






"""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


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import CLIPImageProcessor


class  snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   def __init__(     self			:					str,	_lowerCamelCase			:					List[Any],	_lowerCamelCase			:					Any=7,	_lowerCamelCase			:					Optional[Any]=3,	_lowerCamelCase			:					Optional[Any]=18,	_lowerCamelCase			:					Optional[Any]=30,	_lowerCamelCase			:					Any=4_00,	_lowerCamelCase			:					str=True,	_lowerCamelCase			:					Optional[int]=None,	_lowerCamelCase			:					Optional[int]=True,	_lowerCamelCase			:					List[Any]=None,	_lowerCamelCase			:					Union[str, Any]=True,	_lowerCamelCase			:					Any=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],	_lowerCamelCase			:					Any=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],	_lowerCamelCase			:					List[str]=True,	):

     '''simple docstring'''
     __A						      =			size if size is not None else {'''shortest_edge''': 20}
     __A						      =			crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
     __A						      =			parent
     __A						      =			batch_size
     __A						      =			num_channels
     __A						      =			image_size
     __A						      =			min_resolution
     __A						      =			max_resolution
     __A						      =			do_resize
     __A						      =			size
     __A						      =			do_center_crop
     __A						      =			crop_size
     __A						      =			do_normalize
     __A						      =			image_mean
     __A						      =			image_std
     __A						      =			do_convert_rgb


   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple  ):

     '''simple docstring'''
     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_center_crop": self.do_center_crop,
         "crop_size": self.crop_size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_convert_rgb": self.do_convert_rgb,
     }






   def     _SCREAMING_SNAKE_CASE    (     self			:					Dict,	_lowerCamelCase			:					Optional[int]=False,	_lowerCamelCase			:					Union[str, Any]=False,	_lowerCamelCase			:					str=False  ):

     '''simple docstring'''
     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

     if equal_resolution:
       __A						      =			[]
       for i in range(self.batch_size  ):
         image_inputs.append(
             np.random.randint(
                 2_55,	size=(self.num_channels, self.max_resolution, self.max_resolution),	dtype=np.uinta  )  )
     else:
       __A						      =			[]
       for i in range(self.batch_size  ):
         __A      ,   __A						      =			np.random.choice(np.arange(self.min_resolution,	self.max_resolution  ),	2  )
         image_inputs.append(np.random.randint(2_55,	size=(self.num_channels, width, height),	dtype=np.uinta  )  )

     if not numpify and not torchify:
       # PIL expects the channel dimension as last dimension
       __A						      =			[Image.fromarray(np.moveaxis(_lowerCamelCase,	0,	-1  )  ) for x in image_inputs]

     if torchify:
       __A						      =			[torch.from_numpy(_lowerCamelCase  ) for x in image_inputs]

     return image_inputs





@require_torch
@require_vision
class  snake_case		(							_lowerCAmelCase       ,     unittest.TestCase						):


   '''simple docstring'''

   A_						:     List[str]									=			CLIPImageProcessor if is_vision_available() else None


   def     _SCREAMING_SNAKE_CASE    (     self			:					Any  ):

     '''simple docstring'''
     __A						      =			CLIPImageProcessingTester(self  )


   @property
   def     _SCREAMING_SNAKE_CASE    (     self			:					str  ):

     '''simple docstring'''
     return self.image_processor_tester.prepare_image_processor_dict()


   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[Any]  ):

     '''simple docstring'''
     __A						      =			self.image_processing_class(**self.image_processor_dict  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''do_resize'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''size'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''do_center_crop'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''center_crop'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''do_normalize'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''image_mean'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''image_std'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''do_convert_rgb'''  )  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					str  ):

     '''simple docstring'''
     __A						      =			self.image_processing_class.from_dict(self.image_processor_dict  )
     self.assertEqual(image_processor.size,	{'''shortest_edge''': 20}  )
     self.assertEqual(image_processor.crop_size,	{'''height''': 18, '''width''': 18}  )

