


"""simple docstring"""




import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 			__snake_case   (UpperCAmelCase_			,		UpperCAmelCase_			,		unittest.TestCase  ):
							__a					 =       StableDiffusionDiffEditPipeline
							__a					 =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'height', 'width', 'image'} | {'image_latents'}
							__a					 =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'image'} | {'image_latents'}
							__a					 =       frozenset(
							    []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							__a					 =       frozenset([]  )




							def   __a   (     self:    Dict   ):
										torch.manual_seed(0   )
										__lowerCamelCase          =      UNetaDConditionModel(
										    block_out_channels=(32, 64)     ,							layers_per_block=2     ,							sample_size=32     ,							in_channels=4     ,							out_channels=4     ,							down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")     ,							up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")     ,							cross_attention_dim=32     ,							attention_head_dim=(2, 4)     ,							use_linear_projection=SCREAMING_SNAKE_CASE_     ,							)
										__lowerCamelCase          =      DDIMScheduler(
										    beta_start=0.00_085     ,							beta_end=0.012     ,							beta_schedule="""scaled_linear"""     ,							clip_sample=SCREAMING_SNAKE_CASE_     ,							set_alpha_to_one=SCREAMING_SNAKE_CASE_     ,							)
										__lowerCamelCase          =      DDIMInverseScheduler(
										    beta_start=0.00_085     ,							beta_end=0.012     ,							beta_schedule="""scaled_linear"""     ,							clip_sample=SCREAMING_SNAKE_CASE_     ,							set_alpha_to_zero=SCREAMING_SNAKE_CASE_     ,							)
										torch.manual_seed(0   )
										__lowerCamelCase          =      AutoencoderKL(
										    block_out_channels=[32, 64]     ,							in_channels=3     ,							out_channels=3     ,							down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]     ,							up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]     ,							latent_channels=4     ,							sample_size=1_28     ,							)
										torch.manual_seed(0   )
										__lowerCamelCase          =      CLIPTextConfig(
										    bos_token_id=0     ,							eos_token_id=2     ,							hidden_size=32     ,							intermediate_size=37     ,							layer_norm_eps=1E-05     ,							num_attention_heads=4     ,							num_hidden_layers=5     ,							pad_token_id=1     ,							vocab_size=10_00     ,							hidden_act="""gelu"""     ,							projection_dim=5_12     ,							)
										__lowerCamelCase          =      CLIPTextModel(SCREAMING_SNAKE_CASE_   )
										__lowerCamelCase          =      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

										__lowerCamelCase          =      {
										    """unet""": unet,
										    """scheduler""": scheduler,
										    """inverse_scheduler""": inverse_scheduler,
										    """vae""": vae,
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """safety_checker""": None,
										    """feature_extractor""": None,
										}

										return components




							def   __a   (     self:    Optional[int]     ,							A_:    List[str]     ,							A_:    Union[str, Any]=0   ):
										__lowerCamelCase          =      floats_tensor((1, 16, 16)     ,							rng=random.Random(SCREAMING_SNAKE_CASE_   )   ).to(SCREAMING_SNAKE_CASE_   )
										__lowerCamelCase          =      floats_tensor((1, 2, 4, 16, 16)     ,							rng=random.Random(SCREAMING_SNAKE_CASE_   )   ).to(SCREAMING_SNAKE_CASE_   )
										if str(SCREAMING_SNAKE_CASE_   ).startswith("""mps"""   ):
													__lowerCamelCase          =      torch.manual_seed(SCREAMING_SNAKE_CASE_   )
										else:
													__lowerCamelCase          =      torch.Generator(device=SCREAMING_SNAKE_CASE_   ).manual_seed(SCREAMING_SNAKE_CASE_   )
										__lowerCamelCase          =      {
										    """prompt""": """a dog and a newt""",
										    """mask_image""": mask,
										    """image_latents""": latents,
										    """generator""": generator,
										    """num_inference_steps""": 2,
										    """inpaint_strength""": 1.0,
										    """guidance_scale""": 6.0,
										    """output_type""": """numpy""",
										}

