

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
			import torch

			from transformers.activations import gelu_new, gelu_python, get_activation






@require_torch
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]						)
				SCREAMING_SNAKE_CASE :   Dict					  =							get_activation("gelu"						)
				self.assertTrue(torch.allclose(gelu_python(a						)   ,   torch_builtin(a						)						)						)
				self.assertFalse(torch.allclose(gelu_python(a						)   ,   gelu_new(a						)						)						)
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]						)
				SCREAMING_SNAKE_CASE :   int					  =							get_activation("gelu"						)
				SCREAMING_SNAKE_CASE :   str					  =							get_activation("gelu_10"						)

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							torch_builtin(a						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							geluaa(a						)

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							torch.where(y_gelu_aa < 10.0   ,   1   ,   0						)

				self.assertTrue(torch.max(a						).item() == 10.0						)
				self.assertTrue(torch.allclose(y_gelu * clipped_mask   ,   y_gelu_aa * clipped_mask						)						)
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							Union[str, Any]:

				"""simple docstring"""

				get_activation("gelu"						)
				get_activation("gelu_10"						)
				get_activation("gelu_fast"						)
				get_activation("gelu_new"						)
				get_activation("gelu_python"						)
				get_activation("gelu_pytorch_tanh"						)
				get_activation("linear"						)
				get_activation("mish"						)
				get_activation("quick_gelu"						)
				get_activation("relu"						)
				get_activation("sigmoid"						)
				get_activation("silu"						)
				get_activation("swish"						)
				get_activation("tanh"						)
				with self.assertRaises(a						):
					get_activation("bogus"						)
				with self.assertRaises(a						):
					get_activation(a						)






			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							get_activation("gelu"						)
				SCREAMING_SNAKE_CASE :   Dict					  =							1
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							get_activation("gelu"						)
				self.assertEqual(acta.a   ,   1						)
				with self.assertRaises(a						):
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							acta.a


a_      =     frozenset(
    [
        'prompt',
        'height',
        'width',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
        'cross_attention_kwargs',
    ]
)

a_      =     frozenset(['prompt', 'negative_prompt'])

a_      =     frozenset([])

a_      =     frozenset(['image'])

a_      =     frozenset(
    [
        'image',
        'height',
        'width',
        'guidance_scale',
    ]
)

a_      =     frozenset(['image'])

a_      =     frozenset(
    [
        'prompt',
        'image',
        'height',
        'width',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
    ]
)

a_      =     frozenset(['prompt', 'image', 'negative_prompt'])

a_      =     frozenset(
    [
        # Text guided image variation with an image mask
        'prompt',
        'image',
        'mask_image',
        'height',
        'width',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
    ]
)

a_      =     frozenset(['prompt', 'image', 'mask_image', 'negative_prompt'])

a_      =     frozenset(
    [
        # image variation with an image mask
        'image',
        'mask_image',
        'height',
        'width',
        'guidance_scale',
    ]
)

a_      =     frozenset(['image', 'mask_image'])

a_      =     frozenset(
    [
        'example_image',
        'image',
        'mask_image',
        'height',
        'width',
        'guidance_scale',
    ]
)

a_      =     frozenset(['example_image', 'image', 'mask_image'])

a_      =     frozenset(['class_labels'])

a_      =     frozenset(['class_labels'])

a_      =     frozenset(['batch_size'])

a_      =     frozenset([])

a_      =     frozenset(['batch_size'])

a_      =     frozenset([])

a_      =     frozenset(
    [
        'prompt',
        'audio_length_in_s',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
        'cross_attention_kwargs',
    ]
)

a_      =     frozenset(['prompt', 'negative_prompt'])
a_      =     frozenset(['input_tokens'])

a_      =     frozenset(['input_tokens'])