code: 
  
  
  
  
  """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
  
  
  if is_torch_available():
  							import torch
  
  if is_vision_available():
  							from PIL import Image
  
  							from transformers import TvltImageProcessor
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     List[str]       ,     lowercase__  :     Union[str, Any]=10       ,     lowercase__  :     Optional[Any]=10       ,     lowercase__  :     str=False       ,     lowercase__  :     Any=False   ):
  		snake_case		:						Union[str, Any]								=				[]
  		for i in range(image_processor_tester.num_frames   ):
  				video.append(np.random.randint(255       ,     size=(image_processor_tester.num_channels, width, height)       ,     dtype=np.uinta   )   )
  
  		if not numpify and not torchify:
  				# PIL expects the channel dimension as last dimension
  				snake_case		:						List[str]								=				[Image.fromarray(np.moveaxis(lowercase__       ,     0       ,     -1   )   ) for frame in video]
  
  		if torchify:
  				snake_case		:						Optional[int]								=				[torch.from_numpy(lowercase__   ) for frame in video]
  
  		return video
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     List[Any]       ,     lowercase__  :     Tuple=False       ,     lowercase__  :     Dict=False       ,     lowercase__  :     str=False   ):
  		assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
  		snake_case		:						List[Any]								=				[]
  		for i in range(image_processor_tester.batch_size   ):
  				if equal_resolution:
  						snake_case		:						Optional[int]								=				image_processor_tester.max_resolution
  				else:
  						snake_case			,				snake_case		:						str								=				np.random.choice(
  						    np.arange(image_processor_tester.min_resolution       ,     image_processor_tester.max_resolution   )       ,     2   )
  						snake_case		:						List[str]								=				prepare_video(
  						    image_processor_tester=lowercase__       ,     width=lowercase__       ,     height=lowercase__       ,     numpify=lowercase__       ,     torchify=lowercase__       ,     )
  				video_inputs.append(lowercase__   )
  
  		return video_inputs
  class 			lowerCamelCase__    (  unittest.TestCase			):
  
  
  			def __init__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=7				, SCREAMING_SNAKE_CASE=3				, SCREAMING_SNAKE_CASE=4				, SCREAMING_SNAKE_CASE=18				, SCREAMING_SNAKE_CASE=30				, SCREAMING_SNAKE_CASE=400				, SCREAMING_SNAKE_CASE=True				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=True				, SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]				, SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]				, SCREAMING_SNAKE_CASE=True				, SCREAMING_SNAKE_CASE=None				, ):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						List[Any]								=				size if size is not None else {"shortest_edge": 18}
  					snake_case		:						Optional[int]								=				crop_size if crop_size is not None else {"height": 18, "width": 18}
  
  					snake_case		:						Tuple								=				parent
  					snake_case		:						List[str]								=				batch_size
  					snake_case		:						Any								=				num_channels
  					snake_case		:						Dict								=				num_frames
  					snake_case		:						int								=				image_size
  					snake_case		:						Optional[Any]								=				min_resolution
  					snake_case		:						Tuple								=				max_resolution
  					snake_case		:						Any								=				do_resize
  					snake_case		:						Any								=				size
  					snake_case		:						Optional[int]								=				do_normalize
  					snake_case		:						Union[str, Any]								=				image_mean
  					snake_case		:						str								=				image_std
  					snake_case		:						List[str]								=				do_center_crop
  					snake_case		:						Union[str, Any]								=				crop_size
  
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""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,
  					    "do_center_crop": self.do_center_crop,
  					    "crop_size": self.crop_size,
  					}
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class 			lowerCamelCase__    (  lowerCamelCase_				,  unittest.TestCase			):
  			a__ :      List[str]								=     TvltImageProcessor if is_vision_available() else None
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						int								=				TvltImageProcessorTester(self							)
  
  
  			@property
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return self.image_processor_tester.prepare_image_processor_dict()
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[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				, "do_center_crop"							)							)
  					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "size"							)							)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Dict								=				self.image_processing_class(**self.image_processor_dict							)
  					# create random PIL videos
  					snake_case		:						List[str]								=				prepare_video_inputs(self.image_processor_tester				, equal_resolution=SCREAMING_SNAKE_CASE							)
  					for video in video_inputs:
  							self.assertIsInstance(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  							self.assertIsInstance(video[0]				, Image.Image							)
  
