code: 
  import random
  from typing import List, Optional, Union
  
  import numpy as np
  import torch
  
  from ..state import AcceleratorState
  from .constants import CUDA_DISTRIBUTED_TYPES
  from .dataclasses import DistributedType, RNGType
  from .imports import is_npu_available, is_tpu_available, is_xpu_available
  
  
  if is_tpu_available(check_device=False):
  						import torch_xla.core.xla_model as xm
  
  def    __snake_case       (  __magic_name__		,						__magic_name__ = False	):
  
  
  
  
  
  
  
  				'''simple docstring'''
  
  
  				if device_specific:
  								seed += AcceleratorState().process_index
  				random.seed(__magic_name__	)
  				np.random.seed(__magic_name__	)
  				torch.manual_seed(__magic_name__	)
  				if is_xpu_available():
  								torch.xpu.manual_seed_all(__magic_name__	)
  				elif is_npu_available():
  								torch.npu.manual_seed_all(__magic_name__	)
  				else:
  								torch.cuda.manual_seed_all(__magic_name__	)
  				# ^^ safe to call this function even if cuda is not available
  				if is_tpu_available():
  								xm.set_rng_state(__magic_name__	)
  
  def    __snake_case       (  __magic_name__ = None		,						__magic_name__ = None	):
  
  
  
  
  
  
  
  				'''simple docstring'''
  
  
  				if rng_type == RNGType.TORCH:
  								lowercase						    =			torch.get_rng_state()
  				elif rng_type == RNGType.CUDA:
  								lowercase						    =			torch.cuda.get_rng_state()
  				elif rng_type == RNGType.XLA:
  								assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
  								lowercase						    =			torch.tensor(xm.get_rng_state()	)
  				elif rng_type == RNGType.NPU:
  								assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
  								lowercase						    =			torch.npu.get_rng_state()
  				elif rng_type == RNGType.XPU:
  								assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
  								lowercase						    =			torch.xpu.get_rng_state()
  				elif rng_type == RNGType.GENERATOR:
  								assert generator is not None, "Need a generator to synchronize its seed."
  								lowercase						    =			generator.get_state()
  
  				# Broadcast the rng state from device 0 to other devices
  				lowercase						    =			AcceleratorState()
  				if state.distributed_type == DistributedType.TPU:
  								lowercase						    =			rng_state.to(xm.xla_device()	)
  								xm.collective_broadcast([rng_state]	)
  								xm.mark_step()
  								lowercase						    =			rng_state.cpu()
  				elif (
  				    state.distributed_type in CUDA_DISTRIBUTED_TYPES
  				    or state.distributed_type == DistributedType.MULTI_NPU
  				    or state.distributed_type == DistributedType.MULTI_XPU
  				):
  								lowercase						    =			rng_state.to(state.device	)
  								torch.distributed.broadcast(__magic_name__		,						0	)
  								lowercase						    =			rng_state.cpu()
  				elif state.distributed_type == DistributedType.MULTI_CPU:
  								torch.distributed.broadcast(__magic_name__		,						0	)
  
  				# Set the broadcast rng state
  				if rng_type == RNGType.TORCH:
  								torch.set_rng_state(__magic_name__	)
  				elif rng_type == RNGType.CUDA:
  								torch.cuda.set_rng_state(__magic_name__	)
  				elif rng_type == RNGType.NPU:
  								torch.npu.set_rng_state(__magic_name__	)
  				elif rng_type == RNGType.XPU:
  								torch.xpu.set_rng_state(__magic_name__	)
  				elif rng_type == RNGType.XLA:
  								xm.set_rng_state(rng_state.item()	)
  				elif rng_type == RNGType.GENERATOR:
  								generator.set_state(__magic_name__	)
  
  
  
  
  def    __snake_case       (  __magic_name__		,						__magic_name__ = None	):
  
  
  
  
  
  
  
  				'''simple docstring'''
  
  
  				for rng_type in rng_types:
  								synchronize_rng_state(RNGType(__magic_name__	)		,						generator=__magic_name__	)
  
style_context: 
  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      UpperCamelCase_			(			unittest.TestCase		):
  
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						def __init__( self       :Any		,						lowerCAmelCase__       :Any		,						lowerCAmelCase__       :Optional[int]=7		,						lowerCAmelCase__       :Tuple=3		,						lowerCAmelCase__       :List[str]=10		,						lowerCAmelCase__       :Dict=18		,						lowerCAmelCase__       :int=30		,						lowerCAmelCase__       :List[str]=400		,						lowerCAmelCase__       :Union[str, Any]=True		,						lowerCAmelCase__       :List[Any]=None		,						lowerCAmelCase__       :List[Any]=True		,						lowerCAmelCase__       :int=[0.5, 0.5, 0.5]		,						lowerCAmelCase__       :Optional[Any]=[0.5, 0.5, 0.5]		,						lowerCAmelCase__       :Dict=None		,						)     ->Union[str, Any]:
  										lowercase						    =			size if size is not None else {"shortest_edge": 18}
  										lowercase						    =			crop_size if crop_size is not None else {"height": 18, "width": 18}
  
  										lowercase						    =			parent
  										lowercase						    =			batch_size
  										lowercase						    =			num_channels
  										lowercase						    =			num_frames
  										lowercase						    =			image_size
  										lowercase						    =			min_resolution
  										lowercase						    =			max_resolution
  										lowercase						    =			do_resize
  										lowercase						    =			size
  										lowercase						    =			do_normalize
  										lowercase						    =			image_mean
  										lowercase						    =			image_std
  										lowercase						    =			crop_size
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :List[Any]    )     ->int:
  										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      UpperCamelCase_			(			__a		,						unittest.TestCase		):
  
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCamelCase			:     Union[str, Any]							      =				VideoMAEImageProcessor if is_vision_available() else None
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :List[str]    )     ->List[Any]:
  										lowercase						    =			VideoMAEImageProcessingTester(self    )
  
  
  
  
  
  						@property
  						def 					SCREAMING_SNAKE_CASE( self       :List[Any]    )     ->Any:
  										return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Dict    )     ->str:
  										lowercase						    =			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 					SCREAMING_SNAKE_CASE( self       :str    )     ->Tuple:
  										lowercase						    =			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}    )
  
  										lowercase						    =			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 					SCREAMING_SNAKE_CASE( self       :int    )     ->Any:
  										pass
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :List[Any]    )     ->List[Any]:
  										# Initialize image_processing
  										lowercase						    =			self.image_processing_class(**self.image_processor_dict    )
  										# create random PIL videos
  										lowercase						    =			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
  										lowercase						    =			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
  										lowercase						    =			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 					SCREAMING_SNAKE_CASE( self       :Dict    )     ->Optional[int]:
  										# Initialize image_processing
  										lowercase						    =			self.image_processing_class(**self.image_processor_dict    )
  										# create random numpy tensors
  										lowercase						    =			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
  										lowercase						    =			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
  										lowercase						    =			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 					SCREAMING_SNAKE_CASE( self       :Any    )     ->Dict:
  										# Initialize image_processing
  										lowercase						    =			self.image_processing_class(**self.image_processor_dict    )
  										# create random PyTorch tensors
  										lowercase						    =			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
  										lowercase						    =			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
  										lowercase						    =			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"],
  										    )		,						)
  