
"""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,
)


A_	   = {
    "configuration_convnextv2": [
        "CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ConvNextV2Config",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_	   = [
						    "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

						A_	   = _LazyModule(__name__, globals()["__file__"], _import_structure)





