

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    (lowerCAmelCase					:  Tuple,    **lowerCAmelCase					:  str     )     ->  Optional[Any]:
      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,    **lowerCAmelCase,    )







class        _UpperCAmelCase (   __lowercase			):
  '''simple docstring'''







  @staticmethod
  def 	UpperCamelCase       ( UpperCamelCase__	: ArgumentParser ):
        A    =				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	: int		,						UpperCamelCase__	: str		,						UpperCamelCase__	: str		,						UpperCamelCase__	: str		,						UpperCamelCase__	: str		,						UpperCamelCase__	: str		,						UpperCamelCase__	: bool		,						UpperCamelCase__	: bool		,						UpperCamelCase__	: bool		,						UpperCamelCase__	: bool		,						**UpperCamelCase__	: Tuple		,						):
        A    =				dataset
        A    =				name
        A    =				cache_dir
        A    =				beam_pipeline_options
        A    =				data_dir
        A    =				all_configs
        A    =				save_infos
        A    =				ignore_verifications
        A    =				force_redownload
        A    =				config_kwargs







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

              A    =				Path(UpperCamelCase__ ).name + '.py'

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






import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors



@require_tokenizers
class        _UpperCAmelCase (   __lowercase     ,				unittest.TestCase			):
  '''simple docstring'''







  SCREAMING_SNAKE_CASE							:				List[Any]					      =				BartTokenizer
  SCREAMING_SNAKE_CASE							:				List[str]					      =				BartTokenizerFast
  SCREAMING_SNAKE_CASE							:				Any					      =				True
  SCREAMING_SNAKE_CASE							:				Union[str, Any]					      =				filter_roberta_detectors







  def 	UpperCamelCase       ( self	: str ):
        super().setUp()
        A    =				[
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            '\u0120',
            '\u0120l',
            '\u0120n',
            '\u0120lo',
            '\u0120low',
            'er',
            '\u0120lowest',
            '\u0120newer',
            '\u0120wider',
            '<unk>',
        ]
        A    =				dict(zip(UpperCamelCase__		,						range(len(UpperCamelCase__ ) ) ) )
        A    =				['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
        A    =				{'unk_token': '<unk>'}

        A    =				os.path.join(self.tmpdirname		,						VOCAB_FILES_NAMES['vocab_file'] )
        A    =				os.path.join(self.tmpdirname		,						VOCAB_FILES_NAMES['merges_file'] )
        with open(self.vocab_file		,						'w'		,						encoding='utf-8' ) as fp:
              fp.write(json.dumps(UpperCamelCase__ ) + '\n' )
        with open(self.merges_file		,						'w'		,						encoding='utf-8' ) as fp:
              fp.write('\n'.join(UpperCamelCase__ ) )







  def 	UpperCamelCase       ( self	: Dict		,						**UpperCamelCase__	: List[Any] ):
        kwargs.update(self.special_tokens_map )
        return self.tokenizer_class.from_pretrained(self.tmpdirname		,						**UpperCamelCase__ )







  def 	UpperCamelCase       ( self	: Any		,						**UpperCamelCase__	: Union[str, Any] ):
        kwargs.update(self.special_tokens_map )
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,						**UpperCamelCase__ )







  def 	UpperCamelCase       ( self	: int		,						UpperCamelCase__	: str ):
        return "lower newer", "lower newer"







  @cached_property
  def 	UpperCamelCase       ( self	: Optional[Any] ):
        return BartTokenizer.from_pretrained('facebook/bart-large' )







  @cached_property
  def 	UpperCamelCase       ( self	: str ):
        return BartTokenizerFast.from_pretrained('facebook/bart-large' )







