

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 					a	(						unittest.TestCase					):


						def 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


										_UpperCAmelCase							:		Dict					=     torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]						)
										_UpperCAmelCase							:		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						):







										'''simple docstring'''


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

										_UpperCAmelCase							:		Optional[Any]					=     torch_builtin(A_						)
										_UpperCAmelCase							:		Union[str, Any]					=     geluaa(A_						)

										_UpperCAmelCase							:		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						):







										'''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						):







										'''simple docstring'''


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





import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
			a_      =     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.',
			)

			a_      =     parser.parse_args()

			a_      =     UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

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

			a_      =     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)