





import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

_A :   Any  			=  logging.get_logger(__name__)


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



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					):







   """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__       :						Optional[int]        =							TOKENIZER_CLASSES
   else:
      lowerCamelCase__       :						Optional[int]        =							{tokenizer_name: getattr(UpperCAmelCase      ,  tokenizer_name + '''Fast'''					)}

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

   for tokenizer_name in tokenizer_names:
      lowerCamelCase__       :						Tuple        =							TOKENIZER_CLASSES[tokenizer_name]

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

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

         lowerCamelCase__       :						int        =							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 :   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 :   int  			=  parser.parse_args()

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










from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_A :   Tuple  			=  logging.get_logger(__name__)

_A :   List[str]  			=  {
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,lowerCAmelCase_ ):
       _UpperCAmelCase				:  Dict			   =   "nat"

       _UpperCAmelCase				:  Tuple			   =   {
           "num_attention_heads": "num_heads",
           "num_hidden_layers": "num_layers",
       }







       def __init__(				self					:     List[Any]		, A					:     Union[str, Any]=4		, A					:     str=3		, A					:     List[Any]=6_4		, A					:     Optional[Any]=[3, 4, 6, 5]		, A					:     int=[2, 4, 8, 1_6]		, A					:     Optional[int]=7		, A					:     List[Any]=3.0		, A					:     str=True		, A					:     str=0.0		, A					:     Any=0.0		, A					:     int=0.1		, A					:     Tuple="gelu"		, A					:     List[Any]=0.02		, A					:     str=1e-5		, A					:     Optional[int]=0.0		, A					:     Optional[Any]=None		, A					:     Dict=None		, **A					:     str		, )       ->Union[str, Any]:
          super().__init__(**A      )

          lowerCamelCase__       :						Any        =							patch_size
          lowerCamelCase__       :						Optional[Any]        =							num_channels
          lowerCamelCase__       :						Any        =							embed_dim
          lowerCamelCase__       :						str        =							depths
          lowerCamelCase__       :						Union[str, Any]        =							len(A      )
          lowerCamelCase__       :						int        =							num_heads
          lowerCamelCase__       :						Optional[int]        =							kernel_size
          lowerCamelCase__       :						Optional[int]        =							mlp_ratio
          lowerCamelCase__       :						List[Any]        =							qkv_bias
          lowerCamelCase__       :						Tuple        =							hidden_dropout_prob
          lowerCamelCase__       :						List[str]        =							attention_probs_dropout_prob
          lowerCamelCase__       :						List[Any]        =							drop_path_rate
          lowerCamelCase__       :						Union[str, Any]        =							hidden_act
          lowerCamelCase__       :						Union[str, Any]        =							layer_norm_eps
          lowerCamelCase__       :						List[Any]        =							initializer_range
          # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          lowerCamelCase__       :						str        =							int(embed_dim * 2 ** (len(A      ) - 1)      )
          lowerCamelCase__       :						Dict        =							layer_scale_init_value
          lowerCamelCase__       :						Dict        =							['''stem'''] + [F"stage{idx}" for idx in range(1		, len(A      ) + 1      )]
          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							get_aligned_output_features_output_indices(
              out_features=A		, out_indices=A		, stage_names=self.stage_names      )



