
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 							lowercase   (						unittest.TestCase  ):



		def __init__(					self							, A_							, A_=7							, A_=3							, A_=10							, A_=18							, A_=30							, A_=400							, A_=True							, A_=None							, A_=True							, A_=[0.5, 0.5, 0.5]							, A_=[0.5, 0.5, 0.5]							, A_=None							, )    ->   Any:
				"""simple docstring"""
				UpperCamelCase            =	size if size is not None else {'''shortest_edge''': 18}
				UpperCamelCase            =	crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

				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:
				"""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 							lowercase   (						_lowerCAmelCase    ,  unittest.TestCase  ):
		__lowercase			:			Optional[int]      	=							VideoMAEImageProcessor if is_vision_available() else None



		def __UpperCamelCase					(					self			)    ->   Dict:
				"""simple docstring"""
				UpperCamelCase            =	VideoMAEImageProcessingTester(self			)



		@property
		def __UpperCamelCase					(					self			)    ->   Union[str, Any]:
				"""simple docstring"""
				return self.image_processor_tester.prepare_image_processor_dict()



		def __UpperCamelCase					(					self			)    ->   int:
				"""simple docstring"""
				UpperCamelCase            =	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			)    ->   int:
				"""simple docstring"""
				UpperCamelCase            =	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            =	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			)    ->   str:
				"""simple docstring"""
				pass



		def __UpperCamelCase					(					self			)    ->   Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase            =	self.image_processing_class(**self.image_processor_dict			)
				# create random PIL videos
				UpperCamelCase            =	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
				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(__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[int]:
				"""simple docstring"""
				UpperCamelCase            =	self.image_processing_class(**self.image_processor_dict			)
				# create random numpy tensors
				UpperCamelCase            =	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
				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(__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]:
				"""simple docstring"""
				UpperCamelCase            =	self.image_processing_class(**self.image_processor_dict			)
				# create random PyTorch tensors
				UpperCamelCase            =	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
				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(__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"""




from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments



def 				__a       (			):







					"""simple docstring"""





					lowerCamelCase__		:   Union[str, Any]  				=					HfArgumentParser(_lowercase						)
					try:
										lowerCamelCase__		:   Optional[int]  				=					parser.parse_args_into_dataclasses()[0]
					except ValueError as e:
										lowerCamelCase__		:   Tuple  				=					'''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
										lowerCamelCase__		:   Tuple  				=					''' '''.join(str(_lowercase						).split(''' '''						)[:-1]						)
										lowerCamelCase__		:   Union[str, Any]  				=					''''''
										lowerCamelCase__		:   int  				=					eval(str(_lowercase						).split(''' '''						)[-1]						)
										lowerCamelCase__		:   Any  				=					[]
										for arg in depreciated_args:
															# arg[2:] removes '--'
															if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
																				# arg[5:] removes '--no_'
																				full_error_msg += arg_error_msg.format(arg[5:]						)
															else:
																				wrong_args.append(_lowercase						)
										if len(_lowercase						) > 0:
															lowerCamelCase__		:   Any  				=					full_error_msg + begin_error_msg + str(_lowercase						)
										raise ValueError(_lowercase						)

					lowerCamelCase__		:   List[Any]  				=					PyTorchBenchmark(args=_lowercase						)
					benchmark.run()


if __name__ == "__main__":
					main()






