





"""simple docstring"""





from __future__ import annotations


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						list							,						SCREAMING_SNAKE_CASE					:						list      ):







      '''simple docstring'''




      lowerCAmelCase  						=	[None] * (len(SCREAMING_SNAKE_CASE      ) + len(SCREAMING_SNAKE_CASE      ))

      lowerCAmelCase  						=	0  # pointer to current index for left Half
      lowerCAmelCase  						=	0  # pointer to current index for the right Half
      lowerCAmelCase  						=	0  # pointer to current index for the sorted array Half

      while pointera < len(SCREAMING_SNAKE_CASE      ) and pointera < len(SCREAMING_SNAKE_CASE      ):
            if left_half[pointera] < right_half[pointera]:
                  lowerCAmelCase  						=	left_half[pointera]
                  pointera += 1
                  index += 1
            else:
                  lowerCAmelCase  						=	right_half[pointera]
                  pointera += 1
                  index += 1
      while pointera < len(SCREAMING_SNAKE_CASE      ):
            lowerCAmelCase  						=	left_half[pointera]
            pointera += 1
            index += 1

      while pointera < len(SCREAMING_SNAKE_CASE      ):
            lowerCAmelCase  						=	right_half[pointera]
            pointera += 1
            index += 1

      return sorted_array


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						list      ):







      '''simple docstring'''




      if len(SCREAMING_SNAKE_CASE      ) <= 1:
            return array
      # the actual formula to calculate the middle element = left + (right - left) // 2
      # this avoids integer overflow in case of large N
      lowerCAmelCase  						=	0 + (len(SCREAMING_SNAKE_CASE      ) - 0) // 2

      # Split the array into halves till the array length becomes equal to One
      # merge the arrays of single length returned by mergeSort function and
      # pass them into the merge arrays function which merges the array
      lowerCAmelCase  						=	array[:middle]
      lowerCAmelCase  						=	array[middle:]

      return merge(merge_sort(SCREAMING_SNAKE_CASE      )							,						merge_sort(SCREAMING_SNAKE_CASE      )      )


if __name__ == "__main__":
       import doctest

       doctest.testmod()













"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import ConvNextImageProcessor

class 		lowercase ( unittest.TestCase ):





      def __init__(	self				,						lowercase				,						lowercase=7				,						lowercase=3				,						lowercase=18				,						lowercase=30				,						lowercase=400				,						lowercase=True				,						lowercase=None				,						lowercase=0.875				,						lowercase=True				,						lowercase=[0.5, 0.5, 0.5]				,						lowercase=[0.5, 0.5, 0.5]				,						)		->					List[Any]:
            lowerCAmelCase  						=	size if size is not None else {"""shortest_edge""": 20}
            lowerCAmelCase  						=	parent
            lowerCAmelCase  						=	batch_size
            lowerCAmelCase  						=	num_channels
            lowerCAmelCase  						=	image_size
            lowerCAmelCase  						=	min_resolution
            lowerCAmelCase  						=	max_resolution
            lowerCAmelCase  						=	do_resize
            lowerCAmelCase  						=	size
            lowerCAmelCase  						=	crop_pct
            lowerCAmelCase  						=	do_normalize
            lowerCAmelCase  						=	image_mean
            lowerCAmelCase  						=	image_std





      def    _snake_case      (	self			)		->					Optional[Any]:
            return {
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_normalize": self.do_normalize,
                "do_resize": self.do_resize,
                "size": self.size,
                "crop_pct": self.crop_pct,
            }







@require_torch
@require_vision
class 		lowercase ( _UpperCAmelCase					,  unittest.TestCase ):
      _SCREAMING_SNAKE_CASE         =				ConvNextImageProcessor if is_vision_available() else None





      def    _snake_case      (	self			)		->					List[str]:
            lowerCAmelCase  						=	ConvNextImageProcessingTester(self			)





