"      ,      lowerCAmelCase_   :					int="<unk>"      ,      lowerCAmelCase_   :					Union[str, Any]="<pad>"      ,      lowerCAmelCase_   :					Union[str, Any]="<mask>"      ,      lowerCAmelCase_   :					Any=["<s>NOTUSED", "</s>NOTUSED"]      ,      **lowerCAmelCase_   :					Any      ,      )		->     Union[str, Any]:
				# Mask token behave like a normal word, i.e. include the space before it
				UpperCAmelCase_	:					Dict     	=   AddedToken(lowerCAmelCase_      ,      lstrip=lowerCAmelCase_      ,      rstrip=lowerCAmelCase_		) if isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		) else mask_token

				super().__init__(
				    lowerCAmelCase_      ,      tokenizer_file=lowerCAmelCase_      ,      bos_token=lowerCAmelCase_      ,      eos_token=lowerCAmelCase_      ,      sep_token=lowerCAmelCase_      ,      cls_token=lowerCAmelCase_      ,      unk_token=lowerCAmelCase_      ,      pad_token=lowerCAmelCase_      ,      mask_token=lowerCAmelCase_      ,      additional_special_tokens=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )

				UpperCAmelCase_	:					str     	=   vocab_file
				UpperCAmelCase_	:					Optional[Any]     	=   False if not self.vocab_file else True





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					Optional[List[int]] = None		)		->     List[int]:

				if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				UpperCAmelCase_	:					Tuple     	=   [self.cls_token_id]
				UpperCAmelCase_	:					Any     	=   [self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					Optional[List[int]] = None		)		->     List[int]:
				UpperCAmelCase_	:					List[Any]     	=   [self.sep_token_id]
				UpperCAmelCase_	:					Optional[int]     	=   [self.cls_token_id]

				if token_ids_a is None:
							return len(cls + token_ids_a + sep		) * [0]
				return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					Optional[str] = None		)		->     Tuple[str]:
				if not self.can_save_slow_tokenizer:
							raise ValueError(
							    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
							    "tokenizer."		)

				if not os.path.isdir(lowerCAmelCase_		):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
							return
				UpperCAmelCase_	:					Dict     	=   os.path.join(
				    lowerCAmelCase_      ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

				if os.path.abspath(self.vocab_file		) != os.path.abspath(lowerCAmelCase_		):
							copyfile(self.vocab_file      ,      lowerCAmelCase_		)

				return (out_vocab_file,)



