

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 SwinaSRImageProcessor
      from transformers.image_transforms import get_image_size


class     lowerCamelCase_   ( unittest.TestCase):




 def __init__( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase=7     ,     _UpperCAmelCase=3     ,     _UpperCAmelCase=18     ,     _UpperCAmelCase=30     ,     _UpperCAmelCase=400     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=1 / 255     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=8     ,     ):
   __UpperCAmelCase  :     Optional[int]		=    parent
   __UpperCAmelCase  :     Dict		=    batch_size
   __UpperCAmelCase  :     Dict		=    num_channels
   __UpperCAmelCase  :     List[str]		=    image_size
   __UpperCAmelCase  :     Dict		=    min_resolution
   __UpperCAmelCase  :     Any		=    max_resolution
   __UpperCAmelCase  :     List[str]		=    do_rescale
   __UpperCAmelCase  :     str		=    rescale_factor
   __UpperCAmelCase  :     Any		=    do_pad
   __UpperCAmelCase  :     Dict		=    pad_size




 def 						a					( self       ):
   return {
       "do_rescale": self.do_rescale,
       "rescale_factor": self.rescale_factor,
       "do_pad": self.do_pad,
       "pad_size": self.pad_size,
   }




 def 						a					( self     ,     _UpperCAmelCase=False     ,     _UpperCAmelCase=False     ,     _UpperCAmelCase=False       ):
   assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

   if equal_resolution:
     __UpperCAmelCase  :     Optional[int]		=    []
     for i in range(self.batch_size       ):
       image_inputs.append(
           np.random.randint(
               255     ,     size=(self.num_channels, self.max_resolution, self.max_resolution)     ,     dtype=np.uinta       )       )
   else:
     __UpperCAmelCase  :     Optional[int]		=    []
     for i in range(self.batch_size       ):
       __UpperCAmelCase  :     List[str]		=    np.random.choice(np.arange(self.min_resolution     ,     self.max_resolution       )     ,     2       )
       image_inputs.append(np.random.randint(255     ,     size=(self.num_channels, width, height)     ,     dtype=np.uinta       )       )

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

   if torchify:
     __UpperCAmelCase  :     List[Any]		=    [torch.from_numpy(_UpperCAmelCase       ) for x in image_inputs]

   return image_inputs



@require_torch
@require_vision
class     lowerCamelCase_   ( _lowerCAmelCase  ,							unittest.TestCase):
 _lowerCAmelCase       		=  SwinaSRImageProcessor if is_vision_available() else None




 def 						a					( self       ):
   __UpperCAmelCase  :     Optional[int]		=    SwinaSRImageProcessingTester(self       )




 @property
 def 						a					( self       ):
   return self.image_processor_tester.prepare_image_processor_dict()




 def 						a					( self       ):
   __UpperCAmelCase  :     Union[str, Any]		=    self.image_processing_class(**self.image_processor_dict       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'do_rescale'       )       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'rescale_factor'       )       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'do_pad'       )       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'pad_size'       )       )




 def 						a					( self       ):
   pass




 def 						a					( self     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     str		=    get_image_size(_UpperCAmelCase       )
   __UpperCAmelCase  :     Union[str, Any]		=    self.image_processor_tester.pad_size

   __UpperCAmelCase  :     List[str]		=    (old_height // size + 1) * size - old_height
   __UpperCAmelCase  :     List[Any]		=    (old_width // size + 1) * size - old_width
   return old_height + pad_height, old_width + pad_width




 def 						a					( self       ):
   # Initialize image_processor
   __UpperCAmelCase  :     str		=    self.image_processing_class(**self.image_processor_dict       )
   # create random PIL images
   __UpperCAmelCase  :     int		=    self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase       )
   for image in image_inputs:
     self.assertIsInstance(_UpperCAmelCase     ,     Image.Image       )

   # Test not batched input
   __UpperCAmelCase  :     Optional[int]		=    image_processor(image_inputs[0]     ,     return_tensors='pt'       ).pixel_values
   __UpperCAmelCase  :     Any		=    self.calculate_expected_size(np.array(image_inputs[0]       )       )
   self.assertEqual(
       encoded_images.shape     ,     (
           1,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )     ,     )




 def 						a					( self       ):
   # Initialize image_processor
   __UpperCAmelCase  :     Any		=    self.image_processing_class(**self.image_processor_dict       )
   # create random numpy tensors
   __UpperCAmelCase  :     Optional[Any]		=    self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase     ,     numpify=_UpperCAmelCase       )
   for image in image_inputs:
     self.assertIsInstance(_UpperCAmelCase     ,     np.ndarray       )

   # Test not batched input
   __UpperCAmelCase  :     str		=    image_processor(image_inputs[0]     ,     return_tensors='pt'       ).pixel_values
   __UpperCAmelCase  :     List[str]		=    self.calculate_expected_size(image_inputs[0]       )
   self.assertEqual(
       encoded_images.shape     ,     (
           1,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )     ,     )




 def 						a					( self       ):
   # Initialize image_processor
   __UpperCAmelCase  :     Union[str, Any]		=    self.image_processing_class(**self.image_processor_dict       )
   # create random PyTorch tensors
   __UpperCAmelCase  :     str		=    self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase     ,     torchify=_UpperCAmelCase       )
   for image in image_inputs:
     self.assertIsInstance(_UpperCAmelCase     ,     torch.Tensor       )

   # Test not batched input
   __UpperCAmelCase  :     Dict		=    image_processor(image_inputs[0]     ,     return_tensors='pt'       ).pixel_values
   __UpperCAmelCase  :     Optional[int]		=    self.calculate_expected_size(image_inputs[0]       )
   self.assertEqual(
       encoded_images.shape     ,     (
           1,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )     ,     )








def    _snake_case		(snake_case_				:    list						,					snake_case_				:    list     )       ->		list:
  __UpperCAmelCase  :     int		=    len(snake_case_     )
  __UpperCAmelCase  :     int		=    len(snake_case_     )
  __UpperCAmelCase  :     int		=    (
      first_input_list_length
      if first_input_list_length > second_input_list_length
      else second_input_list_length
  )
  __UpperCAmelCase  :     list		=    []
  for char_count in range(snake_case_     ):
    if char_count < first_input_list_length:
      output_result_list.append(first_input_list[char_count]     )
    if char_count < second_input_list_length:
      output_result_list.append(second_input_list[char_count]     )

  return output_result_list


if __name__ == "__main__":
      print(alternative_list_arrange(['''A''', '''B''', '''C'''], [1, 2, 3, 4, 5]), end=''' ''')





