






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 						A_					(						lowercase_	,  **lowercase_			)				->List[str]:







 """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	,  **lowercase_	,  )
class 	a_(		lowercase__	):



     """simple docstring"""



     @staticmethod
     def     __UpperCamelCase						(		lowerCAmelCase__					:	ArgumentParser)							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		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					:	Union[str, Any]  ,  lowerCAmelCase__					:	str  ,  lowerCAmelCase__					:	str  ,  lowerCAmelCase__					:	str  ,  lowerCAmelCase__					:	str  ,  lowerCAmelCase__					:	str  ,  lowerCAmelCase__					:	bool  ,  lowerCAmelCase__					:	bool  ,  lowerCAmelCase__					:	bool  ,  lowerCAmelCase__					:	bool  ,  **lowerCAmelCase__					:	int  ,  )							->	str:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		dataset
      SCREAMING_SNAKE_CASE				    =		name
      SCREAMING_SNAKE_CASE				    =		cache_dir
      SCREAMING_SNAKE_CASE				    =		beam_pipeline_options
      SCREAMING_SNAKE_CASE				    =		data_dir
      SCREAMING_SNAKE_CASE				    =		all_configs
      SCREAMING_SNAKE_CASE				    =		save_infos
      SCREAMING_SNAKE_CASE				    =		ignore_verifications
      SCREAMING_SNAKE_CASE				    =		force_redownload
      SCREAMING_SNAKE_CASE				    =		config_kwargs





     def     __UpperCamelCase						(		self					:	Optional[int])							->	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				    =		self._dataset, self._name
      SCREAMING_SNAKE_CASE				    =		dataset_module_factory(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		import_main_class(dataset_module.module_path)
      SCREAMING_SNAKE_CASE				    =		[]
      if self._beam_pipeline_options:
       SCREAMING_SNAKE_CASE				    =		beam.options.pipeline_options.PipelineOptions(
           flags=[f'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(',') if opt])
      else:
       SCREAMING_SNAKE_CASE				    =		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:
       SCREAMING_SNAKE_CASE				    =		os.path.join(builder_cls.get_imported_module_dir()  ,  config.DATASETDICT_INFOS_FILENAME)

       SCREAMING_SNAKE_CASE				    =		Path(lowerCAmelCase__).name + '.py'

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






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
 from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


lowerCamelCase_				:							Any       =    logging.get_logger(__name__)

lowerCamelCase_				:							Optional[Any]       =    {
    "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/config.json",
    "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/config.json",
    "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json",
    "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json",
    "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json",
    "microsoft/deberta-xlarge-mnli": "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json",
}







class  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			"""deberta"""

     def __init__(  self							,       SCREAMING_SNAKE_CASE_=50_265							,       SCREAMING_SNAKE_CASE_=768							,       SCREAMING_SNAKE_CASE_=12							,       SCREAMING_SNAKE_CASE_=12							,       SCREAMING_SNAKE_CASE_=3_072							,       SCREAMING_SNAKE_CASE_="gelu"							,       SCREAMING_SNAKE_CASE_=0.1							,       SCREAMING_SNAKE_CASE_=0.1							,       SCREAMING_SNAKE_CASE_=512							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_=0.0_2							,       SCREAMING_SNAKE_CASE_=1E-7							,       SCREAMING_SNAKE_CASE_=False							,       SCREAMING_SNAKE_CASE_=-1							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_="gelu"							,       **SCREAMING_SNAKE_CASE_							,       ):
            super().__init__(**SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   hidden_size
            __snake_case						       =   num_hidden_layers
            __snake_case						       =   num_attention_heads
            __snake_case						       =   intermediate_size
            __snake_case						       =   hidden_act
            __snake_case						       =   hidden_dropout_prob
            __snake_case						       =   attention_probs_dropout_prob
            __snake_case						       =   max_position_embeddings
            __snake_case						       =   type_vocab_size
            __snake_case						       =   initializer_range
            __snake_case						       =   relative_attention
            __snake_case						       =   max_relative_positions
            __snake_case						       =   pad_token_id
            __snake_case						       =   position_biased_input

            # Backwards compatibility
            if type(SCREAMING_SNAKE_CASE_    ) == str:
                   __snake_case						       =   [x.strip() for x in pos_att_type.lower().split('|'    )]

            __snake_case						       =   pos_att_type
            __snake_case						       =   vocab_size
            __snake_case						       =   layer_norm_eps

            __snake_case						       =   kwargs.get('pooler_hidden_size'							,       SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   pooler_dropout
            __snake_case						       =   pooler_hidden_act







class  _lowerCamelCase			(lowerCamelCase					):

     @property
     def 						__lowerCamelCase				(  self    ):
            if self.task == "multiple-choice":
                   __snake_case						       =   {0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   __snake_case						       =   {0: 'batch', 1: 'sequence'}
            if self._config.type_vocab_size > 0:
                   return OrderedDict(
                       [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)]    )
            else:
                   return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)]    )

     @property
     def 						__lowerCamelCase				(  self    ):
            return 12

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = -1							,       SCREAMING_SNAKE_CASE_ = -1							,       SCREAMING_SNAKE_CASE_ = -1							,       SCREAMING_SNAKE_CASE_ = False							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = 3							,       SCREAMING_SNAKE_CASE_ = 40							,       SCREAMING_SNAKE_CASE_ = 40							,       SCREAMING_SNAKE_CASE_ = None							,       ):
            __snake_case						       =   super().generate_dummy_inputs(preprocessor=SCREAMING_SNAKE_CASE_							,       framework=SCREAMING_SNAKE_CASE_    )
            if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                   del dummy_inputs["token_type_ids"]
            return dummy_inputs
