


'''simple docstring'''
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   lowercase      (				unittest.TestCase   ):

		"""simple docstring"""

		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :List[str]				   =					torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]				)
							UpperCamelCase__  :int				   =					get_activation('''gelu'''				)
							self.assertTrue(torch.allclose(gelu_python(UpperCamelCase_				)       ,  torch_builtin(UpperCamelCase_				)				)				)
							self.assertFalse(torch.allclose(gelu_python(UpperCamelCase_				)       ,  gelu_new(UpperCamelCase_				)				)				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]				)
							UpperCamelCase__  :Union[str, Any]				   =					get_activation('''gelu'''				)
							UpperCamelCase__  :Tuple				   =					get_activation('''gelu_10'''				)

							UpperCamelCase__  :Union[str, Any]				   =					torch_builtin(UpperCamelCase_				)
							UpperCamelCase__  :Dict				   =					geluaa(UpperCamelCase_				)

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

							self.assertTrue(torch.max(UpperCamelCase_				).item() == 10.0				)
							self.assertTrue(torch.allclose(y_gelu * clipped_mask       ,  y_gelu_aa * clipped_mask				)				)




		def        lowerCAmelCase__							(						self				):

							'''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(UpperCamelCase_				):
												get_activation('''bogus'''				)
							with self.assertRaises(UpperCamelCase_				):
												get_activation(UpperCamelCase_				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :str				   =					get_activation('''gelu'''				)
							UpperCamelCase__  :Optional[Any]				   =					1
							UpperCamelCase__  :Union[str, Any]				   =					get_activation('''gelu'''				)
							self.assertEqual(acta.a       ,  1				)
							with self.assertRaises(UpperCamelCase_				):
												UpperCamelCase__  :Tuple				   =					acta.a



'''simple docstring'''
import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
		__snake_case				 =	argparse.ArgumentParser()

		parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

		parser.add_argument(
		    '''--txt2img_unclip''',
		    default='''kakaobrain/karlo-v1-alpha''',
		    type=str,
		    required=False,
		    help='''The pretrained txt2img unclip.''',
		)

		__snake_case				 =	parser.parse_args()

		__snake_case				 =	UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

		__snake_case				 =	CLIPImageProcessor()
		__snake_case				 =	CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

		__snake_case				 =	UnCLIPImageVariationPipeline(
		    decoder=txtaimg.decoder,
		    text_encoder=txtaimg.text_encoder,
		    tokenizer=txtaimg.tokenizer,
		    text_proj=txtaimg.text_proj,
		    feature_extractor=feature_extractor,
		    image_encoder=image_encoder,
		    super_res_first=txtaimg.super_res_first,
		    super_res_last=txtaimg.super_res_last,
		    decoder_scheduler=txtaimg.decoder_scheduler,
		    super_res_scheduler=txtaimg.super_res_scheduler,
		)

		imgaimg.save_pretrained(args.dump_path)