


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 TvltImageProcessor






def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase=10 ,     __lowerCamelCase=10 ,     __lowerCamelCase=False ,     __lowerCamelCase=False       )		->List[Any]:


 '''simple docstring'''
 snake_case   : List[str]	  =      []
 for i in range(image_processor_tester.num_frames       ):
  video.append(np.random.randint(255 ,     size=(image_processor_tester.num_channels, width, height) ,     dtype=np.uinta       )       )

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  snake_case   : str	  =      [Image.fromarray(np.moveaxis(__lowerCamelCase ,     0 ,     -1       )       ) for frame in video]

 if torchify:
  snake_case   : Any	  =      [torch.from_numpy(__lowerCamelCase       ) for frame in video]

 return video






def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase=False ,     __lowerCamelCase=False ,     __lowerCamelCase=False       )		->Union[str, Any]:


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

 snake_case   : int	  =      []
 for i in range(image_processor_tester.batch_size       ):
  if equal_resolution:
   snake_case   : Union[str, Any]	  =      image_processor_tester.max_resolution
  else:
   snake_case  ,					snake_case   : Union[str, Any]	  =      np.random.choice(
       np.arange(image_processor_tester.min_resolution ,     image_processor_tester.max_resolution       ) ,     2       )
   snake_case   : Tuple	  =      prepare_video(
       image_processor_tester=__lowerCamelCase ,     width=__lowerCamelCase ,     height=__lowerCamelCase ,     numpify=__lowerCamelCase ,     torchify=__lowerCamelCase ,     )
  video_inputs.append(__lowerCamelCase       )

 return video_inputs

class      lowerCamelCase	(  unittest.TestCase     ):

       def __init__(			self	,						a__	,						a__=7	,						a__=3	,						a__=4	,						a__=18	,						a__=30	,						a__=4_00	,						a__=True	,						a__=None	,						a__=True	,						a__=[0.5, 0.5, 0.5]	,						a__=[0.5, 0.5, 0.5]	,						a__=True	,						a__=None	,						):






        '''simple docstring'''






        snake_case   : Optional[Any]	  =      size if size is not None else {'''shortest_edge''': 18}
        snake_case   : int	  =      crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

        snake_case   : Tuple	  =      parent
        snake_case   : Optional[int]	  =      batch_size
        snake_case   : Union[str, Any]	  =      num_channels
        snake_case   : Tuple	  =      num_frames
        snake_case   : List[str]	  =      image_size
        snake_case   : int	  =      min_resolution
        snake_case   : List[Any]	  =      max_resolution
        snake_case   : List[str]	  =      do_resize
        snake_case   : Tuple	  =      size
        snake_case   : int	  =      do_normalize
        snake_case   : str	  =      image_mean
        snake_case   : Union[str, Any]	  =      image_std
        snake_case   : Optional[Any]	  =      do_center_crop
        snake_case   : Optional[int]	  =      crop_size



       def 				UpperCAmelCase				(			self					):






        '''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,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
        }







@require_torch
@require_vision
class      lowerCamelCase	(  SCREAMING_SNAKE_CASE,			unittest.TestCase     ):
       _UpperCamelCase    		=      TvltImageProcessor if is_vision_available() else None

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : int	  =      TvltImageProcessorTester(self					)

       @property
       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        return self.image_processor_tester.prepare_image_processor_dict()

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : str	  =      self.image_processing_class(**self.image_processor_dict					)
        self.assertTrue(hasattr(a__	,						'''image_mean'''					)					)
        self.assertTrue(hasattr(a__	,						'''image_std'''					)					)
        self.assertTrue(hasattr(a__	,						'''do_normalize'''					)					)
        self.assertTrue(hasattr(a__	,						'''do_resize'''					)					)
        self.assertTrue(hasattr(a__	,						'''do_center_crop'''					)					)
        self.assertTrue(hasattr(a__	,						'''size'''					)					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Optional[Any]	  =      self.image_processing_class(**self.image_processor_dict					)
        # create random PIL videos
        snake_case   : List[str]	  =      prepare_video_inputs(self.image_processor_tester	,						equal_resolution=a__					)
        for video in video_inputs:
         self.assertIsInstance(a__	,						a__					)
         self.assertIsInstance(video[0]	,						Image.Image					)

        # Test not batched input
        snake_case   : List[str]	  =      image_processor(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
        snake_case   : List[str]	  =      image_processor(a__	,						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 				UpperCAmelCase				(			self					):






        '''simple docstring'''






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

        # Test not batched input
        snake_case   : Tuple	  =      image_processor(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
        snake_case   : Dict	  =      image_processor(a__	,						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 				UpperCAmelCase				(			self					):






        '''simple docstring'''






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

        # Test not batched input
        snake_case   : Any	  =      image_processor(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
        snake_case   : List[Any]	  =      image_processor(a__	,						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'''],
            )	,						)










from itertools import permutations






def    _UpperCAmelCase	(   )		->str:


 '''simple docstring'''
 snake_case   : Any	  =      list(map(''''''.join ,     permutations('''0123456789'''       )       )       )
 return result[999999]


if __name__ == "__main__":
  print(solution())






