



"""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       UpperCamelCase__			:

						"""simple docstring"""


						@staticmethod
						def   snake_case__	(      *SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)      ->	List[str]:
							pass

def       _lowerCamelCase				(							UpperCAmelCase_ :       Image     )	-> str:






	"""simple docstring"""





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






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			A__	:					int							  =     MODEL_FOR_DEPTH_ESTIMATION_MAPPING


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	int:
				A__											=					DepthEstimationPipeline(model=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)
				return depth_estimator, [
				    "./tests/fixtures/tests_samples/COCO/000000039769.png",
				    "./tests/fixtures/tests_samples/COCO/000000039769.png",
				]


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	List[Any]:
				A__											=					depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
				self.assertEqual({"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)}						,	SCREAMING_SNAKE_CASE__		)
				import datasets

				A__											=					datasets.load_dataset("hf-internal-testing/fixtures_image_utils"						,	"image"						,	split="test"		)
				A__											=					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		)},
				    ]						,	SCREAMING_SNAKE_CASE__						,	)


			@require_tf
			@unittest.skip("Depth estimation is not implemented in TF"		)
			def   snake_case__	(      self		)      ->	Dict:
				pass


			@slow
			@require_torch
			def   snake_case__	(      self		)      ->	Optional[int]:
				A__											=					"Intel/dpt-large"
				A__											=					pipeline("depth-estimation"						,	model=SCREAMING_SNAKE_CASE__		)
				A__											=					depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"		)
				A__											=					hashimage(outputs["depth"]		)

				# This seems flaky.
				# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
				self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()		)						,	2_9.3_0_4		)
				self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()		)						,	2.6_6_2		)







			@require_torch
			def   snake_case__	(      self		)      ->	str:
				# This is highly irregular to have no small tests.
				self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"		)









"""simple docstring"""







from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_pt_objects import *  # noqa F403
else:
			from .scheduling_consistency_models import CMStochasticIterativeScheduler
			from .scheduling_ddim import DDIMScheduler
			from .scheduling_ddim_inverse import DDIMInverseScheduler
			from .scheduling_ddim_parallel import DDIMParallelScheduler
			from .scheduling_ddpm import DDPMScheduler
			from .scheduling_ddpm_parallel import DDPMParallelScheduler
			from .scheduling_deis_multistep import DEISMultistepScheduler
			from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
			from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
			from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
			from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
			from .scheduling_euler_discrete import EulerDiscreteScheduler
			from .scheduling_heun_discrete import HeunDiscreteScheduler
			from .scheduling_ipndm import IPNDMScheduler
			from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
			from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
			from .scheduling_karras_ve import KarrasVeScheduler
			from .scheduling_pndm import PNDMScheduler
			from .scheduling_repaint import RePaintScheduler
			from .scheduling_sde_ve import ScoreSdeVeScheduler
			from .scheduling_sde_vp import ScoreSdeVpScheduler
			from .scheduling_unclip import UnCLIPScheduler
			from .scheduling_unipc_multistep import UniPCMultistepScheduler
			from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
			from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_flax_objects import *  # noqa F403
else:
			from .scheduling_ddim_flax import FlaxDDIMScheduler
			from .scheduling_ddpm_flax import FlaxDDPMScheduler
			from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
			from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
			from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
			from .scheduling_pndm_flax import FlaxPNDMScheduler
			from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
			from .scheduling_utils_flax import (
			    FlaxKarrasDiffusionSchedulers,
			    FlaxSchedulerMixin,
			    FlaxSchedulerOutput,
			    broadcast_to_shape_from_left,
			)


try:
			if not (is_torch_available() and is_scipy_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
			from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
			if not (is_torch_available() and is_torchsde_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
			from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler




