

"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

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






class     _UpperCAmelCase			(		_a):
			__a :       Union[str, Any]									=							4_2
			__a :       Optional[int]									=							4_2
			def __init__(		self      ,		_A      ,		_A     )    ->    Union[str, Any]:






				'''simple docstring'''







				super().__init__()

				_UpperCAmelCase    :							Union[str, Any]									=					PNDMScheduler.from_config(scheduler.config     )

				self.register_modules(unet=lowerCamelCase__      ,		scheduler=lowerCamelCase__     )
			@torch.no_grad()
			def __call__(		self      ,		_A = 1      ,		_A = 50      ,		_A = None      ,		_A = "pil"      ,		_A = True      ,		**_A      ,		)    ->    Optional[int]:






				'''simple docstring'''







				_UpperCAmelCase    :							str									=					randn_tensor(
				    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)      ,		generator=lowerCamelCase__      ,		device=self.device      ,		)

				self.scheduler.set_timesteps(lowerCamelCase__     )
				for t in self.progress_bar(self.scheduler.timesteps     ):
					_UpperCAmelCase    :							List[str]									=					self.unet(lowerCamelCase__      ,		lowerCamelCase__     ).sample

					_UpperCAmelCase    :							Tuple									=					self.scheduler.step(lowerCamelCase__      ,		lowerCamelCase__      ,		lowerCamelCase__     ).prev_sample

				_UpperCAmelCase    :							Tuple									=					(image / 2 + 0.5).clamp(0      ,		1     )
				_UpperCAmelCase    :							str									=					image.cpu().permute(0      ,		2      ,		3      ,		1     ).numpy()
				if output_type == "pil":
					_UpperCAmelCase    :							str									=					self.numpy_to_pil(lowerCamelCase__     )

				if not return_dict:
					return (image,)

				return ImagePipelineOutput(images=lowerCamelCase__     )








"""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 ViTImageProcessor






class     _UpperCAmelCase			(		unittest.TestCase):
			def __init__(		self      ,		_A      ,		_A=7      ,		_A=3      ,		_A=18      ,		_A=30      ,		_A=4_00      ,		_A=True      ,		_A=None      ,		_A=True      ,		_A=[0.5, 0.5, 0.5]      ,		_A=[0.5, 0.5, 0.5]      ,		)    ->    Optional[Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							Optional[Any]									=					size if size is not None else {"""height""": 18, """width""": 18}
				_UpperCAmelCase    :							Tuple									=					parent
				_UpperCAmelCase    :							Tuple									=					batch_size
				_UpperCAmelCase    :							Optional[int]									=					num_channels
				_UpperCAmelCase    :							Any									=					image_size
				_UpperCAmelCase    :							str									=					min_resolution
				_UpperCAmelCase    :							Dict									=					max_resolution
				_UpperCAmelCase    :							Optional[Any]									=					do_resize
				_UpperCAmelCase    :							str									=					size
				_UpperCAmelCase    :							Optional[int]									=					do_normalize
				_UpperCAmelCase    :							Optional[Any]									=					image_mean
				_UpperCAmelCase    :							Any									=					image_std
			def 							__snake_case (		self     )    ->    List[Any]:






				'''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     _UpperCAmelCase			(		__a    ,     unittest.TestCase):
			__a :       List[str]									=							ViTImageProcessor if is_vision_available() else None
			def 							__snake_case (		self     )    ->    Any:






				'''simple docstring'''







				_UpperCAmelCase    :							Tuple									=					ViTImageProcessingTester(self     )
			@property
			def 							__snake_case (		self     )    ->    List[str]:






				'''simple docstring'''







				return self.image_processor_tester.prepare_image_processor_dict()
			def 							__snake_case (		self     )    ->    str:






				'''simple docstring'''







				_UpperCAmelCase    :							int									=					self.image_processing_class(**self.image_processor_dict     )
				self.assertTrue(hasattr(_A      ,		"""image_mean"""     )     )
				self.assertTrue(hasattr(_A      ,		"""image_std"""     )     )
				self.assertTrue(hasattr(_A      ,		"""do_normalize"""     )     )
				self.assertTrue(hasattr(_A      ,		"""do_resize"""     )     )
				self.assertTrue(hasattr(_A      ,		"""size"""     )     )
			def 							__snake_case (		self     )    ->    Dict:






				'''simple docstring'''







				_UpperCAmelCase    :							Optional[int]									=					self.image_processing_class.from_dict(self.image_processor_dict     )
				self.assertEqual(image_processor.size      ,		{"""height""": 18, """width""": 18}     )

				_UpperCAmelCase    :							Any									=					self.image_processing_class.from_dict(self.image_processor_dict      ,		size=42     )
				self.assertEqual(image_processor.size      ,		{"""height""": 42, """width""": 42}     )
			def 							__snake_case (		self     )    ->    str:






				'''simple docstring'''







				pass
			def 							__snake_case (		self     )    ->    Optional[Any]:






				'''simple docstring'''







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

				# Test not batched input
				_UpperCAmelCase    :							Optional[Any]									=					image_processing(image_inputs[0]      ,		return_tensors="""pt"""     ).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,		(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )      ,		)

				# Test batched
				_UpperCAmelCase    :							Union[str, Any]									=					image_processing(_A      ,		return_tensors="""pt"""     ).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,		(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )      ,		)
			def 							__snake_case (		self     )    ->    str:






				'''simple docstring'''







				_UpperCAmelCase    :							Union[str, Any]									=					self.image_processing_class(**self.image_processor_dict     )
				# create random numpy tensors
				_UpperCAmelCase    :							List[Any]									=					prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_A      ,		numpify=_A     )
				for image in image_inputs:
					self.assertIsInstance(_A      ,		np.ndarray     )

				# Test not batched input
				_UpperCAmelCase    :							Any									=					image_processing(image_inputs[0]      ,		return_tensors="""pt"""     ).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,		(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )      ,		)

				# Test batched
				_UpperCAmelCase    :							int									=					image_processing(_A      ,		return_tensors="""pt"""     ).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,		(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )      ,		)
			def 							__snake_case (		self     )    ->    List[str]:






				'''simple docstring'''







				_UpperCAmelCase    :							Union[str, Any]									=					self.image_processing_class(**self.image_processor_dict     )
				# create random PyTorch tensors
				_UpperCAmelCase    :							Tuple									=					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
				_UpperCAmelCase    :							Optional[int]									=					image_processing(image_inputs[0]      ,		return_tensors="""pt"""     ).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,		(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )      ,		)

				# Test batched
				_UpperCAmelCase    :							Tuple									=					image_processing(_A      ,		return_tensors="""pt"""     ).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,		(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )      ,		)





