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 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Tuple ,	lowerCAmelCase_: Optional[Any]=1_0 ,	lowerCAmelCase_: List[str]=1_0 ,	lowerCAmelCase_: Any=False ,	lowerCAmelCase_: Tuple=False     ):
      snake_case_     :			List[Any]       =       []
      for i in range(image_processor_tester.num_frames     ):
            video.append(np.random.randint(2_5_5 ,	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_     :			List[Any]       =       [Image.fromarray(np.moveaxis(lowerCAmelCase_ ,	0 ,	-1     )     ) for frame in video]

      if torchify:
            snake_case_     :			Tuple       =       [torch.from_numpy(lowerCAmelCase_     ) for frame in video]

      return video






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: List[Any] ,	lowerCAmelCase_: Optional[Any]=False ,	lowerCAmelCase_: Dict=False ,	lowerCAmelCase_: Dict=False     ):
      assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

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

      return video_inputs



class 					snake_case__       (				unittest.TestCase							):



   def __init__(				self   :   List[Any]			,	A__   :   int			,	A__   :   List[str]=7			,	A__   :   str=3			,	A__   :   Dict=4			,	A__   :   str=18			,	A__   :   Any=30			,	A__   :   Union[str, Any]=4_00			,	A__   :   Optional[Any]=True			,	A__   :   Any=None			,	A__   :   int=True			,	A__   :   int=[0.5, 0.5, 0.5]			,	A__   :   List[str]=[0.5, 0.5, 0.5]			,	A__   :   Any=True			,	A__   :   Optional[int]=None			,	)							->     Tuple:




         '''simple docstring'''




         snake_case_     :			List[Any]       =       size if size is not None else {"shortest_edge": 18}
         snake_case_     :			Optional[Any]       =       crop_size if crop_size is not None else {"height": 18, "width": 18}

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



   def 					UpperCAmelCase__       (				self   :   Union[str, Any]   )							->     str:




         '''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 					snake_case__       (				_UpperCamelCase					,						unittest.TestCase							):
   _SCREAMING_SNAKE_CASE	: Optional[Any]						    =      TvltImageProcessor if is_vision_available() else None



   def 					UpperCAmelCase__       (				self   :   Any   )							->     Dict:




         '''simple docstring'''




         snake_case_     :			int       =       TvltImageProcessorTester(self   )



   @property
   def 					UpperCAmelCase__       (				self   :   str   )							->     str:




         '''simple docstring'''




         return self.image_processor_tester.prepare_image_processor_dict()



   def 					UpperCAmelCase__       (				self   :   str   )							->     List[str]:




         '''simple docstring'''




         snake_case_     :			Any       =       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   :   Dict   )							->     Union[str, Any]:




         '''simple docstring'''




         snake_case_     :			str       =       self.image_processing_class(**self.image_processor_dict   )
         # create random PIL videos
         snake_case_     :			Optional[int]       =       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_     :			Optional[int]       =       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_     :			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   :   str   )							->     Dict:




         '''simple docstring'''




         snake_case_     :			Optional[int]       =       self.image_processing_class(**self.image_processor_dict   )
         # create random numpy tensors
         snake_case_     :			Optional[int]       =       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_     :			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_     :			Optional[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"],
             )			,	)



   def 					UpperCAmelCase__       (				self   :   Any   )							->     Dict:




         '''simple docstring'''




         snake_case_     :			str       =       self.image_processing_class(**self.image_processor_dict   )
         # create random PyTorch tensors
         snake_case_     :			Any       =       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_     :			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_     :			int       =       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 __future__ import annotations

UpperCAmelCase							=       "Alexander Joslin"






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: int     ):

      if number <= 0:
            snake_case_     :			Dict       =       f"n must be greater than 0. Got n = {number}"
            raise ValueError(lowerCAmelCase_     )

      snake_case_     :			Tuple       =       [number + 1] * (number + 1)

      # starting position
      snake_case_     :			Union[str, Any]       =       0
      for i in range(1 ,	lowerCAmelCase_     ):
            snake_case_     :			int       =       min(table[i + 1] ,	table[i] + 1     )
            # check if out of bounds
            if i * 2 <= number:
                  snake_case_     :			Tuple       =       min(table[i * 2] ,	table[i] + 1     )
            # check if out of bounds
            if i * 3 <= number:
                  snake_case_     :			Dict       =       min(table[i * 3] ,	table[i] + 1     )
      return table[number]


if __name__ == "__main__":
     import doctest

     doctest.testmod()



