
"""simple docstring"""





import torch
from torch import autograd


class A_      (autograd.Function ):




  '''simple docstring'''







  @staticmethod
  def 			UpperCamelCase__   (						lowercase_							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[Any]     	=       inputs.numel()
     UpperCAmelCase_   :   Optional[Any]     	=       int(0.0_05 * nb_elems    ) + 1
     if sigmoid:
        UpperCAmelCase_   :   int     	=       (torch.sigmoid(lowercase_    ) > threshold).type(inputs.type()    )
     else:
        UpperCAmelCase_   :   Any     	=       (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_   :   Tuple     	=       inputs.flatten().kthvalue(max(nb_elems - nb_min							,  1    )    ).values
        UpperCAmelCase_   :   Optional[int]     	=       (inputs > k_threshold).type(inputs.type()    )
     return mask







  @staticmethod
  def 			UpperCamelCase__   (						lowercase_							,  lowercase_    ):

     """simple docstring"""
     return gradOutput, None, None




class A_      (autograd.Function ):




  '''simple docstring'''







  @staticmethod
  def 			UpperCamelCase__   (						lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Dict     	=       inputs.clone()
     UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[int]     	=       inputs.flatten().sort(descending=lowercase_    )
     UpperCAmelCase_   :   Dict     	=       int(threshold * inputs.numel()    )

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







  @staticmethod
  def 			UpperCamelCase__   (						lowercase_							,  lowercase_    ):

     """simple docstring"""
     return gradOutput, None




class A_      (lowercase__ ):




  '''simple docstring'''







  @staticmethod
  def 			UpperCamelCase__   (						lowercase_							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Union[str, Any]     	=       inputs.clone()
     UpperCAmelCase_	,  UpperCAmelCase_   :   List[Any]     	=       inputs.abs().flatten().sort(descending=lowercase_    )
     UpperCAmelCase_   :   Dict     	=       int(threshold * inputs.numel()    )

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





"""simple docstring"""




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_  :      Tuple							=      {
    '''configuration_convnextv2''': [
        '''CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ConvNextV2Config''',
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_  :      int							=      [
         '''CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''ConvNextV2ForImageClassification''',
         '''ConvNextV2Model''',
         '''ConvNextV2PreTrainedModel''',
         '''ConvNextV2Backbone''',
     ]


if TYPE_CHECKING:
     from .configuration_convnextva import (
         CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
         ConvNextVaConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_convnextva import (
              CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConvNextVaBackbone,
              ConvNextVaForImageClassification,
              ConvNextVaModel,
              ConvNextVaPreTrainedModel,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE_  :      Dict							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure)




