






"""simple docstring"""
import torch
from torch import autograd



class 		a_      (	autograd.Function ):







      '''simple docstring'''

      @staticmethod
      def 			a__							(lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_					):

       '''simple docstring'''



       lowerCamelCase__     :			Dict     				=						inputs.numel()
       lowerCamelCase__     :			Any     				=						int(0.005 * nb_elems					) + 1
       if sigmoid:
        lowerCamelCase__     :			Dict     				=						(torch.sigmoid(lowerCamelCase_					) > threshold).type(inputs.type()					)
       else:
        lowerCamelCase__     :			Dict     				=						(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__     :			Optional[int]     				=						inputs.flatten().kthvalue(max(nb_elems - nb_min,     1					)					).values
        lowerCamelCase__     :			Dict     				=						(inputs > k_threshold).type(inputs.type()					)
       return mask





      @staticmethod
      def 			a__							(lowerCamelCase_,     lowerCamelCase_					):

       '''simple docstring'''



       return gradOutput, None, None



class 		a_      (	autograd.Function ):







      '''simple docstring'''

      @staticmethod
      def 			a__							(lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_					):

       '''simple docstring'''



       lowerCamelCase__     :			Dict     				=						inputs.clone()
       lowerCamelCase__				,     lowerCamelCase__     :			List[Any]     				=						inputs.flatten().sort(descending=lowerCamelCase_					)
       lowerCamelCase__     :			Optional[int]     				=						int(threshold * inputs.numel()					)

       # flat_out and mask access the same memory.
       lowerCamelCase__     :			Optional[int]     				=						mask.flatten()
       lowerCamelCase__     :			Any     				=						0
       lowerCamelCase__     :			Any     				=						1
       return mask





      @staticmethod
      def 			a__							(lowerCamelCase_,     lowerCamelCase_					):

       '''simple docstring'''



       return gradOutput, None



class 		a_      (	snake_case_ ):







      '''simple docstring'''

      @staticmethod
      def 			a__							(lowerCamelCase_,     lowerCamelCase_					):

       '''simple docstring'''



       lowerCamelCase__     :			int     				=						inputs.clone()
       lowerCamelCase__				,     lowerCamelCase__     :			Dict     				=						inputs.abs().flatten().sort(descending=lowerCamelCase_					)
       lowerCamelCase__     :			Union[str, Any]     				=						int(threshold * inputs.numel()					)

       # flat_out and mask access the same memory.
       lowerCamelCase__     :			Dict     				=						mask.flatten()
       lowerCamelCase__     :			int     				=						0
       lowerCamelCase__     :			List[str]     				=						1
       return mask









"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_			: Dict				=			{"configuration_qdqbert": ["QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "QDQBertConfig"]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			: Tuple				=			[
          "QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "QDQBertForMaskedLM",
          "QDQBertForMultipleChoice",
          "QDQBertForNextSentencePrediction",
          "QDQBertForQuestionAnswering",
          "QDQBertForSequenceClassification",
          "QDQBertForTokenClassification",
          "QDQBertLayer",
          "QDQBertLMHeadModel",
          "QDQBertModel",
          "QDQBertPreTrainedModel",
          "load_tf_weights_in_qdqbert",
      ]


if TYPE_CHECKING:
      from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_qdqbert import (
                QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                QDQBertForMaskedLM,
                QDQBertForMultipleChoice,
                QDQBertForNextSentencePrediction,
                QDQBertForQuestionAnswering,
                QDQBertForSequenceClassification,
                QDQBertForTokenClassification,
                QDQBertLayer,
                QDQBertLMHeadModel,
                QDQBertModel,
                QDQBertPreTrainedModel,
                load_tf_weights_in_qdqbert,
            )


else:
      import sys

      A_			: Tuple				=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

