
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_UpperCAmelCase :			Union[str, Any]              =	{
    "configuration_mvp": ["MVP_PRETRAINED_CONFIG_ARCHIVE_MAP", "MvpConfig", "MvpOnnxConfig"],
    "tokenization_mvp": ["MvpTokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase :			Any              =	["MvpTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase :			Optional[Any]              =	[
         "MVP_PRETRAINED_MODEL_ARCHIVE_LIST",
         "MvpForCausalLM",
         "MvpForConditionalGeneration",
         "MvpForQuestionAnswering",
         "MvpForSequenceClassification",
         "MvpModel",
         "MvpPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
     from .tokenization_mvp import MvpTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mvp_fast import MvpTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mvp import (
              MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
              MvpForCausalLM,
              MvpForConditionalGeneration,
              MvpForQuestionAnswering,
              MvpForSequenceClassification,
              MvpModel,
              MvpPreTrainedModel,
          )

else:
     import sys

     _UpperCAmelCase :			Any              =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation


class     lowercase (		unittest.TestCase       ):



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('swish'	)

				self.assertIsInstance(A_    ,				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 __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('silu'	)

				self.assertIsInstance(A_    ,				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 __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('mish'	)

				self.assertIsInstance(A_    ,				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 __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('gelu'	)

				self.assertIsInstance(A_    ,				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	)
