






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 ViTImageProcessor


class      lowercase__     (			unittest.TestCase	):

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase=13		,    __UpperCAmelCase=3		,    __UpperCAmelCase=224		,    __UpperCAmelCase=30		,    __UpperCAmelCase=400		,    __UpperCAmelCase=True		,    __UpperCAmelCase=None		,    __UpperCAmelCase=True		,    __UpperCAmelCase=[0.5, 0.5, 0.5]		,    __UpperCAmelCase=[0.5, 0.5, 0.5]		,    )->					Union[str, Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						size if size is not None else {"height": 18, "width": 18}
							lowerCAmelCase__       =						parent
							lowerCAmelCase__       =						batch_size
							lowerCAmelCase__       =						num_channels
							lowerCAmelCase__       =						image_size
							lowerCAmelCase__       =						min_resolution
							lowerCAmelCase__       =						max_resolution
							lowerCAmelCase__       =						do_resize
							lowerCAmelCase__       =						size
							lowerCAmelCase__       =						do_normalize
							lowerCAmelCase__       =						image_mean
							lowerCAmelCase__       =						image_std


	def 		UpperCAmelCase  (							self       )->					List[str]:







							'''simple docstring'''





							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							}





@require_torch
@require_vision
class      lowercase__     (			_UpperCAmelCase,					unittest.TestCase	):
	a_         =ViTImageProcessor if is_vision_available() else None

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						EfficientFormerImageProcessorTester(self       )

	@property
	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							return self.image_proc_tester.prepare_image_processor_dict()

	def 		UpperCAmelCase  (							self       )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						self.image_processing_class(**self.image_processor_dict       )
							self.assertTrue(hasattr(__UpperCAmelCase		,    "image_mean"       )       )
							self.assertTrue(hasattr(__UpperCAmelCase		,    "image_std"       )       )
							self.assertTrue(hasattr(__UpperCAmelCase		,    "do_normalize"       )       )
							self.assertTrue(hasattr(__UpperCAmelCase		,    "do_resize"       )       )
							self.assertTrue(hasattr(__UpperCAmelCase		,    "size"       )       )

	def 		UpperCAmelCase  (							self       )->					Optional[Any]:







							'''simple docstring'''





							pass

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







							'''simple docstring'''





							lowerCAmelCase__       =						self.image_processing_class(**self.image_processor_dict       )
							# create random PIL images
							lowerCAmelCase__       =						prepare_image_inputs(self.image_proc_tester		,    equal_resolution=__UpperCAmelCase       )
							for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase		,    Image.Image       )

							# Test not batched input
							lowerCAmelCase__       =						image_processor(image_inputs[0]		,    return_tensors="pt"       ).pixel_values
							self.assertEqual(
							    encoded_images.shape		,    (
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size["height"],
							        self.image_proc_tester.size["width"],
							    )		,    )

							# Test batched
							lowerCAmelCase__       =						image_processor(__UpperCAmelCase		,    return_tensors="pt"       ).pixel_values
							self.assertEqual(
							    encoded_images.shape		,    (
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size["height"],
							        self.image_proc_tester.size["width"],
							    )		,    )

	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						self.image_processing_class(**self.image_processor_dict       )
							# create random numpy tensors
							lowerCAmelCase__       =						prepare_image_inputs(self.image_proc_tester		,    equal_resolution=__UpperCAmelCase		,    numpify=__UpperCAmelCase       )
							for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase		,    np.ndarray       )

							# Test not batched input
							lowerCAmelCase__       =						image_processor(image_inputs[0]		,    return_tensors="pt"       ).pixel_values
							self.assertEqual(
							    encoded_images.shape		,    (
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size["height"],
							        self.image_proc_tester.size["width"],
							    )		,    )

							# Test batched
							lowerCAmelCase__       =						image_processor(__UpperCAmelCase		,    return_tensors="pt"       ).pixel_values
							self.assertEqual(
							    encoded_images.shape		,    (
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size["height"],
							        self.image_proc_tester.size["width"],
							    )		,    )


	def 		UpperCAmelCase  (							self       )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						self.image_processing_class(**self.image_processor_dict       )
							# create random PyTorch tensors
							lowerCAmelCase__       =						prepare_image_inputs(self.image_proc_tester		,    equal_resolution=__UpperCAmelCase		,    torchify=__UpperCAmelCase       )
							for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase		,    torch.Tensor       )

							# Test not batched input
							lowerCAmelCase__       =						image_processor(image_inputs[0]		,    return_tensors="pt"       ).pixel_values
							self.assertEqual(
							    encoded_images.shape		,    (
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size["height"],
							        self.image_proc_tester.size["width"],
							    )		,    )

							# Test batched
							lowerCAmelCase__       =						image_processor(__UpperCAmelCase		,    return_tensors="pt"       ).pixel_values
							self.assertEqual(
							    encoded_images.shape		,    (
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size["height"],
							        self.image_proc_tester.size["width"],
							    )		,    )














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)






