


'''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	(					__A				,      **__A   )							->		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				,      **SCREAMING_SNAKE_CASE_				,      )



class  lowercase_					(   a__       ):


    @staticmethod
    def        __a							(					a   ):
          UpperCamelCase__  =    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  , a  , a  , a  , a  , a  , a  , a  , a  , a  , **a  , ):
          UpperCamelCase__  =    dataset
          UpperCamelCase__  =    name
          UpperCamelCase__  =    cache_dir
          UpperCamelCase__  =    beam_pipeline_options
          UpperCamelCase__  =    data_dir
          UpperCamelCase__  =    all_configs
          UpperCamelCase__  =    save_infos
          UpperCamelCase__  =    ignore_verifications
          UpperCamelCase__  =    force_redownload
          UpperCamelCase__  =    config_kwargs


    def        __a							(					self   ):
          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__  =    self._dataset, self._name
          UpperCamelCase__  =    dataset_module_factory(__A   )
          UpperCamelCase__  =    import_main_class(dataset_module.module_path   )
          UpperCamelCase__  =    []
          if self._beam_pipeline_options:
                UpperCamelCase__  =    beam.options.pipeline_options.PipelineOptions(
                    flags=[f'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(","   ) if opt]   )
          else:
                UpperCamelCase__  =    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:
                UpperCamelCase__  =    os.path.join(builder_cls.get_imported_module_dir()  , config.DATASETDICT_INFOS_FILENAME   )

                UpperCamelCase__  =    Path(__A   ).name + ".py"

                UpperCamelCase__  =    os.path.join(__A  , __A   )
                if os.path.isfile(__A   ):
                      UpperCamelCase__  =    os.path.dirname(__A   )
                elif os.path.isfile(__A   ):
                      UpperCamelCase__  =    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__  =    os.path.join(__A  , config.DATASETDICT_INFOS_FILENAME   )
                copyfile(__A  , __A   )
                print(f'''Dataset Infos file saved at {user_dataset_infos_path}'''   )










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a__  : Any				=	{
    'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
    'tokenization_codegen': ['CodeGenTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__  : Tuple				=	['CodeGenTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__  : Dict				=	[
      'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
      'CodeGenForCausalLM',
      'CodeGenModel',
      'CodeGenPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
  from .tokenization_codegen import CodeGenTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_codegen_fast import CodeGenTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_codegen import (
        CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
        CodeGenForCausalLM,
        CodeGenModel,
        CodeGenPreTrainedModel,
    )

else:
  import sys

  a__  : Optional[int]				=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





