






"""simple docstring"""

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


lowercase    			=       logging.get_logger(__name__)

lowercase    			=       {
    '''salesforce/blip2-opt-2.7b''': '''https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json''',
}

class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				'''blip_2_vision_model'''







				def __init__(							self							:						Dict						,    __lowerCamelCase							:						Union[str, Any]=1_4_0_8						,    __lowerCamelCase							:						Any=6_1_4_4						,    __lowerCamelCase							:						int=3_9						,    __lowerCamelCase							:						Any=1_6						,    __lowerCamelCase							:						Union[str, Any]=2_2_4						,    __lowerCamelCase							:						Optional[Any]=1_4						,    __lowerCamelCase							:						Any="gelu"						,    __lowerCamelCase							:						Tuple=0.0_0001						,    __lowerCamelCase							:						Dict=0.0						,    __lowerCamelCase							:						List[str]=1e-10						,    __lowerCamelCase							:						Tuple=True						,    **__lowerCamelCase							:						Optional[int]						,    )				->					Optional[Any]:
										super().__init__(**__lowerCamelCase	)

										__magic_name__            =     hidden_size
										__magic_name__            =     intermediate_size
										__magic_name__            =     num_hidden_layers
										__magic_name__            =     num_attention_heads
										__magic_name__            =     patch_size
										__magic_name__            =     image_size
										__magic_name__            =     initializer_range
										__magic_name__            =     attention_dropout
										__magic_name__            =     layer_norm_eps
										__magic_name__            =     hidden_act
										__magic_name__            =     qkv_bias







				@classmethod
				def     _snake_case					(							cls							:						int						,    __lowerCamelCase							:						Union[str, os.PathLike]						,    **__lowerCamelCase							:						Tuple	)				->					"PretrainedConfig":
										cls._set_token_in_kwargs(__lowerCamelCase	)

										__magic_name__      ,    __magic_name__            =     cls.get_config_dict(__lowerCamelCase						,    **__lowerCamelCase	)

										# get the vision config dict if we are loading from Blip2Config
										if config_dict.get("model_type"	) == "blip-2":
																__magic_name__            =     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 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				'''blip_2_qformer'''







				def __init__(							self							:						int						,    __lowerCamelCase							:						Any=3_0_5_2_2						,    __lowerCamelCase							:						Union[str, Any]=7_6_8						,    __lowerCamelCase							:						Any=1_2						,    __lowerCamelCase							:						Dict=1_2						,    __lowerCamelCase							:						Any=3_0_7_2						,    __lowerCamelCase							:						Optional[int]="gelu"						,    __lowerCamelCase							:						Dict=0.1						,    __lowerCamelCase							:						Any=0.1						,    __lowerCamelCase							:						Tuple=5_1_2						,    __lowerCamelCase							:						Dict=0.02						,    __lowerCamelCase							:						str=1e-12						,    __lowerCamelCase							:						Any=0						,    __lowerCamelCase							:						Union[str, Any]="absolute"						,    __lowerCamelCase							:						Any=2						,    __lowerCamelCase							:						Dict=1_4_0_8						,    **__lowerCamelCase							:						List[Any]						,    )				->					Optional[int]:
										super().__init__(pad_token_id=__lowerCamelCase						,    **__lowerCamelCase	)

										__magic_name__            =     vocab_size
										__magic_name__            =     hidden_size
										__magic_name__            =     num_hidden_layers
										__magic_name__            =     num_attention_heads
										__magic_name__            =     hidden_act
										__magic_name__            =     intermediate_size
										__magic_name__            =     hidden_dropout_prob
										__magic_name__            =     attention_probs_dropout_prob
										__magic_name__            =     max_position_embeddings
										__magic_name__            =     initializer_range
										__magic_name__            =     layer_norm_eps
										__magic_name__            =     position_embedding_type
										__magic_name__            =     cross_attention_frequency
										__magic_name__            =     encoder_hidden_size







				@classmethod
				def     _snake_case					(							cls							:						List[str]						,    __lowerCamelCase							:						Union[str, os.PathLike]						,    **__lowerCamelCase							:						Optional[Any]	)				->					"PretrainedConfig":
										cls._set_token_in_kwargs(__lowerCamelCase	)

										__magic_name__      ,    __magic_name__            =     cls.get_config_dict(__lowerCamelCase						,    **__lowerCamelCase	)

										# get the qformer config dict if we are loading from Blip2Config
										if config_dict.get("model_type"	) == "blip-2":
																__magic_name__            =     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(__lowerCamelCase						,    **__lowerCamelCase	)





class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				'''blip-2'''
				UpperCAmelCase__        =				True







				def __init__(							self							:						Optional[int]						,    __lowerCamelCase							:						int=None						,    __lowerCamelCase							:						Dict=None						,    __lowerCamelCase							:						Any=None						,    __lowerCamelCase							:						Any=3_2						,    **__lowerCamelCase							:						Dict	)				->					List[str]:
										super().__init__(**__lowerCamelCase	)

