

import torch
from torch import autograd





class      snake_case_      (					autograd.Function				):


							"""simple docstring"""





							@staticmethod
							def 							A__	(			_A       ,    _A       ,    _A       ,    _A				):
												__lowerCAmelCase       =				inputs.numel()
												__lowerCAmelCase       =				int(0.005 * nb_elems				) + 1
												if sigmoid:
																	__lowerCAmelCase       =				(torch.sigmoid(_lowerCAmelCase				) > 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 							A__	(			_A       ,    _A				):
												return gradOutput, None, None





class      snake_case_      (					autograd.Function				):


							"""simple docstring"""





							@staticmethod
							def 							A__	(			_A       ,    _A       ,    _A				):
												__lowerCAmelCase       =				inputs.clone()
												__lowerCAmelCase       =				inputs.flatten().sort(descending=_lowerCAmelCase				)
												__lowerCAmelCase       =				int(threshold * inputs.numel()				)

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




							@staticmethod
							def 							A__	(			_A       ,    _A				):
												return gradOutput, None





class      snake_case_      (					__UpperCamelCase				):


							"""simple docstring"""





							@staticmethod
							def 							A__	(			_A       ,    _A				):
												__lowerCAmelCase       =				inputs.clone()
												__lowerCAmelCase       =				inputs.abs().flatten().sort(descending=_lowerCAmelCase				)
												__lowerCAmelCase       =				int(threshold * inputs.numel()				)

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







import warnings

from .generation import FlaxGenerationMixin







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       # warning at import time
       warnings.warn(
           "Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will "
           "be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."   ,							__UpperCamelCase   ,							)



