


import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image
else:



	class 							A   :
						'''simple docstring'''






						@staticmethod
						def   lowerCamelCase__					(*_UpperCAmelCase   :						Optional[Any]				, **_UpperCAmelCase   :						int		)						->       Optional[int]:




								"""simple docstring"""


								pass






def 			UpperCamelCase (     __magic_name__					:							Image					)       ->  str:
		"""simple docstring"""




		lowercase__					      =		hashlib.mda(image.tobytes()					)
		return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					A__      =			MODEL_FOR_DEPTH_ESTIMATION_MAPPING

					def   lowerCamelCase__					(self   :						Union[str, Any]				, _UpperCAmelCase   :						Union[str, Any]				, _UpperCAmelCase   :						Union[str, Any]				, _UpperCAmelCase   :						Dict		)						->       str:




							"""simple docstring"""


							lowercase__					      =		DepthEstimationPipeline(model=__lowerCamelCase				, image_processor=__lowerCamelCase		)
							return depth_estimator, [
							    "./tests/fixtures/tests_samples/COCO/000000039769.png",
							    "./tests/fixtures/tests_samples/COCO/000000039769.png",
							]

					def   lowerCamelCase__					(self   :						Dict				, _UpperCAmelCase   :						Union[str, Any]				, _UpperCAmelCase   :						str		)						->       Optional[int]:




							"""simple docstring"""


							lowercase__					      =		depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
							self.assertEqual({"""predicted_depth""": ANY(torch.Tensor		), """depth""": ANY(Image.Image		)}				, __lowerCamelCase		)
							import datasets

							lowercase__					      =		datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""				, """image"""				, split="""test"""		)
							lowercase__					      =		depth_estimator(
							    [
							        Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		),
							        """http://images.cocodataset.org/val2017/000000039769.jpg""",
							        # RGBA
							        dataset[0]["""file"""],
							        # LA
							        dataset[1]["""file"""],
							        # L
							        dataset[2]["""file"""],
							    ]		)
							self.assertEqual(
							    [
							        {"""predicted_depth""": ANY(torch.Tensor		), """depth""": ANY(Image.Image		)},
							        {"""predicted_depth""": ANY(torch.Tensor		), """depth""": ANY(Image.Image		)},
							        {"""predicted_depth""": ANY(torch.Tensor		), """depth""": ANY(Image.Image		)},
							        {"""predicted_depth""": ANY(torch.Tensor		), """depth""": ANY(Image.Image		)},
							        {"""predicted_depth""": ANY(torch.Tensor		), """depth""": ANY(Image.Image		)},
							    ]				, __lowerCamelCase				, )

					@require_tf
					@unittest.skip("""Depth estimation is not implemented in TF"""		)
					def   lowerCamelCase__					(self   :						str		)						->       int:




							"""simple docstring"""


							pass

					@slow
					@require_torch
					def   lowerCamelCase__					(self   :						Tuple		)						->       Optional[Any]:




							"""simple docstring"""


							lowercase__					      =		"""Intel/dpt-large"""
							lowercase__					      =		pipeline("""depth-estimation"""				, model=__lowerCamelCase		)
							lowercase__					      =		depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""		)
							lowercase__					      =		hashimage(outputs["""depth"""]		)

							# This seems flaky.
							# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
							self.assertEqual(nested_simplify(outputs["""predicted_depth"""].max().item()		)				, 29.304		)
							self.assertEqual(nested_simplify(outputs["""predicted_depth"""].min().item()		)				, 2.662		)


					@require_torch
					def   lowerCamelCase__					(self   :						Union[str, Any]		)						->       Optional[int]:




							"""simple docstring"""


							self.skipTest("""There is not hf-internal-testing tiny model for either GLPN nor DPT"""		)





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			42
					A__      =			42

					def __init__(self   :						Union[str, Any]				, _UpperCAmelCase   :						UNetaDModel				, _UpperCAmelCase   :						ScoreSdeVeScheduler		)						->       List[str]:




							"""simple docstring"""


							super().__init__()
							self.register_modules(unet=_UpperCAmelCase				, scheduler=_UpperCAmelCase		)


					@torch.no_grad()
					def __call__(self   :						Optional[Any]				, _UpperCAmelCase   :						int = 1				, _UpperCAmelCase   :						int = 2000				, _UpperCAmelCase   :						Optional[Union[torch.Generator, List[torch.Generator]]] = None				, _UpperCAmelCase   :						Optional[str] = "pil"				, _UpperCAmelCase   :						bool = True				, **_UpperCAmelCase   :						Any				, )						->       Union[ImagePipelineOutput, Tuple]:




							"""simple docstring"""


							lowercase__					      =		self.unet.config.sample_size
							lowercase__					      =		(batch_size, 3, img_size, img_size)

							lowercase__					      =		self.unet

							lowercase__					      =		randn_tensor(_UpperCAmelCase				, generator=_UpperCAmelCase		) * self.scheduler.init_noise_sigma
							lowercase__					      =		sample.to(self.device		)

							self.scheduler.set_timesteps(_UpperCAmelCase		)
							self.scheduler.set_sigmas(_UpperCAmelCase		)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
									lowercase__					      =		self.scheduler.sigmas[i] * torch.ones(shape[0]				, device=self.device		)

									# correction step
									for _ in range(self.scheduler.config.correct_steps		):
											lowercase__					      =		self.unet(_UpperCAmelCase				, _UpperCAmelCase		).sample
											lowercase__					      =		self.scheduler.step_correct(_UpperCAmelCase				, _UpperCAmelCase				, generator=_UpperCAmelCase		).prev_sample

									# prediction step
									lowercase__					      =		model(_UpperCAmelCase				, _UpperCAmelCase		).sample
									lowercase__					      =		self.scheduler.step_pred(_UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase				, generator=_UpperCAmelCase		)

									lowercase__      ,     lowercase__					      =		output.prev_sample, output.prev_sample_mean

							lowercase__					      =		sample_mean.clamp(0				, 1		)
							lowercase__					      =		sample.cpu().permute(0				, 2				, 3				, 1		).numpy()
							if output_type == "pil":
									lowercase__					      =		self.numpy_to_pil(_UpperCAmelCase		)

							if not return_dict:
									return (sample,)

							return ImagePipelineOutput(images=_UpperCAmelCase		)

