

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 					__lowercase  :





									"""simple docstring"""

									@staticmethod
									def       snake_case			(					*__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       Dict:
																pass






def        snake_case__		(    lowerCamelCase_		):
							A      :					Optional[int]      		=					hashlib.mda(image.tobytes()		)
							return m.hexdigest()







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





							"""simple docstring"""

							UpperCAmelCase_						:		Union[str, Any]								=	MODEL_FOR_DEPTH_ESTIMATION_MAPPING




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)		->       Optional[int]:
														A      :					int      		=					DepthEstimationPipeline(model=UpperCAmelCase__    ,						image_processor=UpperCAmelCase__						)
														return depth_estimator, [
														    "./tests/fixtures/tests_samples/COCO/000000039769.png",
														    "./tests/fixtures/tests_samples/COCO/000000039769.png",
														]




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)		->       Tuple:
														A      :					List[str]      		=					depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
														self.assertEqual({'''predicted_depth''': ANY(torch.Tensor						), '''depth''': ANY(Image.Image						)}    ,						UpperCAmelCase__						)
														import datasets

														A      :					List[str]      		=					datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''    ,						'''image'''    ,						split='''test'''						)
														A      :					Dict      		=					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       snake_case			(					self						)		->       List[str]:
														pass




							@slow
							@require_torch
							def       snake_case			(					self						)		->       Tuple:
														A      :					Tuple      		=					'''Intel/dpt-large'''
														A      :					str      		=					pipeline('''depth-estimation'''    ,						model=UpperCAmelCase__						)
														A      :					Any      		=					depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''						)
														A      :					List[str]      		=					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						)		->       Optional[int]:
														self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''						)












class 							UpperCamelCase_       :  # Public class to implement a graph

    '''simple docstring'''






    def __init__(     self :      str   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      list[list[bool]])		->None:





           '''simple docstring'''

           A__ 					=       row
           A__ 					=       col
           A__ 					=       graph


    def 			SCREAMING_SNAKE_CASE				(     self :      Any   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      list[list[bool]])		->bool:





           '''simple docstring'''

           return (
               0 <= i < self.ROW
               and 0 <= j < self.COL
               and not visited[i][j]
               and self.graph[i][j]
           )


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int]   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      list[list[bool]])		->None:





           '''simple docstring'''

           A__ 					=       [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
           A__ 					=       [-1, 0, 1, -1, 1, -1, 0, 1]
           A__ 					=       True  # Make those cells visited
           for k in range(8):
                  if self.is_safe(i + row_nbr[k]   ,					j + col_nbr[k]   ,					UpperCAmelCase__):
                         self.diffs(i + row_nbr[k]   ,					j + col_nbr[k]   ,					UpperCAmelCase__)





    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any])		->int:  # And finally, count all islands.





           '''simple docstring'''

           A__ 					=       [[False for j in range(self.COL)] for i in range(self.ROW)]
           A__ 					=       0
           for i in range(self.ROW):
                  for j in range(self.COL):
                         if visited[i][j] is False and self.graph[i][j] == 1:
                                self.diffs(UpperCAmelCase__   ,					UpperCAmelCase__   ,					UpperCAmelCase__)
                                count += 1
           return count


