import torch
from torch import autograd




class        lowercase_  (  autograd.Function						):



	"""simple docstring"""





	@staticmethod
	def       SCREAMING_SNAKE_CASE_    (							__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->Any:
								lowerCAmelCase     		=		inputs.numel()
								lowerCAmelCase     		=		int(0.0_0_5 * nb_elems    ) + 1
								if sigmoid:
															lowerCAmelCase     		=		(torch.sigmoid(__SCREAMING_SNAKE_CASE    ) > threshold).type(inputs.type()    )
								else:
															lowerCAmelCase     		=		(inputs > threshold).type(inputs.type()    )
								if mask.sum() < nb_min:
															# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
															lowerCAmelCase     		=		inputs.flatten().kthvalue(max(nb_elems - nb_min		,      1    )    ).values
															lowerCAmelCase     		=		(inputs > k_threshold).type(inputs.type()    )
								return mask




	@staticmethod
	def       SCREAMING_SNAKE_CASE_    (							__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->int:
								return gradOutput, None, None




class        lowercase_  (  autograd.Function						):



	"""simple docstring"""





	@staticmethod
	def       SCREAMING_SNAKE_CASE_    (							__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->Tuple:
								lowerCAmelCase     		=		inputs.clone()
								lowerCAmelCase		,				lowerCAmelCase     		=		inputs.flatten().sort(descending=__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		int(threshold * inputs.numel()    )

								# flat_out and mask access the same memory.
								lowerCAmelCase     		=		mask.flatten()
								lowerCAmelCase     		=		0
								lowerCAmelCase     		=		1
								return mask




	@staticmethod
	def       SCREAMING_SNAKE_CASE_    (							__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->str:
								return gradOutput, None




class        lowercase_  (  UpperCamelCase_						):



	"""simple docstring"""





	@staticmethod
	def       SCREAMING_SNAKE_CASE_    (							__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->int:
								lowerCAmelCase     		=		inputs.clone()
								lowerCAmelCase		,				lowerCAmelCase     		=		inputs.abs().flatten().sort(descending=__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		int(threshold * inputs.numel()    )

								# flat_out and mask access the same memory.
								lowerCAmelCase     		=		mask.flatten()
								lowerCAmelCase     		=		0
								lowerCAmelCase     		=		1
								return mask




lowercase__      :    dict[str, float]          =     {
    "kilogram": 1,
    "gram": pow(1_0, 3),
    "milligram": pow(1_0, 6),
    "metric-ton": pow(1_0, -3),
    "long-ton": 0.0_0_0_9_8_4_2_0_7_3,
    "short-ton": 0.0_0_1_1_0_2_3_1_2_2,
    "pound": 2.2_0_4_6_2_4_4_2_0_2,
    "stone": 0.1_5_7_4_7_3_1_7_2_8,
    "ounce": 3_5.2_7_3_9_9_0_7_2_3,
    "carrat": 5_0_0_0,
    "atomic-mass-unit": 6.0_22_13_66_52e26,
}

lowercase__      :    dict[str, float]          =     {
    "kilogram": 1,
    "gram": pow(1_0, -3),
    "milligram": pow(1_0, -6),
    "metric-ton": pow(1_0, 3),
    "long-ton": 1_0_1_6.0_4_6_0_8,
    "short-ton": 9_0_7.1_8_4,
    "pound": 0.4_5_3_5_9_2,
    "stone": 6.3_5_0_2_9,
    "ounce": 0.0_2_8_3_4_9_5,
    "carrat": 0.0_0_0_2,
    "atomic-mass-unit": 1.6_60_54_01_99e-27,
}

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__							)					->     float:
							if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
														lowerCAmelCase     		=		(
														    f"Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
														    f"Supported values are: {', '.join(snake_case__							)}"
														)
														raise ValueError(snake_case__							)
							return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
	import doctest

	doctest.testmod()



