




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  _lowerCAmelCase     :

           @staticmethod
           def 	__magic_name__(   *__UpperCAmelCase      ,				**__UpperCAmelCase		):
             pass



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	str:
  lowerCAmelCase__				:  List[Any]		       =     hashlib.mda(image.tobytes()      )
  return m.hexdigest()






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

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Union[str, Any]		       =     DepthEstimationPipeline(model=__UpperCAmelCase      ,				image_processor=__UpperCAmelCase		)
        return depth_estimator, [
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Any		       =     depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
        self.assertEqual({'''predicted_depth''': ANY(torch.Tensor		), '''depth''': ANY(Image.Image		)}      ,				__UpperCAmelCase		)
        import datasets

        lowerCAmelCase__				:  int		       =     datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''      ,				'''image'''      ,				split='''test'''		)
        lowerCAmelCase__				:  Union[str, 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		)},
            ]      ,				__UpperCAmelCase      ,				)

      @require_tf
      @unittest.skip('''Depth estimation is not implemented in TF'''		)
      def 	__magic_name__(   self		):
        pass

      @slow
      @require_torch
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Any		       =     '''Intel/dpt-large'''
        lowerCAmelCase__				:  int		       =     pipeline('''depth-estimation'''      ,				model=__UpperCAmelCase		)
        lowerCAmelCase__				:  Optional[int]		       =     depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''		)
        lowerCAmelCase__				:  str		       =     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 	__magic_name__(   self		):
        # This is highly irregular to have no small tests.
        self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''		)







import numpy as np






class  _lowerCAmelCase     :

      def __init__(   self		):
        lowerCAmelCase__				:  List[Any]		       =     (0, 0)
        lowerCAmelCase__				:  Optional[int]		       =     None
        lowerCAmelCase__				:  Optional[Any]		       =     0
        lowerCAmelCase__				:  Optional[int]		       =     0
        lowerCAmelCase__				:  Tuple		       =     0

      def __eq__(   self      ,				__UpperCAmelCase		):
        return self.position == cell.position







      def 	__magic_name__(   self		):
        print(self.position		)






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase=(5, 5)		):
        lowerCAmelCase__				:  List[str]		       =     np.zeros(__UpperCAmelCase		)
        lowerCAmelCase__				:  List[Any]		       =     world_size[0]
        lowerCAmelCase__				:  str		       =     world_size[1]

      def 	__magic_name__(   self		):
        print(self.w		)







      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[Any]		       =     [
            (-1, -1),
            (-1, 0),
            (-1, 1),
            (0, -1),
            (0, 1),
            (1, -1),
            (1, 0),
            (1, 1),
        ]
        lowerCAmelCase__				:  Tuple		       =     cell.position[0]
        lowerCAmelCase__				:  Optional[int]		       =     cell.position[1]
        lowerCAmelCase__				:  Any		       =     []
        for n in neughbour_cord:
          lowerCAmelCase__				:  int		       =     current_x + n[0]
          lowerCAmelCase__				:  Dict		       =     current_y + n[1]
          if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
            lowerCAmelCase__				:  Optional[int]		       =     Cell()
            lowerCAmelCase__				:  Optional[int]		       =     (x, y)
            lowerCAmelCase__				:  Any		       =     cell
            neighbours.append(__UpperCAmelCase		)
        return neighbours



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	Union[str, Any]:
  lowerCAmelCase__				:  int		       =     []
  lowerCAmelCase__				:  int		       =     []
  _open.append(UpperCamelCase      )

  while _open:
    lowerCAmelCase__				:  Dict		       =     np.argmin([n.f for n in _open]      )
    lowerCAmelCase__				:  Dict		       =     _open[min_f]
    _closed.append(_open.pop(UpperCamelCase      )      )
    if current == goal:
      break
    for n in world.get_neigbours(UpperCamelCase      ):
      for c in _closed:
        if c == n:
          continue
      lowerCAmelCase__				:  Any		       =     current.g + 1
      lowerCAmelCase__       ,			lowerCAmelCase__				:  int		       =     n.position
      lowerCAmelCase__       ,			lowerCAmelCase__				:  Union[str, Any]		       =     goal.position
      lowerCAmelCase__				:  Tuple		       =     (ya - ya) ** 2 + (xa - xa) ** 2
      lowerCAmelCase__				:  Optional[Any]		       =     n.h + n.g

      for c in _open:
        if c == n and c.f < n.f:
          continue
      _open.append(UpperCamelCase      )
  lowerCAmelCase__				:  List[Any]		       =     []
  while current.parent is not None:
    path.append(current.position      )
    lowerCAmelCase__				:  List[Any]		       =     current.parent
  path.append(current.position      )
  return path[::-1]


if __name__ == "__main__":
     lowerCAmelCase_					    = Gridworld()
     # Start position and goal
     lowerCAmelCase_					    = Cell()
     lowerCAmelCase_					    = (0, 0)
     lowerCAmelCase_					    = Cell()
     lowerCAmelCase_					    = (4, 4)
     print(F"""path from {start.position} to {goal.position}""")
     lowerCAmelCase_					    = astar(world, start, goal)
     #   Just for visual reasons.
     for i in s:
          lowerCAmelCase_					    = 1
     print(world.w)