  @require_torch
  def 	UpperCamelCase       ( self	: Dict ):
        A    =				['A long paragraph for summarization.', 'Another paragraph for summarization.']
        A    =				[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				tokenizer(UpperCamelCase__		,						max_length=len(UpperCamelCase__ )		,						padding=UpperCamelCase__		,						return_tensors='pt' )
              self.assertIsInstance(UpperCamelCase__		,						UpperCamelCase__ )

              self.assertEqual((2, 9)		,						batch.input_ids.shape )
              self.assertEqual((2, 9)		,						batch.attention_mask.shape )
              A    =				batch.input_ids.tolist()[0]
              self.assertListEqual(UpperCamelCase__		,						UpperCamelCase__ )
              # Test that special tokens are reset







  @require_torch
  def 	UpperCamelCase       ( self	: Tuple ):
        A    =				['A long paragraph for summarization.', 'Another paragraph for summarization.']
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				tokenizer(UpperCamelCase__		,						padding=UpperCamelCase__		,						return_tensors='pt' )
              # check if input_ids are returned and no labels
              self.assertIn('input_ids'		,						UpperCamelCase__ )
              self.assertIn('attention_mask'		,						UpperCamelCase__ )
              self.assertNotIn('labels'		,						UpperCamelCase__ )
              self.assertNotIn('decoder_attention_mask'		,						UpperCamelCase__ )







  @require_torch
  def 	UpperCamelCase       ( self	: Dict ):
        A    =				[
            'Summary of the text.',
            'Another summary.',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				tokenizer(text_target=UpperCamelCase__		,						max_length=32		,						padding='max_length'		,						return_tensors='pt' )
              self.assertEqual(32		,						targets['input_ids'].shape[1] )







  @require_torch
  def 	UpperCamelCase       ( self	: int ):
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				tokenizer(
                  ['I am a small frog' * 1024, 'I am a small frog']		,						padding=UpperCamelCase__		,						truncation=UpperCamelCase__		,						return_tensors='pt' )
              self.assertIsInstance(UpperCamelCase__		,						UpperCamelCase__ )
              self.assertEqual(batch.input_ids.shape		,						(2, 1024) )







  @require_torch
  def 	UpperCamelCase       ( self	: Optional[Any] ):
        A    =				['A long paragraph for summarization.']
        A    =				[
            'Summary of the text.',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				tokenizer(UpperCamelCase__		,						return_tensors='pt' )
              A    =				tokenizer(text_target=UpperCamelCase__		,						return_tensors='pt' )
              A    =				inputs['input_ids']
              A    =				targets['input_ids']
              self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
              self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
              self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
              self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )







  def 	UpperCamelCase       ( self	: List[str] ):
        pass







  def 	UpperCamelCase       ( self	: Tuple ):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
                    A    =				self.rust_tokenizer_class.from_pretrained(UpperCamelCase__		,						**UpperCamelCase__ )
                    A    =				self.tokenizer_class.from_pretrained(UpperCamelCase__		,						**UpperCamelCase__ )
                    A    =				'A, <mask> AllenNLP sentence.'
                    A    =				tokenizer_r.encode_plus(UpperCamelCase__		,						add_special_tokens=UpperCamelCase__		,						return_token_type_ids=UpperCamelCase__ )
                    A    =				tokenizer_p.encode_plus(UpperCamelCase__		,						add_special_tokens=UpperCamelCase__		,						return_token_type_ids=UpperCamelCase__ )

                    # token_type_ids should put 0 everywhere
                    self.assertEqual(sum(tokens_r['token_type_ids'] )		,						sum(tokens_p['token_type_ids'] ) )

                    # attention_mask should put 1 everywhere, so sum over length should be 1
                    self.assertEqual(
                        sum(tokens_r['attention_mask'] ) / len(tokens_r['attention_mask'] )		,						sum(tokens_p['attention_mask'] ) / len(tokens_p['attention_mask'] )		,						)

                    A    =				tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
                    A    =				tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'] )

                    # Rust correctly handles the space before the mask while python doesnt
                    self.assertSequenceEqual(tokens_p['input_ids']		,						[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )
                    self.assertSequenceEqual(tokens_r['input_ids']		,						[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )

                    self.assertSequenceEqual(
                        UpperCamelCase__		,						['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
                    self.assertSequenceEqual(
                        UpperCamelCase__		,						['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )



