


'''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()

A__						:   Union[str, Any]				=    logging.get_logger(__name__)


A__						:   Union[str, Any]				=    {name: getattr(transformers, name + """Fast""") for name in SLOW_TO_FAST_CONVERTERS}



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Union[str, Any] ,       UpperCAmelCase_       :							List[str] ,       UpperCAmelCase_       :							Dict ,       UpperCAmelCase_       :							Any				)	->						Union[str, Any]:
     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  :      List[str]														=						TOKENIZER_CLASSES
     else:
          __lowerCamelCase  :      List[Any]														=						{tokenizer_name: getattr(UpperCAmelCase_ ,       tokenizer_name + 'Fast'				)}

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

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

          __lowerCamelCase  :      Optional[Any]														=						True
          if checkpoint_name is None:
               __lowerCamelCase  :      Dict														=						list(tokenizer_class.max_model_input_sizes.keys()				)
          else:
               __lowerCamelCase  :      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
               __lowerCamelCase  :      Any														=						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:
                    __lowerCamelCase							,			__lowerCamelCase  :      Dict														=						checkpoint.split('/'				)
                    __lowerCamelCase  :      List[Any]														=						os.path.join(UpperCAmelCase_ ,       UpperCAmelCase_				)
               elif add_prefix:
                    __lowerCamelCase  :      int														=						checkpoint
                    __lowerCamelCase  :      Tuple														=						dump_path
               else:
                    __lowerCamelCase  :      List[str]														=						None
                    __lowerCamelCase  :      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]:
                    __lowerCamelCase  :      Any														=						list(tokenizer.pretrained_vocab_files_map.values()				)[0][checkpoint]
                    __lowerCamelCase  :      List[Any]														=						file_path.split(UpperCAmelCase_				)[-1][0]
                    if next_char == "/":
                         __lowerCamelCase  :      Tuple														=						os.path.join(UpperCAmelCase_ ,       UpperCAmelCase_				)
                         __lowerCamelCase  :      str														=						None

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

               __lowerCamelCase  :      Union[str, Any]														=						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__":
      A__						:   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.""",
      )
      A__						:   List[str]				=    parser.parse_args()

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














'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowerCAmelCase									=   {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   ['DeiTFeatureExtractor']
		__lowerCAmelCase									=   ['DeiTImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   [
		    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'DeiTForImageClassification',
		    'DeiTForImageClassificationWithTeacher',
		    'DeiTForMaskedImageModeling',
		    'DeiTModel',
		    'DeiTPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   [
		    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFDeiTForImageClassification',
		    'TFDeiTForImageClassificationWithTeacher',
		    'TFDeiTForMaskedImageModeling',
		    'TFDeiTModel',
		    'TFDeiTPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

		__lowerCAmelCase									=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




