code: 
  
  
  '''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 	a__				(				unittest.TestCase      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			def __init__(     self       : Optional[Any] ,						lowerCAmelCase_       : int ,						lowerCAmelCase_       : Dict=7 ,						lowerCAmelCase_       : str=3 ,						lowerCAmelCase_       : List[Any]=10 ,						lowerCAmelCase_       : Union[str, Any]=18 ,						lowerCAmelCase_       : Optional[int]=30 ,						lowerCAmelCase_       : List[str]=400 ,						lowerCAmelCase_       : Any=True ,						lowerCAmelCase_       : Union[str, Any]=None ,						lowerCAmelCase_       : Union[str, Any]=True ,						lowerCAmelCase_       : List[Any]=[0.5, 0.5, 0.5] ,						lowerCAmelCase_       : List[str]=[0.5, 0.5, 0.5] ,						lowerCAmelCase_       : Tuple=None ,						)			->		str:
  						__A=  size if size is not None else {'shortest_edge': 18}
  						__A=  crop_size if crop_size is not None else {'height': 18, 'width': 18}
  
  						__A=  parent
  						__A=  batch_size
  						__A=  num_channels
  						__A=  num_frames
  						__A=  image_size
  						__A=  min_resolution
  						__A=  max_resolution
  						__A=  do_resize
  						__A=  size
  						__A=  do_normalize
  						__A=  image_mean
  						__A=  image_std
  						__A=  crop_size
  
  			def 				lowerCAmelCase    (     self       : Optional[int]  )			->		Dict:
  						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 	a__				(				a__			,     unittest.TestCase      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							List[str]							= VideoMAEImageProcessor if is_vision_available() else None
  			def 				lowerCAmelCase    (     self       : Optional[int]  )			->		Dict:
  						__A=  VideoMAEImageProcessingTester(self  )
  			@property
  			def 				lowerCAmelCase    (     self       : Optional[int]  )			->		List[str]:
  						return self.image_processor_tester.prepare_image_processor_dict()
  			def 				lowerCAmelCase    (     self       : Optional[int]  )			->		Union[str, Any]:
  						__A=  self.image_processing_class(**self.image_processor_dict  )
  						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,						'image_mean'  )  )
  						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,						'image_std'  )  )
  						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,						'do_normalize'  )  )
  						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,						'do_resize'  )  )
  						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,						'do_center_crop'  )  )
  						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,						'size'  )  )
  			def 				lowerCAmelCase    (     self       : Any  )			->		int:
  						__A=  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}  )
  
  						__A=  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 				lowerCAmelCase    (     self       : int  )			->		Union[str, Any]:
  						pass
  			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Union[str, Any]:
  						# Initialize image_processing
  						__A=  self.image_processing_class(**self.image_processor_dict  )
  						# create random PIL videos
  						__A=  prepare_video_inputs(self.image_processor_tester ,						equal_resolution=SCREAMING_SNAKE_CASE_  )
  						for video in video_inputs:
  									self.assertIsInstance(SCREAMING_SNAKE_CASE_ ,						SCREAMING_SNAKE_CASE_  )
  									self.assertIsInstance(video[0] ,						Image.Image  )
  
  						# Test not batched input
  						__A=  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
  						__A=  image_processing(SCREAMING_SNAKE_CASE_ ,						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 				lowerCAmelCase    (     self       : List[Any]  )			->		Dict:
  						# Initialize image_processing
  						__A=  self.image_processing_class(**self.image_processor_dict  )
  						# create random numpy tensors
  						__A=  prepare_video_inputs(self.image_processor_tester ,						equal_resolution=SCREAMING_SNAKE_CASE_ ,						numpify=SCREAMING_SNAKE_CASE_  )
  						for video in video_inputs:
  									self.assertIsInstance(SCREAMING_SNAKE_CASE_ ,						SCREAMING_SNAKE_CASE_  )
  									self.assertIsInstance(video[0] ,						np.ndarray  )
  
  						# Test not batched input
  						__A=  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
  						__A=  image_processing(SCREAMING_SNAKE_CASE_ ,						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 				lowerCAmelCase    (     self       : str  )			->		Optional[Any]:
  						# Initialize image_processing
  						__A=  self.image_processing_class(**self.image_processor_dict  )
  						# create random PyTorch tensors
  						__A=  prepare_video_inputs(self.image_processor_tester ,						equal_resolution=SCREAMING_SNAKE_CASE_ ,						torchify=SCREAMING_SNAKE_CASE_  )
  						for video in video_inputs:
  									self.assertIsInstance(SCREAMING_SNAKE_CASE_ ,						SCREAMING_SNAKE_CASE_  )
  									self.assertIsInstance(video[0] ,						torch.Tensor  )
  
  						# Test not batched input
  						__A=  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
  						__A=  image_processing(SCREAMING_SNAKE_CASE_ ,						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'],
  						    ) ,						)
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  
  import os
  from argparse import ArgumentParser
  from pathlib import Path
  from shutil import copyfile
  from typing import List
  
  from datasets import config
  from datasets.builder import DatasetBuilder
  from datasets.commands import BaseDatasetsCLICommand
  from datasets.download.download_config import DownloadConfig
  from datasets.download.download_manager import DownloadMode
  from datasets.load import dataset_module_factory, import_main_class
  from datasets.utils.info_utils import VerificationMode
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Any,**_SCREAMING_SNAKE_CASE				:   Tuple   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			return RunBeamCommand(
  			    args.dataset,args.name,args.cache_dir,args.beam_pipeline_options,args.data_dir,args.all_configs,args.save_info or args.save_infos,args.ignore_verifications,args.force_redownload,**_SCREAMING_SNAKE_CASE,)
  
  
  
  
  