										return inputs




							def   __a   (     self:    str     ,							A_:    Optional[int]     ,							A_:    Optional[Any]=0   ):
										__lowerCamelCase          =      floats_tensor((1, 3, 32, 32)     ,							rng=random.Random(SCREAMING_SNAKE_CASE_   )   ).to(SCREAMING_SNAKE_CASE_   )
										__lowerCamelCase          =      image.cpu().permute(0     ,							2     ,							3     ,							1   )[0]
										__lowerCamelCase          =      Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_   )   ).convert("""RGB"""   )
										if str(SCREAMING_SNAKE_CASE_   ).startswith("""mps"""   ):
													__lowerCamelCase          =      torch.manual_seed(SCREAMING_SNAKE_CASE_   )
										else:
													__lowerCamelCase          =      torch.Generator(device=SCREAMING_SNAKE_CASE_   ).manual_seed(SCREAMING_SNAKE_CASE_   )
										__lowerCamelCase          =      {
										    """image""": image,
										    """source_prompt""": """a cat and a frog""",
										    """target_prompt""": """a dog and a newt""",
										    """generator""": generator,
										    """num_inference_steps""": 2,
										    """num_maps_per_mask""": 2,
										    """mask_encode_strength""": 1.0,
										    """guidance_scale""": 6.0,
										    """output_type""": """numpy""",
										}

										return inputs




							def   __a   (     self:    Tuple     ,							A_:    Union[str, Any]     ,							A_:    Any=0   ):
										__lowerCamelCase          =      floats_tensor((1, 3, 32, 32)     ,							rng=random.Random(SCREAMING_SNAKE_CASE_   )   ).to(SCREAMING_SNAKE_CASE_   )
										__lowerCamelCase          =      image.cpu().permute(0     ,							2     ,							3     ,							1   )[0]
										__lowerCamelCase          =      Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_   )   ).convert("""RGB"""   )
										if str(SCREAMING_SNAKE_CASE_   ).startswith("""mps"""   ):
													__lowerCamelCase          =      torch.manual_seed(SCREAMING_SNAKE_CASE_   )
										else:
													__lowerCamelCase          =      torch.Generator(device=SCREAMING_SNAKE_CASE_   ).manual_seed(SCREAMING_SNAKE_CASE_   )
										__lowerCamelCase          =      {
										    """image""": image,
										    """prompt""": """a cat and a frog""",
										    """generator""": generator,
										    """num_inference_steps""": 2,
										    """inpaint_strength""": 1.0,
										    """guidance_scale""": 6.0,
										    """decode_latents""": True,
										    """output_type""": """numpy""",
										}
										return inputs




							def   __a   (     self:    Union[str, Any]   ):




										if not hasattr(self.pipeline_class     ,							"""_optional_components"""   ):
													return

										__lowerCamelCase          =      self.get_dummy_components()
										__lowerCamelCase          =      self.pipeline_class(**SCREAMING_SNAKE_CASE_   )
										pipe.to(SCREAMING_SNAKE_CASE_   )
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

										# set all optional components to None and update pipeline config accordingly
										for optional_component in pipe._optional_components:
													setattr(SCREAMING_SNAKE_CASE_     ,							SCREAMING_SNAKE_CASE_     ,							SCREAMING_SNAKE_CASE_   )
										pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}   )

										__lowerCamelCase          =      self.get_dummy_inputs(SCREAMING_SNAKE_CASE_   )
										__lowerCamelCase          =      pipe(**SCREAMING_SNAKE_CASE_   )[0]

										with tempfile.TemporaryDirectory() as tmpdir:
													pipe.save_pretrained(SCREAMING_SNAKE_CASE_   )
													__lowerCamelCase          =      self.pipeline_class.from_pretrained(SCREAMING_SNAKE_CASE_   )
													pipe_loaded.to(SCREAMING_SNAKE_CASE_   )
													pipe_loaded.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

										for optional_component in pipe._optional_components:
													self.assertTrue(
													    getattr(SCREAMING_SNAKE_CASE_     ,							SCREAMING_SNAKE_CASE_   ) is None     ,							f'`{optional_component}` did not stay set to None after loading.'     ,							)

										__lowerCamelCase          =      self.get_dummy_inputs(SCREAMING_SNAKE_CASE_   )
										__lowerCamelCase          =      pipe_loaded(**SCREAMING_SNAKE_CASE_   )[0]

										__lowerCamelCase          =      np.abs(output - output_loaded   ).max()
										self.assertLess(SCREAMING_SNAKE_CASE_     ,							1E-4   )




							def   __a   (     self:    Optional[int]   ):
										__lowerCamelCase          =      """cpu"""

										__lowerCamelCase          =      self.get_dummy_components()
										__lowerCamelCase          =      self.pipeline_class(**SCREAMING_SNAKE_CASE_   )
										pipe.to(SCREAMING_SNAKE_CASE_   )
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

										__lowerCamelCase          =      self.get_dummy_mask_inputs(SCREAMING_SNAKE_CASE_   )
										__lowerCamelCase          =      pipe.generate_mask(**SCREAMING_SNAKE_CASE_   )
										__lowerCamelCase          =      mask[0, -3:, -3:]

										self.assertEqual(mask.shape     ,							(1, 16, 16)   )
										__lowerCamelCase          =      np.array([0] * 9   )
										__lowerCamelCase          =      np.abs(mask_slice.flatten() - expected_slice   ).max()
										self.assertLessEqual(SCREAMING_SNAKE_CASE_     ,							1E-3   )
										self.assertEqual(mask[0, -3, -4]     ,							0   )




							def   __a   (     self:    Dict   ):
										__lowerCamelCase          =      """cpu"""

