




import unittest

import numpy as np
import requests

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

							from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
							__A						:							int							=  False

if is_vision_available():
							from PIL import Image

							from transformers import PixaStructImageProcessor




class 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							def __init__(					self       :	Tuple       ,     a       :	str       ,     a       :	Dict=7       ,     a       :	Tuple=3       ,     a       :	Dict=18       ,     a       :	Dict=30       ,     a       :	Tuple=400       ,     a       :	Optional[int]=None       ,     a       :	List[Any]=True       ,     a       :	int=True       ,     a       :	Optional[int]=None       ,     )			->					str:
										SCREAMING_SNAKE_CASE           =					size if size is not None else {"""height""": 20, """width""": 20}
										SCREAMING_SNAKE_CASE           =					parent
										SCREAMING_SNAKE_CASE           =					batch_size
										SCREAMING_SNAKE_CASE           =					num_channels
										SCREAMING_SNAKE_CASE           =					image_size
										SCREAMING_SNAKE_CASE           =					min_resolution
										SCREAMING_SNAKE_CASE           =					max_resolution
										SCREAMING_SNAKE_CASE           =					size
										SCREAMING_SNAKE_CASE           =					do_normalize
										SCREAMING_SNAKE_CASE           =					do_convert_rgb
										SCREAMING_SNAKE_CASE           =					[512, 1_024, 2_048, 4_096]
										SCREAMING_SNAKE_CASE           =					patch_size if patch_size is not None else {"""height""": 16, """width""": 16}





							def    _UpperCAmelCase     (					self       :	Tuple			)			->					Optional[Any]:
										return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







							def    _UpperCAmelCase     (					self       :	str			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					"""https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
										SCREAMING_SNAKE_CASE           =					Image.open(requests.get(__A       ,     stream=__A			).raw			).convert("""RGB"""			)
										return raw_image





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11		,      reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''		,      )
@require_torch
@require_vision
class 			UpperCAmelCase_      (   A		,      unittest.TestCase       ):




							'''simple docstring'''



							a__						  =     PixaStructImageProcessor if is_vision_available() else None





							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Any:
										SCREAMING_SNAKE_CASE           =					PixaStructImageProcessingTester(self			)





							@property
							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					str:
										return self.image_processor_tester.prepare_image_processor_dict()





							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					self.image_processing_class(**self.image_processor_dict			)
										self.assertTrue(hasattr(__A       ,     """do_normalize"""			)			)
										self.assertTrue(hasattr(__A       ,     """do_convert_rgb"""			)			)





							def    _UpperCAmelCase     (					self       :	Any			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					self.image_processor_tester.prepare_dummy_image()

										SCREAMING_SNAKE_CASE           =					self.image_processing_class(**self.image_processor_dict			)
										SCREAMING_SNAKE_CASE           =					2_048

										SCREAMING_SNAKE_CASE           =					image_processor(__A       ,     return_tensors="""pt"""       ,     max_patches=__A			)
										self.assertTrue(torch.allclose(inputs.flattened_patches.mean()       ,     torch.tensor(0.0606			)       ,     atol=1E-3       ,     rtol=1E-3			)			)





							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					List[str]:
										# Initialize image_processor
										SCREAMING_SNAKE_CASE           =					self.image_processing_class(**self.image_processor_dict			)
										# create random PIL images
										SCREAMING_SNAKE_CASE           =					prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=__A			)
										for image in image_inputs:
													self.assertIsInstance(__A       ,     Image.Image			)

										# Test not batched input
										SCREAMING_SNAKE_CASE           =					(
										    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
										    * self.image_processor_tester.num_channels
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
													# Test not batched input
													SCREAMING_SNAKE_CASE           =					image_processor(
													    image_inputs[0]       ,     return_tensors="""pt"""       ,     max_patches=__A			).flattened_patches
													self.assertEqual(
													    encoded_images.shape       ,     (1, max_patch, expected_hidden_dim)       ,     )

