
'''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 TvltImageProcessor





def 				A_					(	snake_case ,    snake_case=10 ,    snake_case=10 ,    snake_case=False ,    snake_case=False	):
  SCREAMING_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
    SCREAMING_SNAKE_CASE:Union[str, Any]   =   [Image.fromarray(np.moveaxis(A_ ,    0 ,    -1	)	) for frame in video]

  if torchify:
    SCREAMING_SNAKE_CASE:Union[str, Any]   =   [torch.from_numpy(A_	) for frame in video]

  return video





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

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

  return video_inputs





class 							_snake_case	(    unittest.TestCase  ):
   def __init__(	self     : int       ,SCREAMING_SNAKE_CASE__     : Dict       ,SCREAMING_SNAKE_CASE__     : Any=7       ,SCREAMING_SNAKE_CASE__     : List[str]=3       ,SCREAMING_SNAKE_CASE__     : List[str]=4       ,SCREAMING_SNAKE_CASE__     : Dict=18       ,SCREAMING_SNAKE_CASE__     : List[str]=30       ,SCREAMING_SNAKE_CASE__     : int=400       ,SCREAMING_SNAKE_CASE__     : str=True       ,SCREAMING_SNAKE_CASE__     : Optional[int]=None       ,SCREAMING_SNAKE_CASE__     : Dict=True       ,SCREAMING_SNAKE_CASE__     : Optional[Any]=[0.5, 0.5, 0.5]       ,SCREAMING_SNAKE_CASE__     : List[Any]=[0.5, 0.5, 0.5]       ,SCREAMING_SNAKE_CASE__     : Optional[int]=True       ,SCREAMING_SNAKE_CASE__     : Dict=None       ,):
     SCREAMING_SNAKE_CASE:Dict   =   size if size is not None else {"shortest_edge": 18}
     SCREAMING_SNAKE_CASE:Tuple   =   crop_size if crop_size is not None else {"height": 18, "width": 18}

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







   def       __UpperCamelCase  (	self     : Dict							):
     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	(    _A		,					unittest.TestCase  ):
   _A    :       int      				=   TvltImageProcessor if is_vision_available() else None
   def       __UpperCamelCase  (	self     : Union[str, Any]							):
     SCREAMING_SNAKE_CASE:Optional[Any]   =   TvltImageProcessorTester(self							)
   @property
   def       __UpperCamelCase  (	self     : Optional[int]							):
     return self.image_processor_tester.prepare_image_processor_dict()
   def       __UpperCamelCase  (	self     : List[str]							):
     SCREAMING_SNAKE_CASE:Any   =   self.image_processing_class(**self.image_processor_dict							)
     self.assertTrue(hasattr(UpperCamelCase__       ,"image_mean"							)							)
     self.assertTrue(hasattr(UpperCamelCase__       ,"image_std"							)							)
     self.assertTrue(hasattr(UpperCamelCase__       ,"do_normalize"							)							)
     self.assertTrue(hasattr(UpperCamelCase__       ,"do_resize"							)							)
     self.assertTrue(hasattr(UpperCamelCase__       ,"do_center_crop"							)							)
     self.assertTrue(hasattr(UpperCamelCase__       ,"size"							)							)
   def       __UpperCamelCase  (	self     : Optional[Any]							):
     SCREAMING_SNAKE_CASE:Any   =   self.image_processing_class(**self.image_processor_dict							)
     # create random PIL videos
     SCREAMING_SNAKE_CASE:List[Any]   =   prepare_video_inputs(self.image_processor_tester       ,equal_resolution=UpperCamelCase__							)
     for video in video_inputs:
       self.assertIsInstance(UpperCamelCase__       ,UpperCamelCase__							)
       self.assertIsInstance(video[0]       ,Image.Image							)

     # Test not batched input
     SCREAMING_SNAKE_CASE:Optional[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
     SCREAMING_SNAKE_CASE:Dict   =   image_processor(UpperCamelCase__       ,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     : Union[str, Any]							):
     SCREAMING_SNAKE_CASE:Any   =   self.image_processing_class(**self.image_processor_dict							)
     # create random numpy tensors
     SCREAMING_SNAKE_CASE:Any   =   prepare_video_inputs(self.image_processor_tester       ,equal_resolution=UpperCamelCase__       ,numpify=UpperCamelCase__							)
     for video in video_inputs:
       self.assertIsInstance(UpperCamelCase__       ,UpperCamelCase__							)
       self.assertIsInstance(video[0]       ,np.ndarray							)

     # Test not batched input
     SCREAMING_SNAKE_CASE:Dict   =   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
     SCREAMING_SNAKE_CASE:Dict   =   image_processor(UpperCamelCase__       ,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     : Optional[Any]							):
     SCREAMING_SNAKE_CASE:Any   =   self.image_processing_class(**self.image_processor_dict							)
     # create random PyTorch tensors
     SCREAMING_SNAKE_CASE:List[str]   =   prepare_video_inputs(self.image_processor_tester       ,equal_resolution=UpperCamelCase__       ,torchify=UpperCamelCase__							)
     for video in video_inputs:
       self.assertIsInstance(UpperCamelCase__       ,UpperCamelCase__							)
       self.assertIsInstance(video[0]       ,torch.Tensor							)

     # Test not batched input
     SCREAMING_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
     SCREAMING_SNAKE_CASE:Optional[Any]   =   image_processor(UpperCamelCase__       ,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"],
         )       ,)









'''simple docstring'''

def 				A_					(	snake_case	):
  return "".join(chr(ord(snake_case	) + 32	) if "A" <= char <= "Z" else char for char in word	)


if __name__ == "__main__":
    from doctest import testmod

    testmod()






