





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    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							unittest.TestCase    ):







 '''simple docstring'''
 lowercase_		=			StableDiffusionDiffEditPipeline
 lowercase_		=			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
 lowercase_		=			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
 lowercase_		=			frozenset(
     []    )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 lowercase_		=			frozenset([]    )

 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any])       ->Union[str, Any]:



   '''simple docstring'''



   torch.manual_seed(0)
   lowerCamelCase__:      Union[str, Any]			  =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=UpperCAmelCase_       , )
   lowerCamelCase__:      List[str]			  =DDIMScheduler(
       beta_start=0.0_0085       , beta_end=0.012       , beta_schedule="scaled_linear"       , clip_sample=UpperCAmelCase_       , set_alpha_to_one=UpperCAmelCase_       , )
   lowerCamelCase__:      Optional[Any]			  =DDIMInverseScheduler(
       beta_start=0.0_0085       , beta_end=0.012       , beta_schedule="scaled_linear"       , clip_sample=UpperCAmelCase_       , set_alpha_to_zero=UpperCAmelCase_       , )
   torch.manual_seed(0)
   lowerCamelCase__:      Optional[int]			  =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=128       , )
   torch.manual_seed(0)
   lowerCamelCase__:      Dict			  =CLIPTextConfig(
       bos_token_id=0       , eos_token_id=2       , hidden_size=32       , intermediate_size=37       , layer_norm_eps=1E-0_5       , num_attention_heads=4       , num_hidden_layers=5       , pad_token_id=1       , vocab_size=1_000       , hidden_act="gelu"       , projection_dim=512       , )
   lowerCamelCase__:      Dict			  =CLIPTextModel(UpperCAmelCase_)
   lowerCamelCase__:      List[Any]			  =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

   lowerCamelCase__:      str			  ={
       "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 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int]       , UpperCAmelCase_				:   Tuple       , UpperCAmelCase_				:   Any=0)       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  =floats_tensor((1, 16, 16)       , rng=random.Random(UpperCAmelCase_)).to(UpperCAmelCase_)
   lowerCamelCase__:      Tuple			  =floats_tensor((1, 2, 4, 16, 16)       , rng=random.Random(UpperCAmelCase_)).to(UpperCAmelCase_)
   if str(UpperCAmelCase_).startswith("mps"):
     lowerCamelCase__:      List[Any]			  =torch.manual_seed(UpperCAmelCase_)
   else:
     lowerCamelCase__:      Optional[int]			  =torch.Generator(device=UpperCAmelCase_).manual_seed(UpperCAmelCase_)
   lowerCamelCase__:      List[Any]			  ={
       "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 							SCREAMING_SNAKE_CASE_					(self				:   Tuple       , UpperCAmelCase_				:   int       , UpperCAmelCase_				:   Dict=0)       ->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:      List[str]			  =floats_tensor((1, 3, 32, 32)       , rng=random.Random(UpperCAmelCase_)).to(UpperCAmelCase_)
   lowerCamelCase__:      Optional[Any]			  =image.cpu().permute(0       , 2       , 3       , 1)[0]
   lowerCamelCase__:      Optional[int]			  =Image.fromarray(np.uinta(UpperCAmelCase_)).convert("RGB")
   if str(UpperCAmelCase_).startswith("mps"):
     lowerCamelCase__:      Union[str, Any]			  =torch.manual_seed(UpperCAmelCase_)
   else:
     lowerCamelCase__:      Union[str, Any]			  =torch.Generator(device=UpperCAmelCase_).manual_seed(UpperCAmelCase_)
   lowerCamelCase__:      List[str]			  ={
       "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 							SCREAMING_SNAKE_CASE_					(self				:   List[str]       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   List[Any]=0)       ->str:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =floats_tensor((1, 3, 32, 32)       , rng=random.Random(UpperCAmelCase_)).to(UpperCAmelCase_)
   lowerCamelCase__:      str			  =image.cpu().permute(0       , 2       , 3       , 1)[0]
   lowerCamelCase__:      Any			  =Image.fromarray(np.uinta(UpperCAmelCase_)).convert("RGB")
   if str(UpperCAmelCase_).startswith("mps"):
     lowerCamelCase__:      Dict			  =torch.manual_seed(UpperCAmelCase_)
   else:
     lowerCamelCase__:      int			  =torch.Generator(device=UpperCAmelCase_).manual_seed(UpperCAmelCase_)
   lowerCamelCase__:      Dict			  ={
       "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 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Optional[Any]:



   '''simple docstring'''



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

   lowerCamelCase__:      Dict			  =self.get_dummy_components()
   lowerCamelCase__:      Optional[Any]			  =self.pipeline_class(**UpperCAmelCase_)
   pipe.to(UpperCAmelCase_)
   pipe.set_progress_bar_config(disable=UpperCAmelCase_)

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

   lowerCamelCase__:      Union[str, Any]			  =self.get_dummy_inputs(UpperCAmelCase_)
   lowerCamelCase__:      Optional[Any]			  =pipe(**UpperCAmelCase_)[0]

   with tempfile.TemporaryDirectory() as tmpdir:
     pipe.save_pretrained(UpperCAmelCase_)
     lowerCamelCase__:      int			  =self.pipeline_class.from_pretrained(UpperCAmelCase_)
     pipe_loaded.to(UpperCAmelCase_)
     pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase_)

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

   lowerCamelCase__:      Optional[int]			  =self.get_dummy_inputs(UpperCAmelCase_)
   lowerCamelCase__:      int			  =pipe_loaded(**UpperCAmelCase_)[0]

   lowerCamelCase__:      int			  =np.abs(output - output_loaded).max()
   self.assertLess(UpperCAmelCase_       , 1E-4)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:      List[str]			  ="cpu"

   lowerCamelCase__:      List[Any]			  =self.get_dummy_components()
   lowerCamelCase__:      int			  =self.pipeline_class(**UpperCAmelCase_)
   pipe.to(UpperCAmelCase_)
   pipe.set_progress_bar_config(disable=UpperCAmelCase_)

   lowerCamelCase__:      Optional[int]			  =self.get_dummy_mask_inputs(UpperCAmelCase_)
   lowerCamelCase__:      Dict			  =pipe.generate_mask(**UpperCAmelCase_)
   lowerCamelCase__:      List[Any]			  =mask[0, -3:, -3:]

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

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:      str			  ="cpu"

   lowerCamelCase__:      Optional[Any]			  =self.get_dummy_components()
   lowerCamelCase__:      Optional[Any]			  =self.pipeline_class(**UpperCAmelCase_)
   pipe.to(UpperCAmelCase_)
   pipe.set_progress_bar_config(disable=UpperCAmelCase_)

   lowerCamelCase__:      Tuple			  =self.get_dummy_inversion_inputs(UpperCAmelCase_)
   lowerCamelCase__:      List[Any]			  =pipe.invert(**UpperCAmelCase_).images
   lowerCamelCase__:      Union[str, Any]			  =image[0, -1, -3:, -3:]

   self.assertEqual(image.shape       , (2, 32, 32, 3))
   lowerCamelCase__:      int			  =np.array(
       [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]       , )
   lowerCamelCase__:      Optional[int]			  =np.abs(image_slice.flatten() - expected_slice).max()
   self.assertLessEqual(UpperCAmelCase_       , 1E-3)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->Optional[Any]:



   '''simple docstring'''



   super().test_inference_batch_single_identical(expected_max_diff=5E-3)







 def 							SCREAMING_SNAKE_CASE_					(self				:   str)       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Any			  ="cpu"

   lowerCamelCase__:      Any			  =self.get_dummy_components()

   lowerCamelCase__:      List[Any]			  ={"beta_start": 0.0_0085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
   lowerCamelCase__:      List[Any]			  =DPMSolverMultistepScheduler(**UpperCAmelCase_)
   lowerCamelCase__:      Any			  =DPMSolverMultistepInverseScheduler(**UpperCAmelCase_)

   lowerCamelCase__:      Union[str, Any]			  =self.pipeline_class(**UpperCAmelCase_)
   pipe.to(UpperCAmelCase_)
   pipe.set_progress_bar_config(disable=UpperCAmelCase_)

   lowerCamelCase__:      Dict			  =self.get_dummy_inversion_inputs(UpperCAmelCase_)
   lowerCamelCase__:      Union[str, Any]			  =pipe.invert(**UpperCAmelCase_).images
   lowerCamelCase__:      List[Any]			  =image[0, -1, -3:, -3:]

   self.assertEqual(image.shape       , (2, 32, 32, 3))
   lowerCamelCase__:      Optional[int]			  =np.array(
       [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]       , )
   lowerCamelCase__:      int			  =np.abs(image_slice.flatten() - expected_slice).max()
   self.assertLessEqual(UpperCAmelCase_       , 1E-3)