													# Test batched
													SCREAMING_SNAKE_CASE           =					image_processor(
													    __A       ,     return_tensors="""pt"""       ,     max_patches=__A			).flattened_patches
													self.assertEqual(
													    encoded_images.shape       ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,     )





							def    _UpperCAmelCase     (					self       :	Any			)			->					Tuple:
										# Initialize image_processor
										SCREAMING_SNAKE_CASE           =					self.image_processing_class(**self.image_processor_dict			)
										# create random PIL images
										SCREAMING_SNAKE_CASE           =					prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=__A			)
										for image in image_inputs:
													self.assertIsInstance(__A       ,     Image.Image			)

										# Test not batched input
										SCREAMING_SNAKE_CASE           =					(
										    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
										    * self.image_processor_tester.num_channels
										) + 2

										SCREAMING_SNAKE_CASE           =					True

										for max_patch in self.image_processor_tester.max_patches:
													# Test not batched input
													with self.assertRaises(__A			):
																SCREAMING_SNAKE_CASE           =					image_processor(
																    image_inputs[0]       ,     return_tensors="""pt"""       ,     max_patches=__A			).flattened_patches

													SCREAMING_SNAKE_CASE           =					"""Hello"""

													SCREAMING_SNAKE_CASE           =					image_processor(
													    image_inputs[0]       ,     return_tensors="""pt"""       ,     max_patches=__A       ,     header_text=__A			).flattened_patches
													self.assertEqual(
													    encoded_images.shape       ,     (1, max_patch, expected_hidden_dim)       ,     )

													# Test batched
													SCREAMING_SNAKE_CASE           =					image_processor(
													    __A       ,     return_tensors="""pt"""       ,     max_patches=__A       ,     header_text=__A			).flattened_patches
													self.assertEqual(
													    encoded_images.shape       ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,     )





							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Any:
										# Initialize image_processor
										SCREAMING_SNAKE_CASE           =					self.image_processing_class(**self.image_processor_dict			)
										# create random numpy tensors
										SCREAMING_SNAKE_CASE           =					prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=__A       ,     numpify=__A			)
										for image in image_inputs:
													self.assertIsInstance(__A       ,     np.ndarray			)

										SCREAMING_SNAKE_CASE           =					(
										    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
										    * self.image_processor_tester.num_channels
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
													# Test not batched input
													SCREAMING_SNAKE_CASE           =					image_processor(
													    image_inputs[0]       ,     return_tensors="""pt"""       ,     max_patches=__A			).flattened_patches
													self.assertEqual(
													    encoded_images.shape       ,     (1, max_patch, expected_hidden_dim)       ,     )

													# Test batched
													SCREAMING_SNAKE_CASE           =					image_processor(
													    __A       ,     return_tensors="""pt"""       ,     max_patches=__A			).flattened_patches
													self.assertEqual(
													    encoded_images.shape       ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,     )







							def    _UpperCAmelCase     (					self       :	Tuple			)			->					str:
										# Initialize image_processor
										SCREAMING_SNAKE_CASE           =					self.image_processing_class(**self.image_processor_dict			)
										# create random PyTorch tensors
										SCREAMING_SNAKE_CASE           =					prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=__A       ,     torchify=__A			)
										for image in image_inputs:
													self.assertIsInstance(__A       ,     torch.Tensor			)

										# Test not batched input
										SCREAMING_SNAKE_CASE           =					(
										    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
										    * self.image_processor_tester.num_channels
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
													# Test not batched input
													SCREAMING_SNAKE_CASE           =					image_processor(
													    image_inputs[0]       ,     return_tensors="""pt"""       ,     max_patches=__A			).flattened_patches
													self.assertEqual(
													    encoded_images.shape       ,     (1, max_patch, expected_hidden_dim)       ,     )

													# Test batched
													SCREAMING_SNAKE_CASE           =					image_processor(
													    __A       ,     return_tensors="""pt"""       ,     max_patches=__A			).flattened_patches
													self.assertEqual(
													    encoded_images.shape       ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,     )





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11		,      reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''		,      )
@require_torch
@require_vision
class 			UpperCAmelCase_      (   A		,      unittest.TestCase       ):




