import copy
import os
from typing import Union

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


__lowerCamelCase					:	Tuple       =  logging.get_logger(__name__)

__lowerCamelCase					:	str       =  {
    '''microsoft/git-base''': '''https://huggingface.co/microsoft/git-base/resolve/main/config.json''',
}






class 		a__						(					A__					):
					A	       =							'git_vision_model'

					def __init__(    self      :       Any,_A      :       str=768,_A      :       Union[str, Any]=3072,_A      :       List[Any]=12,_A      :       Dict=12,_A      :       List[Any]=3,_A      :       Union[str, Any]=224,_A      :       Any=16,_A      :       Union[str, Any]="quick_gelu",_A      :       List[str]=1E-5,_A      :       str=0.0,_A      :       Tuple=0.02,**_A      :       Tuple,):







							"""simple docstring"""






							super().__init__(**_A    )

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






					@classmethod
					def 				__UpperCamelCase   (    cls      :       int,_A      :       Union[str, os.PathLike],**_A      :       str    ):







							"""simple docstring"""






							cls._set_token_in_kwargs(_A    )

							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Tuple				      =    cls.get_config_dict(_A,**_A    )

							# get the vision config dict if we are loading from GITConfig
							if config_dict.get("model_type"    ) == "git":
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    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 		a__						(					A__					):
					A	       =							'git'

					def __init__(    self      :       Dict,_A      :       Optional[Any]=None,_A      :       int=3_0522,_A      :       Dict=768,_A      :       str=6,_A      :       str=12,_A      :       Optional[int]=3072,_A      :       List[str]="gelu",_A      :       Tuple=0.1,_A      :       Optional[int]=0.1,_A      :       Any=1024,_A      :       Optional[Any]=0.02,_A      :       Optional[int]=1E-12,_A      :       Any=0,_A      :       List[str]="absolute",_A      :       List[str]=True,_A      :       List[str]=False,_A      :       Optional[int]=101,_A      :       Optional[int]=102,_A      :       List[str]=None,**_A      :       str,):







							"""simple docstring"""






							super().__init__(bos_token_id=_A,eos_token_id=_A,pad_token_id=_A,**_A    )

							if vision_config is None:
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    {}
									logger.info("vision_config is None. initializing the GitVisionConfig with default values."    )

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    GitVisionConfig(**_A    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    vocab_size
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    hidden_size
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    num_hidden_layers
							SCREAMING_SNAKE_CASE_  :		str				      =    num_attention_heads
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    hidden_act
							SCREAMING_SNAKE_CASE_  :		Any				      =    intermediate_size
							SCREAMING_SNAKE_CASE_  :		int				      =    hidden_dropout_prob
							SCREAMING_SNAKE_CASE_  :		Dict				      =    attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE_  :		Dict				      =    max_position_embeddings
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    initializer_range
							SCREAMING_SNAKE_CASE_  :		str				      =    layer_norm_eps
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    position_embedding_type
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    use_cache
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    tie_word_embeddings
							SCREAMING_SNAKE_CASE_  :		Any				      =    num_image_with_embedding

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    bos_token_id
							SCREAMING_SNAKE_CASE_  :		Dict				      =    eos_token_id






					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    copy.deepcopy(self.__dict__    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.vision_config.to_dict()
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.__class__.model_type
							return output

from math import factorial, radians




def       _snake_case	(   lowerCAmelCase							:   float   , lowerCAmelCase							:   int = 1_8   , lowerCAmelCase							:   int = 1_0	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]				      =    angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

		# Converting from degrees to radians
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    radians(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    angle_in_radians
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    3
		SCREAMING_SNAKE_CASE_  :		str				      =    -1

		for _ in range(lowerCAmelCase	):
				result += (b * (angle_in_radians**a)) / factorial(lowerCAmelCase	)

				SCREAMING_SNAKE_CASE_  :		Dict				      =    -b  # One positive term and the next will be negative and so on...
				a += 2  # Increased by 2 for every term.

		return round(lowerCAmelCase   , lowerCAmelCase	)


if __name__ == "__main__":
		__import__('''doctest''').testmod()
