



"""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 BeitImageProcessor



class       SCREAMING_SNAKE_CASE__  (					unittest.TestCase ):

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=7      ,     _SCREAMING_SNAKE_CASE=3      ,     _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=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=False      ,     )  ->      List[str]:





    '''simple docstring'''




    UpperCAmelCase	:					Dict	=	size if size is not None else {"""height""": 20, """width""": 20}
    UpperCAmelCase	:					Optional[int]	=	crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
    UpperCAmelCase	:					Optional[int]	=	parent
    UpperCAmelCase	:					Any	=	batch_size
    UpperCAmelCase	:					Dict	=	num_channels
    UpperCAmelCase	:					Union[str, Any]	=	image_size
    UpperCAmelCase	:					int	=	min_resolution
    UpperCAmelCase	:					Any	=	max_resolution
    UpperCAmelCase	:					int	=	do_resize
    UpperCAmelCase	:					Optional[int]	=	size
    UpperCAmelCase	:					Tuple	=	do_center_crop
    UpperCAmelCase	:					List[str]	=	crop_size
    UpperCAmelCase	:					Dict	=	do_normalize
    UpperCAmelCase	:					List[str]	=	image_mean
    UpperCAmelCase	:					Any	=	image_std
    UpperCAmelCase	:					List[Any]	=	do_reduce_labels




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




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

def       _snake_case						(						):
 UpperCAmelCase	:					Tuple	=	load_dataset("""hf-internal-testing/fixtures_ade20k"""			,		split="""test"""						)

 UpperCAmelCase	:					Any	=	Image.open(dataset[0]["""file"""]						)
 UpperCAmelCase	:					Optional[int]	=	Image.open(dataset[1]["""file"""]						)

 return image, map

def       _snake_case						(						):
 UpperCAmelCase	:					Optional[int]	=	load_dataset("""hf-internal-testing/fixtures_ade20k"""			,		split="""test"""						)

 UpperCAmelCase	:					int	=	Image.open(ds[0]["""file"""]						)
 UpperCAmelCase	:					Optional[int]	=	Image.open(ds[1]["""file"""]						)
 UpperCAmelCase	:					Dict	=	Image.open(ds[2]["""file"""]						)
 UpperCAmelCase	:					List[Any]	=	Image.open(ds[3]["""file"""]						)

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



@require_torch
@require_vision
class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__      ,			unittest.TestCase ):
   __lowerCAmelCase  :   int						=						BeitImageProcessor if is_vision_available() else None

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	BeitImageProcessingTester(self						)

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




    return self.image_processor_tester.prepare_image_processor_dict()

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, Any]	=	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_center_crop"""						)						)
    self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,     """center_crop"""						)						)
    self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,     """do_normalize"""						)						)
    self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,     """image_mean"""						)						)
    self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,     """image_std"""						)						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	self.image_processing_class.from_dict(self.image_processor_dict						)
    self.assertEqual(image_processor.size      ,     {"""height""": 20, """width""": 20}						)
    self.assertEqual(image_processor.crop_size      ,     {"""height""": 18, """width""": 18}						)
    self.assertEqual(image_processor.do_reduce_labels      ,     _SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					str	=	self.image_processing_class.from_dict(
        self.image_processor_dict      ,     size=42      ,     crop_size=84      ,     reduce_labels=_SCREAMING_SNAKE_CASE						)
    self.assertEqual(image_processor.size      ,     {"""height""": 42, """width""": 42}						)
    self.assertEqual(image_processor.crop_size      ,     {"""height""": 84, """width""": 84}						)
    self.assertEqual(image_processor.do_reduce_labels      ,     _SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[Any]:





    '''simple docstring'''




    pass

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




    UpperCAmelCase	:					List[Any]	=	self.image_processing_class(**self.image_processor_dict						)
    # create random PIL images
    UpperCAmelCase	:					List[Any]	=	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	:					List[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
    UpperCAmelCase	:					Any	=	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.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )

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





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	self.image_processing_class(**self.image_processor_dict						)
    # create random numpy tensors
    UpperCAmelCase	:					List[Any]	=	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	:					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
    UpperCAmelCase	:					str	=	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.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )

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





    '''simple docstring'''




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

    # Test not batched input
    UpperCAmelCase	:					int	=	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
    UpperCAmelCase	:					Optional[Any]	=	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.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[int]:





    '''simple docstring'''




    UpperCAmelCase	:					Any	=	self.image_processing_class(**self.image_processor_dict						)
    # create random PyTorch tensors
    UpperCAmelCase	:					Any	=	prepare_image_inputs(self.image_processor_tester      ,     equal_resolution=_SCREAMING_SNAKE_CASE      ,     torchify=_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Tuple	=	[]
    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	:					Optional[int]	=	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.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )
    self.assertEqual(
        encoding["""labels"""].shape      ,     (
            1,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_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	:					Tuple	=	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.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )
    self.assertEqual(
        encoding["""labels"""].shape      ,     (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_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	:					Any	=	prepare_semantic_single_inputs()

    UpperCAmelCase	:					int	=	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.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )
    self.assertEqual(
        encoding["""labels"""].shape      ,     (
            1,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_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	:					List[str]	=	prepare_semantic_batch_inputs()

    UpperCAmelCase	:					Union[str, Any]	=	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.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )
    self.assertEqual(
        encoding["""labels"""].shape      ,     (
            2,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )
    self.assertEqual(encoding["""labels"""].dtype      ,     torch.long						)
    self.assertTrue(encoding["""labels"""].min().item() >= 0						)
    self.assertTrue(encoding["""labels"""].max().item() <= 255						)




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[int]:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	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	:					Dict	=	prepare_semantic_single_inputs()
    UpperCAmelCase	:					Union[str, Any]	=	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	:					List[Any]	=	True
    UpperCAmelCase	:					Union[str, Any]	=	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 inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Union[str, Any]					) ->str:






	'''simple docstring'''






	a,       a      :		str	=				image.size
	a,       a      :		Tuple	=				(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
	a      :		Union[str, Any]	=				image.resize((w, h)    ,       resample=PIL_INTERPOLATION["lanczos"]					)
	a      :		int	=				np.array(_lowercase					).astype(np.floataa					) / 255.0
	a      :		List[str]	=				image[None].transpose(0    ,       3    ,       1    ,       2					)
	a      :		Dict	=				torch.from_numpy(_lowercase					)
	return 2.0 * image - 1.0







class        __UpperCamelCase				(						a__						):






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					)	-> str:
								super().__init__()
								self.register_modules(vqvae=lowerCAmelCase__      ,					unet=lowerCAmelCase__      ,					scheduler=lowerCAmelCase__      )






							@torch.no_grad()
							def __call__(   self      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = 1      ,					lowerCAmelCase__ = 100      ,					lowerCAmelCase__ = 0.0      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = "pil"      ,					lowerCAmelCase__ = True      ,					)	-> Union[Tuple, ImagePipelineOutput]:
								if isinstance(lowerCAmelCase__      ,					PIL.Image.Image      ):
									a      :		int	=				1
								elif isinstance(lowerCAmelCase__      ,					torch.Tensor      ):
									a      :		str	=				image.shape[0]
								else:
									raise ValueError(f"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(lowerCAmelCase__      )}"""      )

								if isinstance(lowerCAmelCase__      ,					PIL.Image.Image      ):
									a      :		Tuple	=				preprocess(lowerCAmelCase__      )

								a,       a      :		Optional[Any]	=				image.shape[-2:]

								# in_channels should be 6: 3 for latents, 3 for low resolution image
								a      :		Tuple	=				(batch_size, self.unet.config.in_channels // 2, height, width)
								a      :		List[str]	=				next(self.unet.parameters()      ).dtype

								a      :		Any	=				randn_tensor(lowerCAmelCase__      ,					generator=lowerCAmelCase__      ,					device=self.device      ,					dtype=lowerCAmelCase__      )

								a      :		Union[str, Any]	=				image.to(device=self.device      ,					dtype=lowerCAmelCase__      )

								# set timesteps and move to the correct device
								self.scheduler.set_timesteps(lowerCAmelCase__      ,					device=self.device      )
								a      :		int	=				self.scheduler.timesteps

								# scale the initial noise by the standard deviation required by the scheduler
								a      :		str	=				latents * self.scheduler.init_noise_sigma

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
								# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
								# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
								# and should be between [0, 1]
								a      :		str	=				"eta" in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
								a      :		List[Any]	=				{}
								if accepts_eta:
									a      :		Any	=				eta

								for t in self.progress_bar(lowerCAmelCase__      ):
									# concat latents and low resolution image in the channel dimension.
									a      :		str	=				torch.cat([latents, image]      ,					dim=1      )
									a      :		int	=				self.scheduler.scale_model_input(lowerCAmelCase__      ,					lowerCAmelCase__      )
									# predict the noise residual
									a      :		Union[str, Any]	=				self.unet(lowerCAmelCase__      ,					lowerCAmelCase__      ).sample
									# compute the previous noisy sample x_t -> x_t-1
									a      :		Tuple	=				self.scheduler.step(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					**lowerCAmelCase__      ).prev_sample

								# decode the image latents with the VQVAE
								a      :		List[Any]	=				self.vqvae.decode(lowerCAmelCase__      ).sample
								a      :		int	=				torch.clamp(lowerCAmelCase__      ,					-1.0      ,					1.0      )
								a      :		Optional[int]	=				image / 2 + 0.5
								a      :		Optional[int]	=				image.cpu().permute(0      ,					2      ,					3      ,					1      ).numpy()

								if output_type == "pil":
									a      :		Union[str, Any]	=				self.numpy_to_pil(lowerCAmelCase__      )

								if not return_dict:
									return (image,)

								return ImagePipelineOutput(images=lowerCAmelCase__      )


