from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_  							=			{
    "configuration_wav2vec2_conformer": [
        "WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Wav2Vec2ConformerConfig",
    ],
}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase_  							=			[
         "WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "Wav2Vec2ConformerForAudioFrameClassification",
         "Wav2Vec2ConformerForCTC",
         "Wav2Vec2ConformerForPreTraining",
         "Wav2Vec2ConformerForSequenceClassification",
         "Wav2Vec2ConformerForXVector",
         "Wav2Vec2ConformerModel",
         "Wav2Vec2ConformerPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_wavaveca_conformer import (
         WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         WavaVecaConformerConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_wavaveca_conformer import (
              WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              WavaVecaConformerForAudioFrameClassification,
              WavaVecaConformerForCTC,
              WavaVecaConformerForPreTraining,
              WavaVecaConformerForSequenceClassification,
              WavaVecaConformerForXVector,
              WavaVecaConformerModel,
              WavaVecaConformerPreTrainedModel,
          )

else:
     import sys

     lowercase_  							=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




import torch
from torch import autograd







class      SCREAMING_SNAKE_CASE__					(  autograd.Function    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Any			,			_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			,			_lowerCAmelCase					:     bool			):
           __snake_case       :				Optional[int] 			=    inputs.numel()
           __snake_case       :				str 			=    int(0.005 * nb_elems			) + 1
           if sigmoid:
               __snake_case       :				Union[str, Any] 			=    (torch.sigmoid(_lowerCAmelCase			) > threshold).type(inputs.type()			)
           else:
               __snake_case       :				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
               __snake_case       :				Dict 			=    inputs.flatten().kthvalue(max(nb_elems - nb_min			,			1			)			).values
               __snake_case       :				List[str] 			=    (inputs > k_threshold).type(inputs.type()			)
           return mask





       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     str			,			_lowerCAmelCase					:     Tuple			):
           return gradOutput, None, None







class      SCREAMING_SNAKE_CASE__					(  autograd.Function    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Optional[int]			,			_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			):
           __snake_case       :				str 			=    inputs.clone()
           __snake_case			, __snake_case       :				Dict 			=    inputs.flatten().sort(descending=_lowerCAmelCase			)
           __snake_case       :				Optional[Any] 			=    int(threshold * inputs.numel()			)

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





       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Any			,			_lowerCAmelCase					:     Optional[Any]			):
           return gradOutput, None







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			):
           __snake_case       :				List[str] 			=    inputs.clone()
           __snake_case			, __snake_case       :				str 			=    inputs.abs().flatten().sort(descending=_lowerCAmelCase			)
           __snake_case       :				List[Any] 			=    int(threshold * inputs.numel()			)

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