										__lowerCamelCase          =      self.get_dummy_components()
										__lowerCamelCase          =      self.pipeline_class(**SCREAMING_SNAKE_CASE_   )
										pipe.to(SCREAMING_SNAKE_CASE_   )
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

										__lowerCamelCase          =      self.get_dummy_inversion_inputs(SCREAMING_SNAKE_CASE_   )
										__lowerCamelCase          =      pipe.invert(**SCREAMING_SNAKE_CASE_   ).images
										__lowerCamelCase          =      image[0, -1, -3:, -3:]

										self.assertEqual(image.shape     ,							(2, 32, 32, 3)   )
										__lowerCamelCase          =      np.array(
										    [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]     ,							)
										__lowerCamelCase          =      np.abs(image_slice.flatten() - expected_slice   ).max()
										self.assertLessEqual(SCREAMING_SNAKE_CASE_     ,							1E-3   )




							def   __a   (     self:    Any   ):
										super().test_inference_batch_single_identical(expected_max_diff=5E-3   )




							def   __a   (     self:    Any   ):
										__lowerCamelCase          =      """cpu"""

										__lowerCamelCase          =      self.get_dummy_components()

										__lowerCamelCase          =      {"""beta_start""": 0.00_085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
										__lowerCamelCase          =      DPMSolverMultistepScheduler(**SCREAMING_SNAKE_CASE_   )
										__lowerCamelCase          =      DPMSolverMultistepInverseScheduler(**SCREAMING_SNAKE_CASE_   )

										__lowerCamelCase          =      self.pipeline_class(**SCREAMING_SNAKE_CASE_   )
										pipe.to(SCREAMING_SNAKE_CASE_   )
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

										__lowerCamelCase          =      self.get_dummy_inversion_inputs(SCREAMING_SNAKE_CASE_   )
										__lowerCamelCase          =      pipe.invert(**SCREAMING_SNAKE_CASE_   ).images
										__lowerCamelCase          =      image[0, -1, -3:, -3:]

										self.assertEqual(image.shape     ,							(2, 32, 32, 3)   )
										__lowerCamelCase          =      np.array(
										    [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]     ,							)
										__lowerCamelCase          =      np.abs(image_slice.flatten() - expected_slice   ).max()
										self.assertLessEqual(SCREAMING_SNAKE_CASE_     ,							1E-3   )




@require_torch_gpu
@slow
class 			__snake_case   (unittest.TestCase  ):




							def   __a   (     self:    Optional[Any]   ):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




							@classmethod
							def   __a   (     cls:    Optional[int]   ):
										__lowerCamelCase          =      load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"""   )

										__lowerCamelCase          =      raw_image.convert("""RGB"""   ).resize((7_68, 7_68)   )

										__lowerCamelCase          =      raw_image




							def   __a   (     self:    str   ):
										__lowerCamelCase          =      torch.manual_seed(0   )

										__lowerCamelCase          =      StableDiffusionDiffEditPipeline.from_pretrained(
										    """stabilityai/stable-diffusion-2-1"""     ,							safety_checker=SCREAMING_SNAKE_CASE_     ,							torch_dtype=torch.floataa   )
										__lowerCamelCase          =      DDIMScheduler.from_config(pipe.scheduler.config   )
										__lowerCamelCase          =      DDIMInverseScheduler.from_config(pipe.scheduler.config   )
										pipe.enable_model_cpu_offload()
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

										__lowerCamelCase          =      """a bowl of fruit"""
										__lowerCamelCase          =      """a bowl of pears"""

										__lowerCamelCase          =      pipe.generate_mask(
										    image=self.raw_image     ,							source_prompt=SCREAMING_SNAKE_CASE_     ,							target_prompt=SCREAMING_SNAKE_CASE_     ,							generator=SCREAMING_SNAKE_CASE_     ,							)

										__lowerCamelCase          =      pipe.invert(
										    prompt=SCREAMING_SNAKE_CASE_     ,							image=self.raw_image     ,							inpaint_strength=0.7     ,							generator=SCREAMING_SNAKE_CASE_   ).latents

										__lowerCamelCase          =      pipe(
										    prompt=SCREAMING_SNAKE_CASE_     ,							mask_image=SCREAMING_SNAKE_CASE_     ,							image_latents=SCREAMING_SNAKE_CASE_     ,							generator=SCREAMING_SNAKE_CASE_     ,							negative_prompt=SCREAMING_SNAKE_CASE_     ,							inpaint_strength=0.7     ,							output_type="""numpy"""     ,							).images[0]

