


'''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	:





														@staticmethod
														def 		a__   (  *_UpperCamelCase		:int  ,**_UpperCamelCase		:List[Any]			):
																pass





def      UpperCAmelCase      (		lowerCamelCase_  :Tuple ):







		'''simple docstring'''
		snake_case_		:							Tuple			       =						hashlib.mda(image.tobytes() )
		return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 						__UpperCamelCase	(						unittest.TestCase							):
							lowercase			:		List[str]   		=  MODEL_FOR_DEPTH_ESTIMATION_MAPPING





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:str  ,_UpperCamelCase		:Dict  ,_UpperCamelCase		:Tuple			):
									snake_case_		:							int			       =						DepthEstimationPipeline(model=__A  ,image_processor=__A			)
									return depth_estimator, [
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									]





							def 		a__   (  self		:Tuple  ,_UpperCamelCase		:Tuple  ,_UpperCamelCase		:Union[str, Any]			):
									snake_case_		:							Optional[Any]			       =						depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
									self.assertEqual({"""predicted_depth""": ANY(torch.Tensor			), """depth""": ANY(Image.Image			)}  ,__A			)
									import datasets

									snake_case_		:							Tuple			       =						datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""  ,"""image"""  ,split="""test"""			)
									snake_case_		:							List[str]			       =						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 		a__   (  self		:Any			):
									pass





							@slow
							@require_torch
							def 		a__   (  self		:List[Any]			):
									snake_case_		:							Any			       =						"""Intel/dpt-large"""
									snake_case_		:							Optional[int]			       =						pipeline("""depth-estimation"""  ,model=__A			)
									snake_case_		:							Union[str, Any]			       =						depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""			)
									snake_case_		:							int			       =						hashimage(outputs["""depth"""]			)

									# This seems flaky.
									# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
									self.assertEqual(nested_simplify(outputs["""predicted_depth"""].max().item()			)  ,29.3_04			)
									self.assertEqual(nested_simplify(outputs["""predicted_depth"""].min().item()			)  ,2.6_62			)





							@require_torch
							def 		a__   (  self		:List[Any]			):
									# 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 abc import ABC, abstractmethod
from argparse import ArgumentParser
class 						__UpperCamelCase	(						lowercase__							):





							@staticmethod
							@abstractmethod
							def 		a__   (  _UpperCamelCase		:ArgumentParser			):
									raise NotImplementedError()





							@abstractmethod
							def 		a__   (  self		:Optional[Any]			):
									raise NotImplementedError()