



"""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    __snake_case       (  __A			:	int       ,		**__A			:	Tuple    )    ->					List[Any]:






				'''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       ,		**__A       ,		)







class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	@staticmethod
	def  _lowerCAmelCase    (			_SCREAMING_SNAKE_CASE							:					ArgumentParser						)	->  List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    parser.add_parser('run_beam'    ,					help='Run a Beam dataset processing pipeline'						)
					run_beam_parser.add_argument('dataset'    ,					type=_SCREAMING_SNAKE_CASE    ,					help='Name of the dataset to download'						)
					run_beam_parser.add_argument('--name'    ,					type=_SCREAMING_SNAKE_CASE    ,					default=_SCREAMING_SNAKE_CASE    ,					help='Dataset config name'						)
					run_beam_parser.add_argument(
					    '--cache_dir'    ,					type=_SCREAMING_SNAKE_CASE    ,					default=_SCREAMING_SNAKE_CASE    ,					help='Cache directory where the datasets are stored'    ,					)
					run_beam_parser.add_argument(
					    '--beam_pipeline_options'    ,					type=_SCREAMING_SNAKE_CASE    ,					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=_SCREAMING_SNAKE_CASE    ,					default=_SCREAMING_SNAKE_CASE    ,					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=_SCREAMING_SNAKE_CASE						)





	def __init__(			self							:					Optional[int]    ,					_SCREAMING_SNAKE_CASE							:					str    ,					_SCREAMING_SNAKE_CASE							:					str    ,					_SCREAMING_SNAKE_CASE							:					str    ,					_SCREAMING_SNAKE_CASE							:					str    ,					_SCREAMING_SNAKE_CASE							:					str    ,					_SCREAMING_SNAKE_CASE							:					bool    ,					_SCREAMING_SNAKE_CASE							:					bool    ,					_SCREAMING_SNAKE_CASE							:					bool    ,					_SCREAMING_SNAKE_CASE							:					bool    ,					**_SCREAMING_SNAKE_CASE							:					Dict    ,					)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[Any] 							=    dataset
					SCREAMING_SNAKE_CASE	:       Dict 							=    name
					SCREAMING_SNAKE_CASE	:       List[str] 							=    cache_dir
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    beam_pipeline_options
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    data_dir
					SCREAMING_SNAKE_CASE	:       str 							=    all_configs
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    save_infos
					SCREAMING_SNAKE_CASE	:       Tuple 							=    ignore_verifications
					SCREAMING_SNAKE_CASE	:       str 							=    force_redownload
					SCREAMING_SNAKE_CASE	:       str 							=    config_kwargs







	def  _lowerCAmelCase    (			self							:					int						)	->  List[str]:





					"""simple docstring"""


					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						)
					SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE	:       List[str] 							=    self._dataset, self._name
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    dataset_module_factory(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       int 							=    import_main_class(dataset_module.module_path						)
					SCREAMING_SNAKE_CASE	:       List[DatasetBuilder] 							=    []
					if self._beam_pipeline_options:
									SCREAMING_SNAKE_CASE	:       Any 							=    beam.options.pipeline_options.PipelineOptions(
									    flags=[f"""--{opt.strip()}""" for opt in self._beam_pipeline_options.split(','						) if opt]						)
					else:
									SCREAMING_SNAKE_CASE	:       str 							=    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=_SCREAMING_SNAKE_CASE    ,					cache_dir=self._cache_dir    ,					base_path=dataset_module.builder_kwargs.get('base_path'						)    ,					)						)
					else:
									builders.append(
									    builder_cls(
									        config_name=_SCREAMING_SNAKE_CASE    ,					data_dir=self._data_dir    ,					beam_options=_SCREAMING_SNAKE_CASE    ,					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=_SCREAMING_SNAKE_CASE    ,					)
									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:
									SCREAMING_SNAKE_CASE	:       Tuple 							=    os.path.join(builder_cls.get_imported_module_dir()    ,					config.DATASETDICT_INFOS_FILENAME						)

									SCREAMING_SNAKE_CASE	:       Dict 							=    Path(_SCREAMING_SNAKE_CASE						).name + '.py'

									SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    os.path.join(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)
									if os.path.isfile(_SCREAMING_SNAKE_CASE						):
													SCREAMING_SNAKE_CASE	:       int 							=    os.path.dirname(_SCREAMING_SNAKE_CASE						)
									elif os.path.isfile(_SCREAMING_SNAKE_CASE						):
													SCREAMING_SNAKE_CASE	:       Any 							=    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
									SCREAMING_SNAKE_CASE	:       Dict 							=    os.path.join(_SCREAMING_SNAKE_CASE    ,					config.DATASETDICT_INFOS_FILENAME						)
									copyfile(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)
									print(f"""Dataset Infos file saved at {user_dataset_infos_path}"""						)










"""simple docstring"""

import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest
class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Dict     	=   (VQDiffusionScheduler,)





	def  _lowerCAmelCase    (			self							:					Tuple    ,					**_SCREAMING_SNAKE_CASE							:					Optional[int]						)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[str] 							=    {
					    'num_vec_classes': 4_097,
					    'num_train_timesteps': 100,
					}

					config.update(**_SCREAMING_SNAKE_CASE						)
					return config





	def  _lowerCAmelCase    (			self							:					Dict    ,					_SCREAMING_SNAKE_CASE							:					Tuple						)	->  List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    4
					SCREAMING_SNAKE_CASE	:       List[str] 							=    8
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    8

					SCREAMING_SNAKE_CASE	:       Tuple 							=    torch.randint(0    ,					_SCREAMING_SNAKE_CASE    ,					(batch_size, height * width)						)

					return sample





	@property
	def  _lowerCAmelCase    (			self							:					str						)	->  int:





					"""simple docstring"""


					assert False





	def  _lowerCAmelCase    (			self							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					Optional[Any]						)	->  Optional[int]:





					"""simple docstring"""


					def model(_SCREAMING_SNAKE_CASE							:					Optional[Any]    ,					_SCREAMING_SNAKE_CASE							:					Optional[Any]    ,					*_SCREAMING_SNAKE_CASE							:					str						):
									SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE	:       Any 							=    sample.shape
									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)						)
									SCREAMING_SNAKE_CASE	:       List[str] 							=    F.log_softmax(logits.double()    ,					dim=1						).float()
									return return_value

					return model





	def  _lowerCAmelCase    (			self							:					List[str]						)	->  Dict:





					"""simple docstring"""


					for timesteps in [2, 5, 100, 1_000]:
									self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Union[str, Any]						)	->  int:





					"""simple docstring"""


					for num_vec_classes in [5, 100, 1_000, 4_000]:
									self.check_over_configs(num_vec_classes=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Dict						)	->  List[Any]:





					"""simple docstring"""


					for t in [0, 50, 99]:
									self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE						)







	def  _lowerCAmelCase    (			self							:					Any						)	->  Dict:





					"""simple docstring"""


					pass





