'''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 MobileNetVaImageProcessor






class      __SCREAMING_SNAKE_CASE						(       unittest.TestCase       ):







  '''simple docstring'''






  def __init__( self      ,		snake_case_      ,		snake_case_=7      ,		snake_case_=3      ,		snake_case_=1_8      ,		snake_case_=3_0      ,		snake_case_=4_0_0      ,		snake_case_=True      ,		snake_case_=None      ,		snake_case_=True      ,		snake_case_=None      ,		):




       '''simple docstring'''

       UpperCAmelCase_							:   Union[str, Any]   =      size if size is not None else {'shortest_edge': 2_0}
       UpperCAmelCase_							:   Tuple   =      crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
       UpperCAmelCase_							:   Any   =      parent
       UpperCAmelCase_							:   Tuple   =      batch_size
       UpperCAmelCase_							:   str   =      num_channels
       UpperCAmelCase_							:   Optional[int]   =      image_size
       UpperCAmelCase_							:   int   =      min_resolution
       UpperCAmelCase_							:   Any   =      max_resolution
       UpperCAmelCase_							:   Union[str, Any]   =      do_resize
       UpperCAmelCase_							:   str   =      size
       UpperCAmelCase_							:   Optional[Any]   =      do_center_crop
       UpperCAmelCase_							:   Union[str, Any]   =      crop_size






  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       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      __SCREAMING_SNAKE_CASE						(       _lowerCamelCase						,	unittest.TestCase       ):







  '''simple docstring'''






  lowerCamelCase_		:List[Any]						    =		MobileNetVaImageProcessor if is_vision_available() else None


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Any   =      MobileNetVaImageProcessingTester(self						)


  @property
  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       return self.image_processor_tester.prepare_image_processor_dict()


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Dict   =      self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(A__      ,		'do_resize'						)						)
       self.assertTrue(hasattr(A__      ,		'size'						)						)
       self.assertTrue(hasattr(A__      ,		'do_center_crop'						)						)
       self.assertTrue(hasattr(A__      ,		'center_crop'						)						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Any   =      self.image_processing_class.from_dict(self.image_processor_dict						)
       self.assertEqual(image_processor.size      ,		{'shortest_edge': 2_0}						)
       self.assertEqual(image_processor.crop_size      ,		{'height': 1_8, 'width': 1_8}						)

       UpperCAmelCase_							:   int   =      self.image_processing_class.from_dict(self.image_processor_dict      ,		size=4_2      ,		crop_size=8_4						)
       self.assertEqual(image_processor.size      ,		{'shortest_edge': 4_2}						)
       self.assertEqual(image_processor.crop_size      ,		{'height': 8_4, 'width': 8_4}						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       pass


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   str   =      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[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.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )      ,		)

       # Test batched
       UpperCAmelCase_							:   Optional[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.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )      ,		)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Optional[int]   =      self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       UpperCAmelCase_							:   int   =      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_							:   Dict   =      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
       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.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )      ,		)






  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Dict   =      self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       UpperCAmelCase_							:   Dict   =      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_							:   List[str]   =      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
       UpperCAmelCase_							:   List[str]   =      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.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )      ,		)








'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

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

class       _a (_lowerCamelCase):





       """simple docstring"""




       def __init__(							self				,							A__				,							A__					)			->	Union[str, Any]:
          super().__init__()
          self.register_modules(unet=A__				,							scheduler=A__					)






       @torch.no_grad()
       def __call__(							self				,							A__ = 1				,							A__ = None				,							A__ = 10_00				,							A__ = "pil"				,							A__ = True				,							)			->	Union[ImagePipelineOutput, Tuple]:
          # Sample gaussian noise to begin loop
          if isinstance(self.unet.config.sample_size				,							A__					):
             _SCREAMING_SNAKE_CASE          = (
                 batch_size,
                 self.unet.config.in_channels,
                 self.unet.config.sample_size,
                 self.unet.config.sample_size,
             )
          else:
             _SCREAMING_SNAKE_CASE          = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

          if self.device.type == "mps":
             # randn does not work reproducibly on mps
             _SCREAMING_SNAKE_CASE          = randn_tensor(A__				,							generator=A__					)
             _SCREAMING_SNAKE_CASE          = image.to(self.device					)
          else:
             _SCREAMING_SNAKE_CASE          = randn_tensor(A__				,							generator=A__				,							device=self.device					)

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

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

             # 2. compute previous image: x_t -> x_t-1
             _SCREAMING_SNAKE_CASE          = self.scheduler.step(A__				,							A__				,							A__				,							generator=A__					).prev_sample

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

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=A__					)






