



'''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 VivitImageProcessor






class 	lowerCAmelCase_(    unittest.TestCase					):
  '''simple docstring'''




  def __init__(   self  ,__UpperCAmelCase  ,__UpperCAmelCase=7  ,__UpperCAmelCase=3  ,__UpperCAmelCase=10  ,__UpperCAmelCase=18  ,__UpperCAmelCase=30  ,__UpperCAmelCase=400  ,__UpperCAmelCase=True  ,__UpperCAmelCase=None  ,__UpperCAmelCase=True  ,__UpperCAmelCase=[0.5, 0.5, 0.5]  ,__UpperCAmelCase=[0.5, 0.5, 0.5]  ,__UpperCAmelCase=None  ,)   ->		Tuple:
      lowerCAmelCase__			:  List[Any]											= size if size is not None else {"""shortest_edge""": 18}
      lowerCAmelCase__			:  str											= crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

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






  def 							UpperCAmelCase_     (   self				)   ->		Any:
      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 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				Dict           =				VivitImageProcessor if is_vision_available() else None




  def 							UpperCAmelCase_     (   self				)   ->		int:
      lowerCAmelCase__			:  Optional[Any]											= VivitImageProcessingTester(self				)




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




  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      lowerCAmelCase__			:  List[str]											= 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				)   ->		List[str]:
      lowerCAmelCase__			:  Dict											= 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}				)

      lowerCAmelCase__			:  List[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 							UpperCAmelCase_     (   self				)   ->		int:
      # Initialize image_processing
      lowerCAmelCase__			:  Optional[int]											= self.image_processing_class(**self.image_processor_dict				)
      # create random PIL videos
      lowerCAmelCase__			:  int											= 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
      lowerCAmelCase__			:  Dict											= 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
      lowerCAmelCase__			:  str											= image_processing(__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				)   ->		str:
      # Initialize image_processing
      lowerCAmelCase__			:  Optional[Any]											= self.image_processing_class(**self.image_processor_dict				)
      # create random numpy tensors
      lowerCAmelCase__			:  List[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
      lowerCAmelCase__			:  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
      lowerCAmelCase__			:  Optional[int]											= image_processing(__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				)   ->		Tuple:
      # Initialize image_processing
      lowerCAmelCase__			:  Dict											= self.image_processing_class(**self.image_processor_dict				)
      # create random PyTorch tensors
      lowerCAmelCase__			:  Tuple											= 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
      lowerCAmelCase__			:  List[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
      lowerCAmelCase__			:  Dict											= image_processing(__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'''





import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union






@dataclass
class 	lowerCAmelCase_:
  '''simple docstring'''




  __lowercase							:				Optional[Union[str, Path]]           =				None
  __lowercase							:				bool           =				False
  __lowercase							:				bool           =				False
  __lowercase							:				bool           =				False
  __lowercase							:				Optional[Dict]           =				None
  __lowercase							:				Optional[str]           =				None
  __lowercase							:				bool           =				False
  __lowercase							:				bool           =				False
  __lowercase							:				bool           =				False
  __lowercase							:				bool           =				True
  __lowercase							:				Optional[int]           =				None
  __lowercase							:				int           =				1
  __lowercase							:				Optional[Union[str, bool]]           =				None
  __lowercase							:				bool           =				False
  __lowercase							:				Optional[Dict]           =				None
  __lowercase							:				Optional[str]           =				None




  def 							UpperCAmelCase_     (   self				)   ->		"DownloadConfig":
      return self.__class__(**{k: copy.deepcopy(__UpperCAmelCase				) for k, v in self.__dict__.items()}				)


