






'''simple docstring'''







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


__lowerCAmelCase       		=      r'\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `" / "`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `" // "`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `"wiki_dpr"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `"train"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `"compressed"`)\n            The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and\n            `"compressed"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a "dummy" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n'





@add_start_docstrings(SCREAMING_SNAKE_CASE_       )
class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				"rag"
			snake_case__  =				True






			def __init__( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=None  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]=True  ,						__SCREAMING_SNAKE_CASE      :			List[str]=None  ,						__SCREAMING_SNAKE_CASE      :			Any=None  ,						__SCREAMING_SNAKE_CASE      :			Any=None  ,						__SCREAMING_SNAKE_CASE      :			int=None  ,						__SCREAMING_SNAKE_CASE      :			str=None  ,						__SCREAMING_SNAKE_CASE      :			Tuple=" / "  ,						__SCREAMING_SNAKE_CASE      :			Any=" // "  ,						__SCREAMING_SNAKE_CASE      :			List[str]=5  ,						__SCREAMING_SNAKE_CASE      :			Dict=300  ,						__SCREAMING_SNAKE_CASE      :			Dict=768  ,						__SCREAMING_SNAKE_CASE      :			Any=8  ,						__SCREAMING_SNAKE_CASE      :			int="wiki_dpr"  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]="train"  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]="compressed"  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]=None  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=None  ,						__SCREAMING_SNAKE_CASE      :			int=False  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]=False  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]=0.0  ,						__SCREAMING_SNAKE_CASE      :			str=True  ,						__SCREAMING_SNAKE_CASE      :			List[Any]=False  ,						__SCREAMING_SNAKE_CASE      :			List[str]=False  ,						__SCREAMING_SNAKE_CASE      :			str=False  ,						__SCREAMING_SNAKE_CASE      :			Any=True  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]=None  ,						**__SCREAMING_SNAKE_CASE      :			Optional[Any]  ,						)			->			List[Any]:
							super().__init__(
							    bos_token_id=__SCREAMING_SNAKE_CASE  ,						pad_token_id=__SCREAMING_SNAKE_CASE  ,						eos_token_id=__SCREAMING_SNAKE_CASE  ,						decoder_start_token_id=__SCREAMING_SNAKE_CASE  ,						forced_eos_token_id=__SCREAMING_SNAKE_CASE  ,						is_encoder_decoder=__SCREAMING_SNAKE_CASE  ,						prefix=__SCREAMING_SNAKE_CASE  ,						vocab_size=__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE  ,						)
							assert (
							    "question_encoder" in kwargs and "generator" in kwargs
							), "Config has to be initialized with question_encoder and generator config"
							a_		:  Any											=      kwargs.pop('''question_encoder'''					)
							a_		:  Dict											=      question_encoder_config.pop('''model_type'''					)
							a_		:  Optional[int]											=      kwargs.pop('''generator'''					)
							a_		:  str											=      decoder_config.pop('''model_type'''					)

							from ..auto.configuration_auto import AutoConfig

							a_		:  List[str]											=      AutoConfig.for_model(__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)
							a_		:  Tuple											=      AutoConfig.for_model(__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)

							a_		:  List[str]											=      reduce_loss
							a_		:  Any											=      label_smoothing
							a_		:  Any											=      exclude_bos_score
							a_		:  Union[str, Any]											=      do_marginalize

							a_		:  Dict											=      title_sep
							a_		:  Union[str, Any]											=      doc_sep
							a_		:  Optional[Any]											=      n_docs
							a_		:  Tuple											=      max_combined_length

							a_		:  Dict											=      dataset
							a_		:  List[str]											=      dataset_split
							a_		:  int											=      index_name

							a_		:  int											=      retrieval_vector_size
							a_		:  int											=      retrieval_batch_size
							a_		:  int											=      passages_path
							a_		:  Optional[int]											=      index_path
							a_		:  List[str]											=      use_dummy_dataset

							a_		:  Optional[int]											=      output_retrieved

							a_		:  Any											=      do_deduplication

							a_		:  str											=      use_cache

							if self.forced_eos_token_id is None:
											a_		:  Optional[Any]											=      getattr(self.generator  ,						'''forced_eos_token_id'''  ,						__SCREAMING_SNAKE_CASE					)






			@classmethod
			def 			SCREAMING_SNAKE_CASE      ( cls      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			PretrainedConfig  ,						__SCREAMING_SNAKE_CASE      :			PretrainedConfig  ,						**__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			PretrainedConfig:
							return cls(question_encoder=question_encoder_config.to_dict()  ,						generator=generator_config.to_dict()  ,						**__SCREAMING_SNAKE_CASE					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			Any:
							a_		:  List[str]											=      copy.deepcopy(self.__dict__					)
							a_		:  List[str]											=      self.question_encoder.to_dict()
							a_		:  str											=      self.generator.to_dict()
							a_		:  Dict											=      self.__class__.model_type
							return output











'''simple docstring'''







from math import pi, sqrt, tan





def 		_UpperCAmelCase   (							__A							:   float      ):
				if side_length < 0:
								raise ValueError('''surface_area_cube() only accepts non-negative values'''      )
				return 6 * side_length**2





