
"""simple docstring"""
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import GLPNImageProcessor
class 	lowerCAmelCase_						(							unittest.TestCase      ):



							'''simple docstring'''





							def __init__(      self  ,							snake_case_  ,							snake_case_=7  ,							snake_case_=3  ,							snake_case_=18  ,							snake_case_=30  ,							snake_case_=400  ,							snake_case_=True  ,							snake_case_=32  ,							snake_case_=True  ,							)    ->   Dict:
													__lowerCAmelCase   					=   parent
													__lowerCAmelCase   					=   batch_size
													__lowerCAmelCase   					=   num_channels
													__lowerCAmelCase   					=   image_size
													__lowerCAmelCase   					=   min_resolution
													__lowerCAmelCase   					=   max_resolution
													__lowerCAmelCase   					=   do_resize
													__lowerCAmelCase   					=   size_divisor
													__lowerCAmelCase   					=   do_rescale




							def 					A__		(      self							)    ->   Dict:
													return {
													    "do_resize": self.do_resize,
													    "size_divisor": self.size_divisor,
													    "do_rescale": self.do_rescale,
													}
@require_torch
@require_vision
class 	lowerCAmelCase_						(							_a							,							unittest.TestCase      ):



							'''simple docstring'''





							_snake_case			=						GLPNImageProcessor if is_vision_available() else None




							def 					A__		(      self							)    ->   Optional[Any]:
													__lowerCAmelCase   					=   GLPNImageProcessingTester(self							)




							@property
							def 					A__		(      self							)    ->   Union[str, Any]:
													return self.image_processor_tester.prepare_image_processor_dict()




							def 					A__		(      self							)    ->   List[str]:
													__lowerCAmelCase   					=   self.image_processing_class(**self.image_processor_dict							)
													self.assertTrue(hasattr(snake_case_  ,							"""do_resize"""							)							)
													self.assertTrue(hasattr(snake_case_  ,							"""size_divisor"""							)							)
													self.assertTrue(hasattr(snake_case_  ,							"""resample"""							)							)
													self.assertTrue(hasattr(snake_case_  ,							"""do_rescale"""							)							)




							def 					A__		(      self							)    ->   List[Any]:
													pass




							def 					A__		(      self							)    ->   int:
													__lowerCAmelCase   					=   self.image_processing_class(**self.image_processor_dict							)
													# create random PIL images
													__lowerCAmelCase   					=   prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=snake_case_							)
													for image in image_inputs:
																			self.assertIsInstance(snake_case_  ,							Image.Image							)

													# Test not batched input (GLPNImageProcessor doesn't support batching)
													__lowerCAmelCase   					=   image_processing(image_inputs[0]  ,							return_tensors="""pt"""							).pixel_values
													self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0							)
													self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0							)




							def 					A__		(      self							)    ->   Dict:
													__lowerCAmelCase   					=   self.image_processing_class(**self.image_processor_dict							)
													# create random numpy tensors
													__lowerCAmelCase   					=   prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=snake_case_  ,							numpify=snake_case_							)
													for image in image_inputs:
																			self.assertIsInstance(snake_case_  ,							np.ndarray							)

													# Test not batched input (GLPNImageProcessor doesn't support batching)
													__lowerCAmelCase   					=   image_processing(image_inputs[0]  ,							return_tensors="""pt"""							).pixel_values
													self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0							)
													self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0							)




							def 					A__		(      self							)    ->   Optional[Any]:
													__lowerCAmelCase   					=   self.image_processing_class(**self.image_processor_dict							)
													# create random PyTorch tensors
													__lowerCAmelCase   					=   prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=snake_case_  ,							torchify=snake_case_							)
													for image in image_inputs:
																			self.assertIsInstance(snake_case_  ,							torch.Tensor							)

													# Test not batched input (GLPNImageProcessor doesn't support batching)
													__lowerCAmelCase   					=   image_processing(image_inputs[0]  ,							return_tensors="""pt"""							).pixel_values
													self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0							)
													self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0							)







import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


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

	lowerCAmelCase_    =				parser.parse_args()

	lowerCAmelCase_    =				UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

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

	lowerCAmelCase_    =				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)