      @property
      def    _snake_case      (	self			)		->					List[str]:
            return self.image_processor_tester.prepare_image_processor_dict()





      def    _snake_case      (	self			)		->					int:
            lowerCAmelCase  						=	self.image_processing_class(**self.image_processor_dict			)
            self.assertTrue(hasattr(lowercase				,						"""do_resize"""			)			)
            self.assertTrue(hasattr(lowercase				,						"""size"""			)			)
            self.assertTrue(hasattr(lowercase				,						"""crop_pct"""			)			)
            self.assertTrue(hasattr(lowercase				,						"""do_normalize"""			)			)
            self.assertTrue(hasattr(lowercase				,						"""image_mean"""			)			)
            self.assertTrue(hasattr(lowercase				,						"""image_std"""			)			)





      def    _snake_case      (	self			)		->					int:
            lowerCAmelCase  						=	self.image_processing_class.from_dict(self.image_processor_dict			)
            self.assertEqual(image_processor.size				,						{"""shortest_edge""": 20}			)

            lowerCAmelCase  						=	self.image_processing_class.from_dict(self.image_processor_dict				,						size=42			)
            self.assertEqual(image_processor.size				,						{"""shortest_edge""": 42}			)





      def    _snake_case      (	self			)		->					Optional[int]:
            pass





      def    _snake_case      (	self			)		->					Optional[int]:
            # Initialize image_processing
            lowerCAmelCase  						=	self.image_processing_class(**self.image_processor_dict			)
            # create random PIL images
            lowerCAmelCase  						=	prepare_image_inputs(self.image_processor_tester				,						equal_resolution=lowercase			)
            for image in image_inputs:
                  self.assertIsInstance(lowercase				,						Image.Image			)

            # Test not batched input
            lowerCAmelCase  						=	image_processing(image_inputs[0]				,						return_tensors="""pt"""			).pixel_values
            self.assertEqual(
                encoded_images.shape				,						(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )				,						)

            # Test batched
            lowerCAmelCase  						=	image_processing(lowercase				,						return_tensors="""pt"""			).pixel_values
            self.assertEqual(
                encoded_images.shape				,						(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )				,						)





      def    _snake_case      (	self			)		->					str:
            # Initialize image_processing
            lowerCAmelCase  						=	self.image_processing_class(**self.image_processor_dict			)
            # create random numpy tensors
            lowerCAmelCase  						=	prepare_image_inputs(self.image_processor_tester				,						equal_resolution=lowercase				,						numpify=lowercase			)
            for image in image_inputs:
                  self.assertIsInstance(lowercase				,						np.ndarray			)

            # Test not batched input
            lowerCAmelCase  						=	image_processing(image_inputs[0]				,						return_tensors="""pt"""			).pixel_values
            self.assertEqual(
                encoded_images.shape				,						(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )				,						)

            # Test batched
            lowerCAmelCase  						=	image_processing(lowercase				,						return_tensors="""pt"""			).pixel_values
            self.assertEqual(
                encoded_images.shape				,						(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )				,						)





      def    _snake_case      (	self			)		->					Optional[int]:
            # Initialize image_processing
            lowerCAmelCase  						=	self.image_processing_class(**self.image_processor_dict			)
            # create random PyTorch tensors
            lowerCAmelCase  						=	prepare_image_inputs(self.image_processor_tester				,						equal_resolution=lowercase				,						torchify=lowercase			)
            for image in image_inputs:
                  self.assertIsInstance(lowercase				,						torch.Tensor			)

            # Test not batched input
            lowerCAmelCase  						=	image_processing(image_inputs[0]				,						return_tensors="""pt"""			).pixel_values
            self.assertEqual(
                encoded_images.shape				,						(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )				,						)

            # Test batched
            lowerCAmelCase  						=	image_processing(lowercase				,						return_tensors="""pt"""			).pixel_values
            self.assertEqual(
                encoded_images.shape				,						(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )				,						)






