"""simple docstring"""






import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor


class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = True    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = 32    ,			_UpperCAmelCase = True    ,			_UpperCAmelCase = 1 / 255    ,			_UpperCAmelCase = True    ,			_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=7    ,			_UpperCAmelCase=30    ,			_UpperCAmelCase=400    ,			_UpperCAmelCase=3    ,			):
       lowercase__:   Dict					=      parent
       lowercase__:   Any					=      do_resize
       lowercase__:   Tuple					=      size if size is not None else {'''shortest_edge''': 288}
       lowercase__:   str					=      size_divisor
       lowercase__:   List[Any]					=      do_rescale
       lowercase__:   Union[str, Any]					=      rescale_factor
       lowercase__:   str					=      do_normalize
       lowercase__:   int					=      do_center_crop
       lowercase__:   str					=      image_mean
       lowercase__:   int					=      image_std
       lowercase__:   Dict					=      do_pad
       lowercase__:   int					=      batch_size
       lowercase__:   Optional[int]					=      num_channels
       lowercase__:   int					=      min_resolution
       lowercase__:   Union[str, Any]					=      max_resolution






    def _snake_case			(   self							):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "size_divisor": self.size_divisor,
       }






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=False							):
       if not batched:
          lowercase__:   List[str]					=      self.size['''shortest_edge''']
          lowercase__:   Tuple					=      image_inputs[0]
          if isinstance(SCREAMING_SNAKE_CASE__    ,			Image.Image							):
             lowercase__, lowercase__:   Optional[int]					=      image.size
          else:
             lowercase__, lowercase__:   str					=      image.shape[1], image.shape[2]
          lowercase__:   Tuple					=      size / min(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__							)
          if h < w:
             lowercase__, lowercase__:   int					=      size, scale * w
          else:
             lowercase__, lowercase__:   Optional[Any]					=      scale * h, size

          lowercase__:   Optional[Any]					=      int((1333 / 800) * size							)
          if max(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__							) > max_size:
             lowercase__:   str					=      max_size / max(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__							)
             lowercase__:   Tuple					=      newh * scale
             lowercase__:   int					=      neww * scale

          lowercase__, lowercase__:   Tuple					=      int(newh + 0.5							), int(neww + 0.5							)
          lowercase__, lowercase__:   int					=      (
              newh // self.size_divisor * self.size_divisor,
              neww // self.size_divisor * self.size_divisor,
          )

       else:
          lowercase__:   Dict					=      []
          for image in image_inputs:
             lowercase__, lowercase__:   Any					=      self.get_expected_values([image]							)
             expected_values.append((expected_height, expected_width)							)
          lowercase__:   Dict					=      max(SCREAMING_SNAKE_CASE__    ,			key=lambda _UpperCAmelCase							: item[0]							)[0]
          lowercase__:   Optional[int]					=      max(SCREAMING_SNAKE_CASE__    ,			key=lambda _UpperCAmelCase							: item[1]							)[1]

       return expected_height, expected_width



@require_torch
@require_vision
class     UpperCAmelCase						(UpperCAmelCase__    ,unittest.TestCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Optional[int]						    = BridgeTowerImageProcessor if is_vision_available() else None






    def _snake_case			(   self							):
       lowercase__:   Optional[int]					=      BridgeTowerImageProcessingTester(self							)






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






    def _snake_case			(   self							):
       lowercase__:   List[Any]					=      self.image_processing_class(**self.image_processor_dict							)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__    ,			'''image_mean'''							)							)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__    ,			'''image_std'''							)							)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__    ,			'''do_normalize'''							)							)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__    ,			'''do_resize'''							)							)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__    ,			'''size'''							)							)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__    ,			'''size_divisor'''							)							)






    def _snake_case			(   self							):
       pass






    def _snake_case			(   self							):
       # Initialize image processor
       lowercase__:   Any					=      self.image_processing_class(**self.image_processor_dict							)
       # create random PIL images
       lowercase__:   Optional[int]					=      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
       lowercase__:   Tuple					=      image_processing(image_inputs[0]    ,			return_tensors='''pt'''							).pixel_values

       lowercase__, lowercase__:   str					=      self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__							)
       self.assertEqual(
           encoded_images.shape    ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,			)

