
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__			(    snake_case   ,	**snake_case    ):





  """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   ,	**_UpperCAmelCase   ,	)





class   __UpperCamelCase				(    UpperCAmelCase__   ):






     """simple docstring"""







     @staticmethod
     def 				UpperCAmelCase__    (							_A    :    Dict				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						parser.add_parser('''run_beam'''      ,		help='''Run a Beam dataset processing pipeline'''				)
       run_beam_parser.add_argument('''dataset'''      ,		type=snake_case_      ,		help='''Name of the dataset to download'''				)
       run_beam_parser.add_argument('''--name'''      ,		type=snake_case_      ,		default=snake_case_      ,		help='''Dataset config name'''				)
       run_beam_parser.add_argument(
           '''--cache_dir'''      ,		type=snake_case_      ,		default=snake_case_      ,		help='''Cache directory where the datasets are stored'''      ,		)
       run_beam_parser.add_argument(
           '''--beam_pipeline_options'''      ,		type=snake_case_      ,		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=snake_case_      ,		default=snake_case_      ,		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=snake_case_				)



     def __init__(							self    :    str      ,		_A    :    Dict      ,		_A    :    Union[str, Any]      ,		_A    :    List[Any]      ,		_A    :    Any      ,		_A    :    Tuple      ,		_A    :    Dict      ,		_A    :    List[str]      ,		_A    :    Union[str, Any]      ,		_A    :    Union[str, Any]      ,		**_A    :    int      ,		):







       """simple docstring"""




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




     def 				UpperCAmelCase__    (							self    :    List[Any]				):







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

         __SCREAMING_SNAKE_CASE     :					int			=						Path(snake_case_				).name + '.py'

         __SCREAMING_SNAKE_CASE     :					List[str]			=						os.path.join(snake_case_      ,		snake_case_				)
         if os.path.isfile(snake_case_				):
           __SCREAMING_SNAKE_CASE     :					Optional[int]			=						os.path.dirname(snake_case_				)
         elif os.path.isfile(snake_case_				):
           __SCREAMING_SNAKE_CASE     :					Union[str, 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
         __SCREAMING_SNAKE_CASE     :					List[str]			=						os.path.join(snake_case_      ,		config.DATASETDICT_INFOS_FILENAME				)
         copyfile(snake_case_      ,		snake_case_				)
         print(F'''Dataset Infos file saved at {user_dataset_infos_path}'''				)




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     lowerCAmelCase_     =	['''image_processor''', '''tokenizer''']
     lowerCAmelCase_     =	'''LayoutLMv2ImageProcessor'''
     lowerCAmelCase_     =	('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')



     def __init__(							self    :    Optional[Any]      ,		_A    :    List[Any]=None      ,		_A    :    List[str]=None      ,		**_A    :    int				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						None
       if "feature_extractor" in kwargs:
         warnings.warn(
             '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
             ''' instead.'''      ,		_A      ,		)
         __SCREAMING_SNAKE_CASE     :					Tuple			=						kwargs.pop('''feature_extractor'''				)

       __SCREAMING_SNAKE_CASE     :					List[str]			=						image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
         raise ValueError('''You need to specify an `image_processor`.'''				)
       if tokenizer is None:
         raise ValueError('''You need to specify a `tokenizer`.'''				)

       super().__init__(_A      ,		_A				)



     def __call__(							self    :    Optional[int]      ,		_A    :    Tuple      ,		_A    :    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,		_A    :    Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None      ,		_A    :    Union[List[List[int]], List[List[List[int]]]] = None      ,		_A    :    Optional[Union[List[int], List[List[int]]]] = None      ,		_A    :    bool = True      ,		_A    :    Union[bool, str, PaddingStrategy] = False      ,		_A    :    Union[bool, str, TruncationStrategy] = False      ,		_A    :    Optional[int] = None      ,		_A    :    int = 0      ,		_A    :    Optional[int] = None      ,		_A    :    Optional[bool] = None      ,		_A    :    Optional[bool] = None      ,		_A    :    bool = False      ,		_A    :    bool = False      ,		_A    :    bool = False      ,		_A    :    bool = False      ,		_A    :    bool = True      ,		_A    :    Optional[Union[str, TensorType]] = None      ,		**_A    :    Tuple      ,		):







       """simple docstring"""




       if self.image_processor.apply_ocr and (boxes is not None):
         raise ValueError(
             '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''				)

       if self.image_processor.apply_ocr and (word_labels is not None):
         raise ValueError(
             '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''				)

       if return_overflowing_tokens is True and return_offsets_mapping is False:
         raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''				)

       # first, apply the image processor
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.image_processor(images=_A      ,		return_tensors=_A				)

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
         if isinstance(_A      ,		_A				):
           __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						[text]  # add batch dimension (as the image processor always adds a batch dimension)
         __SCREAMING_SNAKE_CASE     :					Tuple			=						features['''words''']

       __SCREAMING_SNAKE_CASE     :					str			=						self.tokenizer(
           text=text if text is not None else features['''words''']      ,		text_pair=text_pair if text_pair is not None else None      ,		boxes=boxes if boxes is not None else features['''boxes''']      ,		word_labels=_A      ,		add_special_tokens=_A      ,		padding=_A      ,		truncation=_A      ,		max_length=_A      ,		stride=_A      ,		pad_to_multiple_of=_A      ,		return_token_type_ids=_A      ,		return_attention_mask=_A      ,		return_overflowing_tokens=_A      ,		return_special_tokens_mask=_A      ,		return_offsets_mapping=_A      ,		return_length=_A      ,		verbose=_A      ,		return_tensors=_A      ,		**_A      ,		)

       # add pixel values
       __SCREAMING_SNAKE_CASE     :					str			=						features.pop('''pixel_values'''				)
       if return_overflowing_tokens is True:
         __SCREAMING_SNAKE_CASE     :					str			=						self.get_overflowing_images(_A      ,		encoded_inputs['''overflow_to_sample_mapping''']				)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						images

       return encoded_inputs



     def 				UpperCAmelCase__    (							self    :    List[str]      ,		_A    :    List[Any]      ,		_A    :    Dict				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						[]
       for sample_idx in overflow_to_sample_mapping:
         images_with_overflow.append(images[sample_idx]				)

       if len(_A				) != len(_A				):
         raise ValueError(
             '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
             F''' {len(_A				)} and {len(_A				)}'''				)

       return images_with_overflow



     def 				UpperCAmelCase__    (							self    :    int      ,		*_A    :    Dict      ,		**_A    :    Optional[Any]				):







       """simple docstring"""




       return self.tokenizer.batch_decode(*_A      ,		**_A				)



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]      ,		*_A    :    Union[str, Any]      ,		**_A    :    List[str]				):







       """simple docstring"""




       return self.tokenizer.decode(*_A      ,		**_A				)



     @property
     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]



     @property
     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       warnings.warn(
           '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''      ,		_A      ,		)
       return self.image_processor_class




     @property
     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''      ,		_A      ,		)
       return self.image_processor
