




"""simple docstring"""




import torch
from torch import autograd

class 	_lowerCamelCase  (     autograd.Function				):

      """simple docstring"""

      @staticmethod
      def  snake_case		(       snake_case   :	Optional[Any]	,    snake_case   :	torch.tensor	,    snake_case   :	float	,    snake_case   :	bool			):
          __UpperCamelCase											=  inputs.numel()
          __UpperCamelCase											=  int(0.0_05 * nb_elems			) + 1
          if sigmoid:
              __UpperCamelCase											=  (torch.sigmoid(snake_case			) > threshold).type(inputs.type()			)
          else:
              __UpperCamelCase											=  (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											=  inputs.flatten().kthvalue(max(nb_elems - nb_min	,    1			)			).values
              __UpperCamelCase											=  (inputs > k_threshold).type(inputs.type()			)
          return mask







      @staticmethod
      def  snake_case		(       snake_case   :	Tuple	,    snake_case   :	Union[str, Any]			):
          return gradOutput, None, None







class 	_lowerCamelCase  (     autograd.Function				):

      """simple docstring"""

      @staticmethod
      def  snake_case		(       snake_case   :	Dict	,    snake_case   :	torch.tensor	,    snake_case   :	float			):
          __UpperCamelCase											=  inputs.clone()
          __UpperCamelCase						, __UpperCamelCase											=  inputs.flatten().sort(descending=snake_case			)
          __UpperCamelCase											=  int(threshold * inputs.numel()			)

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







      @staticmethod
      def  snake_case		(       snake_case   :	Tuple	,    snake_case   :	Optional[int]			):
          return gradOutput, None







class 	_lowerCamelCase  (     SCREAMING_SNAKE_CASE__				):

      """simple docstring"""

      @staticmethod
      def  snake_case		(       snake_case   :	torch.tensor	,    snake_case   :	float			):
          __UpperCamelCase											=  inputs.clone()
          __UpperCamelCase						, __UpperCamelCase											=  inputs.abs().flatten().sort(descending=snake_case			)
          __UpperCamelCase											=  int(threshold * inputs.numel()			)

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












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_										=       {
    "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       ["MarianTokenizer"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       [
     "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
     "MarianForCausalLM",
     "MarianModel",
     "MarianMTModel",
     "MarianPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]

if TYPE_CHECKING:
 from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_marian import MarianTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_marian import (
      MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
      MarianForCausalLM,
      MarianModel,
      MarianMTModel,
      MarianPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
 import sys

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






