'''simple docstring'''




import copy
import os
from typing import Union

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


_A								=   logging.get_logger(__name__)

_A								=   {
    '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       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   A							:       int     =    '''bridgetower_vision_model'''
   def __init__(self       ,				_a=768       ,				_a=12       ,				_a=3       ,				_a=16       ,				_a=288       ,				_a=1       ,				_a=1e-05       ,				_a=False       ,				_a=True       ,				_a=False       ,				**_a       ,				)    ->     Optional[Any]:
        super().__init__(**_a				)
        lowercase_					:       int     				=					hidden_size
        lowercase_					:       int     				=					num_hidden_layers
        lowercase_					:       Any     				=					num_channels
        lowercase_					:       Any     				=					patch_size
        lowercase_					:       List[str]     				=					image_size
        lowercase_					:       Any     				=					initializer_factor
        lowercase_					:       int     				=					layer_norm_eps
        lowercase_					:       List[Any]     				=					stop_gradient
        lowercase_					:       List[Any]     				=					share_layernorm
        lowercase_					:       int     				=					remove_last_layer





   @classmethod
   def 		_lowerCamelCase (cls       ,				_a       ,				**_a				)    ->     "PretrainedConfig":
        lowercase_				,lowercase_					:       Union[str, Any]     				=					cls.get_config_dict(_a       ,				**_a				)

        if config_dict.get('model_type'				) == "bridgetower":
             lowercase_					:       Tuple     				=					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(_a       ,				**_a				)



class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   A							:       Union[str, Any]     =    '''bridgetower_text_model'''
   def __init__(self       ,				_a=50_265       ,				_a=768       ,				_a=12       ,				_a=12       ,				_a=1       ,				_a=3_072       ,				_a="gelu"       ,				_a=0.1       ,				_a=0.1       ,				_a=514       ,				_a=1       ,				_a=1e-05       ,				_a=1       ,				_a=0       ,				_a=2       ,				_a="absolute"       ,				_a=True       ,				**_a       ,				)    ->     Optional[Any]:
        super().__init__(**_a				)

        lowercase_					:       List[str]     				=					vocab_size
        lowercase_					:       List[Any]     				=					hidden_size
        lowercase_					:       Tuple     				=					num_hidden_layers
        lowercase_					:       List[str]     				=					num_attention_heads
        lowercase_					:       List[str]     				=					hidden_act
        lowercase_					:       List[Any]     				=					initializer_factor
        lowercase_					:       List[str]     				=					intermediate_size
        lowercase_					:       Optional[int]     				=					hidden_dropout_prob
        lowercase_					:       Optional[Any]     				=					attention_probs_dropout_prob
        lowercase_					:       Any     				=					max_position_embeddings
        lowercase_					:       Optional[Any]     				=					type_vocab_size
        lowercase_					:       Dict     				=					layer_norm_eps
        lowercase_					:       List[Any]     				=					position_embedding_type
        lowercase_					:       Optional[Any]     				=					use_cache
        lowercase_					:       List[str]     				=					pad_token_id
        lowercase_					:       Optional[Any]     				=					bos_token_id
        lowercase_					:       List[Any]     				=					eos_token_id





   @classmethod
   def 		_lowerCamelCase (cls       ,				_a       ,				**_a				)    ->     "PretrainedConfig":
        lowercase_				,lowercase_					:       List[Any]     				=					cls.get_config_dict(_a       ,				**_a				)

        if config_dict.get('model_type'				) == "bridgetower":
             lowercase_					:       Union[str, Any]     				=					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(_a       ,				**_a				)



class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   A							:       int     =    '''bridgetower'''
   def __init__(self       ,				_a=True       ,				_a="gelu"       ,				_a=768       ,				_a=1       ,				_a=1e-05       ,				_a=False       ,				_a="add"       ,				_a=12       ,				_a=6       ,				_a=False       ,				_a=False       ,				_a=None       ,				_a=None       ,				**_a       ,				)    ->     int:
        # TODO: remove this once the Hub files are updated.
        lowercase_					:       List[str]     				=					kwargs.pop('text_config_dict'       ,				_a				)
        lowercase_					:       Union[str, Any]     				=					kwargs.pop('vision_config_dict'       ,				_a				)

        super().__init__(**_a				)
        lowercase_					:       int     				=					share_cross_modal_transformer_layers
        lowercase_					:       Optional[int]     				=					hidden_act
        lowercase_					:       Optional[Any]     				=					hidden_size
        lowercase_					:       List[str]     				=					initializer_factor
        lowercase_					:       Union[str, Any]     				=					layer_norm_eps
        lowercase_					:       Union[str, Any]     				=					share_link_tower_layers
        lowercase_					:       Dict     				=					link_tower_type
        lowercase_					:       Dict     				=					num_attention_heads
        lowercase_					:       Tuple     				=					num_hidden_layers
        lowercase_					:       List[Any]     				=					tie_word_embeddings
        lowercase_					:       Dict     				=					init_layernorm_from_vision_encoder

        if text_config is None:
             lowercase_					:       List[Any]     				=					{}
             logger.info('`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.'				)

        if vision_config is None:
             lowercase_					:       Union[str, Any]     				=					{}
             logger.info('`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.'				)

        lowercase_					:       Tuple     				=					BridgeTowerTextConfig(**_a				)
        lowercase_					:       Optional[Any]     				=					BridgeTowerVisionConfig(**_a				)
   @classmethod
   def 		_lowerCamelCase (cls       ,				_a       ,				_a       ,				**_a				)    ->     Union[str, Any]:
        return cls(text_config=text_config.to_dict()       ,				vision_config=vision_config.to_dict()       ,				**_a				)





   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       str     				=					copy.deepcopy(self.__dict__				)
        lowercase_					:       Any     				=					self.text_config.to_dict()
        lowercase_					:       Optional[int]     				=					self.vision_config.to_dict()
        lowercase_					:       List[Any]     				=					self.__class__.model_type
        return output





'''simple docstring'''




def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[Any]     				=					len(SCREAMING_SNAKE_CASE_							)
     for _ in range(SCREAMING_SNAKE_CASE_							):
          for i in range(_ % 2  ,       arr_size - 1  ,       2							):
               if arr[i + 1] < arr[i]:
                    lowercase_				,lowercase_					:       Optional[Any]     				=					arr[i + 1], arr[i]

     return arr


if __name__ == "__main__":
 _A								=   list(range(1_0, 0, -1))
 print(F"""Original: {arr}. Sorted: {odd_even_transposition(arr)}""")




