
import torch
from torch import autograd





class 			__snake_case  (      autograd.Function     ):




					@staticmethod
					def       __a      (       __UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		)					->  Any:



									'''simple docstring'''







									snake_case__ :     str 							=				inputs.numel()
									snake_case__ :     List[str] 							=				int(0.0_0_5 * nb_elems		) + 1
									if sigmoid:
													snake_case__ :     str 							=				(torch.sigmoid(__SCREAMING_SNAKE_CASE		) > threshold).type(inputs.type()		)
									else:
													snake_case__ :     Dict 							=				(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
													snake_case__ :     Union[str, Any] 							=				inputs.flatten().kthvalue(max(nb_elems - nb_min   ,	1		)		).values
													snake_case__ :     int 							=				(inputs > k_threshold).type(inputs.type()		)
									return mask




					@staticmethod
					def       __a      (       __UpperCamelCase   ,	__UpperCamelCase		)					->  int:



									'''simple docstring'''







									return gradOutput, None, None





class 			__snake_case  (      autograd.Function     ):




					@staticmethod
					def       __a      (       __UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		)					->  Tuple:



									'''simple docstring'''







									snake_case__ :     Optional[int] 							=				inputs.clone()
									snake_case__							,  snake_case__ :     Dict 							=				inputs.flatten().sort(descending=__SCREAMING_SNAKE_CASE		)
									snake_case__ :     Tuple 							=				int(threshold * inputs.numel()		)

									# flat_out and mask access the same memory.
									snake_case__ :     int 							=				mask.flatten()
									snake_case__ :     Dict 							=				0
									snake_case__ :     Dict 							=				1
									return mask




					@staticmethod
					def       __a      (       __UpperCamelCase   ,	__UpperCamelCase		)					->  str:



									'''simple docstring'''







									return gradOutput, None





class 			__snake_case  (      UpperCamelCase_     ):




					@staticmethod
					def       __a      (       __UpperCamelCase   ,	__UpperCamelCase		)					->  int:



									'''simple docstring'''







									snake_case__ :     Union[str, Any] 							=				inputs.clone()
									snake_case__							,  snake_case__ :     str 							=				inputs.abs().flatten().sort(descending=__SCREAMING_SNAKE_CASE		)
									snake_case__ :     str 							=				int(threshold * inputs.numel()		)

									# flat_out and mask access the same memory.
									snake_case__ :     Tuple 							=				mask.flatten()
									snake_case__ :     Dict 							=				0
									snake_case__ :     Any 							=				1
									return mask



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


lowerCAmelCase__					:       Optional[int]		=       {}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					:       Tuple		=       ['''MBart50Tokenizer''']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					:       Tuple		=       ['''MBart50TokenizerFast''']


if TYPE_CHECKING:
				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mbartaa import MBartaaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
				import sys

				lowerCAmelCase__					:       Optional[int]		=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


