






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 VideoMAEImageProcessor





class     __a						(					unittest.TestCase ):







 def __init__(	self      :     List[str]							,     SCREAMING_SNAKE_CASE      :     List[str]							,     SCREAMING_SNAKE_CASE      :     int=7							,     SCREAMING_SNAKE_CASE      :     List[str]=3							,     SCREAMING_SNAKE_CASE      :     Union[str, Any]=10							,     SCREAMING_SNAKE_CASE      :     Tuple=18							,     SCREAMING_SNAKE_CASE      :     Tuple=30							,     SCREAMING_SNAKE_CASE      :     Tuple=4_00							,     SCREAMING_SNAKE_CASE      :     Any=True							,     SCREAMING_SNAKE_CASE      :     int=None							,     SCREAMING_SNAKE_CASE      :     Optional[Any]=True							,     SCREAMING_SNAKE_CASE      :     Any=[0.5, 0.5, 0.5]							,     SCREAMING_SNAKE_CASE      :     Any=[0.5, 0.5, 0.5]							,     SCREAMING_SNAKE_CASE      :     Any=None							,     ):


      '''simple docstring'''






      UpperCamelCase__    :     List[str]        =						size if size is not None else {"shortest_edge": 18}
      UpperCamelCase__    :     Tuple        =						crop_size if crop_size is not None else {"height": 18, "width": 18}

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







 def       __lowercase      (	self      :     Dict		):


      '''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     __a						(					A__ ,     unittest.TestCase ):
 _lowerCAmelCase : Union[str, Any] 				=				VideoMAEImageProcessor if is_vision_available() else None







 def       __lowercase      (	self      :     Any		):


      '''simple docstring'''






      UpperCamelCase__    :     Any        =						VideoMAEImageProcessingTester(self		)







 @property
 def       __lowercase      (	self      :     str		):


      '''simple docstring'''






      return self.image_processor_tester.prepare_image_processor_dict()







 def       __lowercase      (	self      :     Dict		):


      '''simple docstring'''






      UpperCamelCase__    :     Optional[int]        =						self.image_processing_class(**self.image_processor_dict		)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,     "image_mean"		)		)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,     "image_std"		)		)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,     "do_normalize"		)		)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,     "do_resize"		)		)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,     "do_center_crop"		)		)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,     "size"		)		)







 def       __lowercase      (	self      :     str		):


      '''simple docstring'''






      UpperCamelCase__    :     List[Any]        =						self.image_processing_class.from_dict(self.image_processor_dict		)
      self.assertEqual(image_processor.size							,     {"shortest_edge": 18}		)
      self.assertEqual(image_processor.crop_size							,     {"height": 18, "width": 18}		)

      UpperCamelCase__    :     Union[str, Any]        =						self.image_processing_class.from_dict(self.image_processor_dict							,     size=42							,     crop_size=84		)
      self.assertEqual(image_processor.size							,     {"shortest_edge": 42}		)
      self.assertEqual(image_processor.crop_size							,     {"height": 84, "width": 84}		)







 def       __lowercase      (	self      :     Any		):


      '''simple docstring'''






      pass







 def       __lowercase      (	self      :     int		):


      '''simple docstring'''






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

      # Test not batched input
      UpperCamelCase__    :     List[Any]        =						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__    :     Any        =						image_processing(SCREAMING_SNAKE_CASE							,     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      :     Dict		):


      '''simple docstring'''






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

      # Test not batched input
      UpperCamelCase__    :     Optional[int]        =						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__    :     Any        =						image_processing(SCREAMING_SNAKE_CASE							,     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      :     str		):


      '''simple docstring'''






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

      # Test not batched input
      UpperCamelCase__    :     str        =						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__    :     Dict        =						image_processing(SCREAMING_SNAKE_CASE							,     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 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			str:
     return "".join(chr(ord(__lowerCAmelCase    ) + 32    ) if "A" <= char <= "Z" else char for char in word    )


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

 testmod()