



'''simple docstring'''





import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation







class        UpperCAmelCase_			(			unittest.TestCase      ):
    """simple docstring"""







    def lowerCamelCase			(       self   :	Union[str, Any]		):
          snake_case__    :       int       		= get_activation("""swish"""		)

          self.assertIsInstance(snake_case_					,	nn.SiLU		)

          self.assertEqual(act(torch.tensor(-100					,	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 lowerCamelCase			(       self   :	Optional[Any]		):
          snake_case__    :       int       		= get_activation("""silu"""		)

          self.assertIsInstance(snake_case_					,	nn.SiLU		)

          self.assertEqual(act(torch.tensor(-100					,	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 lowerCamelCase			(       self   :	Dict		):
          snake_case__    :       Union[str, Any]       		= get_activation("""mish"""		)

          self.assertIsInstance(snake_case_					,	nn.Mish		)

          self.assertEqual(act(torch.tensor(-200					,	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 lowerCamelCase			(       self   :	Union[str, Any]		):
          snake_case__    :       int       		= get_activation("""gelu"""		)

          self.assertIsInstance(snake_case_					,	nn.GELU		)

          self.assertEqual(act(torch.tensor(-100					,	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		)





'''simple docstring'''





from typing import TYPE_CHECKING

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


__a  			=						{
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a  			=						[
           "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
           "GPTBigCodeForSequenceClassification",
           "GPTBigCodeForTokenClassification",
           "GPTBigCodeForCausalLM",
           "GPTBigCodeModel",
           "GPTBigCodePreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_bigcode import (
                  GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTBigCodeForCausalLM,
                  GPTBigCodeForSequenceClassification,
                  GPTBigCodeForTokenClassification,
                  GPTBigCodeModel,
                  GPTBigCodePreTrainedModel,
              )


else:
       import sys

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