




def      _a				(						SCREAMING_SNAKE_CASE_							:    List[Any]					):
					__lowerCAmelCase           =	0
					__lowerCAmelCase           =	len(SCREAMING_SNAKE_CASE_					)

					for i in range(n - 1					):
										for j in range(i + 1				,				SCREAMING_SNAKE_CASE_					):
															if arr[i] > arr[j]:
																				num_inversions += 1

					return num_inversions







def      _a				(						SCREAMING_SNAKE_CASE_							:    str					):
					if len(SCREAMING_SNAKE_CASE_					) <= 1:
										return arr, 0
					__lowerCAmelCase           =	len(SCREAMING_SNAKE_CASE_					) // 2
					__lowerCAmelCase           =	arr[0:mid]
					__lowerCAmelCase           =	arr[mid:]

					__lowerCAmelCase       ,							__lowerCAmelCase           =	count_inversions_recursive(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase       ,							__lowerCAmelCase           =	count_inversions_recursive(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase       ,							__lowerCAmelCase           =	_count_cross_inversions(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)

					__lowerCAmelCase           =	inversion_p + inversions_q + cross_inversions
					return c, num_inversions







def      _a				(						SCREAMING_SNAKE_CASE_							:    List[str]				,				SCREAMING_SNAKE_CASE_							:    Dict					):
					__lowerCAmelCase           =	[]
					__lowerCAmelCase           =	__lowerCAmelCase           =	__lowerCAmelCase           =	0
					while i < len(SCREAMING_SNAKE_CASE_					) and j < len(SCREAMING_SNAKE_CASE_					):
										if p[i] > q[j]:
															# if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
															# These are all inversions. The claim emerges from the
															# property that P is sorted.
															num_inversion += len(SCREAMING_SNAKE_CASE_					) - i
															r.append(q[j]					)
															j += 1
										else:
															r.append(p[i]					)
															i += 1

					if i < len(SCREAMING_SNAKE_CASE_					):
										r.extend(p[i:]					)
					else:
										r.extend(q[j:]					)

					return r, num_inversion







def      _a				(						):
					__lowerCAmelCase           =	[10, 2, 1, 5, 5, 2, 11]

					# this arr has 8 inversions:
					# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

					__lowerCAmelCase           =	count_inversions_bf(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase       ,							__lowerCAmelCase           =	count_inversions_recursive(SCREAMING_SNAKE_CASE_					)

					assert num_inversions_bf == num_inversions_recursive == 8

					print("number of inversions = "				,				SCREAMING_SNAKE_CASE_					)

					# testing an array with zero inversion (a sorted arr_1)

					arr_a.sort()
					__lowerCAmelCase           =	count_inversions_bf(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase       ,							__lowerCAmelCase           =	count_inversions_recursive(SCREAMING_SNAKE_CASE_					)

					assert num_inversions_bf == num_inversions_recursive == 0
					print("number of inversions = "				,				SCREAMING_SNAKE_CASE_					)

					# an empty list should also have zero inversions
					__lowerCAmelCase           =	[]
					__lowerCAmelCase           =	count_inversions_bf(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase       ,							__lowerCAmelCase           =	count_inversions_recursive(SCREAMING_SNAKE_CASE_					)

					assert num_inversions_bf == num_inversions_recursive == 0
					print("number of inversions = "				,				SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
					main()










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     __SCREAMING_SNAKE_CASE(       *_A		,      **_A      ):




																"""simple docstring"""



																pass







def      _a				(						SCREAMING_SNAKE_CASE_							:    Image					):
					__lowerCAmelCase           =	hashlib.mda(image.tobytes()					)
					return m.hexdigest()


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class       a__   ( unittest.TestCase				):
						_a							:   Tuple =   MODEL_FOR_DEPTH_ESTIMATION_MAPPING






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



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






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A      ):




											"""simple docstring"""



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






						@require_tf
						@unittest.skip("Depth estimation is not implemented in TF"      )
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											pass






						@slow
						@require_torch
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	"Intel/dpt-large"
											__lowerCAmelCase           =	pipeline("depth-estimation"		,      model=_A      )
											__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()      )		,      29.3_04      )
											self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()      )		,      2.6_62      )






						@require_torch
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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




