


'''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				:					int    						=			logging.get_logger(__name__)


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






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      int  ,lowerCamelCase					:      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:
              _A					:     Optional[Any]     = TOKENIZER_CLASSES
       else:
              _A					:     int     = {tokenizer_name: getattr(lowerCamelCase  ,tokenizer_name + 'Fast'       )}

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

       for tokenizer_name in tokenizer_names:
              _A					:     str     = TOKENIZER_CLASSES[tokenizer_name]

              _A					:     Optional[int]     = True
              if checkpoint_name is None:
                     _A					:     Any     = list(tokenizer_class.max_model_input_sizes.keys()       )
              else:
                     _A					:     Any     = [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
                     _A					:     Dict     = tokenizer_class.from_pretrained(lowerCamelCase  ,force_download=lowerCamelCase       )

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

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

                     _A					:     int     = tokenizer.save_pretrained(
                         lowerCamelCase  ,legacy_format=lowerCamelCase  ,filename_prefix=lowerCamelCase       )
                     logger.info(F'=> File names {file_names}'       )

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


if __name__ == "__main__":
   A				:					str    						=			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				:					Union[str, Any]    						=			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_torch_available, is_vision_available


A				:					Optional[int]    						=			{
    '''configuration_mask2former''': [
        '''MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Mask2FormerConfig''',
    ],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Tuple    						=			['''Mask2FormerImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Dict    						=			[
       '''MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''Mask2FormerForUniversalSegmentation''',
       '''Mask2FormerModel''',
       '''Mask2FormerPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_maskaformer import MaskaFormerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_maskaformer import (
          MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          MaskaFormerForUniversalSegmentation,
          MaskaFormerModel,
          MaskaFormerPreTrainedModel,
      )


else:
   import sys

   A				:					Optional[Any]    						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure)






