


"""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 			A__	:


         """simple docstring"""

         @staticmethod
         def 		a__						(	*__a:						Optional[int]     ,		**__a:						Optional[int]	)->    Optional[Any]:
               pass




def   snake_case		(     UpperCamelCase__       :						Image    )   ->      str:
      lowerCamelCase					:					List[Any]        =	hashlib.mda(image.tobytes()    )
      return m.hexdigest()






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


     """simple docstring"""
     snake_case__	:					Optional[int]													=MODEL_FOR_DEPTH_ESTIMATION_MAPPING

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

           lowerCamelCase					:					Union[str, Any]        =	datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""     ,		"""image"""     ,		split="""test"""	)
           lowerCamelCase					:					Optional[int]        =	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:						Optional[Any]	)->    List[str]:
           pass

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


__lowerCamelCase      :List[Any]	    =		{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

__lowerCamelCase      :Union[str, Any]	    =		{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}




def   snake_case		(     UpperCamelCase__       :						dict[int, list[int]]			,				UpperCamelCase__       :						int			,				UpperCamelCase__       :						list[bool]    )   ->      list[int]:
      lowerCamelCase					:					Tuple        =	True
      lowerCamelCase					:					Any        =	[]

      for neighbour in graph[vert]:
            if not visited[neighbour]:
                  order += topology_sort(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )

      order.append(UpperCamelCase__    )

      return order




def   snake_case		(     UpperCamelCase__       :						dict[int, list[int]]			,				UpperCamelCase__       :						int			,				UpperCamelCase__       :						list[bool]    )   ->      list[int]:
      lowerCamelCase					:					List[Any]        =	True
      lowerCamelCase					:					int        =	[vert]

      for neighbour in reversed_graph[vert]:
            if not visited[neighbour]:
                  component += find_components(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )

      return component






def   snake_case		(     UpperCamelCase__       :						dict[int, list[int]]    )   ->      list[list[int]]:
      lowerCamelCase					:					int        =	len(UpperCamelCase__    ) * [False]
      lowerCamelCase					:					dict[int, list[int]]        =	{vert: [] for vert in range(len(UpperCamelCase__    )    )}

      for vert, neighbours in graph.items():
            for neighbour in neighbours:
                  reversed_graph[neighbour].append(UpperCamelCase__    )

      lowerCamelCase					:					int        =	[]
      for i, was_visited in enumerate(UpperCamelCase__    ):
            if not was_visited:
                  order += topology_sort(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )

      lowerCamelCase					:					Tuple        =	[]
      lowerCamelCase					:					str        =	len(UpperCamelCase__    ) * [False]

      for i in range(len(UpperCamelCase__    )    ):
            lowerCamelCase					:					Any        =	order[len(UpperCamelCase__    ) - i - 1]
            if not visited[vert]:
                  lowerCamelCase					:					List[str]        =	find_components(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )
                  components_list.append(UpperCamelCase__    )

      return components_list




