




from math import factorial, pi







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							float  ,						snake_case_      :							int = 30     ):

	if not isinstance(snake_case_  ,						(int, float)     ):
		raise ValueError("maclaurin_sin() requires either an int or float for theta"     )

	if not isinstance(snake_case_  ,						snake_case_     ) or accuracy <= 0:
		raise ValueError("maclaurin_sin() requires a positive int for accuracy"     )

	snake_case__				:   int     =						float(snake_case_     )
	snake_case__				:   int     =						theta // (2 * pi)
	theta -= 2 * div * pi
	return sum(
	    (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1     ) for r in range(snake_case_     )     )







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							float  ,						snake_case_      :							int = 30     ):

	if not isinstance(snake_case_  ,						(int, float)     ):
		raise ValueError("maclaurin_cos() requires either an int or float for theta"     )

	if not isinstance(snake_case_  ,						snake_case_     ) or accuracy <= 0:
		raise ValueError("maclaurin_cos() requires a positive int for accuracy"     )

	snake_case__				:   int     =						float(snake_case_     )
	snake_case__				:   Union[str, Any]     =						theta // (2 * pi)
	theta -= 2 * div * pi
	return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r     ) for r in range(snake_case_     )     )


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	print(maclaurin_sin(10))
	print(maclaurin_sin(-10))
	print(maclaurin_sin(10, 15))
	print(maclaurin_sin(-10, 15))

	print(maclaurin_cos(5))
	print(maclaurin_cos(-5))
	print(maclaurin_cos(10, 15))
	print(maclaurin_cos(-10, 15))











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


__lowerCamelCase      :    Union[str, Any]   		=   logging.get_logger(__name__)

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



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	"instructblip_vision_model"



					def __init__(      self  :						List[Any]							,	__A  :						Dict=1_4_0_8							,	__A  :						Tuple=6_1_4_4							,	__A  :						str=3_9							,	__A  :						int=1_6							,	__A  :						str=2_2_4							,	__A  :						Any=1_4							,	__A  :						Dict="gelu"							,	__A  :						List[Any]=1e-6							,	__A  :						Any=0.0							,	__A  :						List[Any]=1e-1_0							,	__A  :						Union[str, Any]=True							,	**__A  :						Tuple							,	):
						super().__init__(**__A )

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






					@classmethod
					def  _lowercase  (      cls  :						List[str]							,	__A  :						Union[str, os.PathLike]							,	**__A  :						Optional[Any] ):
						cls._set_token_in_kwargs(__A )

						snake_case__,		snake_case__				:   str     =						cls.get_config_dict(__A							,	**__A )

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



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	"instructblip_qformer"



					def __init__(      self  :						Any							,	__A  :						Union[str, Any]=3_0_5_2_2							,	__A  :						Union[str, Any]=7_6_8							,	__A  :						Optional[int]=1_2							,	__A  :						Dict=1_2							,	__A  :						Dict=3_0_7_2							,	__A  :						List[str]="gelu"							,	__A  :						Union[str, Any]=0.1							,	__A  :						Tuple=0.1							,	__A  :						Any=5_1_2							,	__A  :						Optional[int]=0.0_2							,	__A  :						List[str]=1e-1_2							,	__A  :						Any=0							,	__A  :						Optional[Any]="absolute"							,	__A  :						str=2							,	__A  :						Any=1_4_0_8							,	**__A  :						List[str]							,	):
						super().__init__(pad_token_id=__A							,	**__A )

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






					@classmethod
					def  _lowercase  (      cls  :						List[Any]							,	__A  :						Union[str, os.PathLike]							,	**__A  :						Optional[int] ):
						cls._set_token_in_kwargs(__A )

						snake_case__,		snake_case__				:   Tuple     =						cls.get_config_dict(__A							,	**__A )

						# get the qformer config dict if we are loading from InstructBlipConfig
						if config_dict.get("model_type" ) == "instructblip":
							snake_case__				:   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(__A							,	**__A )



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	"instructblip"
					a_			=	True



					def __init__(      self  :						List[str]							,	__A  :						Optional[Any]=None							,	__A  :						Tuple=None							,	__A  :						Optional[int]=None							,	__A  :						Optional[Any]=3_2							,	**__A  :						Optional[int] ):
						super().__init__(**__A )

						if vision_config is None:
							snake_case__				:   Any     =						{}
							logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values." )

						if qformer_config is None:
							snake_case__				:   Optional[Any]     =						{}
							logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values." )

						if text_config is None:
							snake_case__				:   Optional[int]     =						{}
							logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`)." )

						snake_case__				:   List[Any]     =						InstructBlipVisionConfig(**__A )
						snake_case__				:   Union[str, Any]     =						InstructBlipQFormerConfig(**__A )
						snake_case__				:   Dict     =						text_config["model_type"] if "model_type" in text_config else "opt"
						snake_case__				:   List[Any]     =						CONFIG_MAPPING[text_model_type](**__A )

						snake_case__				:   Union[str, Any]     =						self.text_config.tie_word_embeddings
						snake_case__				:   Tuple     =						self.text_config.is_encoder_decoder

						snake_case__				:   str     =						num_query_tokens
						snake_case__				:   Dict     =						self.vision_config.hidden_size
						snake_case__				:   List[Any]     =						self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
						snake_case__				:   int     =						1.0
						snake_case__				:   Optional[int]     =						0.0_2



					@classmethod
					def  _lowercase  (      cls  :						List[str]							,	__A  :						InstructBlipVisionConfig							,	__A  :						InstructBlipQFormerConfig							,	__A  :						PretrainedConfig							,	**__A  :						int							,	):
						return cls(
						    vision_config=vision_config.to_dict()							,	qformer_config=qformer_config.to_dict()							,	text_config=text_config.to_dict()							,	**__A							,	)






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



