






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 ConditionalDetrImageProcessor


class      lowercase__     (			unittest.TestCase	):

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase=7		,    __UpperCAmelCase=3		,    __UpperCAmelCase=30		,    __UpperCAmelCase=400		,    __UpperCAmelCase=True		,    __UpperCAmelCase=None		,    __UpperCAmelCase=True		,    __UpperCAmelCase=[0.5, 0.5, 0.5]		,    __UpperCAmelCase=[0.5, 0.5, 0.5]		,    __UpperCAmelCase=True		,    __UpperCAmelCase=1 / 255		,    __UpperCAmelCase=True		,    )->					Union[str, Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
							lowerCAmelCase__       =						parent
							lowerCAmelCase__       =						batch_size
							lowerCAmelCase__       =						num_channels
							lowerCAmelCase__       =						min_resolution
							lowerCAmelCase__       =						max_resolution
							lowerCAmelCase__       =						do_resize
							lowerCAmelCase__       =						size
							lowerCAmelCase__       =						do_normalize
							lowerCAmelCase__       =						image_mean
							lowerCAmelCase__       =						image_std
							lowerCAmelCase__       =						do_rescale
							lowerCAmelCase__       =						rescale_factor
							lowerCAmelCase__       =						do_pad

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







							'''simple docstring'''





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


	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase=False       )->					List[Any]:







							'''simple docstring'''





							if not batched:
													lowerCAmelCase__       =						image_inputs[0]
													if isinstance(__UpperCAmelCase		,    Image.Image       ):
																			lowerCAmelCase__      ,							lowerCAmelCase__       =						image.size
													else:
																			lowerCAmelCase__      ,							lowerCAmelCase__       =						image.shape[1], image.shape[2]
													if w < h:
																			lowerCAmelCase__       =						int(self.size["shortest_edge"] * h / w       )
																			lowerCAmelCase__       =						self.size["shortest_edge"]
													elif w > h:
																			lowerCAmelCase__       =						self.size["shortest_edge"]
																			lowerCAmelCase__       =						int(self.size["shortest_edge"] * w / h       )
													else:
																			lowerCAmelCase__       =						self.size["shortest_edge"]
																			lowerCAmelCase__       =						self.size["shortest_edge"]

							else:
													lowerCAmelCase__       =						[]
													for image in image_inputs:
																			lowerCAmelCase__      ,							lowerCAmelCase__       =						self.get_expected_values([image]       )
																			expected_values.append((expected_height, expected_width)       )
													lowerCAmelCase__       =						max(__UpperCAmelCase		,    key=lambda __UpperCAmelCase       : item[0]       )[0]
													lowerCAmelCase__       =						max(__UpperCAmelCase		,    key=lambda __UpperCAmelCase       : item[1]       )[1]

							return expected_height, expected_width





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

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







							'''simple docstring'''





							lowerCAmelCase__       =						ConditionalDetrImageProcessingTester(self       )

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







							'''simple docstring'''





							return self.image_processor_tester.prepare_image_processor_dict()

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







							'''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[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.image_processing_class.from_dict(self.image_processor_dict       )
							self.assertEqual(image_processor.size		,    {"shortest_edge": 18, "longest_edge": 1333}       )
							self.assertEqual(image_processor.do_pad		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						self.image_processing_class.from_dict(
							    self.image_processor_dict		,    size=42		,    max_size=84		,    pad_and_return_pixel_mask=__UpperCAmelCase       )
							self.assertEqual(image_processor.size		,    {"shortest_edge": 42, "longest_edge": 84}       )
							self.assertEqual(image_processor.do_pad		,    __UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							pass

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







							'''simple docstring'''





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

							# Test not batched input
							lowerCAmelCase__       =						image_processing(image_inputs[0]		,    return_tensors="pt"       ).pixel_values

							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.image_processor_tester.get_expected_values(__UpperCAmelCase       )

							self.assertEqual(
							    encoded_images.shape		,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,    )

							# Test batched
							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.image_processor_tester.get_expected_values(__UpperCAmelCase		,    batched=__UpperCAmelCase       )

							lowerCAmelCase__       =						image_processing(__UpperCAmelCase		,    return_tensors="pt"       ).pixel_values
							self.assertEqual(
							    encoded_images.shape		,    (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )		,    )

	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





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

							# Test not batched input
							lowerCAmelCase__       =						image_processing(image_inputs[0]		,    return_tensors="pt"       ).pixel_values

							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.image_processor_tester.get_expected_values(__UpperCAmelCase       )

							self.assertEqual(
							    encoded_images.shape		,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,    )

							# Test batched
							lowerCAmelCase__       =						image_processing(__UpperCAmelCase		,    return_tensors="pt"       ).pixel_values

							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.image_processor_tester.get_expected_values(__UpperCAmelCase		,    batched=__UpperCAmelCase       )

							self.assertEqual(
							    encoded_images.shape		,    (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )		,    )

