





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   __snake_case				(			unittest.TestCase   ):






    '''simple docstring'''





    def __init__(   self						:		Tuple			,						snake_case						:		Dict			,						snake_case						:		Optional[int]=13			,						snake_case						:		Tuple=3			,						snake_case						:		Dict=18			,						snake_case						:		Any=30			,						snake_case						:		Union[str, Any]=400			,						snake_case						:		Any=True			,						snake_case						:		Any=None			,						snake_case						:		str=True			,						snake_case						:		int=[0.5, 0.5, 0.5]			,						snake_case						:		int=[0.5, 0.5, 0.5]			,						):

      """simple docstring"""





      __lowerCAmelCase			=				size if size is not None else {'''height''': 18, '''width''': 18}
      __lowerCAmelCase			=				parent
      __lowerCAmelCase			=				batch_size
      __lowerCAmelCase			=				num_channels
      __lowerCAmelCase			=				image_size
      __lowerCAmelCase			=				min_resolution
      __lowerCAmelCase			=				max_resolution
      __lowerCAmelCase			=				do_resize
      __lowerCAmelCase			=				size
      __lowerCAmelCase			=				do_normalize
      __lowerCAmelCase			=				image_mean
      __lowerCAmelCase			=				image_std





    def 	A		(   self						:		Optional[Any]							):

      """simple docstring"""





      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   __snake_case				(			_a       ,				unittest.TestCase   ):






    '''simple docstring'''
    _lowerCAmelCase     =	EfficientNetImageProcessor if is_vision_available() else None





    def 	A		(   self						:		Tuple							):

      """simple docstring"""





      __lowerCAmelCase			=				EfficientNetImageProcessorTester(self							)





    @property
    def 	A		(   self						:		Dict							):

      """simple docstring"""





      return self.image_processor_tester.prepare_image_processor_dict()





    def 	A		(   self						:		Union[str, Any]							):

      """simple docstring"""





      __lowerCAmelCase			=				self.image_processing_class(**self.image_processor_dict							)
      self.assertTrue(hasattr(snake_case			,						'''image_mean'''							)							)
      self.assertTrue(hasattr(snake_case			,						'''image_std'''							)							)
      self.assertTrue(hasattr(snake_case			,						'''do_normalize'''							)							)
      self.assertTrue(hasattr(snake_case			,						'''do_resize'''							)							)
      self.assertTrue(hasattr(snake_case			,						'''size'''							)							)





    def 	A		(   self						:		Dict							):

      """simple docstring"""





      __lowerCAmelCase			=				self.image_processing_class.from_dict(self.image_processor_dict							)
      self.assertEqual(image_processor.size			,						{'''height''': 18, '''width''': 18}							)

      __lowerCAmelCase			=				self.image_processing_class.from_dict(self.image_processor_dict			,						size=42							)
      self.assertEqual(image_processor.size			,						{'''height''': 42, '''width''': 42}							)





    def 	A		(   self						:		Any							):

      """simple docstring"""





      __lowerCAmelCase			=				self.image_processing_class(**self.image_processor_dict							)
      # create random PIL images
      __lowerCAmelCase			=				prepare_image_inputs(self.image_processor_tester			,						equal_resolution=snake_case							)
      for image in image_inputs:
        self.assertIsInstance(snake_case			,						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['''height'''],
              self.image_processor_tester.size['''width'''],
          )			,						)

      # Test batched
      __lowerCAmelCase			=				image_processing(snake_case			,						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						:		Optional[int]							):

      """simple docstring"""





      __lowerCAmelCase			=				self.image_processing_class(**self.image_processor_dict							)
      # create random numpy tensors
      __lowerCAmelCase			=				prepare_image_inputs(self.image_processor_tester			,						equal_resolution=snake_case			,						numpify=snake_case							)
      for image in image_inputs:
        self.assertIsInstance(snake_case			,						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['''height'''],
              self.image_processor_tester.size['''width'''],
          )			,						)

      # Test batched
      __lowerCAmelCase			=				image_processing(snake_case			,						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						:		Any							):

      """simple docstring"""





      __lowerCAmelCase			=				self.image_processing_class(**self.image_processor_dict							)
      # create random PyTorch tensors
      __lowerCAmelCase			=				prepare_image_inputs(self.image_processor_tester			,						equal_resolution=snake_case			,						torchify=snake_case							)
      for image in image_inputs:
        self.assertIsInstance(snake_case			,						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['''height'''],
              self.image_processor_tester.size['''width'''],
          )			,						)

      # Test batched
      __lowerCAmelCase			=				image_processing(snake_case			,						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  (							__lowercase:		list):



  '''simple docstring'''






  if len(__lowercase) <= 1:
    return data
  else:
    return [
        *quick_sort([e for e in data[1:] if e <= data[0]]),
        data[0],
        *quick_sort([e for e in data[1:] if e > data[0]]),
    ]


if __name__ == "__main__":
      import doctest

      doctest.testmod()
