





import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE__       :			Tuple			      =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       :			List[Any]			      =						{
    "salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json",
}







class   __lowerCAmelCase			(					UpperCAmelCase_  ):
   _UpperCamelCase :						List[Any]     		=				'blip_2_vision_model'





   def __init__( self					,			snake_case=1_408					,			snake_case=6_144					,			snake_case=39					,			snake_case=16					,			snake_case=224					,			snake_case=14					,			snake_case="gelu"					,			snake_case=0.00_001					,			snake_case=0.0					,			snake_case=1E-10					,			snake_case=True					,			**snake_case					,			)				->  Optional[int]:
      """simple docstring"""





      super().__init__(**_lowercase      )

      a__	:						Optional[int]      		=  hidden_size
      a__	:						Dict      		=  intermediate_size
      a__	:						str      		=  num_hidden_layers
      a__	:						List[Any]      		=  num_attention_heads
      a__	:						Any      		=  patch_size
      a__	:						Dict      		=  image_size
      a__	:						Optional[Any]      		=  initializer_range
      a__	:						str      		=  attention_dropout
      a__	:						Dict      		=  layer_norm_eps
      a__	:						Tuple      		=  hidden_act
      a__	:						Optional[int]      		=  qkv_bias





   @classmethod
   def 	_snake_case ( cls					,			snake_case					,			**snake_case      )				->  "PretrainedConfig":
      """simple docstring"""





      cls._set_token_in_kwargs(_lowercase      )

      a__	:						Any      		=  cls.get_config_dict(_lowercase					,			**_lowercase      )

      # get the vision config dict if we are loading from Blip2Config
      if config_dict.get("model_type"      ) == "blip-2":
         a__	:						Union[str, Any]      		=  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(_lowercase					,			**_lowercase      )







class   __lowerCAmelCase			(					UpperCAmelCase_  ):
   _UpperCamelCase :						int     		=				'blip_2_qformer'





   def __init__( self					,			snake_case=30_522					,			snake_case=768					,			snake_case=12					,			snake_case=12					,			snake_case=3_072					,			snake_case="gelu"					,			snake_case=0.1					,			snake_case=0.1					,			snake_case=512					,			snake_case=0.02					,			snake_case=1E-12					,			snake_case=0					,			snake_case="absolute"					,			snake_case=2					,			snake_case=1_408					,			**snake_case					,			)				->  Any:
      """simple docstring"""





      super().__init__(pad_token_id=_lowercase					,			**_lowercase      )

      a__	:						List[Any]      		=  vocab_size
      a__	:						Tuple      		=  hidden_size
      a__	:						Optional[int]      		=  num_hidden_layers
      a__	:						Tuple      		=  num_attention_heads
      a__	:						Dict      		=  hidden_act
      a__	:						Optional[Any]      		=  intermediate_size
      a__	:						Optional[int]      		=  hidden_dropout_prob
      a__	:						Any      		=  attention_probs_dropout_prob
      a__	:						List[Any]      		=  max_position_embeddings
      a__	:						Any      		=  initializer_range
      a__	:						Tuple      		=  layer_norm_eps
      a__	:						Optional[int]      		=  position_embedding_type
      a__	:						List[str]      		=  cross_attention_frequency
      a__	:						Optional[Any]      		=  encoder_hidden_size





   @classmethod
   def 	_snake_case ( cls					,			snake_case					,			**snake_case      )				->  "PretrainedConfig":
      """simple docstring"""





      cls._set_token_in_kwargs(_lowercase      )

      a__	:						str      		=  cls.get_config_dict(_lowercase					,			**_lowercase      )

      # get the qformer config dict if we are loading from Blip2Config
      if config_dict.get("model_type"      ) == "blip-2":
         a__	:						Union[str, Any]      		=  config_dict['qformer_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(_lowercase					,			**_lowercase      )







class   __lowerCAmelCase			(					UpperCAmelCase_  ):
   _UpperCamelCase :						Optional[Any]     		=				'blip-2'
   _UpperCamelCase :						str     		=				True





   def __init__( self					,			snake_case=None					,			snake_case=None					,			snake_case=None					,			snake_case=32					,			**snake_case      )				->  int:
      """simple docstring"""





      super().__init__(**_lowercase      )

      if vision_config is None:
         a__	:						Tuple      		=  {}
         logger.info("vision_config is None. initializing the Blip2VisionConfig with default values."      )

      if qformer_config is None:
         a__	:						str      		=  {}
         logger.info("qformer_config is None. Initializing the Blip2QFormerConfig with default values."      )

      if text_config is None:
         a__	:						Tuple      		=  {}
         logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`)."      )

      a__	:						List[str]      		=  BlipaVisionConfig(**_lowercase      )
      a__	:						Union[str, Any]      		=  BlipaQFormerConfig(**_lowercase      )
      a__	:						List[Any]      		=  text_config['model_type'] if 'model_type' in text_config else 'opt'
      a__	:						Any      		=  CONFIG_MAPPING[text_model_type](**_lowercase      )

      a__	:						int      		=  self.text_config.tie_word_embeddings
      a__	:						int      		=  self.text_config.is_encoder_decoder

      a__	:						Tuple      		=  num_query_tokens
      a__	:						Tuple      		=  self.vision_config.hidden_size
      a__	:						Union[str, Any]      		=  self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
      a__	:						Any      		=  1.0
      a__	:						Union[str, Any]      		=  0.02





   @classmethod
   def 	_snake_case ( cls					,			snake_case					,			snake_case					,			snake_case					,			**snake_case					,			)				->  Optional[int]:
      """simple docstring"""





      return cls(
          vision_config=vision_config.to_dict()					,			qformer_config=qformer_config.to_dict()					,			text_config=text_config.to_dict()					,			**_lowercase					,			)





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      a__	:						List[Any]      		=  copy.deepcopy(self.__dict__      )
      a__	:						int      		=  self.vision_config.to_dict()
      a__	:						Any      		=  self.qformer_config.to_dict()
      a__	:						Optional[int]      		=  self.text_config.to_dict()
      a__	:						str      		=  self.__class__.model_type
      return output








from PIL import Image






def       _A  (      lowerCamelCase							, lowerCamelCase   ):

   def brightness(lowerCamelCase   ) -> float:
      return 128 + level + (c - 128)

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


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

