





"""simple docstring"""







class 							snake_case_ :







   """simple docstring"""




   def __init__(		self)							->      Union[str, Any]:
       UpperCamelCase										=  []
       UpperCamelCase										=  0
       UpperCamelCase										=  0







   def __str__(		self)							->      Dict:
       UpperCamelCase										=  '''<''' + str(self.entries)[1:-1] + '''>'''
       return printed







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Any:
       self.entries.append(lowerCamelCase_)
       UpperCamelCase										=  self.length + 1







   def      UpperCAmelCase__ (		self)							->      Optional[int]:
       UpperCamelCase										=  self.length - 1
       UpperCamelCase										=  self.entries[self.front]
       # self.front-=1
       # self.entries = self.entries[self.front:]
       UpperCamelCase										=  self.entries[1:]
       return dequeued







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      List[str]:
       for _ in range(lowerCamelCase_):
           self.put(self.get())







   def      UpperCAmelCase__ (		self)							->      Tuple:
       return self.entries[0]







   def      UpperCAmelCase__ (		self)							->      Optional[int]:
       return self.length





"""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, prepare_video_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import VideoMAEImageProcessor

class 							snake_case_ (				unittest.TestCase ):







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_=7					,				lowerCamelCase_=3					,				lowerCamelCase_=1_0					,				lowerCamelCase_=1_8					,				lowerCamelCase_=3_0					,				lowerCamelCase_=4_0_0					,				lowerCamelCase_=True					,				lowerCamelCase_=None					,				lowerCamelCase_=True					,				lowerCamelCase_=[0.5, 0.5, 0.5]					,				lowerCamelCase_=[0.5, 0.5, 0.5]					,				lowerCamelCase_=None					,				)							->      Optional[Any]:
       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      UpperCAmelCase__ (		self)							->      Tuple:
       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 							snake_case_ (				lowerCamelCase_  ,   unittest.TestCase ):







   """simple docstring"""




   A_										=					VideoMAEImageProcessor if is_vision_available() else None







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       UpperCamelCase										=  VideoMAEImageProcessingTester(self)







   @property
   def      UpperCAmelCase__ (		self)							->      Optional[int]:
       return self.image_processor_tester.prepare_image_processor_dict()







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







   def      UpperCAmelCase__ (		self)							->      Dict:
       # Initialize image_processing
       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      UpperCAmelCase__ (		self)							->      Dict:
       # Initialize image_processing
       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      UpperCAmelCase__ (		self)							->      Optional[int]:
       # Initialize image_processing
       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'''],
           )					,				)