import torch
from torch import autograd


class _lowercase					(       autograd.Function						):





							"""simple docstring"""




							@staticmethod
							def     _UpperCAmelCase       (    UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase  ):







														'''simple docstring'''






														_lowercase				=	inputs.numel()
														_lowercase				=	int(0.005 * nb_elems  ) + 1
														if sigmoid:
																					_lowercase				=	(torch.sigmoid(SCREAMING_SNAKE_CASE_  ) > threshold).type(inputs.type()  )
														else:
																					_lowercase				=	(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
																					_lowercase				=	inputs.flatten().kthvalue(max(nb_elems - nb_min     ,   1  )  ).values
																					_lowercase				=	(inputs > k_threshold).type(inputs.type()  )
														return mask



							@staticmethod
							def     _UpperCAmelCase       (    UpperCAmelCase     ,   UpperCAmelCase  ):







														'''simple docstring'''






														return gradOutput, None, None






class _lowercase					(       autograd.Function						):





							"""simple docstring"""




							@staticmethod
							def     _UpperCAmelCase       (    UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase  ):







														'''simple docstring'''






														_lowercase				=	inputs.clone()
														_lowercase				,       _lowercase				=	inputs.flatten().sort(descending=SCREAMING_SNAKE_CASE_  )
														_lowercase				=	int(threshold * inputs.numel()  )

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



							@staticmethod
							def     _UpperCAmelCase       (    UpperCAmelCase     ,   UpperCAmelCase  ):







														'''simple docstring'''






														return gradOutput, None






class _lowercase					(       lowerCamelCase__						):





							"""simple docstring"""




							@staticmethod
							def     _UpperCAmelCase       (    UpperCAmelCase     ,   UpperCAmelCase  ):







														'''simple docstring'''






														_lowercase				=	inputs.clone()
														_lowercase				,       _lowercase				=	inputs.abs().flatten().sort(descending=SCREAMING_SNAKE_CASE_  )
														_lowercase				=	int(threshold * inputs.numel()  )

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



import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


A_:      Tuple        =			logging.get_logger(__name__)


class _lowercase					(       _UpperCAmelCase						):





							"""simple docstring"""




							def __init__(    self     ,   *UpperCAmelCase     ,   **UpperCAmelCase  ):







														'''simple docstring'''






														warnings.warn(
														    """The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
														    """ Please use MobileNetV2ImageProcessor instead."""     ,   UpperCAmelCase     ,   )
														super().__init__(*UpperCAmelCase     ,   **UpperCAmelCase  )


