




import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
 pass

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


lowerCamelCase				:		Optional[int]     =   logging.get_logger(__name__)

lowerCamelCase				:		Optional[Any]     =   {
    "kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						'''align_text_model'''

 def __init__(				self					:  Dict						,							A_					:  str=30522						,							A_					:  List[str]=768						,							A_					:  List[str]=12						,							A_					:  int=12						,							A_					:  List[Any]=3072						,							A_					:  Optional[Any]="gelu"						,							A_					:  Any=0.1						,							A_					:  str=0.1						,							A_					:  Dict=512						,							A_					:  int=2						,							A_					:  List[Any]=0.02						,							A_					:  List[Any]=1E-12						,							A_					:  Optional[Any]=0						,							A_					:  List[Any]="absolute"						,							A_					:  Optional[int]=True						,							**A_					:  Optional[Any]						,							)   ->	Any:

       """simple docstring"""






       super().__init__(**A_  )

       lowerCamelCase_		     =    vocab_size
       lowerCamelCase_		     =    hidden_size
       lowerCamelCase_		     =    num_hidden_layers
       lowerCamelCase_		     =    num_attention_heads
       lowerCamelCase_		     =    hidden_act
       lowerCamelCase_		     =    intermediate_size
       lowerCamelCase_		     =    hidden_dropout_prob
       lowerCamelCase_		     =    attention_probs_dropout_prob
       lowerCamelCase_		     =    max_position_embeddings
       lowerCamelCase_		     =    type_vocab_size
       lowerCamelCase_		     =    initializer_range
       lowerCamelCase_		     =    layer_norm_eps
       lowerCamelCase_		     =    position_embedding_type
       lowerCamelCase_		     =    use_cache
       lowerCamelCase_		     =    pad_token_id





 @classmethod
 def 				a__ (				cls					:  Optional[Any]						,							A_					:  Union[str, os.PathLike]						,							**A_					:  Optional[Any]  )   ->	"PretrainedConfig":

       """simple docstring"""






       cls._set_token_in_kwargs(A_  )

       lowerCamelCase_     ,				lowerCamelCase_		     =    cls.get_config_dict(A_						,							**A_  )

       # get the text config dict if we are loading from AlignConfig
       if config_dict.get('model_type'  ) == "align":
             lowerCamelCase_		     =    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   A(						UpperCamelCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						'''align_vision_model'''

 def __init__(				self					:  int						,							A_					:  int = 3						,							A_					:  int = 600						,							A_					:  float = 2.0						,							A_					:  float = 3.1						,							A_					:  int = 8						,							A_					:  List[int] = [3, 3, 5, 3, 5, 5, 3]						,							A_					:  List[int] = [32, 16, 24, 40, 80, 112, 192]						,							A_					:  List[int] = [16, 24, 40, 80, 112, 192, 320]						,							A_					:  List[int] = []						,							A_					:  List[int] = [1, 2, 2, 2, 1, 2, 1]						,							A_					:  List[int] = [1, 2, 2, 3, 3, 4, 1]						,							A_					:  List[int] = [1, 6, 6, 6, 6, 6, 6]						,							A_					:  float = 0.25						,							A_					:  str = "swish"						,							A_					:  int = 2560						,							A_					:  str = "mean"						,							A_					:  float = 0.02						,							A_					:  float = 0.001						,							A_					:  float = 0.99						,							A_					:  float = 0.2						,							**A_					:  Union[str, Any]						,							)   ->	Optional[Any]:

       """simple docstring"""






       super().__init__(**A_  )

       lowerCamelCase_		     =    num_channels
       lowerCamelCase_		     =    image_size
       lowerCamelCase_		     =    width_coefficient
       lowerCamelCase_		     =    depth_coefficient
       lowerCamelCase_		     =    depth_divisor
       lowerCamelCase_		     =    kernel_sizes
       lowerCamelCase_		     =    in_channels
       lowerCamelCase_		     =    out_channels
       lowerCamelCase_		     =    depthwise_padding
       lowerCamelCase_		     =    strides
       lowerCamelCase_		     =    num_block_repeats
       lowerCamelCase_		     =    expand_ratios
       lowerCamelCase_		     =    squeeze_expansion_ratio
       lowerCamelCase_		     =    hidden_act
       lowerCamelCase_		     =    hidden_dim
       lowerCamelCase_		     =    pooling_type
       lowerCamelCase_		     =    initializer_range
       lowerCamelCase_		     =    batch_norm_eps
       lowerCamelCase_		     =    batch_norm_momentum
       lowerCamelCase_		     =    drop_connect_rate
       lowerCamelCase_		     =    sum(A_  ) * 4





 @classmethod
 def 				a__ (				cls					:  Dict						,							A_					:  Union[str, os.PathLike]						,							**A_					:  str  )   ->	"PretrainedConfig":

       """simple docstring"""






       cls._set_token_in_kwargs(A_  )

       lowerCamelCase_     ,				lowerCamelCase_		     =    cls.get_config_dict(A_						,							**A_  )

       # get the vision config dict if we are loading from AlignConfig
       if config_dict.get('model_type'  ) == "align":
             lowerCamelCase_		     =    config_dict['vision_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   A(						UpperCamelCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						'''align'''
 UpperCamelCase						   =						True

 def __init__(				self					:  int						,							A_					:  List[Any]=None						,							A_					:  Union[str, Any]=None						,							A_					:  Dict=640						,							A_					:  Tuple=1.0						,							A_					:  List[str]=0.02						,							**A_					:  Optional[Any]						,							)   ->	str:

       """simple docstring"""






       super().__init__(**A_  )

       if text_config is None:
             lowerCamelCase_		     =    {}
             logger.info('text_config is None. Initializing the AlignTextConfig with default values.'  )

       if vision_config is None:
             lowerCamelCase_		     =    {}
             logger.info('vision_config is None. Initializing the AlignVisionConfig with default values.'  )

       lowerCamelCase_		     =    AlignTextConfig(**A_  )
       lowerCamelCase_		     =    AlignVisionConfig(**A_  )

       lowerCamelCase_		     =    projection_dim
       lowerCamelCase_		     =    temperature_init_value
       lowerCamelCase_		     =    initializer_range

 @classmethod
 def 				a__ (				cls					:  Optional[int]						,							A_					:  AlignTextConfig						,							A_					:  AlignVisionConfig						,							**A_					:  List[Any]  )   ->	Union[str, Any]:

       """simple docstring"""






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





 def 				a__ (				self					:  Any  )   ->	Dict:

       """simple docstring"""






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









def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       list     ):


      '''simple docstring'''



      for i in range(len(lowercase     ) - 1	,							0	,							-1     ):
            lowerCamelCase_		     =    False

            for j in range(lowercase	,							0	,							-1     ):
                  if unsorted[j] < unsorted[j - 1]:
                        lowerCamelCase_     ,				lowerCamelCase_		     =    unsorted[j - 1], unsorted[j]
                        lowerCamelCase_		     =    True

            for j in range(lowercase     ):
                  if unsorted[j] > unsorted[j + 1]:
                        lowerCamelCase_     ,				lowerCamelCase_		     =    unsorted[j + 1], unsorted[j]
                        lowerCamelCase_		     =    True

            if not swapped:
                  break
      return unsorted


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 lowerCamelCase				:		Any     =   input("Enter numbers separated by a comma:\n").strip()
 lowerCamelCase				:		Dict     =   [int(item) for item in user_input.split(",")]
 print(F"""{cocktail_shaker_sort(unsorted) = }""")



