






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


__UpperCAmelCase     							=						logging.get_logger(__name__)

__UpperCAmelCase     							=						{
    'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Tuple							=  '''instructblip_vision_model'''



		def __init__(							self    ,		_UpperCamelCase=1_4_0_8    ,		_UpperCamelCase=6_1_4_4    ,		_UpperCamelCase=3_9    ,		_UpperCamelCase=1_6    ,		_UpperCamelCase=2_2_4    ,		_UpperCamelCase=1_4    ,		_UpperCamelCase="gelu"    ,		_UpperCamelCase=1E-6    ,		_UpperCamelCase=0.0    ,		_UpperCamelCase=1E-10    ,		_UpperCamelCase=True    ,		**_UpperCamelCase    ,		)     ->							Optional[Any]:
							super().__init__(**_UpperCamelCase       )

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



		@classmethod
		def  __UpperCAmelCase      (							cls    ,		_UpperCamelCase    ,		**_UpperCamelCase       )     ->							"PretrainedConfig":
							cls._set_token_in_kwargs(_UpperCamelCase       )

							UpperCAmelCase_	,  UpperCAmelCase_   :				Tuple						=						cls.get_config_dict(_UpperCamelCase    ,		**_UpperCamelCase       )

							# get the vision config dict if we are loading from InstructBlipConfig
							if config_dict.get('model_type'       ) == "instructblip":
												UpperCAmelCase_   :				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(_UpperCamelCase    ,		**_UpperCamelCase       )





class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Optional[Any]							=  '''instructblip_qformer'''



		def __init__(							self    ,		_UpperCamelCase=3_0_5_2_2    ,		_UpperCamelCase=7_6_8    ,		_UpperCamelCase=1_2    ,		_UpperCamelCase=1_2    ,		_UpperCamelCase=3_0_7_2    ,		_UpperCamelCase="gelu"    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=5_1_2    ,		_UpperCamelCase=0.02    ,		_UpperCamelCase=1E-12    ,		_UpperCamelCase=0    ,		_UpperCamelCase="absolute"    ,		_UpperCamelCase=2    ,		_UpperCamelCase=1_4_0_8    ,		**_UpperCamelCase    ,		)     ->							Tuple:
							super().__init__(pad_token_id=_UpperCamelCase    ,		**_UpperCamelCase       )

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



		@classmethod
		def  __UpperCAmelCase      (							cls    ,		_UpperCamelCase    ,		**_UpperCamelCase       )     ->							"PretrainedConfig":
							cls._set_token_in_kwargs(_UpperCamelCase       )

							UpperCAmelCase_	,  UpperCAmelCase_   :				List[Any]						=						cls.get_config_dict(_UpperCamelCase    ,		**_UpperCamelCase       )

							# get the qformer config dict if we are loading from InstructBlipConfig
							if config_dict.get('model_type'       ) == "instructblip":
												UpperCAmelCase_   :				List[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(_UpperCamelCase    ,		**_UpperCamelCase       )





class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Tuple							=  '''instructblip'''
		_snake_case   :   List[str]							=  True



		def __init__(							self    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None    ,		_UpperCamelCase=3_2    ,		**_UpperCamelCase       )     ->							List[str]:
							super().__init__(**_UpperCamelCase       )

							if vision_config is None:
												UpperCAmelCase_   :				Optional[Any]						=						{}
												logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.'       )

							if qformer_config is None:
												UpperCAmelCase_   :				Optional[int]						=						{}
												logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'       )

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

							UpperCAmelCase_   :				str						=						InstructBlipVisionConfig(**_UpperCamelCase       )
							UpperCAmelCase_   :				Tuple						=						InstructBlipQFormerConfig(**_UpperCamelCase       )
							UpperCAmelCase_   :				List[Any]						=						text_config['model_type'] if 'model_type' in text_config else 'opt'
							UpperCAmelCase_   :				List[str]						=						CONFIG_MAPPING[text_model_type](**_UpperCamelCase       )

							UpperCAmelCase_   :				Union[str, Any]						=						self.text_config.tie_word_embeddings
							UpperCAmelCase_   :				List[str]						=						self.text_config.is_encoder_decoder

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



		@classmethod
		def  __UpperCAmelCase      (							cls    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		**_UpperCamelCase    ,		)     ->							Dict:
							return cls(
							    vision_config=vision_config.to_dict()    ,		qformer_config=qformer_config.to_dict()    ,		text_config=text_config.to_dict()    ,		**_UpperCamelCase    ,		)



		def  __UpperCAmelCase      (							self       )     ->							str:
							UpperCAmelCase_   :				Any						=						copy.deepcopy(self.__dict__       )
							UpperCAmelCase_   :				Dict						=						self.vision_config.to_dict()
							UpperCAmelCase_   :				Any						=						self.qformer_config.to_dict()
							UpperCAmelCase_   :				str						=						self.text_config.to_dict()
							UpperCAmelCase_   :				Tuple						=						self.__class__.model_type
							return output














from __future__ import annotations

from collections.abc import Callable

__UpperCAmelCase     							=						list[list[float | int]]




def 					lowercase__				(					__snake_case   :							Matrix					,	__snake_case   :							Matrix  ):


