

import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

SCREAMING_SNAKE_CASE		:Any						=     logging.get_logger(__name__)


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




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						str:



    """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:
        UpperCamelCase_			  =						TOKENIZER_CLASSES
    else:
        UpperCamelCase_			  =						{tokenizer_name: getattr(SCREAMING_SNAKE_CASE_			,						tokenizer_name + "Fast"			)}

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

    for tokenizer_name in tokenizer_names:
        UpperCamelCase_			  =						TOKENIZER_CLASSES[tokenizer_name]

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

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

            UpperCamelCase_			  =						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__":
 SCREAMING_SNAKE_CASE		:Optional[int]						=     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.""",
 )
 SCREAMING_SNAKE_CASE		:Optional[int]						=     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__)
