
import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

lowercase   =	logging.get_logger(__name__)


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



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Union[str, Any]      ,					UpperCAmelCase__					:   Tuple      ,					UpperCAmelCase__					:   str      ,					UpperCAmelCase__					:   List[Any]							)	->     str:






							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(SCREAMING_SNAKE_CASE_      ,					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(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:
																												lowerCamelCase_	, lowerCamelCase_					=       checkpoint.split("""/"""							)
																												lowerCamelCase_					=       os.path.join(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_							)
																					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(SCREAMING_SNAKE_CASE_							)[-1][0]
																												if next_char == "/":
																																			lowerCamelCase_					=       os.path.join(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_							)
																																			lowerCamelCase_					=       None

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

																					lowerCamelCase_					=       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__":
		lowercase   =	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   =	parser.parse_args()

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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE		:Optional[int]						=     {
    """configuration_rembert""": ["""REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RemBertConfig""", """RemBertOnnxConfig"""]
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:str						=     ["""RemBertTokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Optional[Any]						=     ["""RemBertTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Dict						=     [
     """REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """RemBertForCausalLM""",
     """RemBertForMaskedLM""",
     """RemBertForMultipleChoice""",
     """RemBertForQuestionAnswering""",
     """RemBertForSequenceClassification""",
     """RemBertForTokenClassification""",
     """RemBertLayer""",
     """RemBertModel""",
     """RemBertPreTrainedModel""",
     """load_tf_weights_in_rembert""",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Optional[int]						=     [
     """TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFRemBertForCausalLM""",
     """TFRemBertForMaskedLM""",
     """TFRemBertForMultipleChoice""",
     """TFRemBertForQuestionAnswering""",
     """TFRemBertForSequenceClassification""",
     """TFRemBertForTokenClassification""",
     """TFRemBertLayer""",
     """TFRemBertModel""",
     """TFRemBertPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_rembert import RemBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_rembert_fast import RemBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rembert import (
      REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      RemBertForCausalLM,
      RemBertForMaskedLM,
      RemBertForMultipleChoice,
      RemBertForQuestionAnswering,
      RemBertForSequenceClassification,
      RemBertForTokenClassification,
      RemBertLayer,
      RemBertModel,
      RemBertPreTrainedModel,
      load_tf_weights_in_rembert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_rembert import (
      TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRemBertForCausalLM,
      TFRemBertForMaskedLM,
      TFRemBertForMultipleChoice,
      TFRemBertForQuestionAnswering,
      TFRemBertForSequenceClassification,
      TFRemBertForTokenClassification,
      TFRemBertLayer,
      TFRemBertModel,
      TFRemBertPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE		:List[Any]						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
