






import torch
from torch import autograd

class      UpperCamelCase_							(				autograd.Function       ):




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







						@staticmethod
						def      snake_case__(    lowercase		,	lowercase       )			->						Union[str, Any]:
													return gradOutput, None, None

class      UpperCamelCase_							(				autograd.Function       ):




						@staticmethod
						def      snake_case__(    lowercase		,	lowercase		,	lowercase       )			->						List[Any]:
													_a  :    List[Any]			=						inputs.clone()
													_a  :    Optional[Any]			=						inputs.flatten().sort(descending=lowerCAmelCase_       )
													_a  :    Tuple			=						int(threshold * inputs.numel()       )

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







						@staticmethod
						def      snake_case__(    lowercase		,	lowercase       )			->						Tuple:
													return gradOutput, None

class      UpperCamelCase_							(				snake_case__       ):




						@staticmethod
						def      snake_case__(    lowercase		,	lowercase       )			->						Tuple:
													_a  :    Union[str, Any]			=						inputs.clone()
													_a  :    List[Any]			=						inputs.abs().flatten().sort(descending=lowerCAmelCase_       )
													_a  :    Optional[int]			=						int(threshold * inputs.numel()       )

													# flat_out and mask access the same memory.
													_a  :    Dict			=						mask.flatten()
													_a  :    Tuple			=						0
													_a  :    List[str]			=						1
													return mask
from typing import TYPE_CHECKING

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


UpperCAmelCase				 =   {
    """configuration_gptsan_japanese""": ["""GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTSanJapaneseConfig"""],
    """tokenization_gptsan_japanese""": ["""GPTSanJapaneseTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				 =   [
      """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPTSanJapaneseForConditionalGeneration""",
      """GPTSanJapaneseModel""",
      """GPTSanJapanesePreTrainedModel""",
  ]
  UpperCAmelCase				 =   [
      """GPTSanJapaneseTokenizer""",
  ]


if TYPE_CHECKING:
  from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
  from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gptsan_japanese import (
        GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTSanJapaneseForConditionalGeneration,
        GPTSanJapaneseModel,
        GPTSanJapanesePreTrainedModel,
    )
    from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
  import sys

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