


'''simple docstring'''

import unittest

import numpy as np
from datasets import load_dataset

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 SegformerImageProcessor







class        UpperCAmelCase       (				unittest.TestCase):

 def __init__(       self       :		Any,    a_       :		Tuple,    a_       :		Optional[int]=7,    a_       :		Optional[int]=3,    a_       :		Optional[int]=30,    a_       :		Tuple=400,    a_       :		Dict=True,    a_       :		str=None,    a_       :		Optional[Any]=True,    a_       :		Optional[int]=[0.5, 0.5, 0.5],    a_       :		int=[0.5, 0.5, 0.5],    a_       :		Tuple=False,    ):





    """simple docstring"""



    UpperCamelCase__         =  size if size is not None else {"height": 30, "width": 30}
    UpperCamelCase__         =  parent
    UpperCamelCase__         =  batch_size
    UpperCamelCase__         =  num_channels
    UpperCamelCase__         =  min_resolution
    UpperCamelCase__         =  max_resolution
    UpperCamelCase__         =  do_resize
    UpperCamelCase__         =  size
    UpperCamelCase__         =  do_normalize
    UpperCamelCase__         =  image_mean
    UpperCamelCase__         =  image_std
    UpperCamelCase__         =  do_reduce_labels

 def 						lowercase_  (       self       :		List[str]    ):





    """simple docstring"""



    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_reduce_labels": self.do_reduce_labels,
    }







def 				SCREAMING_SNAKE_CASE__(						)						->    Tuple:





   '''simple docstring'''







   UpperCamelCase__         =  load_dataset("hf-internal-testing/fixtures_ade20k"			,						split="test"      )

   UpperCamelCase__         =  Image.open(dataset[0]["file"]      )
   UpperCamelCase__         =  Image.open(dataset[1]["file"]      )

   return image, map



def 				SCREAMING_SNAKE_CASE__(						)						->    Any:





   '''simple docstring'''







   UpperCamelCase__         =  load_dataset("hf-internal-testing/fixtures_ade20k"			,						split="test"      )

   UpperCamelCase__         =  Image.open(dataset[0]["file"]      )
   UpperCamelCase__         =  Image.open(dataset[1]["file"]      )
   UpperCamelCase__         =  Image.open(dataset[2]["file"]      )
   UpperCamelCase__         =  Image.open(dataset[3]["file"]      )

   return [imagea, imagea], [mapa, mapa]







@require_torch
@require_vision
class        UpperCAmelCase       (				__lowerCAmelCase						,	unittest.TestCase):
 _lowerCamelCase       :    List[Any]			    =    SegformerImageProcessor if is_vision_available() else None

 def 						lowercase_  (       self       :		int    ):





    """simple docstring"""



    UpperCamelCase__         =  SegformerImageProcessingTester(self    )

 @property
 def 						lowercase_  (       self       :		Union[str, Any]    ):





    """simple docstring"""



    return self.image_processor_tester.prepare_image_processor_dict()

 def 						lowercase_  (       self       :		List[Any]    ):





    """simple docstring"""



    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_normalize"    )    )
    self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE,    "image_mean"    )    )
    self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE,    "image_std"    )    )
    self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE,    "do_reduce_labels"    )    )

 def 						lowercase_  (       self       :		int    ):





    """simple docstring"""



    UpperCamelCase__         =  self.image_processing_class.from_dict(self.image_processor_dict    )
    self.assertEqual(image_processor.size,    {"height": 30, "width": 30}    )
    self.assertEqual(image_processor.do_reduce_labels,    __SCREAMING_SNAKE_CASE    )

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

 def 						lowercase_  (       self       :		Optional[int]    ):





    """simple docstring"""



    pass

 def 						lowercase_  (       self       :		List[Any]    ):





    """simple docstring"""



    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.size["height"],
            self.image_processor_tester.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.size["height"],
            self.image_processor_tester.size["width"],
        ),    )

 def 						lowercase_  (       self       :		Optional[int]    ):





    """simple docstring"""



    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.size["height"],
            self.image_processor_tester.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.size["height"],
            self.image_processor_tester.size["width"],
        ),    )

 def 						lowercase_  (       self       :		str    ):





    """simple docstring"""



    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.size["height"],
            self.image_processor_tester.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.size["height"],
            self.image_processor_tester.size["width"],
        ),    )

 def 						lowercase_  (       self       :		List[Any]    ):





    """simple docstring"""



    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    )
    UpperCamelCase__         =  []
    for image in image_inputs:
       self.assertIsInstance(__SCREAMING_SNAKE_CASE,    torch.Tensor    )
       maps.append(torch.zeros(image.shape[-2:]    ).long()    )