							'''simple docstring'''



							a__						  =     PixaStructImageProcessor if is_vision_available() else None





							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					PixaStructImageProcessingTester(self       ,     num_channels=4			)
										SCREAMING_SNAKE_CASE           =					3





							@property
							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					str:
										return self.image_processor_tester.prepare_image_processor_dict()





							def    _UpperCAmelCase     (					self       :	str			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					self.image_processing_class(**self.image_processor_dict			)
										self.assertTrue(hasattr(__A       ,     """do_normalize"""			)			)
										self.assertTrue(hasattr(__A       ,     """do_convert_rgb"""			)			)







							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					str:
										# Initialize image_processor
										SCREAMING_SNAKE_CASE           =					self.image_processing_class(**self.image_processor_dict			)
										# create random PIL images
										SCREAMING_SNAKE_CASE           =					prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=__A			)
										for image in image_inputs:
													self.assertIsInstance(__A       ,     Image.Image			)

										# Test not batched input
										SCREAMING_SNAKE_CASE           =					(
										    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
										    * (self.image_processor_tester.num_channels - 1)
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
													# Test not batched input
													SCREAMING_SNAKE_CASE           =					image_processor(
													    image_inputs[0]       ,     return_tensors="""pt"""       ,     max_patches=__A			).flattened_patches
													self.assertEqual(
													    encoded_images.shape       ,     (1, max_patch, expected_hidden_dim)       ,     )

													# Test batched
													SCREAMING_SNAKE_CASE           =					image_processor(
													    __A       ,     return_tensors="""pt"""       ,     max_patches=__A			).flattened_patches
													self.assertEqual(
													    encoded_images.shape       ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,     )












from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     42
							a__						  =     42





							def __init__(					self       :	Any       ,     a       :	UNetaDModel       ,     a       :	ScoreSdeVeScheduler			)			->					str:
										super().__init__()
										self.register_modules(unet=a       ,     scheduler=a			)







							@torch.no_grad()
							def __call__(					self       :	List[Any]       ,     a       :	int = 1       ,     a       :	int = 2_000       ,     a       :	Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,     a       :	Optional[str] = "pil"       ,     a       :	bool = True       ,     **a       :	Optional[int]       ,     )			->					Union[ImagePipelineOutput, Tuple]:
										SCREAMING_SNAKE_CASE           =					self.unet.config.sample_size
										SCREAMING_SNAKE_CASE           =					(batch_size, 3, img_size, img_size)

										SCREAMING_SNAKE_CASE           =					self.unet

										SCREAMING_SNAKE_CASE           =					randn_tensor(a       ,     generator=a			) * self.scheduler.init_noise_sigma
										SCREAMING_SNAKE_CASE           =					sample.to(self.device			)

										self.scheduler.set_timesteps(a			)
										self.scheduler.set_sigmas(a			)

										for i, t in enumerate(self.progress_bar(self.scheduler.timesteps			)			):
													SCREAMING_SNAKE_CASE           =					self.scheduler.sigmas[i] * torch.ones(shape[0]       ,     device=self.device			)

													# correction step
													for _ in range(self.scheduler.config.correct_steps			):
																SCREAMING_SNAKE_CASE           =					self.unet(a       ,     a			).sample
																SCREAMING_SNAKE_CASE           =					self.scheduler.step_correct(a       ,     a       ,     generator=a			).prev_sample

													# prediction step
													SCREAMING_SNAKE_CASE           =					model(a       ,     a			).sample
													SCREAMING_SNAKE_CASE           =					self.scheduler.step_pred(a       ,     a       ,     a       ,     generator=a			)

													SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					output.prev_sample, output.prev_sample_mean

										SCREAMING_SNAKE_CASE           =					sample_mean.clamp(0       ,     1			)
										SCREAMING_SNAKE_CASE           =					sample.cpu().permute(0       ,     2       ,     3       ,     1			).numpy()
										if output_type == "pil":
													SCREAMING_SNAKE_CASE           =					self.numpy_to_pil(a			)

										if not return_dict:
													return (sample,)

										return ImagePipelineOutput(images=a			)