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

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

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

										__magic_name__            =     BlipaVisionConfig(**__lowerCamelCase	)
										__magic_name__            =     BlipaQFormerConfig(**__lowerCamelCase	)
										__magic_name__            =     text_config["model_type"] if "model_type" in text_config else "opt"
										__magic_name__            =     CONFIG_MAPPING[text_model_type](**__lowerCamelCase	)

										__magic_name__            =     self.text_config.tie_word_embeddings
										__magic_name__            =     self.text_config.is_encoder_decoder

										__magic_name__            =     num_query_tokens
										__magic_name__            =     self.vision_config.hidden_size
										__magic_name__            =     self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
										__magic_name__            =     1.0
										__magic_name__            =     0.02







				@classmethod
				def     _snake_case					(							cls							:						List[str]						,    __lowerCamelCase							:						BlipaVisionConfig						,    __lowerCamelCase							:						BlipaQFormerConfig						,    __lowerCamelCase							:						PretrainedConfig						,    **__lowerCamelCase							:						Optional[int]						,    )				->					List[str]:
										return cls(
										    vision_config=vision_config.to_dict()						,    qformer_config=qformer_config.to_dict()						,    text_config=text_config.to_dict()						,    **__lowerCamelCase						,    )







				def     _snake_case					(							self							:						int	)				->					str:
										__magic_name__            =     copy.deepcopy(self.__dict__	)
										__magic_name__            =     self.vision_config.to_dict()
										__magic_name__            =     self.qformer_config.to_dict()
										__magic_name__            =     self.text_config.to_dict()
										__magic_name__            =     self.__class__.model_type
										return output











"""simple docstring"""

import colorsys

from PIL import Image  # type: ignore






def _lowerCAmelCase	(  __lowerCamelCase:float ,						__lowerCamelCase:float ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     x
						__magic_name__            =     y
						for step in range(__lowerCamelCase   ):  # noqa: B007
												__magic_name__            =     a * a - b * b + x
												__magic_name__            =     2 * a * b + y
												__magic_name__            =     a_new

												# divergence happens for all complex number with an absolute value
												# greater than 4
												if a * a + b * b > 4:
																		break
						return step / (max_step - 1)






def _lowerCAmelCase	(  __lowerCamelCase:float   ):

						'''simple docstring'''






						if distance == 1:
												return (0, 0, 0)
						else:
												return (2_5_5, 2_5_5, 2_5_5)






def _lowerCAmelCase	(  __lowerCamelCase:float   ):

						'''simple docstring'''






						if distance == 1:
												return (0, 0, 0)
						else:
												return tuple(round(i * 2_5_5   ) for i in colorsys.hsv_to_rgb(__lowerCamelCase ,						1 ,						1   )   )






def _lowerCAmelCase	(  __lowerCamelCase:int = 8_0_0 ,						__lowerCamelCase:int = 6_0_0 ,						__lowerCamelCase:float = -0.6 ,						__lowerCamelCase:float = 0 ,						__lowerCamelCase:float = 3.2 ,						__lowerCamelCase:int = 5_0 ,						__lowerCamelCase:bool = True ,						):

						'''simple docstring'''






						__magic_name__            =     Image.new("RGB" ,						(image_width, image_height)   )
						__magic_name__            =     img.load()

						# loop through the image-coordinates
						for image_x in range(__lowerCamelCase   ):
												for image_y in range(__lowerCamelCase   ):
																		# determine the figure-coordinates based on the image-coordinates
																		__magic_name__            =     figure_width / image_width * image_height
																		__magic_name__            =     figure_center_x + (image_x / image_width - 0.5) * figure_width
																		__magic_name__            =     figure_center_y + (image_y / image_height - 0.5) * figure_height

																		__magic_name__            =     get_distance(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase   )

																		# color the corresponding pixel based on the selected coloring-function
																		if use_distance_color_coding:
																								__magic_name__            =     get_color_coded_rgb(__lowerCamelCase   )
																		else:
																								__magic_name__            =     get_black_and_white_rgb(__lowerCamelCase   )

						return img


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			# colored version, full figure
			lowercase    			=       get_image()

			# uncomment for colored version, different section, zoomed in
			# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
			# figure_width = 0.8)

			# uncomment for black and white version, full figure
			# img = get_image(use_distance_color_coding = False)

			# uncomment to save the image
			# img.save("mandelbrot.png")

			img.show()



