


"""simple docstring"""







import torch
from torch import autograd
class 				lowerCamelCase							(      autograd.Function			):


							"""simple docstring"""






							@staticmethod
							def        lowerCAmelCase  (    lowerCamelCase_			:  Tuple		,			lowerCamelCase_			:  torch.tensor		,			lowerCamelCase_			:  float		,			lowerCamelCase_			:  bool					):
													UpperCAmelCase__												=     inputs.numel()
													UpperCAmelCase__												=     int(0.005 * nb_elems					) + 1
													if sigmoid:
																			UpperCAmelCase__												=     (torch.sigmoid(lowerCamelCase_					) > threshold).type(inputs.type()					)
													else:
																			UpperCAmelCase__												=     (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
																			UpperCAmelCase__												=     inputs.flatten().kthvalue(max(nb_elems - nb_min		,			1					)					).values
																			UpperCAmelCase__												=     (inputs > k_threshold).type(inputs.type()					)
													return mask






							@staticmethod
							def        lowerCAmelCase  (    lowerCamelCase_			:  str		,			lowerCamelCase_			:  Tuple					):
													return gradOutput, None, None




class 				lowerCamelCase							(      autograd.Function			):


							"""simple docstring"""






							@staticmethod
							def        lowerCAmelCase  (    lowerCamelCase_			:  str		,			lowerCamelCase_			:  torch.tensor		,			lowerCamelCase_			:  float					):
													UpperCAmelCase__												=     inputs.clone()
													UpperCAmelCase__						,					UpperCAmelCase__												=     inputs.flatten().sort(descending=lowerCamelCase_					)
													UpperCAmelCase__												=     int(threshold * inputs.numel()					)

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






							@staticmethod
							def        lowerCAmelCase  (    lowerCamelCase_			:  Tuple		,			lowerCamelCase_			:  Optional[int]					):
													return gradOutput, None




class 				lowerCamelCase							(      __SCREAMING_SNAKE_CASE			):


							"""simple docstring"""






							@staticmethod
							def        lowerCAmelCase  (    lowerCamelCase_			:  torch.tensor		,			lowerCamelCase_			:  float					):
													UpperCAmelCase__												=     inputs.clone()
													UpperCAmelCase__						,					UpperCAmelCase__												=     inputs.abs().flatten().sort(descending=lowerCamelCase_					)
													UpperCAmelCase__												=     int(threshold * inputs.numel()					)

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



"""simple docstring"""







from typing import TYPE_CHECKING

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


__a      			= {}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      			= ["BarthezTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      			= ["BarthezTokenizerFast"]


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

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_barthez_fast import BarthezTokenizerFast

else:
						import sys

						__a      			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)