  					# Test not batched input
  					snake_case		:						int								=				image_processor(video_inputs[0]				, return_tensors="pt"							).pixel_values
  					self.assertEqual(
  					    encoded_videos.shape				, (
  					        1,
  					        self.image_processor_tester.num_frames,
  					        self.image_processor_tester.num_channels,
  					        self.image_processor_tester.crop_size["height"],
  					        self.image_processor_tester.crop_size["width"],
  					    )				, )
  
  					# Test batched
  					snake_case		:						int								=				image_processor(SCREAMING_SNAKE_CASE				, return_tensors="pt"							).pixel_values
  					self.assertEqual(
  					    encoded_videos.shape				, (
  					        self.image_processor_tester.batch_size,
  					        self.image_processor_tester.num_frames,
  					        self.image_processor_tester.num_channels,
  					        self.image_processor_tester.crop_size["height"],
  					        self.image_processor_tester.crop_size["width"],
  					    )				, )
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						int								=				self.image_processing_class(**self.image_processor_dict							)
  					# create random numpy tensors
  					snake_case		:						str								=				prepare_video_inputs(self.image_processor_tester				, equal_resolution=SCREAMING_SNAKE_CASE				, numpify=SCREAMING_SNAKE_CASE							)
  					for video in video_inputs:
  							self.assertIsInstance(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  							self.assertIsInstance(video[0]				, np.ndarray							)
  
  					# Test not batched input
  					snake_case		:						str								=				image_processor(video_inputs[0]				, return_tensors="pt"							).pixel_values
  					self.assertEqual(
  					    encoded_videos.shape				, (
  					        1,
  					        self.image_processor_tester.num_frames,
  					        self.image_processor_tester.num_channels,
  					        self.image_processor_tester.crop_size["height"],
  					        self.image_processor_tester.crop_size["width"],
  					    )				, )
  
  					# Test batched
  					snake_case		:						List[Any]								=				image_processor(SCREAMING_SNAKE_CASE				, return_tensors="pt"							).pixel_values
  					self.assertEqual(
  					    encoded_videos.shape				, (
  					        self.image_processor_tester.batch_size,
  					        self.image_processor_tester.num_frames,
  					        self.image_processor_tester.num_channels,
  					        self.image_processor_tester.crop_size["height"],
  					        self.image_processor_tester.crop_size["width"],
  					    )				, )
  
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Dict								=				self.image_processing_class(**self.image_processor_dict							)
  					# create random PyTorch tensors
  					snake_case		:						Dict								=				prepare_video_inputs(self.image_processor_tester				, equal_resolution=SCREAMING_SNAKE_CASE				, torchify=SCREAMING_SNAKE_CASE							)
  					for video in video_inputs:
  							self.assertIsInstance(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  							self.assertIsInstance(video[0]				, torch.Tensor							)
  
  					# Test not batched input
  					snake_case		:						List[str]								=				image_processor(video_inputs[0]				, return_tensors="pt"							).pixel_values
  					self.assertEqual(
  					    encoded_videos.shape				, (
  					        1,
  					        self.image_processor_tester.num_frames,
  					        self.image_processor_tester.num_channels,
  					        self.image_processor_tester.crop_size["height"],
  					        self.image_processor_tester.crop_size["width"],
  					    )				, )
  
  					# Test batched
  					snake_case		:						Optional[Any]								=				image_processor(SCREAMING_SNAKE_CASE				, return_tensors="pt"							).pixel_values
  					self.assertEqual(
  					    encoded_videos.shape				, (
  					        self.image_processor_tester.batch_size,
  					        self.image_processor_tester.num_frames,
  					        self.image_processor_tester.num_channels,
  					        self.image_processor_tester.crop_size["height"],
  					        self.image_processor_tester.crop_size["width"],
  					    )				, )
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  import tempfile
  import unittest
  
  from diffusers import (
      DDIMScheduler,
      DDPMScheduler,
      DPMSolverMultistepScheduler,
      EulerAncestralDiscreteScheduler,
      EulerDiscreteScheduler,
      PNDMScheduler,
      logging,
  )
  from diffusers.configuration_utils import ConfigMixin, register_to_config
  from diffusers.utils.testing_utils import CaptureLogger
  class 			lowerCamelCase__    (  lowerCamelCase_			):
  			a__ :      int								=     """config.json"""
  
  
  			@register_to_config
  			def __init__(     self				, SCREAMING_SNAKE_CASE=2				, SCREAMING_SNAKE_CASE=5				, SCREAMING_SNAKE_CASE=(2, 5)				, SCREAMING_SNAKE_CASE="for diffusion"				, SCREAMING_SNAKE_CASE=[1, 3]				, ):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					pass
  
