

'''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 		__SCREAMING_SNAKE_CASE       ( unittest.TestCase			):







    def __init__(					self    :		List[str]		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		int=13		,				UpperCAmelCase__    :		int=3		,				UpperCAmelCase__    :		str=224		,				UpperCAmelCase__    :		str=30		,				UpperCAmelCase__    :		Dict=400		,				UpperCAmelCase__    :		Any=True		,				UpperCAmelCase__    :		Dict=None		,				UpperCAmelCase__    :		Optional[int]=True		,				UpperCAmelCase__    :		Optional[Any]=[0.5, 0.5, 0.5]		,				UpperCAmelCase__    :		Optional[int]=[0.5, 0.5, 0.5]		,				):
          '''simple docstring'''
          lowercase   :       Union[str, Any]								=size if size is not None else {'''height''': 18, '''width''': 18}
          lowercase   :       Optional[Any]								=parent
          lowercase   :       int								=batch_size
          lowercase   :       Union[str, Any]								=num_channels
          lowercase   :       List[str]								=image_size
          lowercase   :       Optional[int]								=min_resolution
          lowercase   :       str								=max_resolution
          lowercase   :       Union[str, Any]								=do_resize
          lowercase   :       Optional[int]								=size
          lowercase   :       Tuple								=do_normalize
          lowercase   :       Optional[int]								=image_mean
          lowercase   :       Union[str, Any]								=image_std







    def    lowerCamelCase_  (					self    :		int      ):
          '''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 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       unittest.TestCase			):
    lowerCamelCase_							=						ViTImageProcessor if is_vision_available() else None







    def    lowerCamelCase_  (					self    :		List[str]      ):
          '''simple docstring'''
          lowercase   :       Any								=EfficientFormerImageProcessorTester(self      )







    @property
    def    lowerCamelCase_  (					self    :		List[str]      ):
          '''simple docstring'''
          return self.image_proc_tester.prepare_image_processor_dict()







    def    lowerCamelCase_  (					self    :		str      ):
          '''simple docstring'''
          lowercase   :       Optional[Any]								=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    lowerCamelCase_  (					self    :		Dict      ):
          '''simple docstring'''
          pass







    def    lowerCamelCase_  (					self    :		str      ):
          '''simple docstring'''
          lowercase   :       Tuple								=self.image_processing_class(**self.image_processor_dict      )
          # create random PIL images
          lowercase   :       List[str]								=prepare_image_inputs(self.image_proc_tester		,				equal_resolution=UpperCAmelCase__      )
          for image in image_inputs:
                self.assertIsInstance(UpperCAmelCase__		,				Image.Image      )

          # Test not batched input
          lowercase   :       int								=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
          lowercase   :       int								=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    lowerCamelCase_  (					self    :		str      ):
          '''simple docstring'''
          lowercase   :       List[Any]								=self.image_processing_class(**self.image_processor_dict      )
          # create random numpy tensors
          lowercase   :       List[Any]								=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
          lowercase   :       str								=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
          lowercase   :       Dict								=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    lowerCamelCase_  (					self    :		Tuple      ):
          '''simple docstring'''
          lowercase   :       List[str]								=self.image_processing_class(**self.image_processor_dict      )
          # create random PyTorch tensors
          lowercase   :       Optional[int]								=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
          lowercase   :       int								=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
          lowercase   :       Tuple								=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'''],
              )		,				)








'''simple docstring'''





from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				def __init__(					self    :		Optional[Any]		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		int      ):
										'''simple docstring'''
										super().__init__()

										# make sure scheduler can always be converted to DDIM
										lowercase   :       Any								=DDIMScheduler.from_config(scheduler.config      )

										self.register_modules(unet=UpperCAmelCase__		,				scheduler=UpperCAmelCase__      )







				@torch.no_grad()
				def __call__(					self    :		List[Any]		,				UpperCAmelCase__    :		int = 1		,				UpperCAmelCase__    :		Optional[Union[torch.Generator, List[torch.Generator]]] = None		,				UpperCAmelCase__    :		float = 0.0		,				UpperCAmelCase__    :		int = 50		,				UpperCAmelCase__    :		Optional[bool] = None		,				UpperCAmelCase__    :		Optional[str] = "pil"		,				UpperCAmelCase__    :		bool = True		,				):
										'''simple docstring'''

										# Sample gaussian noise to begin loop
										if isinstance(self.unet.config.sample_size		,				UpperCAmelCase__      ):
																lowercase   :       Optional[int]								=(
																    batch_size,
																    self.unet.config.in_channels,
																    self.unet.config.sample_size,
																    self.unet.config.sample_size,
																)
										else:
																lowercase   :       Optional[int]								=(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

										if isinstance(UpperCAmelCase__		,				UpperCAmelCase__      ) and len(UpperCAmelCase__      ) != batch_size:
																raise ValueError(
																    F'''You have passed a list of generators of length {len(UpperCAmelCase__      )}, but requested an effective batch'''
																    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''      )

										lowercase   :       str								=randn_tensor(UpperCAmelCase__		,				generator=UpperCAmelCase__		,				device=self.device		,				dtype=self.unet.dtype      )

										# set step values
										self.scheduler.set_timesteps(UpperCAmelCase__      )

										for t in self.progress_bar(self.scheduler.timesteps      ):
																# 1. predict noise model_output
																lowercase   :       Dict								=self.unet(UpperCAmelCase__		,				UpperCAmelCase__      ).sample

																# 2. predict previous mean of image x_t-1 and add variance depending on eta
																# eta corresponds to η in paper and should be between [0, 1]
																# do x_t -> x_t-1
																lowercase   :       Dict								=self.scheduler.step(
																    UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				eta=UpperCAmelCase__		,				use_clipped_model_output=UpperCAmelCase__		,				generator=UpperCAmelCase__      ).prev_sample

										lowercase   :       Optional[Any]								=(image / 2 + 0.5).clamp(0		,				1      )
										lowercase   :       Optional[Any]								=image.cpu().permute(0		,				2		,				3		,				1      ).numpy()
										if output_type == "pil":
																lowercase   :       List[str]								=self.numpy_to_pil(UpperCAmelCase__      )

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=UpperCAmelCase__      )





