




"""simple docstring"""
import copy
import os
from typing import Union

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


a_				:    Tuple										=     logging.get_logger(__name__)

a_				:    Any										=     {
    '''google/pix2struct-textcaps-base''': (
        '''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
    ),
}

class __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						List[Any]     		= 'pix2struct_text_model'
					__a						:						List[str]     		= ['past_key_values']
					__a						:						Tuple     		= {
					    'hidden_size': 'hidden_size',
					    'num_attention_heads': 'num_heads',
					    'num_hidden_layers': 'num_layers',
					}
					def __init__(    self						,	__a=50244						,	__a=768						,	__a=64						,	__a=2048						,	__a=12						,	__a=12						,	__a=32						,	__a=128						,	__a=0.1						,	__a=1E-6						,	__a=1.0						,	__a="gelu_new"						,	__a=0						,	__a=False						,	__a=0						,	__a=1						,	__a=False						,	__a=True						,	**__a						,	):
								__lowerCamelCase					:	List[str]	       =				vocab_size
								__lowerCamelCase					:	Tuple	       =				hidden_size
								__lowerCamelCase					:	Dict	       =				d_kv
								__lowerCamelCase					:	List[Any]	       =				d_ff
								__lowerCamelCase					:	Optional[int]	       =				num_layers
								__lowerCamelCase					:	int	       =				num_heads
								__lowerCamelCase					:	Union[str, Any]	       =				relative_attention_num_buckets
								__lowerCamelCase					:	List[str]	       =				relative_attention_max_distance
								__lowerCamelCase					:	Dict	       =				dropout_rate
								__lowerCamelCase					:	Any	       =				layer_norm_epsilon
								__lowerCamelCase					:	Union[str, Any]	       =				initializer_factor
								__lowerCamelCase					:	Union[str, Any]	       =				use_cache

								__lowerCamelCase					:	Dict	       =				eos_token_id
								__lowerCamelCase					:	str	       =				decoder_start_token_id

								# for backwards compatibility
								__lowerCamelCase					:	Dict	       =				dense_act_fn

								super().__init__(
								    pad_token_id=__a						,	eos_token_id=__a						,	decoder_start_token_id=__a						,	tie_word_embeddings=__a						,	is_decoder=__a						,	**__a						,	)





					@classmethod
					def     snake_case_				(    cls						,	__a						,	**__a     ):
								cls._set_token_in_kwargs(__a     )

								__lowerCamelCase       ,      __lowerCamelCase					:	Any	       =				cls.get_config_dict(__a						,	**__a     )

								# get the text config dict if we are loading from Pix2StructConfig
								if config_dict.get('model_type'     ) == "pix2struct":
											__lowerCamelCase					:	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 __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						Any     		= 'pix2struct_vision_model'
					def __init__(    self						,	__a=768						,	__a=768						,	__a=2048						,	__a=64						,	__a=12						,	__a=12						,	__a="gelu_new"						,	__a=1E-6						,	__a=0.0						,	__a=0.0						,	__a=1E-10						,	__a=1.0						,	__a=4096						,	__a=32						,	__a=128						,	**__a						,	):
								super().__init__(**__a     )

								__lowerCamelCase					:	Optional[Any]	       =				hidden_size
								__lowerCamelCase					:	Optional[int]	       =				patch_embed_hidden_size
								__lowerCamelCase					:	Optional[Any]	       =				d_ff
								__lowerCamelCase					:	Dict	       =				dropout_rate
								__lowerCamelCase					:	Tuple	       =				num_hidden_layers
								__lowerCamelCase					:	List[Any]	       =				num_attention_heads
								__lowerCamelCase					:	Optional[int]	       =				initializer_range
								__lowerCamelCase					:	Optional[int]	       =				initializer_factor
								__lowerCamelCase					:	Optional[int]	       =				attention_dropout
								__lowerCamelCase					:	Dict	       =				layer_norm_eps
								__lowerCamelCase					:	Optional[int]	       =				dense_act_fn
								__lowerCamelCase					:	Dict	       =				seq_len
								__lowerCamelCase					:	List[str]	       =				relative_attention_num_buckets
								__lowerCamelCase					:	str	       =				relative_attention_max_distance
								__lowerCamelCase					:	Any	       =				d_kv





					@classmethod
					def     snake_case_				(    cls						,	__a						,	**__a     ):
								cls._set_token_in_kwargs(__a     )

								__lowerCamelCase       ,      __lowerCamelCase					:	Optional[Any]	       =				cls.get_config_dict(__a						,	**__a     )

								# get the vision config dict if we are loading from Pix2StructConfig
								if config_dict.get('model_type'     ) == "pix2struct":
											__lowerCamelCase					:	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(__a						,	**__a     )



class __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						int     		= 'pix2struct'
					__a						:						List[str]     		= True
					def __init__(    self						,	__a=None						,	__a=None						,	__a=1.0						,	__a=0.02						,	__a=False						,	__a=False						,	__a=True						,	**__a						,	):
								super().__init__(tie_word_embeddings=__a						,	is_encoder_decoder=__a						,	**__a     )

								if text_config is None:
											__lowerCamelCase					:	Optional[int]	       =				{}
											logger.info('text_config is None. Initializing the Pix2StructTextConfig with default values.'     )

								if vision_config is None:
											__lowerCamelCase					:	Tuple	       =				{}
											logger.info('vision_config is None. Initializing the Pix2StructVisionConfig with default values.'     )

								__lowerCamelCase					:	Optional[int]	       =				PixaStructTextConfig(**__a     )
								__lowerCamelCase					:	List[Any]	       =				PixaStructVisionConfig(**__a     )

								__lowerCamelCase					:	Tuple	       =				self.text_config.decoder_start_token_id
								__lowerCamelCase					:	Any	       =				self.text_config.pad_token_id
								__lowerCamelCase					:	Optional[int]	       =				self.text_config.eos_token_id

								__lowerCamelCase					:	Optional[Any]	       =				initializer_factor
								__lowerCamelCase					:	Dict	       =				initializer_range

								__lowerCamelCase					:	int	       =				self.initializer_range
								__lowerCamelCase					:	int	       =				self.initializer_range

								__lowerCamelCase					:	str	       =				is_vqa
					@classmethod
					def     snake_case_				(    cls						,	__a						,	__a						,	**__a     ):
								return cls(text_config=text_config.to_dict()						,	vision_config=vision_config.to_dict()						,	**__a     )





					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Any	       =				copy.deepcopy(self.__dict__     )
								__lowerCamelCase					:	str	       =				self.text_config.to_dict()
								__lowerCamelCase					:	List[Any]	       =				self.vision_config.to_dict()
								__lowerCamelCase					:	Union[str, Any]	       =				self.__class__.model_type
								return output









"""simple docstring"""
from cva import destroyAllWindows, imread, imshow, waitKey





def 						UpperCAmelCase				(			A__:    Tuple     )  ->     Union[str, Any]:
			# getting number of pixels in the image
			__lowerCamelCase       ,      __lowerCamelCase					:	Optional[Any]	       =				img.shape[0], img.shape[1]

			# converting each pixel's color to its negative
			for i in range(A__     ):
						for j in range(A__     ):
									__lowerCamelCase					:	Optional[Any]	       =				[255, 255, 255] - img[i][j]

			return img


if __name__ == "__main__":
					# read original image
					a_				:    Dict										=     imread('''image_data/lena.jpg''', 1)

					# convert to its negative
					a_				:    str										=     convert_to_negative(img)

					# show result image
					imshow('''negative of original image''', img)
					waitKey(0)
					destroyAllWindows()



