
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		:







												@staticmethod
												def    UpperCAmelCase__       (    *snake_case__	:	Any   ,					**snake_case__	:	List[str]			):
													"""simple docstring"""







													pass






def 		_UpperCAmelCase (						UpperCamelCase:  Image	):






	"""simple docstring"""


	__lowerCAmelCase    =				hashlib.mda(image.tobytes()	)
	return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class    a		( unittest.TestCase       ):
							lowercase_		:    Union[str, Any] 				=	MODEL_FOR_DEPTH_ESTIMATION_MAPPING







							def    UpperCAmelCase__       (    self	:	str   ,					snake_case__	:	Optional[int]   ,					snake_case__	:	List[str]   ,					snake_case__	:	Optional[int]			):
								"""simple docstring"""







								__lowerCAmelCase    =				DepthEstimationPipeline(model=lowerCamelCase_   ,					image_processor=lowerCamelCase_			)
								return depth_estimator, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]







							def    UpperCAmelCase__       (    self	:	Union[str, Any]   ,					snake_case__	:	Optional[int]   ,					snake_case__	:	Tuple			):
								"""simple docstring"""







								__lowerCAmelCase    =				depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
								self.assertEqual({"predicted_depth": ANY(torch.Tensor			), "depth": ANY(Image.Image			)}   ,					lowerCamelCase_			)
								import datasets

								__lowerCAmelCase    =				datasets.load_dataset("hf-internal-testing/fixtures_image_utils"   ,					"image"   ,					split="test"			)
								__lowerCAmelCase    =				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			)},
								    ]   ,					lowerCamelCase_   ,					)







							@require_tf
							@unittest.skip("Depth estimation is not implemented in TF"			)
							def    UpperCAmelCase__       (    self	:	Optional[int]			):
								"""simple docstring"""







								pass







							@slow
							@require_torch
							def    UpperCAmelCase__       (    self	:	List[Any]			):
								"""simple docstring"""







								__lowerCAmelCase    =				'''Intel/dpt-large'''
								__lowerCAmelCase    =				pipeline("depth-estimation"   ,					model=lowerCamelCase_			)
								__lowerCAmelCase    =				depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"			)
								__lowerCAmelCase    =				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    UpperCAmelCase__       (    self	:	Any			):
								"""simple docstring"""







								self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"			)







'''simple docstring'''

from __future__ import annotations







def __UpperCamelCase( _A       :					list[int]	,      _A       :					int	,      _A       :					int	,      _A       :					int     ):
   '''simple docstring'''
   if (direction == 1 and array[indexa] > array[indexa]) or (
       direction == 0 and array[indexa] < array[indexa]
   ):
      UpperCAmelCase__					,   UpperCAmelCase__				:					Union[str, Any] 					=	array[indexa], array[indexa]







def __UpperCamelCase( _A       :					list[int]	,      _A       :					int	,      _A       :					int	,      _A       :					int     ):
   '''simple docstring'''
   if length > 1:
      UpperCAmelCase__				:					List[Any] 					=	int(length / 2     )
      for i in range(_A	,      low + middle     ):
         comp_and_swap(_A	,      _A	,      i + middle	,      _A     )
      bitonic_merge(_A	,      _A	,      _A	,      _A     )
      bitonic_merge(_A	,      low + middle	,      _A	,      _A     )







def __UpperCamelCase( _A       :					list[int]	,      _A       :					int	,      _A       :					int	,      _A       :					int     ):
   '''simple docstring'''
   if length > 1:
      UpperCAmelCase__				:					Optional[int] 					=	int(length / 2     )
      bitonic_sort(_A	,      _A	,      _A	,      1     )
      bitonic_sort(_A	,      low + middle	,      _A	,      0     )
      bitonic_merge(_A	,      _A	,      _A	,      _A     )


if __name__ == "__main__":
  UpperCamelCase__  :						Dict					=      input('Enter numbers separated by a comma:\n').strip()
  UpperCamelCase__  :						Tuple					=      [int(item.strip()) for item in user_input.split(',')]

  bitonic_sort(unsorted, 0, len(unsorted), 1)
  print('\nSorted array in ascending order is: ', end='')
  print(*unsorted, sep=', ')

  bitonic_merge(unsorted, 0, len(unsorted), 0)
  print('Sorted array in descending order is: ', end='')
  print(*unsorted, sep=', ')