					'''simple docstring'''


					UpperCAmelCase_   :				int						=						len(__snake_case  )
					UpperCAmelCase_   :				Matrix						=						[[0 for _ in range(size + 1  )] for _ in range(__snake_case  )]
					UpperCAmelCase_   :				int
					UpperCAmelCase_   :				int
					UpperCAmelCase_   :				int
					UpperCAmelCase_   :				int
					UpperCAmelCase_   :				int
					UpperCAmelCase_   :				float

					for row in range(__snake_case  ):
										for col in range(__snake_case  ):
															UpperCAmelCase_   :				Dict						=						matrix[row][col]

										UpperCAmelCase_   :				Union[str, Any]						=						vector[row][0]

					UpperCAmelCase_   :				Union[str, Any]						=						0
					UpperCAmelCase_   :				Union[str, Any]						=						0
					while row < size and col < size:
										# pivoting
										UpperCAmelCase_   :				Optional[Any]						=						max((abs(augmented[rowa][col]  ), rowa) for rowa in range(__snake_case					,	__snake_case  )  )[
										    1
										]
										if augmented[pivot_row][col] == 0:
															col += 1
															continue
										else:
															UpperCAmelCase_	,  UpperCAmelCase_   :				Tuple						=						augmented[pivot_row], augmented[row]

										for rowa in range(row + 1					,	__snake_case  ):
															UpperCAmelCase_   :				Optional[int]						=						augmented[rowa][col] / augmented[row][col]
															UpperCAmelCase_   :				List[str]						=						0
															for cola in range(col + 1					,	size + 1  ):
																				augmented[rowa][cola] -= augmented[row][cola] * ratio

										row += 1
										col += 1

					# back substitution
					for col in range(1					,	__snake_case  ):
										for row in range(__snake_case  ):
															UpperCAmelCase_   :				Union[str, Any]						=						augmented[row][col] / augmented[col][col]
															for cola in range(__snake_case					,	size + 1  ):
																				augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
					return [
					    [round(augmented[row][size] / augmented[row][row]					,	10  )] for row in range(__snake_case  )
					]




def 					lowercase__				(					__snake_case   :							list[int]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				int						=						len(__snake_case  )
					UpperCAmelCase_   :				Matrix						=						[[0 for _ in range(__snake_case  )] for _ in range(__snake_case  )]
					UpperCAmelCase_   :				Matrix						=						[[0] for _ in range(__snake_case  )]
					UpperCAmelCase_   :				Matrix
					UpperCAmelCase_   :				int
					UpperCAmelCase_   :				int
					UpperCAmelCase_   :				int

					for x_val, y_val in enumerate(__snake_case  ):
										for col in range(__snake_case  ):
															UpperCAmelCase_   :				int						=						(x_val + 1) ** (size - col - 1)
										UpperCAmelCase_   :				int						=						y_val

					UpperCAmelCase_   :				List[str]						=						solve(__snake_case					,	__snake_case  )

					def interpolated_func(__snake_case   :							int  ) -> int:
										return sum(
										    round(coeffs[x_val][0]  ) * (var ** (size - x_val - 1))
										    for x_val in range(__snake_case  )  )

					return interpolated_func




def 					lowercase__				(					__snake_case   :							int  ):


					'''simple docstring'''


					return (
					    1
					    - variable
					    + variable**2
					    - variable**3
					    + variable**4
					    - variable**5
					    + variable**6
					    - variable**7
					    + variable**8
					    - variable**9
					    + variable**10
					)




def 					lowercase__				(					__snake_case   :							Callable[[int], int] = question_function					,	__snake_case   :							int = 10  ):


					'''simple docstring'''


					UpperCAmelCase_   :				list[int]						=						[func(__snake_case  ) for x_val in range(1					,	order + 1  )]

					UpperCAmelCase_   :				list[Callable[[int], int]]						=						[
					    interpolate(data_points[:max_coeff]  ) for max_coeff in range(1					,	order + 1  )
					]

					UpperCAmelCase_   :				int						=						0
					UpperCAmelCase_   :				Callable[[int], int]
					UpperCAmelCase_   :				int

					for poly in polynomials:
										UpperCAmelCase_   :				Optional[int]						=						1
										while func(__snake_case  ) == poly(__snake_case  ):
															x_val += 1

										ret += poly(__snake_case  )

					return ret


if __name__ == "__main__":
		print(F'{solution() = }')