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







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->Dict:



   '''simple docstring'''



   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()

 @classmethod
 def 							SCREAMING_SNAKE_CASE_					(cls				:   Optional[int])       ->str:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png")

   lowerCamelCase__:      Optional[int]			  =raw_image.convert("RGB").resize((768, 768))

   lowerCamelCase__:      Any			  =raw_image

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  =torch.manual_seed(0)

   lowerCamelCase__:      List[str]			  =StableDiffusionDiffEditPipeline.from_pretrained(
       "stabilityai/stable-diffusion-2-1"       , safety_checker=UpperCAmelCase_       , torch_dtype=torch.floataa)
   lowerCamelCase__:      int			  =DDIMScheduler.from_config(pipe.scheduler.config)
   lowerCamelCase__:      int			  =DDIMInverseScheduler.from_config(pipe.scheduler.config)
   pipe.enable_model_cpu_offload()
   pipe.set_progress_bar_config(disable=UpperCAmelCase_)

   lowerCamelCase__:      Optional[Any]			  ="a bowl of fruit"
   lowerCamelCase__:      List[str]			  ="a bowl of pears"

   lowerCamelCase__:      Tuple			  =pipe.generate_mask(
       image=self.raw_image       , source_prompt=UpperCAmelCase_       , target_prompt=UpperCAmelCase_       , generator=UpperCAmelCase_       , )

   lowerCamelCase__:      Optional[Any]			  =pipe.invert(
       prompt=UpperCAmelCase_       , image=self.raw_image       , inpaint_strength=0.7       , generator=UpperCAmelCase_).latents

   lowerCamelCase__:      Optional[int]			  =pipe(
       prompt=UpperCAmelCase_       , mask_image=UpperCAmelCase_       , image_latents=UpperCAmelCase_       , generator=UpperCAmelCase_       , negative_prompt=UpperCAmelCase_       , inpaint_strength=0.7       , output_type="numpy"       , ).images[0]

   lowerCamelCase__:      int			  =(
       np.array(
           load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
               "/diffedit/pears.png").resize((768, 768)))
       / 255
   )
   assert np.abs((expected_image - image).max()) < 5E-1







 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      str			  =torch.manual_seed(0)

   lowerCamelCase__:      Optional[Any]			  =StableDiffusionDiffEditPipeline.from_pretrained(
       "stabilityai/stable-diffusion-2-1"       , safety_checker=UpperCAmelCase_       , torch_dtype=torch.floataa)
   lowerCamelCase__:      Dict			  =DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
   lowerCamelCase__:      List[str]			  =DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config)
   pipe.enable_model_cpu_offload()
   pipe.set_progress_bar_config(disable=UpperCAmelCase_)

   lowerCamelCase__:      Dict			  ="a bowl of fruit"
   lowerCamelCase__:      Optional[Any]			  ="a bowl of pears"

   lowerCamelCase__:      int			  =pipe.generate_mask(
       image=self.raw_image       , source_prompt=UpperCAmelCase_       , target_prompt=UpperCAmelCase_       , generator=UpperCAmelCase_       , )

   lowerCamelCase__:      List[str]			  =pipe.invert(
       prompt=UpperCAmelCase_       , image=self.raw_image       , inpaint_strength=0.7       , generator=UpperCAmelCase_       , num_inference_steps=25       , ).latents

   lowerCamelCase__:      List[Any]			  =pipe(
       prompt=UpperCAmelCase_       , mask_image=UpperCAmelCase_       , image_latents=UpperCAmelCase_       , generator=UpperCAmelCase_       , negative_prompt=UpperCAmelCase_       , inpaint_strength=0.7       , num_inference_steps=25       , output_type="numpy"       , ).images[0]

   lowerCamelCase__:      List[Any]			  =(
       np.array(
           load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
               "/diffedit/pears.png").resize((768, 768)))
       / 255
   )
   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 PoolFormerImageProcessor



class    _SCREAMING_SNAKE_CASE						(	unittest.TestCase    ):







 '''simple docstring'''

 def __init__(self				:   List[Any]       , UpperCAmelCase_				:   Any       , UpperCAmelCase_				:   Dict=7       , UpperCAmelCase_				:   List[str]=3       , UpperCAmelCase_				:   str=30       , UpperCAmelCase_				:   List[str]=400       , UpperCAmelCase_				:   Optional[int]=True       , UpperCAmelCase_				:   Optional[int]=None       , UpperCAmelCase_				:   Tuple=0.9       , UpperCAmelCase_				:   str=None       , UpperCAmelCase_				:   int=True       , UpperCAmelCase_				:   Union[str, Any]=[0.5, 0.5, 0.5]       , UpperCAmelCase_				:   Optional[Any]=[0.5, 0.5, 0.5]       , )       ->str:



