




'''simple docstring'''



import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase_			=						logging.get_logger(__name__)

lowerCamelCase_			=						{
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}







class 		lowercase_   (			A       ):


			"""simple docstring"""

			lowerCamelCase_      =						'''conditional_detr'''
			lowerCamelCase_      =						['''past_key_values''']
			lowerCamelCase_      =						{
			    '''hidden_size''': '''d_model''',
			    '''num_attention_heads''': '''encoder_attention_heads''',
			}



			def __init__(   self :     List[Any]  ,				__lowerCamelCase :     str=True  ,				__lowerCamelCase :     List[str]=None  ,				__lowerCamelCase :     Tuple=3  ,				__lowerCamelCase :     List[Any]=3_0_0  ,				__lowerCamelCase :     Optional[int]=6  ,				__lowerCamelCase :     str=2_0_4_8  ,				__lowerCamelCase :     List[Any]=8  ,				__lowerCamelCase :     Tuple=6  ,				__lowerCamelCase :     Tuple=2_0_4_8  ,				__lowerCamelCase :     str=8  ,				__lowerCamelCase :     str=0.0  ,				__lowerCamelCase :     List[str]=0.0  ,				__lowerCamelCase :     Optional[Any]=True  ,				__lowerCamelCase :     Tuple="relu"  ,				__lowerCamelCase :     Union[str, Any]=2_5_6  ,				__lowerCamelCase :     Tuple=0.1  ,				__lowerCamelCase :     Any=0.0  ,				__lowerCamelCase :     Union[str, Any]=0.0  ,				__lowerCamelCase :     str=0.0_2  ,				__lowerCamelCase :     Union[str, Any]=1.0  ,				__lowerCamelCase :     Optional[int]=False  ,				__lowerCamelCase :     Dict="sine"  ,				__lowerCamelCase :     Union[str, Any]="resnet50"  ,				__lowerCamelCase :     List[Any]=True  ,				__lowerCamelCase :     Dict=False  ,				__lowerCamelCase :     Dict=2  ,				__lowerCamelCase :     List[Any]=5  ,				__lowerCamelCase :     int=2  ,				__lowerCamelCase :     str=1  ,				__lowerCamelCase :     str=1  ,				__lowerCamelCase :     Tuple=2  ,				__lowerCamelCase :     Dict=5  ,				__lowerCamelCase :     List[Any]=2  ,				__lowerCamelCase :     Optional[int]=0.2_5  ,				**__lowerCamelCase :     Dict  ,				):

						"""simple docstring"""
						if backbone_config is not None and use_timm_backbone:
									raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."   )

						if not use_timm_backbone:
									if backbone_config is None:
												logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."   )
												_SCREAMING_SNAKE_CASE	=				CONFIG_MAPPING["resnet"](out_features=["stage4"]   )
									elif isinstance(__lowerCamelCase  ,				__lowerCamelCase   ):
												_SCREAMING_SNAKE_CASE	=				backbone_config.get("model_type"   )
												_SCREAMING_SNAKE_CASE	=				CONFIG_MAPPING[backbone_model_type]
												_SCREAMING_SNAKE_CASE	=				config_class.from_dict(__lowerCamelCase   )

						_SCREAMING_SNAKE_CASE	=				use_timm_backbone
						_SCREAMING_SNAKE_CASE	=				backbone_config
						_SCREAMING_SNAKE_CASE	=				num_channels
						_SCREAMING_SNAKE_CASE	=				num_queries
						_SCREAMING_SNAKE_CASE	=				d_model
						_SCREAMING_SNAKE_CASE	=				encoder_ffn_dim
						_SCREAMING_SNAKE_CASE	=				encoder_layers
						_SCREAMING_SNAKE_CASE	=				encoder_attention_heads
						_SCREAMING_SNAKE_CASE	=				decoder_ffn_dim
						_SCREAMING_SNAKE_CASE	=				decoder_layers
						_SCREAMING_SNAKE_CASE	=				decoder_attention_heads
						_SCREAMING_SNAKE_CASE	=				dropout
						_SCREAMING_SNAKE_CASE	=				attention_dropout
						_SCREAMING_SNAKE_CASE	=				activation_dropout
						_SCREAMING_SNAKE_CASE	=				activation_function
						_SCREAMING_SNAKE_CASE	=				init_std
						_SCREAMING_SNAKE_CASE	=				init_xavier_std
						_SCREAMING_SNAKE_CASE	=				encoder_layerdrop
						_SCREAMING_SNAKE_CASE	=				decoder_layerdrop
						_SCREAMING_SNAKE_CASE	=				encoder_layers
						_SCREAMING_SNAKE_CASE	=				auxiliary_loss
						_SCREAMING_SNAKE_CASE	=				position_embedding_type
						_SCREAMING_SNAKE_CASE	=				backbone
						_SCREAMING_SNAKE_CASE	=				use_pretrained_backbone
						_SCREAMING_SNAKE_CASE	=				dilation
						# Hungarian matcher
						_SCREAMING_SNAKE_CASE	=				class_cost
						_SCREAMING_SNAKE_CASE	=				bbox_cost
						_SCREAMING_SNAKE_CASE	=				giou_cost
						# Loss coefficients
						_SCREAMING_SNAKE_CASE	=				mask_loss_coefficient
						_SCREAMING_SNAKE_CASE	=				dice_loss_coefficient
						_SCREAMING_SNAKE_CASE	=				cls_loss_coefficient
						_SCREAMING_SNAKE_CASE	=				bbox_loss_coefficient
						_SCREAMING_SNAKE_CASE	=				giou_loss_coefficient
						_SCREAMING_SNAKE_CASE	=				focal_alpha
						super().__init__(is_encoder_decoder=__lowerCamelCase  ,				**__lowerCamelCase   )