     __A						      =			self.image_processing_class.from_dict(self.image_processor_dict,	size=42,	crop_size=84  )
     self.assertEqual(image_processor.size,	{'''shortest_edge''': 42}  )
     self.assertEqual(image_processor.crop_size,	{'''height''': 84, '''width''': 84}  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[Any]  ):

     '''simple docstring'''
     pass


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str]  ):

     '''simple docstring'''
     __A						      =			self.image_processing_class(**self.image_processor_dict  )
     # create random PIL images
     __A						      =			self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,	Image.Image  )

     # Test not batched input
     __A						      =			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.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         ),	)

     # Test batched
     __A						      =			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.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         ),	)


   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[int]  ):

     '''simple docstring'''
     __A						      =			self.image_processing_class(**self.image_processor_dict  )
     # create random numpy tensors
     __A						      =			self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCamelCase,	numpify=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,	np.ndarray  )

     # Test not batched input
     __A						      =			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.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         ),	)

     # Test batched
     __A						      =			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.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         ),	)






   def     _SCREAMING_SNAKE_CASE    (     self			:					Dict  ):

     '''simple docstring'''
     __A						      =			self.image_processing_class(**self.image_processor_dict  )
     # create random PyTorch tensors
     __A						      =			self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCamelCase,	torchify=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,	torch.Tensor  )

     # Test not batched input
     __A						      =			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.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         ),	)

     # Test batched
     __A						      =			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.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         ),	)





@require_torch
@require_vision
class  snake_case		(							_lowerCAmelCase       ,     unittest.TestCase						):


   '''simple docstring'''

   A_						:     Any									=			CLIPImageProcessor if is_vision_available() else None


   def     _SCREAMING_SNAKE_CASE    (     self			:					Any  ):

     '''simple docstring'''
     __A						      =			CLIPImageProcessingTester(self,	num_channels=4  )
     __A						      =			3


   @property
   def     _SCREAMING_SNAKE_CASE    (     self			:					int  ):

     '''simple docstring'''
     return self.image_processor_tester.prepare_image_processor_dict()


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

     '''simple docstring'''
     __A						      =			self.image_processing_class(**self.image_processor_dict  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''do_resize'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''size'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''do_center_crop'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''center_crop'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''do_normalize'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''image_mean'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''image_std'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''do_convert_rgb'''  )  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple  ):

     '''simple docstring'''
     pass






   def     _SCREAMING_SNAKE_CASE    (     self			:					int  ):

     '''simple docstring'''
     __A						      =			self.image_processing_class(**self.image_processor_dict  )
     # create random PIL images
     __A						      =			self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,	Image.Image  )

     # Test not batched input
     __A						      =			image_processing(image_inputs[0],	return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,	(
             1,
             self.expected_encoded_image_num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         ),	)

     # Test batched
     __A						      =			image_processing(_lowerCamelCase,	return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,	(
             self.image_processor_tester.batch_size,
             self.expected_encoded_image_num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         ),	)












"""simple docstring"""


def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  if not isinstance(__UpperCamelCase		,	__UpperCamelCase			):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 8]'''			)
  if len(__UpperCamelCase			) == 0:
    raise ValueError('''Input list must be a non empty list'''			)
  if len(__UpperCamelCase			) == 1:
    return True
  try:
    __A						      =			series[1] / series[0]
    for index in range(len(__UpperCamelCase			) - 1			):
      if series[index + 1] / series[index] != common_ratio:
        return False
  except ZeroDivisionError:
    return False
  return True



def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  if not isinstance(__UpperCamelCase		,	__UpperCamelCase			):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 8]'''			)
  if len(__UpperCamelCase			) == 0:
    raise ValueError('''Input list must be a non empty list'''			)
  __A						      =			1
  for value in series:
    answer *= value
  return pow(__UpperCamelCase		,	1 / len(__UpperCamelCase			)			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()