  
  
  
  
  
  
  class 			lowerCamelCase__    (  lowerCamelCase_			):
  			a__ :      Dict								=     """config.json"""
  
  
  			@register_to_config
  			def __init__(     self				, SCREAMING_SNAKE_CASE=2				, SCREAMING_SNAKE_CASE=5				, SCREAMING_SNAKE_CASE=(2, 5)				, SCREAMING_SNAKE_CASE="for diffusion"				, SCREAMING_SNAKE_CASE=[1, 3]				, ):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					pass
  
  
  
  
  
  
  
  class 			lowerCamelCase__    (  lowerCamelCase_			):
  			a__ :      Union[str, Any]								=     """config.json"""
  
  
  			@register_to_config
  			def __init__(     self				, SCREAMING_SNAKE_CASE=2				, SCREAMING_SNAKE_CASE=5				, SCREAMING_SNAKE_CASE=(2, 5)				, SCREAMING_SNAKE_CASE="for diffusion"				, SCREAMING_SNAKE_CASE=[1, 3]				, SCREAMING_SNAKE_CASE=[1, 3]				, ):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					pass
  
  
  
  
  
  
  
  class 			lowerCamelCase__    (  lowerCamelCase_			):
  			a__ :      Tuple								=     """config.json"""
  
  
  			@register_to_config
  			def __init__(     self				, SCREAMING_SNAKE_CASE=2				, SCREAMING_SNAKE_CASE=5				, SCREAMING_SNAKE_CASE=(2, 5)				, SCREAMING_SNAKE_CASE="for diffusion"				, SCREAMING_SNAKE_CASE=[1, 5]				, SCREAMING_SNAKE_CASE=[5, 4]				, ):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					pass
  
  
  
  
  
  
  
  class 			lowerCamelCase__    (  unittest.TestCase			):
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					with self.assertRaises(SCREAMING_SNAKE_CASE							):
  							ConfigMixin.load_config("dummy_path"							)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Dict								=				SampleObject()
  					snake_case		:						Dict								=				obj.config
  					assert config["a"] == 2
  					assert config["b"] == 5
  					assert config["c"] == (2, 5)
  					assert config["d"] == "for diffusion"
  					assert config["e"] == [1, 3]
  
  					# init ignore private arguments
  					snake_case		:						Dict								=				SampleObject(_name_or_path="lalala"							)
  					snake_case		:						List[Any]								=				obj.config
  					assert config["a"] == 2
  					assert config["b"] == 5
  					assert config["c"] == (2, 5)
  					assert config["d"] == "for diffusion"
  					assert config["e"] == [1, 3]
  
  					# can override default
  					snake_case		:						str								=				SampleObject(c=6							)
  					snake_case		:						Any								=				obj.config
  					assert config["a"] == 2
  					assert config["b"] == 5
  					assert config["c"] == 6
  					assert config["d"] == "for diffusion"
  					assert config["e"] == [1, 3]
  
  					# can use positional arguments.
  					snake_case		:						int								=				SampleObject(1				, c=6							)
  					snake_case		:						Dict								=				obj.config
  					assert config["a"] == 1
  					assert config["b"] == 5
  					assert config["c"] == 6
  					assert config["d"] == "for diffusion"
  					assert config["e"] == [1, 3]
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						str								=				SampleObject()
  					snake_case		:						List[str]								=				obj.config
  
  					assert config["a"] == 2
  					assert config["b"] == 5
  					assert config["c"] == (2, 5)
  					assert config["d"] == "for diffusion"
  					assert config["e"] == [1, 3]
  
  					with tempfile.TemporaryDirectory() as tmpdirname:
  							obj.save_config(SCREAMING_SNAKE_CASE							)
  							snake_case		:						Union[str, Any]								=				SampleObject.from_config(SampleObject.load_config(SCREAMING_SNAKE_CASE							)							)
  							snake_case		:						Union[str, Any]								=				new_obj.config
  
  					# unfreeze configs
  					snake_case		:						List[str]								=				dict(SCREAMING_SNAKE_CASE							)
  					snake_case		:						Union[str, Any]								=				dict(SCREAMING_SNAKE_CASE							)
  
  					assert config.pop("c"							) == (2, 5)  # instantiated as tuple
  					assert new_config.pop("c"							) == [2, 5]  # saved & loaded as list because of json
  					config.pop("_use_default_values"							)
  					assert config == new_config
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						List[Any]								=				logging.get_logger("diffusers.configuration_utils"							)
  					# 30 for warning
  					logger.setLevel(30							)
  
