





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


if TYPE_CHECKING:
      pass

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


SCREAMING_SNAKE_CASE_						       =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						       =	{
    """kakaobrain/align-base""": """https://huggingface.co/kakaobrain/align-base/resolve/main/config.json""",
}

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     Any		     =      "align_text_model"






     def __init__(							self						:   Optional[int]						,lowerCamelCase__						:   int=30522						,lowerCamelCase__						:   str=768						,lowerCamelCase__						:   Tuple=12						,lowerCamelCase__						:   int=12						,lowerCamelCase__						:   Dict=3072						,lowerCamelCase__						:   Union[str, Any]="gelu"						,lowerCamelCase__						:   List[Any]=0.1						,lowerCamelCase__						:   Union[str, Any]=0.1						,lowerCamelCase__						:   List[str]=512						,lowerCamelCase__						:   List[str]=2						,lowerCamelCase__						:   Dict=0.02						,lowerCamelCase__						:   Dict=1e-1_2						,lowerCamelCase__						:   Optional[int]=0						,lowerCamelCase__						:   Union[str, Any]="absolute"						,lowerCamelCase__						:   Dict=True						,**lowerCamelCase__						:   Dict						,)					->		str:




           '''simple docstring'''


           super().__init__(**lowerCamelCase__	)

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






     @classmethod
     def     SCREAMING_SNAKE_CASE__  (							cls						:   List[str]						,lowerCamelCase__						:   Union[str, os.PathLike]						,**lowerCamelCase__						:   Tuple	)					->		"PretrainedConfig":




           '''simple docstring'''


           cls._set_token_in_kwargs(lowerCamelCase__	)

           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						cls.get_config_dict(lowerCamelCase__						,**lowerCamelCase__	)

           # get the text config dict if we are loading from AlignConfig
           if config_dict.get("""model_type"""	) == "align":
                 SCREAMING_SNAKE_CASE		=						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(lowerCamelCase__						,**lowerCamelCase__	)

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     List[Any]		     =      "align_vision_model"






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




           '''simple docstring'''


           super().__init__(**lowerCamelCase__	)

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






     @classmethod
     def     SCREAMING_SNAKE_CASE__  (							cls						:   List[Any]						,lowerCamelCase__						:   Union[str, os.PathLike]						,**lowerCamelCase__						:   int	)					->		"PretrainedConfig":




           '''simple docstring'''


           cls._set_token_in_kwargs(lowerCamelCase__	)

           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						cls.get_config_dict(lowerCamelCase__						,**lowerCamelCase__	)

           # get the vision config dict if we are loading from AlignConfig
           if config_dict.get("""model_type"""	) == "align":
                 SCREAMING_SNAKE_CASE		=						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(lowerCamelCase__						,**lowerCamelCase__	)

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     Union[str, Any]		     =      "align"
     __snake_case   :     Any		     =      True






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




           '''simple docstring'''


           super().__init__(**lowerCamelCase__	)

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

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

           SCREAMING_SNAKE_CASE		=						AlignTextConfig(**lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						AlignVisionConfig(**lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						projection_dim
           SCREAMING_SNAKE_CASE		=						temperature_init_value
           SCREAMING_SNAKE_CASE		=						initializer_range






     @classmethod
     def     SCREAMING_SNAKE_CASE__  (							cls						:   List[str]						,lowerCamelCase__						:   AlignTextConfig						,lowerCamelCase__						:   AlignVisionConfig						,**lowerCamelCase__						:   Dict	)					->		Optional[Any]:




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		int:




           '''simple docstring'''


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









from PIL import Image



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Image:


      '''simple docstring'''


      def brightness(_SCREAMING_SNAKE_CASE     ) -> float:
            return 1_28 + level + (c - 1_28)

      if not -255.0 <= level <= 255.0:
            raise ValueError("""level must be between -255.0 (black) and 255.0 (white)"""     )
      return img.point(_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
      # Load image
      with Image.open("""image_data/lena.jpg""") as img:
            # Change brightness to 100
            SCREAMING_SNAKE_CASE_						       =	change_brightness(img, 1_0_0)
            brigt_img.save("""image_data/lena_brightness.png""", format="""png""")