										__lowerCamelCase          =      (
										    np.array(
										        load_image(
										            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										            """/diffedit/pears.png"""   ).resize((7_68, 7_68)   )   )
										    / 2_55
										)
										assert np.abs((expected_image - image).max()   ) < 5E-1




							def   __a   (     self:    int   ):
										__lowerCamelCase          =      torch.manual_seed(0   )

										__lowerCamelCase          =      StableDiffusionDiffEditPipeline.from_pretrained(
										    """stabilityai/stable-diffusion-2-1"""     ,							safety_checker=SCREAMING_SNAKE_CASE_     ,							torch_dtype=torch.floataa   )
										__lowerCamelCase          =      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
										__lowerCamelCase          =      DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config   )
										pipe.enable_model_cpu_offload()
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

										__lowerCamelCase          =      """a bowl of fruit"""
										__lowerCamelCase          =      """a bowl of pears"""

										__lowerCamelCase          =      pipe.generate_mask(
										    image=self.raw_image     ,							source_prompt=SCREAMING_SNAKE_CASE_     ,							target_prompt=SCREAMING_SNAKE_CASE_     ,							generator=SCREAMING_SNAKE_CASE_     ,							)

										__lowerCamelCase          =      pipe.invert(
										    prompt=SCREAMING_SNAKE_CASE_     ,							image=self.raw_image     ,							inpaint_strength=0.7     ,							generator=SCREAMING_SNAKE_CASE_     ,							num_inference_steps=25     ,							).latents

										__lowerCamelCase          =      pipe(
										    prompt=SCREAMING_SNAKE_CASE_     ,							mask_image=SCREAMING_SNAKE_CASE_     ,							image_latents=SCREAMING_SNAKE_CASE_     ,							generator=SCREAMING_SNAKE_CASE_     ,							negative_prompt=SCREAMING_SNAKE_CASE_     ,							inpaint_strength=0.7     ,							num_inference_steps=25     ,							output_type="""numpy"""     ,							).images[0]

										__lowerCamelCase          =      (
										    np.array(
										        load_image(
										            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										            """/diffedit/pears.png"""   ).resize((7_68, 7_68)   )   )
										    / 2_55
										)
										assert np.abs((expected_image - image).max()   ) < 5E-1






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    _snake_case (						unittest.TestCase				):


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=13       ,	SCREAMING_SNAKE_CASE_=3       ,	SCREAMING_SNAKE_CASE_=2_24       ,	SCREAMING_SNAKE_CASE_=30       ,	SCREAMING_SNAKE_CASE_=4_00       ,	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]       ,	):


   '''simple docstring'''




   lowercase__	: List[str]     		=   size if size is not None else {"""height""": 18, """width""": 18}
   lowercase__	: int     		=   parent
   lowercase__	: Union[str, Any]     		=   batch_size
   lowercase__	: List[str]     		=   num_channels
   lowercase__	: str     		=   image_size
   lowercase__	: int     		=   min_resolution
   lowercase__	: Dict     		=   max_resolution
   lowercase__	: Tuple     		=   do_resize
   lowercase__	: Union[str, Any]     		=   size
   lowercase__	: Any     		=   do_normalize
   lowercase__	: Tuple     		=   image_mean
   lowercase__	: str     		=   image_std



 def   lowercase__  (       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,
   }



@require_torch
@require_vision
class    _snake_case (						UpperCAmelCase_	,    unittest.TestCase				):
 __lowerCAmelCase	:  Optional[Any]	   =      ViTImageProcessor if is_vision_available() else None


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   EfficientFormerImageProcessorTester(self)


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   return self.image_proc_tester.prepare_image_processor_dict()


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: 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"""))


 def   lowercase__  (       self):


   '''simple docstring'''




   pass


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   self.image_processing_class(**self.image_processor_dict)
   # create random PIL images
   lowercase__	: List[Any]     		=   prepare_image_inputs(self.image_proc_tester       ,	equal_resolution=SCREAMING_SNAKE_CASE_)
   for image in image_inputs:
     self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,	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__	: str     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	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   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Tuple     		=   self.image_processing_class(**self.image_processor_dict)
   # create random numpy tensors
   lowercase__	: str     		=   prepare_image_inputs(self.image_proc_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__	: Optional[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__	: Dict     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	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   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   self.image_processing_class(**self.image_processor_dict)
   # create random PyTorch tensors
   lowercase__	: Dict     		=   prepare_image_inputs(self.image_proc_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__	: 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__	: Any     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	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"""],
       )       ,	)






