

"""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        _lowercase					:







      @staticmethod
      def 	lowerCAmelCase__    (      *UpperCamelCase_ ,						**UpperCamelCase_       ):
       pass





def 	lowercase      (   __UpperCamelCase						) ->      int:
 __magic_name__     =      hashlib.mda(image.tobytes()						)
 return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class        _lowercase					(     unittest.TestCase     ):
     _lowerCamelCase     =       MODEL_FOR_DEPTH_ESTIMATION_MAPPING







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







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

      __magic_name__     =      datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' ,						'''image''' ,						split='''test'''       )
      __magic_name__     =      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       )},
          ] ,						snake_case_ ,						)







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







     @slow
     @require_torch
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      "Intel/dpt-large"
      __magic_name__     =      pipeline('''depth-estimation''' ,						model=snake_case_       )
      __magic_name__     =      depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
      __magic_name__     =      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 	lowerCAmelCase__    (      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'''       )





"""simple docstring"""






class        _lowercase					:







     def __init__(      self ,						UpperCamelCase_       ):
      __magic_name__     =      size
      __magic_name__     =      [0] * size
      __magic_name__     =      [0] * size







     @staticmethod
     def 	lowerCAmelCase__    (      UpperCamelCase_       ):
      return index | (index + 1)







     @staticmethod
     def 	lowerCAmelCase__    (      UpperCamelCase_       ):
      return (index & (index + 1)) - 1







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      value
      while index < self.size:
       __magic_name__     =      self.get_prev(UpperCamelCase_       ) + 1
       if current_left_border == index:
        __magic_name__     =      value
       else:
        __magic_name__     =      max(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       )
       __magic_name__     =      self.get_next(UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      right -= 1  # Because of right is exclusive
      __magic_name__     =      0
      while left <= right:
       __magic_name__     =      self.get_prev(UpperCamelCase_       )
       if left <= current_left:
        __magic_name__     =      max(UpperCamelCase_ ,						self.tree[right]       )
        __magic_name__     =      current_left
       else:
        __magic_name__     =      max(UpperCamelCase_ ,						self.arr[right]       )
        right -= 1
      return result


if __name__ == "__main__":
 import doctest

 doctest.testmod()

