
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 EfficientNetImageProcessor




class    _SCREAMING_SNAKE_CASE					( unittest.TestCase	):
      def __init__(     self						,		lowerCamelCase						,		lowerCamelCase=13						,		lowerCamelCase=3						,		lowerCamelCase=18						,		lowerCamelCase=30						,		lowerCamelCase=4_00						,		lowerCamelCase=True						,		lowerCamelCase=None						,		lowerCamelCase=True						,		lowerCamelCase=[0.5, 0.5, 0.5]						,		lowerCamelCase=[0.5, 0.5, 0.5]						,		):
          snake_case__  =			size if size is not None else {"height": 18, "width": 18}
          snake_case__  =			parent
          snake_case__  =			batch_size
          snake_case__  =			num_channels
          snake_case__  =			image_size
          snake_case__  =			min_resolution
          snake_case__  =			max_resolution
          snake_case__  =			do_resize
          snake_case__  =			size
          snake_case__  =			do_normalize
          snake_case__  =			image_mean
          snake_case__  =			image_std



      def A_     (     self	):
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
          }





@require_torch
@require_vision
class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase    ,   unittest.TestCase	):
      _A  :						List[str]      		=       EfficientNetImageProcessor if is_vision_available() else None
      def A_     (     self	):
          snake_case__  =			EfficientNetImageProcessorTester(self	)
      @property
      def A_     (     self	):
          return self.image_processor_tester.prepare_image_processor_dict()
      def A_     (     self	):
          snake_case__  =			self.image_processing_class(**self.image_processor_dict	)
          self.assertTrue(hasattr(lowerCamelCase						,		"image_mean"	)	)
          self.assertTrue(hasattr(lowerCamelCase						,		"image_std"	)	)
          self.assertTrue(hasattr(lowerCamelCase						,		"do_normalize"	)	)
          self.assertTrue(hasattr(lowerCamelCase						,		"do_resize"	)	)
          self.assertTrue(hasattr(lowerCamelCase						,		"size"	)	)
      def A_     (     self	):
          snake_case__  =			self.image_processing_class.from_dict(self.image_processor_dict	)
          self.assertEqual(image_processor.size						,		{"height": 18, "width": 18}	)

          snake_case__  =			self.image_processing_class.from_dict(self.image_processor_dict						,		size=42	)
          self.assertEqual(image_processor.size						,		{"height": 42, "width": 42}	)
      def A_     (     self	):
          # Initialize image_processing
          snake_case__  =			self.image_processing_class(**self.image_processor_dict	)
          # create random PIL images
          snake_case__  =			prepare_image_inputs(self.image_processor_tester						,		equal_resolution=lowerCamelCase	)
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase						,		Image.Image	)

          # Test not batched input
          snake_case__  =			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["height"],
                  self.image_processor_tester.size["width"],
              )						,		)

          # Test batched
          snake_case__  =			image_processing(lowerCamelCase						,		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["height"],
                  self.image_processor_tester.size["width"],
              )						,		)
      def A_     (     self	):
          # Initialize image_processing
          snake_case__  =			self.image_processing_class(**self.image_processor_dict	)
          # create random numpy tensors
          snake_case__  =			prepare_image_inputs(self.image_processor_tester						,		equal_resolution=lowerCamelCase						,		numpify=lowerCamelCase	)
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase						,		np.ndarray	)

          # Test not batched input
          snake_case__  =			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["height"],
                  self.image_processor_tester.size["width"],
              )						,		)

          # Test batched
          snake_case__  =			image_processing(lowerCamelCase						,		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["height"],
                  self.image_processor_tester.size["width"],
              )						,		)



      def A_     (     self	):
          # Initialize image_processing
          snake_case__  =			self.image_processing_class(**self.image_processor_dict	)
          # create random PyTorch tensors
          snake_case__  =			prepare_image_inputs(self.image_processor_tester						,		equal_resolution=lowerCamelCase						,		torchify=lowerCamelCase	)
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase						,		torch.Tensor	)

          # Test not batched input
          snake_case__  =			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["height"],
                  self.image_processor_tester.size["width"],
              )						,		)

          # Test batched
          snake_case__  =			image_processing(lowerCamelCase						,		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["height"],
                  self.image_processor_tester.size["width"],
              )						,		)





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase							):
    if len(__lowerCAmelCase							) > 1:
        snake_case__  =			len(__lowerCAmelCase							) // 2  # Finds the middle of the array
        snake_case__  =			arr[
            :middle_length
        ]  # Creates an array of the elements in the first half.
        snake_case__  =			arr[
            middle_length:
        ]  # Creates an array of the elements in the second half.
        snake_case__  =			len(__lowerCAmelCase							)
        snake_case__  =			len(__lowerCAmelCase							)
        merge(__lowerCAmelCase							)  # Starts sorting the left.
        merge(__lowerCAmelCase							)  # Starts sorting the right
        snake_case__  =			0  # Left Counter
        snake_case__  =			0  # Right Counter
        snake_case__  =			0  # Position Counter
        while (
            left_index < left_size and right_index < right_size
        ):  # Runs until the lowers size of the left and right are sorted.
            if left_array[left_index] < right_array[right_index]:
                snake_case__  =			left_array[left_index]
                left_index += 1
            else:
                snake_case__  =			right_array[right_index]
                right_index += 1
            index += 1
        while (
            left_index < left_size
        ):  # Adds the left over elements in the left half of the array
            snake_case__  =			left_array[left_index]
            left_index += 1
            index += 1
        while (
            right_index < right_size
        ):  # Adds the left over elements in the right half of the array
            snake_case__  =			right_array[right_index]
            right_index += 1
            index += 1
    return arr


if __name__ == "__main__":
     import doctest

     doctest.testmod()