			@property
			def 							lowerCAmelCase_      (   self :     List[Any]   ):

						"""simple docstring"""
						return self.encoder_attention_heads



			@property
			def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

						"""simple docstring"""
						return self.d_model



			def 							lowerCAmelCase_      (   self :     List[Any]   ):

						"""simple docstring"""
						_SCREAMING_SNAKE_CASE	=				copy.deepcopy(self.__dict__   )
						if self.backbone_config is not None:
									_SCREAMING_SNAKE_CASE	=				self.backbone_config.to_dict()
						_SCREAMING_SNAKE_CASE	=				self.__class__.model_type
						return output







class 		lowercase_   (			A       ):


			"""simple docstring"""

			lowerCamelCase_      =						version.parse('''1.11'''       )



			@property
			def 							lowerCAmelCase_      (   self :     int   ):

						"""simple docstring"""
						return OrderedDict(
						    [
						        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
						        ("pixel_mask", {0: "batch"}),
						    ]   )



			@property
			def 							lowerCAmelCase_      (   self :     Tuple   ):

						"""simple docstring"""
						return 1e-5



			@property
			def 							lowerCAmelCase_      (   self :     Tuple   ):

						"""simple docstring"""
						return 1_2







'''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()

lowerCamelCase_			=						logging.get_logger(__name__)


lowerCamelCase_			=						{name: getattr(transformers, name + 'Fast') for name in SLOW_TO_FAST_CONVERTERS}
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[int]	,					__A    :			Tuple	,					__A    :			List[str]	,					__A    :			List[str]  )			->			List[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:
      _SCREAMING_SNAKE_CASE	=				TOKENIZER_CLASSES
   else:
      _SCREAMING_SNAKE_CASE	=				{tokenizer_name: getattr(__A	,					tokenizer_name + "Fast"  )}

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

   for tokenizer_name in tokenizer_names:
      _SCREAMING_SNAKE_CASE	=				TOKENIZER_CLASSES[tokenizer_name]

      _SCREAMING_SNAKE_CASE	=				True
      if checkpoint_name is None:
         _SCREAMING_SNAKE_CASE	=				list(tokenizer_class.max_model_input_sizes.keys()  )
      else:
         _SCREAMING_SNAKE_CASE	=				[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
         _SCREAMING_SNAKE_CASE	=				tokenizer_class.from_pretrained(__A	,					force_download=__A  )

         # 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:
            _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				checkpoint.split("/"  )
            _SCREAMING_SNAKE_CASE	=				os.path.join(__A	,					__A  )
         elif add_prefix:
            _SCREAMING_SNAKE_CASE	=				checkpoint
            _SCREAMING_SNAKE_CASE	=				dump_path
         else:
            _SCREAMING_SNAKE_CASE	=				None
            _SCREAMING_SNAKE_CASE	=				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]:
            _SCREAMING_SNAKE_CASE	=				list(tokenizer.pretrained_vocab_files_map.values()  )[0][checkpoint]
            _SCREAMING_SNAKE_CASE	=				file_path.split(__A  )[-1][0]
            if next_char == "/":
               _SCREAMING_SNAKE_CASE	=				os.path.join(__A	,					__A  )
               _SCREAMING_SNAKE_CASE	=				None

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

         _SCREAMING_SNAKE_CASE	=				tokenizer.save_pretrained(
             __A	,					legacy_format=__A	,					filename_prefix=__A  )
         logger.info(f"""=> File names {file_names}"""  )

         for file_name in file_names:
            if not file_name.endswith("tokenizer.json"  ):
               os.remove(__A  )
               logger.info(f"""=> removing {file_name}"""  )


if __name__ == "__main__":
 lowerCamelCase_			=						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.',
 )
 lowerCamelCase_			=						parser.parse_args()

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

