






'''simple docstring'''

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     =       {
    'BridgeTower/bridgetower-base': 'https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json',
    'BridgeTower/bridgetower-base-itm-mlm': (
        'https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'
    ),
}






class     a_						(      lowerCamelCase    ):
  lowercase							=				"""bridgetower_vision_model"""

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE=768      ,		_SCREAMING_SNAKE_CASE=12      ,		_SCREAMING_SNAKE_CASE=3      ,		_SCREAMING_SNAKE_CASE=16      ,		_SCREAMING_SNAKE_CASE=288      ,		_SCREAMING_SNAKE_CASE=1      ,		_SCREAMING_SNAKE_CASE=1e-05      ,		_SCREAMING_SNAKE_CASE=False      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=False      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				Union[str, Any]:



        """simple docstring"""






        super().__init__(**_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = hidden_size
        UpperCamelCase      = num_hidden_layers
        UpperCamelCase      = num_channels
        UpperCamelCase      = patch_size
        UpperCamelCase      = image_size
        UpperCamelCase      = initializer_factor
        UpperCamelCase      = layer_norm_eps
        UpperCamelCase      = stop_gradient
        UpperCamelCase      = share_layernorm
        UpperCamelCase      = remove_last_layer


  @classmethod
  def   A__						(				cls      ,		_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)  ->				"PretrainedConfig":



        """simple docstring"""






        UpperCamelCase  ,UpperCamelCase      = cls.get_config_dict(_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)

        if config_dict.get("""model_type"""				) == "bridgetower":
              UpperCamelCase      = config_dict["""text_config"""]

        if "model_type" in config_dict and hasattr(cls      ,		"""model_type"""				) and config_dict["model_type"] != cls.model_type:
              logger.warning(
                  F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                  F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."				)

        return cls.from_dict(_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)






class     a_						(      lowerCamelCase    ):
  lowercase							=				"""bridgetower_text_model"""

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE=50265      ,		_SCREAMING_SNAKE_CASE=768      ,		_SCREAMING_SNAKE_CASE=12      ,		_SCREAMING_SNAKE_CASE=12      ,		_SCREAMING_SNAKE_CASE=1      ,		_SCREAMING_SNAKE_CASE=3072      ,		_SCREAMING_SNAKE_CASE="gelu"      ,		_SCREAMING_SNAKE_CASE=0.1      ,		_SCREAMING_SNAKE_CASE=0.1      ,		_SCREAMING_SNAKE_CASE=514      ,		_SCREAMING_SNAKE_CASE=1      ,		_SCREAMING_SNAKE_CASE=1e-05      ,		_SCREAMING_SNAKE_CASE=1      ,		_SCREAMING_SNAKE_CASE=0      ,		_SCREAMING_SNAKE_CASE=2      ,		_SCREAMING_SNAKE_CASE="absolute"      ,		_SCREAMING_SNAKE_CASE=True      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				List[Any]:



        """simple docstring"""






        super().__init__(**_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = vocab_size
        UpperCamelCase      = hidden_size
        UpperCamelCase      = num_hidden_layers
        UpperCamelCase      = num_attention_heads
        UpperCamelCase      = hidden_act
        UpperCamelCase      = initializer_factor
        UpperCamelCase      = intermediate_size
        UpperCamelCase      = hidden_dropout_prob
        UpperCamelCase      = attention_probs_dropout_prob
        UpperCamelCase      = max_position_embeddings
        UpperCamelCase      = type_vocab_size
        UpperCamelCase      = layer_norm_eps
        UpperCamelCase      = position_embedding_type
        UpperCamelCase      = use_cache
        UpperCamelCase      = pad_token_id
        UpperCamelCase      = bos_token_id
        UpperCamelCase      = eos_token_id


  @classmethod
  def   A__						(				cls      ,		_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)  ->				"PretrainedConfig":



        """simple docstring"""