   '''simple docstring'''



   lowerCamelCase__:      List[Any]			  =size if size is not None else {"shortest_edge": 30}
   lowerCamelCase__:      Dict			  =crop_size if crop_size is not None else {"height": 30, "width": 30}
   lowerCamelCase__:      Any			  =parent
   lowerCamelCase__:      Any			  =batch_size
   lowerCamelCase__:      Optional[Any]			  =num_channels
   lowerCamelCase__:      Tuple			  =min_resolution
   lowerCamelCase__:      Union[str, Any]			  =max_resolution
   lowerCamelCase__:      Union[str, Any]			  =do_resize_and_center_crop
   lowerCamelCase__:      Optional[int]			  =size
   lowerCamelCase__:      str			  =crop_pct
   lowerCamelCase__:      Any			  =crop_size
   lowerCamelCase__:      List[str]			  =do_normalize
   lowerCamelCase__:      List[str]			  =image_mean
   lowerCamelCase__:      Tuple			  =image_std







 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->Optional[int]:



   '''simple docstring'''



   return {
       "size": self.size,
       "do_resize_and_center_crop": self.do_resize_and_center_crop,
       "crop_pct": self.crop_pct,
       "crop_size": self.crop_size,
       "do_normalize": self.do_normalize,
       "image_mean": self.image_mean,
       "image_std": self.image_std,
   }



@require_torch
@require_vision
class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ,							unittest.TestCase    ):







 '''simple docstring'''
 lowercase_		=			PoolFormerImageProcessor if is_vision_available() else None

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int])       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =PoolFormerImageProcessingTester(self)

 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   str)       ->int:



   '''simple docstring'''



   return self.image_processor_tester.prepare_image_processor_dict()

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Any			  =self.image_processing_class(**self.image_processor_dict)
   self.assertTrue(hasattr(UpperCAmelCase_       , "do_resize_and_center_crop"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "size"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "crop_pct"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "do_normalize"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "image_mean"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "image_std"))

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:      List[str]			  =self.image_processing_class.from_dict(self.image_processor_dict)
   self.assertEqual(image_processor.size       , {"shortest_edge": 30})
   self.assertEqual(image_processor.crop_size       , {"height": 30, "width": 30})

   lowerCamelCase__:      Union[str, Any]			  =self.image_processing_class.from_dict(self.image_processor_dict       , size=42       , crop_size=84)
   self.assertEqual(image_processor.size       , {"shortest_edge": 42})
   self.assertEqual(image_processor.crop_size       , {"height": 84, "width": 84})

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Optional[Any]:



   '''simple docstring'''



   pass

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Union[str, Any]			  =self.image_processing_class(**self.image_processor_dict)
   # create random PIL images
   lowerCamelCase__:      Union[str, Any]			  =prepare_image_inputs(self.image_processor_tester       , equal_resolution=UpperCAmelCase_)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase_       , Image.Image)

   # Test not batched input
   lowerCamelCase__:      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
   lowerCamelCase__:      int			  =image_processing(UpperCAmelCase_       , 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 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      Any			  =self.image_processing_class(**self.image_processor_dict)
   # create random numpy tensors
   lowerCamelCase__:      Tuple			  =prepare_image_inputs(self.image_processor_tester       , equal_resolution=UpperCAmelCase_       , numpify=UpperCAmelCase_)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase_       , np.ndarray)

   # Test not batched input
   lowerCamelCase__:      Union[str, Any]			  =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
   lowerCamelCase__:      List[str]			  =image_processing(UpperCAmelCase_       , 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 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any])       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =self.image_processing_class(**self.image_processor_dict)
   # create random PyTorch tensors
   lowerCamelCase__:      Any			  =prepare_image_inputs(self.image_processor_tester       , equal_resolution=UpperCAmelCase_       , torchify=UpperCAmelCase_)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase_       , torch.Tensor)

   # Test not batched input
   lowerCamelCase__:      Any			  =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
   lowerCamelCase__:      str			  =image_processing(UpperCAmelCase_       , 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"],
       )       , )