  					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
  							snake_case		:						Any								=				DDIMScheduler.from_pretrained(
  							    "hf-internal-testing/tiny-stable-diffusion-torch"				, subfolder="scheduler"							)
  
  					assert ddim.__class__ == DDIMScheduler
  					# no warning should be thrown
  					assert cap_logger.out == ""
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						int								=				logging.get_logger("diffusers.configuration_utils"							)
  					# 30 for warning
  					logger.setLevel(30							)
  
  					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
  							snake_case		:						Optional[int]								=				EulerDiscreteScheduler.from_pretrained(
  							    "hf-internal-testing/tiny-stable-diffusion-torch"				, subfolder="scheduler"							)
  
  					assert euler.__class__ == EulerDiscreteScheduler
  					# no warning should be thrown
  					assert cap_logger.out == ""
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Any								=				logging.get_logger("diffusers.configuration_utils"							)
  					# 30 for warning
  					logger.setLevel(30							)
  
  					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
  							snake_case		:						Union[str, Any]								=				EulerAncestralDiscreteScheduler.from_pretrained(
  							    "hf-internal-testing/tiny-stable-diffusion-torch"				, subfolder="scheduler"							)
  
  					assert euler.__class__ == EulerAncestralDiscreteScheduler
  					# no warning should be thrown
  					assert cap_logger.out == ""
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Union[str, Any]								=				logging.get_logger("diffusers.configuration_utils"							)
  					# 30 for warning
  					logger.setLevel(30							)
  
  					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
  							snake_case		:						List[Any]								=				PNDMScheduler.from_pretrained(
  							    "hf-internal-testing/tiny-stable-diffusion-torch"				, subfolder="scheduler"							)
  
  					assert pndm.__class__ == PNDMScheduler
  					# no warning should be thrown
  					assert cap_logger.out == ""
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						str								=				logging.get_logger("diffusers.configuration_utils"							)
  					# 30 for warning
  					logger.setLevel(30							)
  
  					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
  							snake_case		:						List[Any]								=				DDPMScheduler.from_pretrained(
  							    "hf-internal-testing/tiny-stable-diffusion-torch"				, subfolder="scheduler"				, prediction_type="sample"				, beta_end=8				, )
  
  					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger_a:
  							snake_case		:						Optional[int]								=				DDPMScheduler.from_pretrained("google/ddpm-celebahq-256"				, beta_start=88							)
  
  					assert ddpm.__class__ == DDPMScheduler
  					assert ddpm.config.prediction_type == "sample"
  					assert ddpm.config.beta_end == 8
  					assert ddpm_a.config.beta_start == 88
  
  					# no warning should be thrown
  					assert cap_logger.out == ""
  					assert cap_logger_a.out == ""
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Dict								=				logging.get_logger("diffusers.configuration_utils"							)
  					# 30 for warning
  					logger.setLevel(30							)
  
  					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
  							snake_case		:						Union[str, Any]								=				DPMSolverMultistepScheduler.from_pretrained(
  							    "hf-internal-testing/tiny-stable-diffusion-torch"				, subfolder="scheduler"							)
  
  					assert dpm.__class__ == DPMSolverMultistepScheduler
  					# no warning should be thrown
  					assert cap_logger.out == ""
  
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[Any]								=				SampleObject()
  
  					snake_case		:						str								=				{k: v for k, v in config.config.items() if not k.startswith("_"							)}
  
  					# make sure that default config has all keys in `_use_default_values`
  					assert set(config_dict.keys()							) == set(config.config._use_default_values							)
  
  					with tempfile.TemporaryDirectory() as tmpdirname:
  							config.save_config(SCREAMING_SNAKE_CASE							)
  
  							# now loading it with SampleObject2 should put f into `_use_default_values`
  							snake_case		:						Optional[int]								=				SampleObjecta.from_config(SCREAMING_SNAKE_CASE							)
  
  							assert "f" in config._use_default_values
  							assert config.f == [1, 3]
  
  					# now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
  					# **BECAUSE** it is part of `config._use_default_values`
  					snake_case		:						Tuple								=				SampleObjecta.from_config(config.config							)
  					assert new_config.f == [5, 4]
  
  					config.config._use_default_values.pop()
  					snake_case		:						List[Any]								=				SampleObjecta.from_config(config.config							)
  					assert new_config_a.f == [1, 3]
  
  					# Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
  					assert new_config_a.e == [1, 3]
  
  