        UpperCamelCase  ,UpperCamelCase      = cls.get_config_dict(_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)

        if config_dict.get("""model_type"""				) == "bridgetower":
              UpperCamelCase      = config_dict["""text_config"""]

        if "model_type" in config_dict and hasattr(cls      ,		"""model_type"""				) and config_dict["model_type"] != cls.model_type:
              logger.warning(
                  F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                  F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."				)

        return cls.from_dict(_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)






class     a_						(      lowerCamelCase    ):
  lowercase							=				"""bridgetower"""

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE="gelu"      ,		_SCREAMING_SNAKE_CASE=768      ,		_SCREAMING_SNAKE_CASE=1      ,		_SCREAMING_SNAKE_CASE=1e-05      ,		_SCREAMING_SNAKE_CASE=False      ,		_SCREAMING_SNAKE_CASE="add"      ,		_SCREAMING_SNAKE_CASE=12      ,		_SCREAMING_SNAKE_CASE=6      ,		_SCREAMING_SNAKE_CASE=False      ,		_SCREAMING_SNAKE_CASE=False      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=None      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				List[str]:



        """simple docstring"""






        UpperCamelCase      = kwargs.pop("""text_config_dict"""      ,		_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = kwargs.pop("""vision_config_dict"""      ,		_SCREAMING_SNAKE_CASE				)

        super().__init__(**_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = share_cross_modal_transformer_layers
        UpperCamelCase      = hidden_act
        UpperCamelCase      = hidden_size
        UpperCamelCase      = initializer_factor
        UpperCamelCase      = layer_norm_eps
        UpperCamelCase      = share_link_tower_layers
        UpperCamelCase      = link_tower_type
        UpperCamelCase      = num_attention_heads
        UpperCamelCase      = num_hidden_layers
        UpperCamelCase      = tie_word_embeddings
        UpperCamelCase      = init_layernorm_from_vision_encoder

        if text_config is None:
              UpperCamelCase      = {}
              logger.info("""`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."""				)

        if vision_config is None:
              UpperCamelCase      = {}
              logger.info("""`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values."""				)

        UpperCamelCase      = BridgeTowerTextConfig(**_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = BridgeTowerVisionConfig(**_SCREAMING_SNAKE_CASE				)

  @classmethod
  def   A__						(				cls      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)  ->				List[Any]:



        """simple docstring"""






        return cls(text_config=text_config.to_dict()      ,		vision_config=vision_config.to_dict()      ,		**_SCREAMING_SNAKE_CASE				)


  def   A__						(				self				)  ->				Any:



        """simple docstring"""






        UpperCamelCase      = copy.deepcopy(self.__dict__				)
        UpperCamelCase      = self.text_config.to_dict()
        UpperCamelCase      = self.vision_config.to_dict()
        UpperCamelCase      = self.__class__.model_type
        return output








'''simple docstring'''

from timeit import timeit







def    lowercase__			(				__UpperCamelCase   )->			int:
      if number < 0:
            raise ValueError("""the value of input must not be negative"""   )
      UpperCamelCase      = 0
      while number:
            number &= number - 1
            result += 1
      return result







def    lowercase__			(				__UpperCamelCase   )->			int:
      if number < 0:
            raise ValueError("""the value of input must not be negative"""   )
      UpperCamelCase      = 0
      while number:
            if number % 2 == 1:
                  result += 1
            number >>= 1
      return result







def    lowercase__			(				)->			None:

      def do_benchmark(__UpperCamelCase   ) -> None:
            UpperCamelCase      = """import __main__ as z"""
            print(F"Benchmark when {number = }:"   )
            print(F"{get_set_bits_count_using_modulo_operator(__UpperCamelCase   ) = }"   )
            UpperCamelCase      = timeit("""z.get_set_bits_count_using_modulo_operator(25)"""		,						setup=__UpperCamelCase   )
            print(F"timeit() runs in {timing} seconds"   )
            print(F"{get_set_bits_count_using_brian_kernighans_algorithm(__UpperCamelCase   ) = }"   )
            UpperCamelCase      = timeit(
                """z.get_set_bits_count_using_brian_kernighans_algorithm(25)"""		,						setup=__UpperCamelCase		,						)
            print(F"timeit() runs in {timing} seconds"   )

      for number in (25, 37, 58, 0):
            do_benchmark(__UpperCamelCase   )
            print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()
