


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_video_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import VivitImageProcessor






class   __lowercase				(  unittest.TestCase  ):





      def __init__(		self						:					Union[str, Any]					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					Tuple=7					,       __lowerCamelCase						:					int=3					,       __lowerCamelCase						:					List[str]=1_0					,       __lowerCamelCase						:					Any=1_8					,       __lowerCamelCase						:					str=3_0					,       __lowerCamelCase						:					Dict=4_0_0					,       __lowerCamelCase						:					Optional[Any]=True					,       __lowerCamelCase						:					Any=None					,       __lowerCamelCase						:					str=True					,       __lowerCamelCase						:					List[str]=[0.5, 0.5, 0.5]					,       __lowerCamelCase						:					Any=[0.5, 0.5, 0.5]					,       __lowerCamelCase						:					str=None					,       )    ->   List[Any]:






         """simple docstring"""







         UpperCAmelCase							=     size if size is not None else {"""shortest_edge""": 1_8}
         UpperCAmelCase							=     crop_size if crop_size is not None else {"""height""": 1_8, """width""": 1_8}

         UpperCAmelCase							=     parent
         UpperCAmelCase							=     batch_size
         UpperCAmelCase							=     num_channels
         UpperCAmelCase							=     num_frames
         UpperCAmelCase							=     image_size
         UpperCAmelCase							=     min_resolution
         UpperCAmelCase							=     max_resolution
         UpperCAmelCase							=     do_resize
         UpperCAmelCase							=     size
         UpperCAmelCase							=     do_normalize
         UpperCAmelCase							=     image_mean
         UpperCAmelCase							=     image_std
         UpperCAmelCase							=     crop_size





      def        _lowercase				(		self						:					Dict       )    ->   List[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,
             "crop_size": self.crop_size,
         }






@require_torch
@require_vision
class   __lowercase				(  __snake_case ,					unittest.TestCase  ):
      UpperCamelCase								=       VivitImageProcessor if is_vision_available() else None





      def        _lowercase				(		self						:					Dict       )    ->   Dict:






         """simple docstring"""







         UpperCAmelCase							=     VivitImageProcessingTester(self       )





      @property
      def        _lowercase				(		self						:					Optional[int]       )    ->   Any:






         """simple docstring"""







         return self.image_processor_tester.prepare_image_processor_dict()





      def        _lowercase				(		self						:					Optional[int]       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase							=     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					,       """do_center_crop"""       )       )
         self.assertTrue(hasattr(__lowerCamelCase					,       """size"""       )       )





      def        _lowercase				(		self						:					str       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase							=     self.image_processing_class.from_dict(self.image_processor_dict       )
         self.assertEqual(image_processor.size					,       {"""shortest_edge""": 1_8}       )
         self.assertEqual(image_processor.crop_size					,       {"""height""": 1_8, """width""": 1_8}       )

         UpperCAmelCase							=     self.image_processing_class.from_dict(self.image_processor_dict					,       size=4_2					,       crop_size=8_4       )
         self.assertEqual(image_processor.size					,       {"""shortest_edge""": 4_2}       )
         self.assertEqual(image_processor.crop_size					,       {"""height""": 8_4, """width""": 8_4}       )





      def        _lowercase				(		self						:					str       )    ->   str:






         """simple docstring"""







         UpperCAmelCase							=     self.image_processing_class(**self.image_processor_dict       )
         # create random PIL videos
         UpperCAmelCase							=     prepare_video_inputs(self.image_processor_tester					,       equal_resolution=__lowerCamelCase       )
         for video in video_inputs:
            self.assertIsInstance(__lowerCamelCase					,       __lowerCamelCase       )
            self.assertIsInstance(video[0]					,       Image.Image       )

         # Test not batched input
         UpperCAmelCase							=     image_processing(video_inputs[0]					,       return_tensors="""pt"""       ).pixel_values
         self.assertEqual(
             encoded_videos.shape					,       (
                 1,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )					,       )

         # Test batched
         UpperCAmelCase							=     image_processing(__lowerCamelCase					,       return_tensors="""pt"""       ).pixel_values
         self.assertEqual(
             encoded_videos.shape					,       (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )					,       )





      def        _lowercase				(		self						:					List[str]       )    ->   List[Any]:






         """simple docstring"""







         UpperCAmelCase							=     self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         UpperCAmelCase							=     prepare_video_inputs(self.image_processor_tester					,       equal_resolution=__lowerCamelCase					,       numpify=__lowerCamelCase       )
         for video in video_inputs:
            self.assertIsInstance(__lowerCamelCase					,       __lowerCamelCase       )
            self.assertIsInstance(video[0]					,       np.ndarray       )

         # Test not batched input
         UpperCAmelCase							=     image_processing(video_inputs[0]					,       return_tensors="""pt"""       ).pixel_values
         self.assertEqual(
             encoded_videos.shape					,       (
                 1,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )					,       )

         # Test batched
         UpperCAmelCase							=     image_processing(__lowerCamelCase					,       return_tensors="""pt"""       ).pixel_values
         self.assertEqual(
             encoded_videos.shape					,       (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )					,       )





      def        _lowercase				(		self						:					List[Any]       )    ->   Any:






         """simple docstring"""







         UpperCAmelCase							=     self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         UpperCAmelCase							=     prepare_video_inputs(self.image_processor_tester					,       equal_resolution=__lowerCamelCase					,       torchify=__lowerCamelCase       )
         for video in video_inputs:
            self.assertIsInstance(__lowerCamelCase					,       __lowerCamelCase       )
            self.assertIsInstance(video[0]					,       torch.Tensor       )

         # Test not batched input
         UpperCAmelCase							=     image_processing(video_inputs[0]					,       return_tensors="""pt"""       ).pixel_values
         self.assertEqual(
             encoded_videos.shape					,       (
                 1,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )					,       )

         # Test batched
         UpperCAmelCase							=     image_processing(__lowerCamelCase					,       return_tensors="""pt"""       ).pixel_values
         self.assertEqual(
             encoded_videos.shape					,       (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )					,       )








__a     =			[
    (1000, """M"""),
    (900, """CM"""),
    (500, """D"""),
    (400, """CD"""),
    (100, """C"""),
    (90, """XC"""),
    (50, """L"""),
    (40, """XL"""),
    (10, """X"""),
    (9, """IX"""),
    (5, """V"""),
    (4, """IV"""),
    (1, """I"""),
]
def   _UpperCamelCase				(						lowerCAmelCase_   )						->int:
   UpperCAmelCase							=     {"""I""": 1, """V""": 5, """X""": 1_0, """L""": 5_0, """C""": 1_0_0, """D""": 5_0_0, """M""": 1_0_0_0}
   UpperCAmelCase							=     0
   UpperCAmelCase							=     0
   while place < len(lowerCAmelCase_   ):
      if (place + 1 < len(lowerCAmelCase_   )) and (vals[roman[place]] < vals[roman[place + 1]]):
         total += vals[roman[place + 1]] - vals[roman[place]]
         place += 2
      else:
         total += vals[roman[place]]
         place += 1
   return total
def   _UpperCamelCase				(						lowerCAmelCase_   )						->str:
   UpperCAmelCase							=     []
   for arabic, roman in ROMAN:
      ((UpperCAmelCase)				,  (UpperCAmelCase))							=     divmod(lowerCAmelCase_  ,						lowerCAmelCase_   )
      result.append(roman * factor   )
      if number == 0:
         break
   return "".join(lowerCAmelCase_   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()