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







							'''simple docstring'''





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

							# Test not batched input
							lowerCAmelCase__       =						image_processing(image_inputs[0]		,    return_tensors="pt"       ).pixel_values

							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.image_processor_tester.get_expected_values(__UpperCAmelCase       )

							self.assertEqual(
							    encoded_images.shape		,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,    )

							# Test batched
							lowerCAmelCase__       =						image_processing(__UpperCAmelCase		,    return_tensors="pt"       ).pixel_values

							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.image_processor_tester.get_expected_values(__UpperCAmelCase		,    batched=__UpperCAmelCase       )

							self.assertEqual(
							    encoded_images.shape		,    (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )		,    )

	@slow
	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
							with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"		,    "r"       ) as f:
													lowerCAmelCase__       =						json.loads(f.read()       )

							lowerCAmelCase__       =						{"image_id": 39769, "annotations": target}

							# encode them
							lowerCAmelCase__       =						ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50"       )
							lowerCAmelCase__       =						image_processing(images=__UpperCAmelCase		,    annotations=__UpperCAmelCase		,    return_tensors="pt"       )

							# verify pixel values
							lowerCAmelCase__       =						torch.Size([1, 3, 800, 1066]       )
							self.assertEqual(encoding["pixel_values"].shape		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						torch.tensor([0.2_796, 0.3_138, 0.3_481]       )
							self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]		,    __UpperCAmelCase		,    atol=1E-4       )       )

							# verify area
							lowerCAmelCase__       =						torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["area"]		,    __UpperCAmelCase       )       )
							# verify boxes
							lowerCAmelCase__       =						torch.Size([6, 4]       )
							self.assertEqual(encoding["labels"][0]["boxes"].shape		,    __UpperCAmelCase       )
							lowerCAmelCase__       =						torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]		,    __UpperCAmelCase		,    atol=1E-3       )       )
							# verify image_id
							lowerCAmelCase__       =						torch.tensor([39769]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,    __UpperCAmelCase       )       )
							# verify is_crowd
							lowerCAmelCase__       =						torch.tensor([0, 0, 0, 0, 0, 0]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,    __UpperCAmelCase       )       )
							# verify class_labels
							lowerCAmelCase__       =						torch.tensor([75, 75, 63, 65, 17, 17]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,    __UpperCAmelCase       )       )
							# verify orig_size
							lowerCAmelCase__       =						torch.tensor([480, 640]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,    __UpperCAmelCase       )       )
							# verify size
							lowerCAmelCase__       =						torch.tensor([800, 1066]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,    __UpperCAmelCase       )       )


	@slow
	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
							with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"		,    "r"       ) as f:
													lowerCAmelCase__       =						json.loads(f.read()       )

							lowerCAmelCase__       =						{"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

							lowerCAmelCase__       =						pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"       )

							# encode them
							lowerCAmelCase__       =						ConditionalDetrImageProcessor(format="coco_panoptic"       )
							lowerCAmelCase__       =						image_processing(images=__UpperCAmelCase		,    annotations=__UpperCAmelCase		,    masks_path=__UpperCAmelCase		,    return_tensors="pt"       )

							# verify pixel values
							lowerCAmelCase__       =						torch.Size([1, 3, 800, 1066]       )
							self.assertEqual(encoding["pixel_values"].shape		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						torch.tensor([0.2_796, 0.3_138, 0.3_481]       )
							self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]		,    __UpperCAmelCase		,    atol=1E-4       )       )

							# verify area
							lowerCAmelCase__       =						torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["area"]		,    __UpperCAmelCase       )       )
							# verify boxes
							lowerCAmelCase__       =						torch.Size([6, 4]       )
							self.assertEqual(encoding["labels"][0]["boxes"].shape		,    __UpperCAmelCase       )
							lowerCAmelCase__       =						torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]		,    __UpperCAmelCase		,    atol=1E-3       )       )
							# verify image_id
							lowerCAmelCase__       =						torch.tensor([39769]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,    __UpperCAmelCase       )       )
							# verify is_crowd
							lowerCAmelCase__       =						torch.tensor([0, 0, 0, 0, 0, 0]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,    __UpperCAmelCase       )       )
							# verify class_labels
							lowerCAmelCase__       =						torch.tensor([17, 17, 63, 75, 75, 93]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,    __UpperCAmelCase       )       )
							# verify masks
							lowerCAmelCase__       =						822873
							self.assertEqual(encoding["labels"][0]["masks"].sum().item()		,    __UpperCAmelCase       )
							# verify orig_size
							lowerCAmelCase__       =						torch.tensor([480, 640]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,    __UpperCAmelCase       )       )
							# verify size
							lowerCAmelCase__       =						torch.tensor([800, 1066]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,    __UpperCAmelCase       )       )














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)