    # Test not batched input
    UpperCamelCase__         =  image_processing(image_inputs[0],    maps[0],    return_tensors="pt"    )
    self.assertEqual(
        encoding["pixel_values"].shape,    (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size["height"],
            self.image_processor_tester.size["width"],
        ),    )
    self.assertEqual(
        encoding["labels"].shape,    (
            1,
            self.image_processor_tester.size["height"],
            self.image_processor_tester.size["width"],
        ),    )
    self.assertEqual(encoding["labels"].dtype,    torch.long    )
    self.assertTrue(encoding["labels"].min().item() >= 0    )
    self.assertTrue(encoding["labels"].max().item() <= 255    )

    # Test batched
    UpperCamelCase__         =  image_processing(__SCREAMING_SNAKE_CASE,    __SCREAMING_SNAKE_CASE,    return_tensors="pt"    )
    self.assertEqual(
        encoding["pixel_values"].shape,    (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size["height"],
            self.image_processor_tester.size["width"],
        ),    )
    self.assertEqual(
        encoding["labels"].shape,    (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.size["height"],
            self.image_processor_tester.size["width"],
        ),    )
    self.assertEqual(encoding["labels"].dtype,    torch.long    )
    self.assertTrue(encoding["labels"].min().item() >= 0    )
    self.assertTrue(encoding["labels"].max().item() <= 255    )

    # Test not batched input (PIL images)
    UpperCamelCase__  ,						UpperCamelCase__         =  prepare_semantic_single_inputs()

    UpperCamelCase__         =  image_processing(__SCREAMING_SNAKE_CASE,    __SCREAMING_SNAKE_CASE,    return_tensors="pt"    )
    self.assertEqual(
        encoding["pixel_values"].shape,    (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size["height"],
            self.image_processor_tester.size["width"],
        ),    )
    self.assertEqual(
        encoding["labels"].shape,    (
            1,
            self.image_processor_tester.size["height"],
            self.image_processor_tester.size["width"],
        ),    )
    self.assertEqual(encoding["labels"].dtype,    torch.long    )
    self.assertTrue(encoding["labels"].min().item() >= 0    )
    self.assertTrue(encoding["labels"].max().item() <= 255    )

    # Test batched input (PIL images)
    UpperCamelCase__  ,						UpperCamelCase__         =  prepare_semantic_batch_inputs()

    UpperCamelCase__         =  image_processing(__SCREAMING_SNAKE_CASE,    __SCREAMING_SNAKE_CASE,    return_tensors="pt"    )
    self.assertEqual(
        encoding["pixel_values"].shape,    (
            2,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size["height"],
            self.image_processor_tester.size["width"],
        ),    )
    self.assertEqual(
        encoding["labels"].shape,    (
            2,
            self.image_processor_tester.size["height"],
            self.image_processor_tester.size["width"],
        ),    )
    self.assertEqual(encoding["labels"].dtype,    torch.long    )
    self.assertTrue(encoding["labels"].min().item() >= 0    )
    self.assertTrue(encoding["labels"].max().item() <= 255    )

 def 						lowercase_  (       self       :		str    ):





    """simple docstring"""



    UpperCamelCase__         =  self.image_processing_class(**self.image_processor_dict    )

    # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
    UpperCamelCase__  ,						UpperCamelCase__         =  prepare_semantic_single_inputs()
    UpperCamelCase__         =  image_processing(__SCREAMING_SNAKE_CASE,    __SCREAMING_SNAKE_CASE,    return_tensors="pt"    )
    self.assertTrue(encoding["labels"].min().item() >= 0    )
    self.assertTrue(encoding["labels"].max().item() <= 150    )

    UpperCamelCase__         =  True
    UpperCamelCase__         =  image_processing(__SCREAMING_SNAKE_CASE,    __SCREAMING_SNAKE_CASE,    return_tensors="pt"    )
    self.assertTrue(encoding["labels"].min().item() >= 0    )
    self.assertTrue(encoding["labels"].max().item() <= 255    )






"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class    lowercase_ (   __lowerCAmelCase ,unittest.TestCase							):
       snake_case__ :							List[str] 							=   PaintByExamplePipeline
       snake_case__ :							Any 							=   IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
       snake_case__ :							Optional[int] 							=   IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       snake_case__ :							int 							=   frozenset([]							)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        torch.manual_seed(0   )
        snake_case__										= UNetaDConditionModel(
            block_out_channels=(32, 64)      ,    layers_per_block=2      ,    sample_size=32      ,    in_channels=9      ,    out_channels=4      ,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,    cross_attention_dim=32      ,    )
        snake_case__										= PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE   )
        torch.manual_seed(0   )
        snake_case__										= 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      ,    )
        torch.manual_seed(0   )
        snake_case__										= CLIPVisionConfig(
            hidden_size=32      ,    projection_dim=32      ,    intermediate_size=37      ,    layer_norm_eps=1e-05      ,    num_attention_heads=4      ,    num_hidden_layers=5      ,    image_size=32      ,    patch_size=4      ,    )
        snake_case__										= PaintByExampleImageEncoder(__SCREAMING_SNAKE_CASE      ,    proj_size=32   )
        snake_case__										= CLIPImageProcessor(crop_size=32      ,    size=32   )

        snake_case__										= {
            "unet": unet,
            "scheduler": scheduler,
            "vae": vae,
            "image_encoder": image_encoder,
            "safety_checker": None,
            "feature_extractor": feature_extractor,
        }
        return components

