





import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


_UpperCAmelCase				:       Tuple	   =				logging.get_logger(__name__)

class    lowerCAmelCase							(					A__			):
 def __init__(      self  :      List[str]			,     *UpperCAmelCase  :      List[Any]			,     **UpperCAmelCase  :      str	)			->	None:
     warnings.warn(
         'The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
         ' Please use DeformableDetrImageProcessor instead.'			,     lowerCAmelCase__			,     )
     super().__init__(*lowerCAmelCase__			,     **lowerCAmelCase__	)









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        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



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

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



				def 		__magic_name__(       self			:Tuple	)		->			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        _lowercase      (			A__						,			unittest.TestCase       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			List[str]						=		VivitImageProcessor if is_vision_available() else None


				def 		__magic_name__(       self			:List[str]	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     str					=		VivitImageProcessingTester(self	)


				@property
				def 		__magic_name__(       self			:int	)		->			Union[str, Any]:
										return self.image_processor_tester.prepare_image_processor_dict()


				def 		__magic_name__(       self			:List[str]	)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		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 		__magic_name__(       self			:Optional[Any]	)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     Union[str, 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}	)

										__SCREAMING_SNAKE_CASE						:     Tuple					=		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 		__magic_name__(       self			:List[Any]	)		->			Union[str, Any]:
										# Initialize image_processing
										__SCREAMING_SNAKE_CASE						:     Dict					=		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=lowerCAmelCase__	)
										for video in video_inputs:
																self.assertIsInstance(lowerCAmelCase__			,			lowerCAmelCase__	)
																self.assertIsInstance(video[0]			,			Image.Image	)

										# Test not batched input
										__SCREAMING_SNAKE_CASE						:     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
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		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 		__magic_name__(       self			:str	)		->			int:
										# Initialize image_processing
										__SCREAMING_SNAKE_CASE						:     Any					=		self.image_processing_class(**self.image_processor_dict	)
										# create random numpy tensors
										__SCREAMING_SNAKE_CASE						:     List[str]					=		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
										__SCREAMING_SNAKE_CASE						:     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
										__SCREAMING_SNAKE_CASE						:     Any					=		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 		__magic_name__(       self			:Any	)		->			List[str]:
										# Initialize image_processing
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.image_processing_class(**self.image_processor_dict	)
										# create random PyTorch tensors
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		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
										__SCREAMING_SNAKE_CASE						:     Tuple					=		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
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		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'''],
										    )			,			)
