'''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 OwlViTImageProcessor

class       lowerCamelCase_							(	unittest.TestCase  ):






   '''simple docstring'''

   def __init__(  self     ,						__UpperCamelCase     ,						__UpperCamelCase=7     ,						__UpperCamelCase=3     ,						__UpperCamelCase=18     ,						__UpperCamelCase=30     ,						__UpperCamelCase=4_00     ,						__UpperCamelCase=True     ,						__UpperCamelCase=None     ,						__UpperCamelCase=True     ,						__UpperCamelCase=None     ,						__UpperCamelCase=True     ,						__UpperCamelCase=[0.48_145_466, 0.4_578_275, 0.40_821_073]     ,						__UpperCamelCase=[0.26_862_954, 0.26_130_258, 0.27_577_711]     ,						__UpperCamelCase=True     ,						):
       UpperCamelCase__      =			parent
       UpperCamelCase__      =			batch_size
       UpperCamelCase__      =			num_channels
       UpperCamelCase__      =			image_size
       UpperCamelCase__      =			min_resolution
       UpperCamelCase__      =			max_resolution
       UpperCamelCase__      =			do_resize
       UpperCamelCase__      =			size if size is not None else {'height': 18, 'width': 18}
       UpperCamelCase__      =			do_center_crop
       UpperCamelCase__      =			crop_size if crop_size is not None else {'height': 18, 'width': 18}
       UpperCamelCase__      =			do_normalize
       UpperCamelCase__      =			image_mean
       UpperCamelCase__      =			image_std
       UpperCamelCase__      =			do_convert_rgb







   def 	_UpperCamelCase       (  self ):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_convert_rgb": self.do_convert_rgb,
       }

@require_torch
@require_vision
class       lowerCamelCase_							(	a__					,    unittest.TestCase  ):






   '''simple docstring'''

   A :     List[Any]				=	OwlViTImageProcessor if is_vision_available() else None


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			OwlViTImageProcessingTester(self )


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


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"do_resize" ) )
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"size" ) )
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"do_center_crop" ) )
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"center_crop" ) )
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"do_normalize" ) )
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"image_mean" ) )
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"image_std" ) )
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"do_convert_rgb" ) )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.image_processing_class.from_dict(self.image_processor_dict )
       self.assertEqual(image_processor.size     ,						{"height": 18, "width": 18} )
       self.assertEqual(image_processor.crop_size     ,						{"height": 18, "width": 18} )

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


   def 	_UpperCamelCase       (  self ):
       # Initialize image_processing
       UpperCamelCase__      =			self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       UpperCamelCase__      =			prepare_image_inputs(self.image_processor_tester     ,						equal_resolution=SCREAMING_SNAKE_CASE_ )

       for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,						Image.Image )

       # Test not batched input
       UpperCamelCase__      =			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__      =			image_processing(SCREAMING_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 	_UpperCamelCase       (  self ):
       # Initialize image_processing
       UpperCamelCase__      =			self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       UpperCamelCase__      =			prepare_image_inputs(self.image_processor_tester     ,						equal_resolution=SCREAMING_SNAKE_CASE_     ,						numpify=SCREAMING_SNAKE_CASE_ )
       for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,						np.ndarray )

       # Test not batched input
       UpperCamelCase__      =			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__      =			image_processing(SCREAMING_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 	_UpperCamelCase       (  self ):
       # Initialize image_processing
       UpperCamelCase__      =			self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       UpperCamelCase__      =			prepare_image_inputs(self.image_processor_tester     ,						equal_resolution=SCREAMING_SNAKE_CASE_     ,						torchify=SCREAMING_SNAKE_CASE_ )
       for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,						torch.Tensor )

       # Test not batched input
       UpperCamelCase__      =			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__      =			image_processing(SCREAMING_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 PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class       lowerCamelCase_							(	lowerCAmelCase_  ):






   '''simple docstring'''

   A :     UNetaDModel
   A :     PNDMScheduler


   def __init__(  self     ,						__UpperCamelCase     ,						__UpperCamelCase ):
       super().__init__()

       UpperCamelCase__      =			PNDMScheduler.from_config(scheduler.config )

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







   @torch.no_grad()
   def __call__(  self     ,						__UpperCamelCase = 1     ,						__UpperCamelCase = 50     ,						__UpperCamelCase = None     ,						__UpperCamelCase = "pil"     ,						__UpperCamelCase = True     ,						**__UpperCamelCase     ,						):
       UpperCamelCase__      =			randn_tensor(
           (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     ,						generator=__UpperCamelCase     ,						device=self.device     ,						)

       self.scheduler.set_timesteps(__UpperCamelCase )
       for t in self.progress_bar(self.scheduler.timesteps ):
           UpperCamelCase__      =			self.unet(__UpperCamelCase     ,						__UpperCamelCase ).sample

           UpperCamelCase__      =			self.scheduler.step(__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase ).prev_sample

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

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=__UpperCamelCase )