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float						,		__A							:   float      ):
				if length < 0 or breadth < 0 or height < 0:
								raise ValueError('''surface_area_cuboid() only accepts non-negative values'''      )
				return 2 * ((length * breadth) + (breadth * height) + (length * height))





def 		_UpperCAmelCase   (							__A							:   float      ):
				if radius < 0:
								raise ValueError('''surface_area_sphere() only accepts non-negative values'''      )
				return 4 * pi * radius**2





def 		_UpperCAmelCase   (							__A							:   float      ):
				if radius < 0:
								raise ValueError('''surface_area_hemisphere() only accepts non-negative values'''      )
				return 3 * pi * radius**2





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if radius < 0 or height < 0:
								raise ValueError('''surface_area_cone() only accepts non-negative values'''      )
				return pi * radius * (radius + (height**2 + radius**2) ** 0.5)





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float						,		__A							:   float      ):
				if radius_a < 0 or radius_a < 0 or height < 0:
								raise ValueError(
								    '''surface_area_conical_frustum() only accepts non-negative values'''      )
				a_		:  Any											=      (height**2 + (radius_a - radius_a) ** 2) ** 0.5
				return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if radius < 0 or height < 0:
								raise ValueError('''surface_area_cylinder() only accepts non-negative values'''      )
				return 2 * pi * radius * (height + radius)





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if torus_radius < 0 or tube_radius < 0:
								raise ValueError('''surface_area_torus() only accepts non-negative values'''      )
				if torus_radius < tube_radius:
								raise ValueError(
								    '''surface_area_torus() does not support spindle or self intersecting tori'''      )
				return 4 * pow(__A						,		2      ) * torus_radius * tube_radius





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if length < 0 or width < 0:
								raise ValueError('''area_rectangle() only accepts non-negative values'''      )
				return length * width





def 		_UpperCAmelCase   (							__A							:   float      ):
				if side_length < 0:
								raise ValueError('''area_square() only accepts non-negative values'''      )
				return side_length**2





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if base < 0 or height < 0:
								raise ValueError('''area_triangle() only accepts non-negative values'''      )
				return (base * height) / 2





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float						,		__A							:   float      ):
				if sidea < 0 or sidea < 0 or sidea < 0:
								raise ValueError('''area_triangle_three_sides() only accepts non-negative values'''      )
				elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
								raise ValueError('''Given three sides do not form a triangle'''      )
				a_		:  int											=      (sidea + sidea + sidea) / 2
				a_		:  Optional[Any]											=      sqrt(
				    semi_perimeter
				    * (semi_perimeter - sidea)
				    * (semi_perimeter - sidea)
				    * (semi_perimeter - sidea)      )
				return area





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if base < 0 or height < 0:
								raise ValueError('''area_parallelogram() only accepts non-negative values'''      )
				return base * height





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float						,		__A							:   float      ):
				if basea < 0 or basea < 0 or height < 0:
								raise ValueError('''area_trapezium() only accepts non-negative values'''      )
				return 1 / 2 * (basea + basea) * height





def 		_UpperCAmelCase   (							__A							:   float      ):
				if radius < 0:
								raise ValueError('''area_circle() only accepts non-negative values'''      )
				return pi * radius**2





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if radius_x < 0 or radius_y < 0:
								raise ValueError('''area_ellipse() only accepts non-negative values'''      )
				return pi * radius_x * radius_y





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if diagonal_a < 0 or diagonal_a < 0:
								raise ValueError('''area_rhombus() only accepts non-negative values'''      )
				return 1 / 2 * diagonal_a * diagonal_a





def 		_UpperCAmelCase   (							__A							:   int						,		__A							:   float      ):
				if not isinstance(__A						,		__A      ) or sides < 3:
								raise ValueError(
								    '''area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides'''      )
				elif length < 0:
								raise ValueError(
								    '''area_reg_polygon() only accepts non-negative values as \
length of a side'''      )
				return (sides * length**2) / (4 * tan(pi / sides      ))
				return (sides * length**2) / (4 * tan(pi / sides      ))


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

		print('[DEMO] Areas of various geometric shapes: \n')
		print(F"""Rectangle: {area_rectangle(10, 20) = }""")
		print(F"""Square: {area_square(10) = }""")
		print(F"""Triangle: {area_triangle(10, 10) = }""")
		print(F"""Triangle: {area_triangle_three_sides(5, 12, 13) = }""")
		print(F"""Parallelogram: {area_parallelogram(10, 20) = }""")
		print(F"""Rhombus: {area_rhombus(10, 20) = }""")
		print(F"""Trapezium: {area_trapezium(10, 20, 30) = }""")
		print(F"""Circle: {area_circle(20) = }""")
		print(F"""Ellipse: {area_ellipse(10, 20) = }""")
		print('\nSurface Areas of various geometric shapes: \n')
		print(F"""Cube: {surface_area_cube(20) = }""")
		print(F"""Cuboid: {surface_area_cuboid(10, 20, 30) = }""")
		print(F"""Sphere: {surface_area_sphere(20) = }""")
		print(F"""Hemisphere: {surface_area_hemisphere(20) = }""")
		print(F"""Cone: {surface_area_cone(10, 20) = }""")
		print(F"""Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }""")
		print(F"""Cylinder: {surface_area_cylinder(10, 20) = }""")
		print(F"""Torus: {surface_area_torus(20, 10) = }""")
		print(F"""Equilateral Triangle: {area_reg_polygon(3, 10) = }""")
		print(F"""Square: {area_reg_polygon(4, 10) = }""")
		print(F"""Reqular Pentagon: {area_reg_polygon(5, 10) = }""")




