






'''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'''

def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			list:
      UpperCamelCase      = word.split()

      def justify(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   ) -> str:
            UpperCamelCase      = max_width - width
            UpperCamelCase      = len(__UpperCamelCase   )
            if len(__UpperCamelCase   ) == 1:
                  # if there is only word in line
                  # just insert overall_spaces_count for the remainder of line
                  return line[0] + " " * overall_spaces_count
            else:
                  UpperCamelCase      = words_count - 1
                  # num_spaces_between_words_list[i] : tells you to insert
                  # num_spaces_between_words_list[i] spaces
                  # after word on line[i]
                  UpperCamelCase      = spaces_to_insert_between_words * [
                      overall_spaces_count // spaces_to_insert_between_words
                  ]
                  UpperCamelCase      = (
                      overall_spaces_count % spaces_to_insert_between_words
                  )
                  # distribute spaces via round robin to the left words
                  for i in range(__UpperCamelCase   ):
                        num_spaces_between_words_list[i] += 1
                  UpperCamelCase      = []
                  for i in range(__UpperCamelCase   ):
                        # add the word
                        aligned_words_list.append(line[i]   )
                        # add the spaces to insert
                        aligned_words_list.append(num_spaces_between_words_list[i] * """ """   )
                  # just add the last word to the sentence
                  aligned_words_list.append(line[-1]   )
                  # join the aligned words list to form a justified line
                  return "".join(__UpperCamelCase   )

      UpperCamelCase      = []
      UpperCamelCase      = []
      UpperCamelCase      = 0
      for word in words:
            if width + len(__UpperCamelCase   ) + len(__UpperCamelCase   ) <= max_width:
                  # keep adding words until we can fill out max_width
                  # width = sum of length of all words (without overall_spaces_count)
                  # len(word) = length of current word
                  # len(line) = number of overall_spaces_count to insert between words
                  line.append(__UpperCamelCase   )
                  width += len(__UpperCamelCase   )
            else:
                  # justify the line and add it to result
                  answer.append(justify(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )   )
                  # reset new line and new width
                  UpperCamelCase  ,UpperCamelCase      = [word], len(__UpperCamelCase   )
      UpperCamelCase      = max_width - width - len(__UpperCamelCase   )
      answer.append(""" """.join(__UpperCamelCase   ) + (remaining_spaces + 1) * """ """   )
      return answer


if __name__ == "__main__":
     from doctest import testmod

     testmod()
