

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  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[str]      ,						**lowercase__  :List[Any]				)       ->  str:
						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     snake_case__  (      lowerCAmelCase_  ):
				@staticmethod
				def  UpperCamelCase_						(  _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Any				=      parser.add_parser('run_beam'     ,      help='Run a Beam dataset processing pipeline'      )
										run_beam_parser.add_argument('dataset'     ,      type=__a     ,      help='Name of the dataset to download'      )
										run_beam_parser.add_argument('--name'     ,      type=__a     ,      default=__a     ,      help='Dataset config name'      )
										run_beam_parser.add_argument(
										    '--cache_dir'     ,      type=__a     ,      default=__a     ,      help='Cache directory where the datasets are stored'     ,      )
										run_beam_parser.add_argument(
										    '--beam_pipeline_options'     ,      type=__a     ,      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=__a     ,      default=__a     ,      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=__a      )
				def __init__(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      **_UpperCAmelCase     ,      ):




										"""simple docstring"""




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





				def  UpperCamelCase_						(  self      ):




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

																__magic_name__				:							Optional[int]				=      Path(__a      ).name + '.py'

																__magic_name__				:							Dict				=      os.path.join(__a     ,      __a      )
																if os.path.isfile(__a      ):
																						__magic_name__				:							int				=      os.path.dirname(__a      )
																elif os.path.isfile(__a      ):
																						__magic_name__				:							List[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
																__magic_name__				:							str				=      os.path.join(__a     ,      config.DATASETDICT_INFOS_FILENAME      )
																copyfile(__a     ,      __a      )
																print(F"Dataset Infos file saved at {user_dataset_infos_path}"      )






from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
snake_case__     							= load_iris()
iris.keys()


print(F"""Target names: \n {iris.target_names} """)
print(F"""\n Features: \n {iris.feature_names}""")

# Train set e Test set
snake_case__							,     snake_case__							,     snake_case__							,     snake_case__     							= train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

snake_case__     							= KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
snake_case__     							= [[1, 2, 1, 4], [2, 3, 4, 5]]

snake_case__     							= knn.predict(X_new)

print(
    F"""\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"""
    F""" {iris['target_names'][prediction]}"""
)