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= np.array(image.convert("RGB"   )   )
        snake_case__										= image[None].transpose(0      ,    3      ,    1      ,    2   )
        snake_case__										= torch.from_numpy(__SCREAMING_SNAKE_CASE   ).to(dtype=torch.floataa   ) / 127.5 - 1.0
        return image

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE="cpu"      ,    __SCREAMING_SNAKE_CASE=0   ):






        '''simple docstring'''

        snake_case__										= floats_tensor((1, 3, 32, 32)      ,    rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
        snake_case__										= image.cpu().permute(0      ,    2      ,    3      ,    1   )[0]
        snake_case__										= Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert("RGB"   ).resize((64, 64)   )
        snake_case__										= Image.fromarray(np.uinta(image + 4   )   ).convert("RGB"   ).resize((64, 64)   )
        snake_case__										= Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert("RGB"   ).resize((32, 32)   )

        if str(__SCREAMING_SNAKE_CASE   ).startswith("mps"   ):
         snake_case__										= torch.manual_seed(__SCREAMING_SNAKE_CASE   )
        else:
         snake_case__										= torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )
        snake_case__										= {
            "example_image": example_image,
            "image": init_image,
            "mask_image": mask_image,
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 6.0,
            "output_type": "numpy",
        }
        return inputs

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= self.get_dummy_components()

        # make sure here that pndm scheduler skips prk
        snake_case__										= PaintByExamplePipeline(**__SCREAMING_SNAKE_CASE   )
        snake_case__										= pipe.to("cpu"   )
        pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

        snake_case__										= self.get_dummy_inputs()
        snake_case__										= pipe(**__SCREAMING_SNAKE_CASE   )
        snake_case__										= output.images

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

        assert image.shape == (1, 64, 64, 3)
        snake_case__										= np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]   )

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

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= "cpu"
        snake_case__										= self.get_dummy_inputs()
        inputs.pop("mask_image"   )
        snake_case__										= self.convert_to_pt(inputs.pop("image"   )   )
        snake_case__										= image.clamp(0      ,    1   ) / 2

        # make sure here that pndm scheduler skips prk
        snake_case__										= PaintByExamplePipeline(**self.get_dummy_components()   )
        snake_case__										= pipe.to(__SCREAMING_SNAKE_CASE   )
        pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

        snake_case__										= pipe(image=__SCREAMING_SNAKE_CASE      ,    mask_image=mask_image[:, 0]      ,    **__SCREAMING_SNAKE_CASE   )
        snake_case__										= output.images

        snake_case__										= image.cpu().permute(0      ,    2      ,    3      ,    1   )[0]
        snake_case__										= mask_image.cpu().permute(0      ,    2      ,    3      ,    1   )[0]

        snake_case__										= Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert("RGB"   )
        snake_case__										= Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert("RGB"   )

        snake_case__										= pipe(**self.get_dummy_inputs()   )
        snake_case__										= output.images

        assert out_a.shape == (1, 64, 64, 3)
        assert np.abs(out_a.flatten() - out_a.flatten()   ).max() < 5e-2

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        super().test_inference_batch_single_identical(expected_max_diff=3e-3   )



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

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/paint_by_example/dog_in_bucket.png"   )
        snake_case__										= load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/paint_by_example/mask.png"   )
        snake_case__										= load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/paint_by_example/panda.jpg"   )

        snake_case__										= PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"   )
        snake_case__										= pipe.to(__SCREAMING_SNAKE_CASE   )
        pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

        snake_case__										= torch.manual_seed(321   )
        snake_case__										= pipe(
            image=__SCREAMING_SNAKE_CASE      ,    mask_image=__SCREAMING_SNAKE_CASE      ,    example_image=__SCREAMING_SNAKE_CASE      ,    generator=__SCREAMING_SNAKE_CASE      ,    guidance_scale=5.0      ,    num_inference_steps=50      ,    output_type="np"      ,    )

        snake_case__										= output.images

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

        assert image.shape == (1, 512, 512, 3)
        snake_case__										= np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]   )

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