  
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			@staticmethod
  			def 				lowerCAmelCase    (     lowerCAmelCase_       : ArgumentParser  )			->		Tuple:
  						__A=  parser.add_parser('run_beam' ,						help='Run a Beam dataset processing pipeline'  )
  						run_beam_parser.add_argument('dataset' ,						type=lowerCAmelCase_ ,						help='Name of the dataset to download'  )
  						run_beam_parser.add_argument('--name' ,						type=lowerCAmelCase_ ,						default=lowerCAmelCase_ ,						help='Dataset config name'  )
  						run_beam_parser.add_argument(
  						    '--cache_dir' ,						type=lowerCAmelCase_ ,						default=lowerCAmelCase_ ,						help='Cache directory where the datasets are stored' ,						)
  						run_beam_parser.add_argument(
  						    '--beam_pipeline_options' ,						type=lowerCAmelCase_ ,						default='' ,						help='Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`' ,						)
  						run_beam_parser.add_argument(
  						    '--data_dir' ,						type=lowerCAmelCase_ ,						default=lowerCAmelCase_ ,						help='Can be used to specify a manual directory to get the files from' ,						)
  						run_beam_parser.add_argument('--all_configs' ,						action='store_true' ,						help='Test all dataset configurations'  )
  						run_beam_parser.add_argument('--save_info' ,						action='store_true' ,						help='Save the dataset infos file'  )
  						run_beam_parser.add_argument(
  						    '--ignore_verifications' ,						action='store_true' ,						help='Run the test without checksums and splits checks'  )
  						run_beam_parser.add_argument('--force_redownload' ,						action='store_true' ,						help='Force dataset redownload'  )
  						# aliases
  						run_beam_parser.add_argument('--save_infos' ,						action='store_true' ,						help='alias for save_info'  )
  						run_beam_parser.set_defaults(func=lowerCAmelCase_  )
  			def __init__(     self       : Any ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : bool ,						lowerCAmelCase_       : bool ,						lowerCAmelCase_       : bool ,						lowerCAmelCase_       : bool ,						**lowerCAmelCase_       : Tuple ,						)			->		List[Any]:
  						__A=  dataset
  						__A=  name
  						__A=  cache_dir
  						__A=  beam_pipeline_options
  						__A=  data_dir
  						__A=  all_configs
  						__A=  save_infos
  						__A=  ignore_verifications
  						__A=  force_redownload
  						__A=  config_kwargs
  
  			def 				lowerCAmelCase    (     self       : Optional[int]  )			->		Optional[Any]:
  						import apache_beam as beam
  
  						if self._name is not None and self._all_configs:
  									print('Both parameters `name` and `all_configs` can\'t be used at once.'  )
  									exit(1  )
  						__A,  __A=  self._dataset, self._name
  						__A=  dataset_module_factory(lowerCAmelCase_  )
  						__A=  import_main_class(dataset_module.module_path  )
  						__A=  []
  						if self._beam_pipeline_options:
  									__A=  beam.options.pipeline_options.PipelineOptions(
  									    flags=[F"""--{opt.strip()}""" for opt in self._beam_pipeline_options.split(','  ) if opt]  )
  						else:
  									__A=  None
  						if self._all_configs and len(builder_cls.BUILDER_CONFIGS  ) > 0:
  									for builder_config in builder_cls.BUILDER_CONFIGS:
  												builders.append(
  												    builder_cls(
  												        config_name=builder_config.name ,						data_dir=self._data_dir ,						hash=dataset_module.hash ,						beam_options=lowerCAmelCase_ ,						cache_dir=self._cache_dir ,						base_path=dataset_module.builder_kwargs.get('base_path'  ) ,						)  )
  						else:
  									builders.append(
  									    builder_cls(
  									        config_name=lowerCAmelCase_ ,						data_dir=self._data_dir ,						beam_options=lowerCAmelCase_ ,						cache_dir=self._cache_dir ,						base_path=dataset_module.builder_kwargs.get('base_path'  ) ,						**self._config_kwargs ,						)  )
  
  						for builder in builders:
  									builder.download_and_prepare(
  									    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
  									    if not self._force_redownload
  									    else DownloadMode.FORCE_REDOWNLOAD ,						download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH  ) ,						verification_mode=VerificationMode.NO_CHECKS
  									    if self._ignore_verifications
  									    else VerificationMode.ALL_CHECKS ,						try_from_hf_gcs=lowerCAmelCase_ ,						)
  									if self._save_infos:
  												builder._save_infos()
  
  						print('Apache beam run successful.'  )
  
  						# If save_infos=True, the dataset infos file is created next to the loaded module file.
  						# Let's move it to the original directory of the dataset script, to allow the user to
  						# upload them on S3 at the same time afterwards.
  						if self._save_infos:
  									__A=  os.path.join(builder_cls.get_imported_module_dir() ,						config.DATASETDICT_INFOS_FILENAME  )
  
  									__A=  Path(lowerCAmelCase_  ).name + '.py'
  
  									__A=  os.path.join(lowerCAmelCase_ ,						lowerCAmelCase_  )
  									if os.path.isfile(lowerCAmelCase_  ):
  												__A=  os.path.dirname(lowerCAmelCase_  )
  									elif os.path.isfile(lowerCAmelCase_  ):
  												__A=  path
  									else:  # in case of a remote dataset
  												print(F"""Dataset Infos file saved at {dataset_infos_path}"""  )
  												exit(1  )
  
  									# Move datasetinfo back to the user
  									__A=  os.path.join(lowerCAmelCase_ ,						config.DATASETDICT_INFOS_FILENAME  )
  									copyfile(lowerCAmelCase_ ,						lowerCAmelCase_  )
  									print(F"""Dataset Infos file saved at {user_dataset_infos_path}"""  )
  
  
  
  