

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 MobileNetVaImageProcessor



class     _a						(unittest.TestCase				):


					'''simple docstring'''






					def __init__( self    ,       A__    ,       A__=7    ,       A__=3    ,       A__=18    ,       A__=30    ,       A__=400    ,       A__=True    ,       A__=None    ,       A__=True    ,       A__=None    ,       ):
						A__							:    Any        =		size if size is not None else {"""shortest_edge""": 20}
						A__							:    int        =		crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
						A__							:    Dict        =		parent
						A__							:    Any        =		batch_size
						A__							:    Any        =		num_channels
						A__							:    Dict        =		image_size
						A__							:    Any        =		min_resolution
						A__							:    Any        =		max_resolution
						A__							:    Union[str, Any]        =		do_resize
						A__							:    Tuple        =		size
						A__							:    Optional[int]        =		do_center_crop
						A__							:    str        =		crop_size






					def   __A			( self			):
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						}



@require_torch
@require_vision
class     _a						(_snake_case		,   unittest.TestCase				):


					'''simple docstring'''






					UpperCAmelCase__:   Union[str, Any]  							=       MobileNetVaImageProcessor if is_vision_available() else None






					def   __A			( self			):
						A__							:    int        =		MobileNetVaImageProcessingTester(self			)






					@property
					def   __A			( self			):
						return self.image_processor_tester.prepare_image_processor_dict()






					def   __A			( self			):
						A__							:    str        =		self.image_processing_class(**self.image_processor_dict			)
						self.assertTrue(hasattr(__snake_case    ,       """do_resize"""			)			)
						self.assertTrue(hasattr(__snake_case    ,       """size"""			)			)
						self.assertTrue(hasattr(__snake_case    ,       """do_center_crop"""			)			)
						self.assertTrue(hasattr(__snake_case    ,       """crop_size"""			)			)






					def   __A			( self			):
						A__							:    str        =		self.image_processing_class.from_dict(self.image_processor_dict			)
						self.assertEqual(image_processor.size    ,       {"""shortest_edge""": 20}			)
						self.assertEqual(image_processor.crop_size    ,       {"""height""": 18, """width""": 18}			)

						A__							:    Dict        =		self.image_processing_class.from_dict(self.image_processor_dict    ,       size=42    ,       crop_size=84			)
						self.assertEqual(image_processor.size    ,       {"""shortest_edge""": 42}			)
						self.assertEqual(image_processor.crop_size    ,       {"""height""": 84, """width""": 84}			)






					def   __A			( self			):
						pass






					def   __A			( self			):
						# Initialize image_processing
						A__							:    str        =		self.image_processing_class(**self.image_processor_dict			)
						# create random PIL images
						A__							:    List[str]        =		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
						A__							:    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.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )    ,       )

						# Test batched
						A__							:    int        =		image_processing(__snake_case    ,       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.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )    ,       )






					def   __A			( self			):
						# Initialize image_processing
						A__							:    Optional[int]        =		self.image_processing_class(**self.image_processor_dict			)
						# create random numpy tensors
						A__							:    Tuple        =		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
						A__							:    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.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )    ,       )

						# Test batched
						A__							:    Optional[Any]        =		image_processing(__snake_case    ,       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.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )    ,       )






					def   __A			( self			):
						# Initialize image_processing
						A__							:    Optional[Any]        =		self.image_processing_class(**self.image_processor_dict			)
						# create random PyTorch tensors
						A__							:    str        =		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
						A__							:    List[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.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )    ,       )

						# Test batched
						A__							:    str        =		image_processing(__snake_case    ,       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.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )    ,       )







'''simple docstring'''


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     lowercase__  (						_snake_case					):





							'''simple docstring'''


							A_							:      UNetaDModel
							A_							:      ScoreSdeVeScheduler






							def __init__(    self    ,					__snake_case    ,					__snake_case  ):
											super().__init__()
											self.register_modules(unet=__snake_case    ,					scheduler=__snake_case  )






							@torch.no_grad()
							def __call__(    self    ,					__snake_case = 1    ,					__snake_case = 2000    ,					__snake_case = None    ,					__snake_case = "pil"    ,					__snake_case = True    ,					**__snake_case    ,					):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.unet.config.sample_size
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      (batch_size, 3, img_size, img_size)

											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.unet

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      randn_tensor(__snake_case    ,					generator=__snake_case  ) * self.scheduler.init_noise_sigma
											_SCREAMING_SNAKE_CASE :       Any     		=      sample.to(self.device  )

											self.scheduler.set_timesteps(__snake_case  )
											self.scheduler.set_sigmas(__snake_case  )

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

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

															# prediction step
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      model(__snake_case    ,					__snake_case  ).sample
															_SCREAMING_SNAKE_CASE :       Tuple     		=      self.scheduler.step_pred(__snake_case    ,					__snake_case    ,					__snake_case    ,					generator=__snake_case  )

															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       int     		=      output.prev_sample, output.prev_sample_mean

											_SCREAMING_SNAKE_CASE :       Tuple     		=      sample_mean.clamp(0    ,					1  )
											_SCREAMING_SNAKE_CASE :       str     		=      sample.cpu().permute(0    ,					2    ,					3    ,					1  ).numpy()
											if output_type == "pil":
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.numpy_to_pil(__snake_case  )

											if not return_dict:
															return (sample,)

											return ImagePipelineOutput(images=__snake_case  )




