
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       _lowercase					(      __SCREAMING_SNAKE_CASE				,    **__SCREAMING_SNAKE_CASE )	->     Tuple:
			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 			UpperCamelCase_			(						__UpperCamelCase   ):




							'''simple docstring'''

							@staticmethod
							def        lowerCAmelCase__ (				UpperCamelCase) ->	int:
										UpperCamelCase__    :		str     =				parser.add_parser('run_beam'     ,						help='Run a Beam dataset processing pipeline')
										run_beam_parser.add_argument('dataset'     ,						type=UpperCamelCase     ,						help='Name of the dataset to download')
										run_beam_parser.add_argument('--name'     ,						type=UpperCamelCase     ,						default=UpperCamelCase     ,						help='Dataset config name')
										run_beam_parser.add_argument(
										    '--cache_dir'     ,						type=UpperCamelCase     ,						default=UpperCamelCase     ,						help='Cache directory where the datasets are stored'     ,						)
										run_beam_parser.add_argument(
										    '--beam_pipeline_options'     ,						type=UpperCamelCase     ,						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=UpperCamelCase     ,						default=UpperCamelCase     ,						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=UpperCamelCase)
							def __init__(				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						**UpperCamelCase     ,						) ->	Dict:
										UpperCamelCase__    :		Optional[int]     =				dataset
										UpperCamelCase__    :		Optional[Any]     =				name
										UpperCamelCase__    :		List[Any]     =				cache_dir
										UpperCamelCase__    :		Dict     =				beam_pipeline_options
										UpperCamelCase__    :		Tuple     =				data_dir
										UpperCamelCase__    :		Tuple     =				all_configs
										UpperCamelCase__    :		List[str]     =				save_infos
										UpperCamelCase__    :		Any     =				ignore_verifications
										UpperCamelCase__    :		Optional[int]     =				force_redownload
										UpperCamelCase__    :		Any     =				config_kwargs






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

													UpperCamelCase__    :		List[Any]     =				Path(UpperCamelCase).name + '.py'

													UpperCamelCase__    :		Optional[Any]     =				os.path.join(UpperCamelCase     ,						UpperCamelCase)
													if os.path.isfile(UpperCamelCase):
																UpperCamelCase__    :		int     =				os.path.dirname(UpperCamelCase)
													elif os.path.isfile(UpperCamelCase):
																UpperCamelCase__    :		Union[str, 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
													UpperCamelCase__    :		str     =				os.path.join(UpperCamelCase     ,						config.DATASETDICT_INFOS_FILENAME)
													copyfile(UpperCamelCase     ,						UpperCamelCase)
													print(F"""Dataset Infos file saved at {user_dataset_infos_path}""")





import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union



class 			UpperCamelCase_			(						enum.Enum   ):




							'''simple docstring'''

							UpperCamelCase_					   =							"""timm"""
							UpperCamelCase_					   =							"""transformers"""



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Dict:
			if stage_names is None:
						raise ValueError('Stage_names must be set for transformers backbones' )

			if out_features is not None:
						if not isinstance(__SCREAMING_SNAKE_CASE				,    (list,) ):
									raise ValueError(F"""out_features must be a list {type(__SCREAMING_SNAKE_CASE )}""" )
						if any(feat not in stage_names for feat in out_features ):
									raise ValueError(F"""out_features must be a subset of stage_names: {stage_names} got {out_features}""" )

			if out_indices is not None:
						if not isinstance(__SCREAMING_SNAKE_CASE				,    (list, tuple) ):
									raise ValueError(F"""out_indices must be a list or tuple, got {type(__SCREAMING_SNAKE_CASE )}""" )
						if any(idx >= len(__SCREAMING_SNAKE_CASE ) for idx in out_indices ):
									raise ValueError('out_indices must be valid indices for stage_names {stage_names}, got {out_indices}' )

			if out_features is not None and out_indices is not None:
						if len(__SCREAMING_SNAKE_CASE ) != len(__SCREAMING_SNAKE_CASE ):
									raise ValueError('out_features and out_indices should have the same length if both are set' )
						if out_features != [stage_names[idx] for idx in out_indices]:
									raise ValueError('out_features and out_indices should correspond to the same stages if both are set' )



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    )	->     List[Any]:
			if out_indices is None and out_features is None:
						UpperCamelCase__    :		List[Any]     =				[len(__SCREAMING_SNAKE_CASE ) - 1]
						UpperCamelCase__    :		Tuple     =				[stage_names[-1]]
			elif out_indices is None and out_features is not None:
						UpperCamelCase__    :		str     =				[stage_names.index(__SCREAMING_SNAKE_CASE ) for layer in out_features]
			elif out_features is None and out_indices is not None:
						UpperCamelCase__    :		int     =				[stage_names[idx] for idx in out_indices]
			return out_features, out_indices



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    )	->     Tuple[List[str], List[int]]:
			verify_out_features_out_indices(out_features=__SCREAMING_SNAKE_CASE				,    out_indices=__SCREAMING_SNAKE_CASE				,    stage_names=__SCREAMING_SNAKE_CASE )
			UpperCamelCase__   ,			UpperCamelCase__    :		str     =				_align_output_features_output_indices(
			    out_features=__SCREAMING_SNAKE_CASE				,    out_indices=__SCREAMING_SNAKE_CASE				,    stage_names=__SCREAMING_SNAKE_CASE )
			# Verify that the aligned out_features and out_indices are valid
			verify_out_features_out_indices(out_features=__SCREAMING_SNAKE_CASE				,    out_indices=__SCREAMING_SNAKE_CASE				,    stage_names=__SCREAMING_SNAKE_CASE )
			return output_features, output_indices



class 			UpperCamelCase_			:




							'''simple docstring'''

							UpperCamelCase_					   =							None
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	None:
										if getattr(self     ,						'_backbone'     ,						UpperCamelCase) is None:
													raise ValueError('self._backbone must be set before calling _init_timm_backbone')

										# These will diagree with the defaults for the transformers models e.g. for resnet50
										# the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
										# the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
										UpperCamelCase__    :		int     =				[stage['module'] for stage in self._backbone.feature_info.info]
										UpperCamelCase__    :		str     =				[stage['num_chs'] for stage in self._backbone.feature_info.info]
										UpperCamelCase__    :		Optional[int]     =				self._backbone.feature_info.out_indices
										UpperCamelCase__    :		Optional[int]     =				self._backbone.feature_info.module_name()

										# We verify the out indices and out features are valid
										verify_out_features_out_indices(
										    out_features=UpperCamelCase     ,						out_indices=UpperCamelCase     ,						stage_names=self.stage_names)
										UpperCamelCase__   ,			UpperCamelCase__    :		Optional[int]     =				out_features, out_indices
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	None:
										UpperCamelCase__    :		List[str]     =				getattr(UpperCamelCase     ,						'stage_names')
										UpperCamelCase__    :		List[str]     =				getattr(UpperCamelCase     ,						'out_features'     ,						UpperCamelCase)
										UpperCamelCase__    :		Optional[Any]     =				getattr(UpperCamelCase     ,						'out_indices'     ,						UpperCamelCase)

										UpperCamelCase__    :		Tuple     =				stage_names
										UpperCamelCase__   ,			UpperCamelCase__    :		Optional[Any]     =				get_aligned_output_features_output_indices(
										    out_features=UpperCamelCase     ,						out_indices=UpperCamelCase     ,						stage_names=UpperCamelCase)
										# Number of channels for each stage. This is set in the transformer backbone model init
										UpperCamelCase__    :		Dict     =				None
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	None:
										UpperCamelCase__    :		List[Any]     =				config

										UpperCamelCase__    :		Dict     =				getattr(UpperCamelCase     ,						'use_timm_backbone'     ,						UpperCamelCase)
										UpperCamelCase__    :		List[str]     =				BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

										if self.backbone_type == BackboneType.TIMM:
													self._init_timm_backbone(UpperCamelCase)
										elif self.backbone_type == BackboneType.TRANSFORMERS:
													self._init_transformers_backbone(UpperCamelCase)
										else:
													raise ValueError(F"""backbone_type {self.backbone_type} not supported.""")
							@property
							def        lowerCAmelCase__ (				self) ->	List[str]:
										return self._out_features
							@out_features.setter
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	int:
										UpperCamelCase__   ,			UpperCamelCase__    :		str     =				get_aligned_output_features_output_indices(
										    out_features=UpperCamelCase     ,						out_indices=UpperCamelCase     ,						stage_names=self.stage_names)
							@property
							def        lowerCAmelCase__ (				self) ->	List[Any]:
										return self._out_indices
							@out_indices.setter
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	int:
										UpperCamelCase__   ,			UpperCamelCase__    :		Optional[int]     =				get_aligned_output_features_output_indices(
										    out_features=UpperCamelCase     ,						out_indices=UpperCamelCase     ,						stage_names=self.stage_names)
							@property
							def        lowerCAmelCase__ (				self) ->	List[str]:
										# the current backbones will output the number of channels for each stage
										# even if that stage is not in the out_features list.
										return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names)}
							@property
							def        lowerCAmelCase__ (				self) ->	Union[str, Any]:
										return [self.out_feature_channels[name] for name in self.out_features]
							def        lowerCAmelCase__ (				self     ,						*UpperCamelCase     ,						**UpperCamelCase) ->	str:
										UpperCamelCase__    :		List[Any]     =				dict(inspect.signature(self.forward).parameters)
										UpperCamelCase__    :		Any     =				{k: v for k, v in kwargs.items() if k in signature}
										return self(*UpperCamelCase     ,						**UpperCamelCase)
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase = None     ,						UpperCamelCase = None     ,						UpperCamelCase = None     ,						) ->	Union[str, Any]:
										raise NotImplementedError('This method should be implemented by the derived class.')






							def        lowerCAmelCase__ (				self) ->	int:
										UpperCamelCase__    :		Any     =				super().to_dict()
										UpperCamelCase__    :		Optional[Any]     =				output.pop('_out_features')
										UpperCamelCase__    :		Any     =				output.pop('_out_indices')
										return output



class 			UpperCamelCase_			:




							'''simple docstring'''

							@property
							def        lowerCAmelCase__ (				self) ->	Tuple:
										return self._out_features
							@out_features.setter
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	Tuple:
										UpperCamelCase__   ,			UpperCamelCase__    :		Union[str, Any]     =				get_aligned_output_features_output_indices(
										    out_features=UpperCamelCase     ,						out_indices=UpperCamelCase     ,						stage_names=self.stage_names)
							@property
							def        lowerCAmelCase__ (				self) ->	Optional[int]:
										return self._out_indices
							@out_indices.setter
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	int:
										UpperCamelCase__   ,			UpperCamelCase__    :		str     =				get_aligned_output_features_output_indices(
										    out_features=UpperCamelCase     ,						out_indices=UpperCamelCase     ,						stage_names=self.stage_names)






							def        lowerCAmelCase__ (				self) ->	Tuple:
										UpperCamelCase__    :		List[Any]     =				super().to_dict()
										UpperCamelCase__    :		Optional[int]     =				output.pop('_out_features')
										UpperCamelCase__    :		Tuple     =				output.pop('_out_indices')
										return output



