


'''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 			a_   (					SCREAMING_SNAKE_CASE__      :   int	,    **SCREAMING_SNAKE_CASE__      :   Optional[int]  ):
       '''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 A						(     _a  ):






      @staticmethod
      def  lowerCamelCase (      lowercase_		:  List[str]				)      ->		List[str]:
             """simple docstring"""




             _lowerCamelCase :	Any						=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		:  Dict    ,     lowercase_		:  List[str]    ,     lowercase_		:  Tuple    ,     lowercase_		:  int    ,     lowercase_		:  List[str]    ,     lowercase_		:  List[Any]    ,     lowercase_		:  Any    ,     lowercase_		:  Any    ,     lowercase_		:  List[Any]    ,     lowercase_		:  Optional[int]    ,     **lowercase_		:  List[str]    ,     )      ->		Any:
             """simple docstring"""




             _lowerCamelCase :	List[str]						=dataset
             _lowerCamelCase :	List[str]						=name
             _lowerCamelCase :	str						=cache_dir
             _lowerCamelCase :	Optional[Any]						=beam_pipeline_options
             _lowerCamelCase :	Union[str, Any]						=data_dir
             _lowerCamelCase :	int						=all_configs
             _lowerCamelCase :	List[str]						=save_infos
             _lowerCamelCase :	Optional[int]						=ignore_verifications
             _lowerCamelCase :	Union[str, Any]						=force_redownload
             _lowerCamelCase :	Any						=config_kwargs






      def  lowerCamelCase (      self		:  Tuple				)      ->		Tuple:
             """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				)
             _lowerCamelCase :	List[str]						=self._dataset, self._name
             _lowerCamelCase :	List[Any]						=dataset_module_factory(lowerCamelCase__				)
             _lowerCamelCase :	Union[str, Any]						=import_main_class(dataset_module.module_path				)
             _lowerCamelCase :	List[DatasetBuilder]						=[]
             if self._beam_pipeline_options:
                    _lowerCamelCase :	Union[str, Any]						=beam.options.pipeline_options.PipelineOptions(
                        flags=[F'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(','				) if opt]				)
             else:
                    _lowerCamelCase :	Optional[int]						=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:
                    _lowerCamelCase :	Any						=os.path.join(builder_cls.get_imported_module_dir()    ,     config.DATASETDICT_INFOS_FILENAME				)

                    _lowerCamelCase :	Dict						=Path(lowerCamelCase__				).name + ".py"

                    _lowerCamelCase :	Tuple						=os.path.join(lowerCamelCase__    ,     lowerCamelCase__				)
                    if os.path.isfile(lowerCamelCase__				):
                           _lowerCamelCase :	Optional[int]						=os.path.dirname(lowerCamelCase__				)
                    elif os.path.isfile(lowerCamelCase__				):
                           _lowerCamelCase :	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
                    _lowerCamelCase :	Optional[int]						=os.path.join(lowerCamelCase__    ,     config.DATASETDICT_INFOS_FILENAME				)
                    copyfile(lowerCamelCase__    ,     lowerCamelCase__				)
                    print(F'''Dataset Infos file saved at {user_dataset_infos_path}'''				)






from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


lowerCamelCase		      = logging.get_logger(__name__)

lowerCamelCase		      = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase		      = {
    'vocab_file': {
        'openai/clip-vit-base-patch32': 'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json',
    },
    'merges_file': {
        'openai/clip-vit-base-patch32': 'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'openai/clip-vit-base-patch32': (
            'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json'
        ),
    },
}

lowerCamelCase		      = {
    'openai/clip-vit-base-patch32': 77,
}




class A						(     UpperCamelCase_  ):
      UpperCamelCase__							:						Dict   =VOCAB_FILES_NAMES
      UpperCamelCase__							:						Optional[int]   =PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase__							:						Optional[int]   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase__							:						Optional[Any]   =['input_ids', 'attention_mask']
      UpperCamelCase__							:						str   =CLIPTokenizer






      def __init__(      self		:  str    ,     lowercase_		:  Optional[Any]=None    ,     lowercase_		:  Optional[int]=None    ,     lowercase_		:  int=None    ,     lowercase_		:  Optional[int]="<|endoftext|>"    ,     lowercase_		:  Union[str, Any]="<|startoftext|>"    ,     lowercase_		:  Tuple="<|endoftext|>"    ,     lowercase_		:  int="<|endoftext|>"    ,     **lowercase_		:  Optional[Any]    ,     )      ->		Any:
             """simple docstring"""




             super().__init__(
                 lowercase_    ,     lowercase_    ,     tokenizer_file=lowercase_    ,     unk_token=lowercase_    ,     bos_token=lowercase_    ,     eos_token=lowercase_    ,     pad_token=lowercase_    ,     **lowercase_    ,     )

             if not isinstance(self.backend_tokenizer.pre_tokenizer    ,     pre_tokenizers.Sequence				):
                    raise ValueError(
                        'The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'
                        ' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'
                        ' to be compatible with this version.The easiest way to do so is'
                        ' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'
                        ' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'
                        ' transformers.'				)

             self._wrap_decode_method_backend_tokenizer()






      def  lowerCamelCase (      self		:  Any				)      ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase :	int						=self.backend_tokenizer.decode

             def new_decode_method(*lowercase_		:  List[str]    ,     **lowercase_		:  int				):
                    _lowerCamelCase :	Tuple						=orig_decode_method(*lowercase_    ,     **lowercase_				)
                    _lowerCamelCase :	Dict						=text.replace(self.backend_tokenizer.model.end_of_word_suffix    ,     ' '				).strip()
                    return text

             _lowerCamelCase :	List[Any]						=new_decode_method






      def  lowerCamelCase (      self		:  Dict    ,     lowercase_		:  List[int]    ,     lowercase_		:  Optional[List[int]] = None				)      ->		List[int]:
             """simple docstring"""




             _lowerCamelCase :	Union[str, Any]						=[self.bos_token_id]
             _lowerCamelCase :	str						=[self.eos_token_id]

             if token_ids_a is None:
                    return bos_token + token_ids_a + eos_token
             return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token






      def  lowerCamelCase (      self		:  Any    ,     lowercase_		:  List[int]    ,     lowercase_		:  Optional[List[int]] = None				)      ->		List[int]:
             """simple docstring"""




             _lowerCamelCase :	int						=[self.bos_token_id]
             _lowerCamelCase :	Any						=[self.eos_token_id]

             if token_ids_a is None:
                    return len(bos_token + token_ids_a + eos_token				) * [0]
             return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token				) * [0]






      def  lowerCamelCase (      self		:  str    ,     lowercase_		:  str    ,     lowercase_		:  Optional[str] = None				)      ->		Tuple[str]:
             """simple docstring"""




             _lowerCamelCase :	str						=self._tokenizer.model.save(lowercase_    ,     name=lowercase_				)
             return tuple(lowercase_				)


