



"""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"		)








from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader



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






					def __init__(self       :		Any ,   _UpperCAmelCase       :		NestedDataStructureLike[PathLike] ,   _UpperCAmelCase       :		Optional[NamedSplit] = None ,   _UpperCAmelCase       :		Optional[Features] = None ,   _UpperCAmelCase       :		str = None ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		Optional[int] = None ,   **_UpperCAmelCase       :		Optional[int] ,   )		->  List[str]:




							"""simple docstring"""


							super().__init__(
							    _UpperCAmelCase ,   split=_UpperCAmelCase ,   features=_UpperCAmelCase ,   cache_dir=_UpperCAmelCase ,   keep_in_memory=_UpperCAmelCase ,   streaming=_UpperCAmelCase ,   num_proc=_UpperCAmelCase ,   **_UpperCAmelCase ,   )
							lowercase__	     =						path_or_paths if isinstance(_UpperCAmelCase ,   _UpperCAmelCase						) else {self.split: path_or_paths}
							lowercase__	     =						Text(
							    cache_dir=_UpperCAmelCase ,   data_files=_UpperCAmelCase ,   features=_UpperCAmelCase ,   **_UpperCAmelCase ,   )


					def     lowerCamelCase__	(self       :		Tuple						)		->  Any:




							"""simple docstring"""


							if self.streaming:
									lowercase__	     =						self.builder.as_streaming_dataset(split=self.split						)
							# Build regular (map-style) dataset
							else:
									lowercase__	     =						None
									lowercase__	     =						None
									lowercase__	     =						None
									lowercase__	     =						None

									self.builder.download_and_prepare(
									    download_config=_UpperCAmelCase ,   download_mode=_UpperCAmelCase ,   verification_mode=_UpperCAmelCase ,   base_path=_UpperCAmelCase ,   num_proc=self.num_proc ,   )
									lowercase__	     =						self.builder.as_dataset(
									    split=self.split ,   verification_mode=_UpperCAmelCase ,   in_memory=self.keep_in_memory						)
							return dataset

