




'''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


lowercase      :   Tuple      =       logging.get_logger(__name__)

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







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Dict          =					"""conditional_detr"""
     lowerCAmelCase_  :     List[Any]          =					["""past_key_values"""]
     lowerCAmelCase_  :     Optional[Any]          =					{
         """hidden_size""": """d_model""",
         """num_attention_heads""": """encoder_attention_heads""",
     }







     def __init__(				self							,__a=True							,__a=None							,__a=3							,__a=300							,__a=6							,__a=2_048							,__a=8							,__a=6							,__a=2_048							,__a=8							,__a=0.0							,__a=0.0							,__a=True							,__a="relu"							,__a=256							,__a=0.1							,__a=0.0							,__a=0.0							,__a=0.02							,__a=1.0							,__a=False							,__a="sine"							,__a="resnet50"							,__a=True							,__a=False							,__a=2							,__a=5							,__a=2							,__a=1							,__a=1							,__a=2							,__a=5							,__a=2							,__a=0.25							,**__a							,)      ->				List[Any]:
          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."""							)
                    snake_case			:      Tuple        =			CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]							)
               elif isinstance(__a							,__a							):
                    snake_case			:      List[str]        =			backbone_config.get("""model_type"""							)
                    snake_case			:      int        =			CONFIG_MAPPING[backbone_model_type]
                    snake_case			:      List[str]        =			config_class.from_dict(__a							)

          snake_case			:      Any        =			use_timm_backbone
          snake_case			:      Dict        =			backbone_config
          snake_case			:      List[Any]        =			num_channels
          snake_case			:      str        =			num_queries
          snake_case			:      Union[str, Any]        =			d_model
          snake_case			:      Union[str, Any]        =			encoder_ffn_dim
          snake_case			:      Union[str, Any]        =			encoder_layers
          snake_case			:      Any        =			encoder_attention_heads
          snake_case			:      str        =			decoder_ffn_dim
          snake_case			:      List[Any]        =			decoder_layers
          snake_case			:      Optional[int]        =			decoder_attention_heads
          snake_case			:      Optional[Any]        =			dropout
          snake_case			:      int        =			attention_dropout
          snake_case			:      str        =			activation_dropout
          snake_case			:      Tuple        =			activation_function
          snake_case			:      Optional[int]        =			init_std
          snake_case			:      str        =			init_xavier_std
          snake_case			:      Any        =			encoder_layerdrop
          snake_case			:      Tuple        =			decoder_layerdrop
          snake_case			:      Union[str, Any]        =			encoder_layers
          snake_case			:      str        =			auxiliary_loss
          snake_case			:      Optional[Any]        =			position_embedding_type
          snake_case			:      int        =			backbone
          snake_case			:      Union[str, Any]        =			use_pretrained_backbone
          snake_case			:      int        =			dilation
          # Hungarian matcher
          snake_case			:      Union[str, Any]        =			class_cost
          snake_case			:      Optional[Any]        =			bbox_cost
          snake_case			:      Dict        =			giou_cost
          # Loss coefficients
          snake_case			:      Any        =			mask_loss_coefficient
          snake_case			:      Optional[Any]        =			dice_loss_coefficient
          snake_case			:      List[str]        =			cls_loss_coefficient
          snake_case			:      Dict        =			bbox_loss_coefficient
          snake_case			:      str        =			giou_loss_coefficient
          snake_case			:      int        =			focal_alpha
          super().__init__(is_encoder_decoder=__a							,**__a							)







     @property
     def 			snake_case_      (				self							)      ->				int:
          return self.encoder_attention_heads







     @property
     def 			snake_case_      (				self							)      ->				int:
          return self.d_model







     def 			snake_case_      (				self							)      ->				Any:
          snake_case			:      str        =			copy.deepcopy(self.__dict__							)
          if self.backbone_config is not None:
               snake_case			:      List[str]        =			self.backbone_config.to_dict()
          snake_case			:      Optional[Any]        =			self.__class__.model_type
          return output







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Union[str, Any]          =					version.parse("""1.11"""	)







     @property
     def 			snake_case_      (				self							)      ->				Mapping[str, Mapping[int, str]]:
          return OrderedDict(
              [
                  ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                  ("""pixel_mask""", {0: """batch"""}),
              ]							)







     @property
     def 			snake_case_      (				self							)      ->				float:
          return 1E-5







     @property
     def 			snake_case_      (				self							)      ->				int:
          return 12









'''simple docstring'''







import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowercase      :   Any      =       logging.getLogger(__name__)

if __name__ == "__main__":
      lowercase      :   List[Any]      =       argparse.ArgumentParser(
          description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
      )
      parser.add_argument(
          """--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
      )
      parser.add_argument(
          """--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
      )
      parser.add_argument("""--vocab_size""", default=3_0522, type=int)
      lowercase      :   str      =       parser.parse_args()

      logger.info(F"""Loading data from {args.data_file}""")
      with open(args.data_file, """rb""") as fp:
            lowercase      :   int      =       pickle.load(fp)

      logger.info("""Counting occurrences for MLM.""")
      lowercase      :   List[Any]      =       Counter()
      for tk_ids in data:
            counter.update(tk_ids)
      lowercase      :   int      =       [0] * args.vocab_size
      for k, v in counter.items():
            lowercase      :   List[Any]      =       v

      logger.info(F"""Dump to {args.token_counts_dump}""")
      with open(args.token_counts_dump, """wb""") as handle:
            pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)



