

'''simple docstring'''
import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation

class 						__magic_name__   (							unittest.TestCase ):







 def 						_A(       self   ):
   lowercase									=get_activation('''swish'''   )

   self.assertIsInstance(snake_case_  , nn.SiLU   )

   self.assertEqual(act(torch.tensor(-1_00  , dtype=torch.floataa   )   ).item()  , 0   )
   self.assertNotEqual(act(torch.tensor(-1  , dtype=torch.floataa   )   ).item()  , 0   )
   self.assertEqual(act(torch.tensor(0  , dtype=torch.floataa   )   ).item()  , 0   )
   self.assertEqual(act(torch.tensor(20  , dtype=torch.floataa   )   ).item()  , 20   )







 def 						_A(       self   ):
   lowercase									=get_activation('''silu'''   )

   self.assertIsInstance(snake_case_  , nn.SiLU   )

   self.assertEqual(act(torch.tensor(-1_00  , dtype=torch.floataa   )   ).item()  , 0   )
   self.assertNotEqual(act(torch.tensor(-1  , dtype=torch.floataa   )   ).item()  , 0   )
   self.assertEqual(act(torch.tensor(0  , dtype=torch.floataa   )   ).item()  , 0   )
   self.assertEqual(act(torch.tensor(20  , dtype=torch.floataa   )   ).item()  , 20   )







 def 						_A(       self   ):
   lowercase									=get_activation('''mish'''   )

   self.assertIsInstance(snake_case_  , nn.Mish   )

   self.assertEqual(act(torch.tensor(-2_00  , dtype=torch.floataa   )   ).item()  , 0   )
   self.assertNotEqual(act(torch.tensor(-1  , dtype=torch.floataa   )   ).item()  , 0   )
   self.assertEqual(act(torch.tensor(0  , dtype=torch.floataa   )   ).item()  , 0   )
   self.assertEqual(act(torch.tensor(20  , dtype=torch.floataa   )   ).item()  , 20   )







 def 						_A(       self   ):
   lowercase									=get_activation('''gelu'''   )

   self.assertIsInstance(snake_case_  , nn.GELU   )

   self.assertEqual(act(torch.tensor(-1_00  , dtype=torch.floataa   )   ).item()  , 0   )
   self.assertNotEqual(act(torch.tensor(-1  , dtype=torch.floataa   )   ).item()  , 0   )
   self.assertEqual(act(torch.tensor(0  , dtype=torch.floataa   )   ).item()  , 0   )
   self.assertEqual(act(torch.tensor(20  , dtype=torch.floataa   )   ).item()  , 20   )






from typing import TYPE_CHECKING

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


_lowerCamelCase      :						Any							=  {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase      :						int							=  ["""XLNetTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase      :						int							=  ["""XLNetTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase      :						Union[str, Any]							=  [
      """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """XLNetForMultipleChoice""",
      """XLNetForQuestionAnswering""",
      """XLNetForQuestionAnsweringSimple""",
      """XLNetForSequenceClassification""",
      """XLNetForTokenClassification""",
      """XLNetLMHeadModel""",
      """XLNetModel""",
      """XLNetPreTrainedModel""",
      """load_tf_weights_in_xlnet""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase      :						Dict							=  [
      """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFXLNetForMultipleChoice""",
      """TFXLNetForQuestionAnsweringSimple""",
      """TFXLNetForSequenceClassification""",
      """TFXLNetForTokenClassification""",
      """TFXLNetLMHeadModel""",
      """TFXLNetMainLayer""",
      """TFXLNetModel""",
      """TFXLNetPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet import XLNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet_fast import XLNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlnet import (
        XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
        XLNetPreTrainedModel,
        load_tf_weights_in_xlnet,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlnet import (
        TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLNetForMultipleChoice,
        TFXLNetForQuestionAnsweringSimple,
        TFXLNetForSequenceClassification,
        TFXLNetForTokenClassification,
        TFXLNetLMHeadModel,
        TFXLNetMainLayer,
        TFXLNetModel,
        TFXLNetPreTrainedModel,
    )

else:
  import sys

  _lowerCamelCase      :						int							=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