       # Test batched
       lowercase__:   List[str]					=      image_processing(SCREAMING_SNAKE_CASE__    ,			return_tensors='''pt'''							).pixel_values

       lowercase__, lowercase__:   str					=      self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__    ,			batched=SCREAMING_SNAKE_CASE__							)
       self.assertEqual(
           encoded_images.shape    ,			(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )    ,			)






    def _snake_case			(   self							):
       # Initialize image processor
       lowercase__:   Any					=      self.image_processing_class(**self.image_processor_dict							)
       # create random numpy tensors
       lowercase__:   Optional[int]					=      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
       lowercase__:   int					=      image_processing(image_inputs[0]    ,			return_tensors='''pt'''							).pixel_values

       lowercase__, lowercase__:   List[str]					=      self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__							)
       self.assertEqual(
           encoded_images.shape    ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,			)

       # Test batched
       lowercase__:   Tuple					=      image_processing(SCREAMING_SNAKE_CASE__    ,			return_tensors='''pt'''							).pixel_values

       lowercase__, lowercase__:   List[Any]					=      self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__    ,			batched=SCREAMING_SNAKE_CASE__							)
       self.assertEqual(
           encoded_images.shape    ,			(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )    ,			)






    def _snake_case			(   self							):
       # Initialize image processor
       lowercase__:   Any					=      self.image_processing_class(**self.image_processor_dict							)
       # create random PyTorch tensors
       lowercase__:   Union[str, Any]					=      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
       lowercase__:   Dict					=      image_processing(image_inputs[0]    ,			return_tensors='''pt'''							).pixel_values

       lowercase__, lowercase__:   Tuple					=      self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__							)
       self.assertEqual(
           encoded_images.shape    ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,			)

       # Test batched
       lowercase__:   Optional[int]					=      image_processing(SCREAMING_SNAKE_CASE__    ,			return_tensors='''pt'''							).pixel_values

       lowercase__, lowercase__:   Union[str, Any]					=      self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__    ,			batched=SCREAMING_SNAKE_CASE__							)
       self.assertEqual(
           encoded_images.shape    ,			(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )    ,			)

'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 @property
 def a					(      self   :	Any							)  ->    int:
  torch.manual_seed(0							)
  __lowerCAmelCase											=						UNetaDModel(
      block_out_channels=(32, 64)      ,       layers_per_block=2      ,       sample_size=32      ,       in_channels=3      ,       out_channels=3      ,       down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")      ,       up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")      ,       )
  return model





 def a					(      self   :	Dict							)  ->    int:
  __lowerCAmelCase											=						self.dummy_uncond_unet
  __lowerCAmelCase											=						PNDMScheduler()

  __lowerCAmelCase											=						PNDMPipeline(unet=SCREAMING_SNAKE_CASE__      ,       scheduler=SCREAMING_SNAKE_CASE__							)
  pndm.to(SCREAMING_SNAKE_CASE__							)
  pndm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						torch.manual_seed(0							)
  __lowerCAmelCase											=						pndm(generator=SCREAMING_SNAKE_CASE__      ,       num_inference_steps=20      ,       output_type="""numpy"""							).images

  __lowerCAmelCase											=						torch.manual_seed(0							)
  __lowerCAmelCase											=						pndm(generator=SCREAMING_SNAKE_CASE__      ,       num_inference_steps=20      ,       output_type="""numpy"""      ,       return_dict=SCREAMING_SNAKE_CASE__							)[0]

  __lowerCAmelCase											=						image[0, -3:, -3:, -1]
  __lowerCAmelCase											=						image_from_tuple[0, -3:, -3:, -1]

  assert image.shape == (1, 32, 32, 3)
  __lowerCAmelCase											=						np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

  assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
  assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2







@slow
@require_torch
class 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 def a					(      self   :	Any							)  ->    Any:
  __lowerCAmelCase											=						"""google/ddpm-cifar10-32"""

  __lowerCAmelCase											=						UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						PNDMScheduler()

  __lowerCAmelCase											=						PNDMPipeline(unet=SCREAMING_SNAKE_CASE__      ,       scheduler=SCREAMING_SNAKE_CASE__							)
  pndm.to(SCREAMING_SNAKE_CASE__							)
  pndm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						torch.manual_seed(0							)
  __lowerCAmelCase											=						pndm(generator=SCREAMING_SNAKE_CASE__      ,       output_type="""numpy"""							).images

  __lowerCAmelCase											=						image[0, -3:, -3:, -1]

  assert image.shape == (1, 32, 32, 3)
  __lowerCAmelCase											=						np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]							)

  assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2






