






"""simple docstring"""



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 							__SCREAMING_SNAKE_CASE						:


													@staticmethod
													def 						_UpperCamelCase       (			*snake_case      :						List[str]			,    **snake_case      :						Union[str, Any]							):
																			'''simple docstring'''





																			pass




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Image      )		->Tuple:
						A__						:  Optional[Any]		  =  hashlib.mda(image.tobytes()      )
						return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 							__SCREAMING_SNAKE_CASE						(				unittest.TestCase		):
							snake_case_     =							MODEL_FOR_DEPTH_ESTIMATION_MAPPING


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						List[Any]			,    snake_case      :						Optional[Any]			,    snake_case      :						Dict							):
													'''simple docstring'''





													A__						:  str		  =  DepthEstimationPipeline(model=A_			,    image_processor=A_							)
													return depth_estimator, [
													    "./tests/fixtures/tests_samples/COCO/000000039769.png",
													    "./tests/fixtures/tests_samples/COCO/000000039769.png",
													]


							def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						str			,    snake_case      :						List[Any]							):
													'''simple docstring'''





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

													A__						:  Any		  =  datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""			,    """image"""			,    split="""test"""							)
													A__						:  Optional[Any]		  =  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							)},
													    ]			,    A_			,    )


							@require_tf
							@unittest.skip("""Depth estimation is not implemented in TF"""							)
							def 						_UpperCamelCase       (			self      :						Dict							):
													'''simple docstring'''





													pass


							@slow
							@require_torch
							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													A__						:  Optional[int]		  =  """Intel/dpt-large"""
													A__						:  Dict		  =  pipeline("""depth-estimation"""			,    model=A_							)
													A__						:  Optional[Any]		  =  depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""							)
													A__						:  int		  =  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 						_UpperCamelCase       (			self      :						Union[str, Any]							):
													'''simple docstring'''





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







"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

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






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							42
							snake_case_     =							42


							def __init__(			self      :						Union[str, Any]			,    snake_case      :						UNetaDModel			,    snake_case      :						KarrasVeScheduler							):
													'''simple docstring'''





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


							@torch.no_grad()
							def __call__(			self      :						Any			,    snake_case      :						int = 1			,    snake_case      :						int = 50			,    snake_case      :						Optional[Union[torch.Generator, List[torch.Generator]]] = None			,    snake_case      :						Optional[str] = "pil"			,    snake_case      :						bool = True			,    **snake_case      :						Dict			,    ):
													'''simple docstring'''





													A__						:  Optional[int]		  =  self.unet.config.sample_size
													A__						:  Dict		  =  (batch_size, 3, img_size, img_size)

													A__						:  Tuple		  =  self.unet

													# sample x_0 ~ N(0, sigma_0^2 * I)
													A__						:  Union[str, Any]		  =  randn_tensor(snake_case			,    generator=snake_case			,    device=self.device							) * self.scheduler.init_noise_sigma

													self.scheduler.set_timesteps(snake_case							)

													for t in self.progress_bar(self.scheduler.timesteps							):
																			# here sigma_t == t_i from the paper
																			A__						:  Optional[Any]		  =  self.scheduler.schedule[t]
																			A__						:  Union[str, Any]		  =  self.scheduler.schedule[t - 1] if t > 0 else 0

																			# 1. Select temporarily increased noise level sigma_hat
																			# 2. Add new noise to move from sample_i to sample_hat
																			A__  ,  A__						:  Dict		  =  self.scheduler.add_noise_to_input(snake_case			,    snake_case			,    generator=snake_case							)

																			# 3. Predict the noise residual given the noise magnitude `sigma_hat`
																			# The model inputs and output are adjusted by following eq. (213) in [1].
																			A__						:  int		  =  (sigma_hat / 2) * model((sample_hat + 1) / 2			,    sigma_hat / 2							).sample

																			# 4. Evaluate dx/dt at sigma_hat
																			# 5. Take Euler step from sigma to sigma_prev
																			A__						:  Tuple		  =  self.scheduler.step(snake_case			,    snake_case			,    snake_case			,    snake_case							)

																			if sigma_prev != 0:
																									# 6. Apply 2nd order correction
																									# The model inputs and output are adjusted by following eq. (213) in [1].
																									A__						:  Dict		  =  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2			,    sigma_prev / 2							).sample
																									A__						:  Tuple		  =  self.scheduler.step_correct(
																									    snake_case			,    snake_case			,    snake_case			,    snake_case			,    step_output.prev_sample			,    step_output["""derivative"""]			,    )
																			A__						:  Tuple		  =  step_output.prev_sample

													A__						:  Dict		  =  (sample / 2 + 0.5).clamp(0			,    1							)
													A__						:  Dict		  =  sample.cpu().permute(0			,    2			,    3			,    1							).numpy()
													if output_type == "pil":
																			A__						:  Optional[Any]		  =  self.numpy_to_pil(snake_case							)

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=snake_case							)






