

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				:			Dict     ,				**_A				:			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     ,				**_A     ,				)







class 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   @staticmethod
   def 			_UpperCAmelCase			(    lowercase_	)							->   Tuple:






     """simple docstring"""






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



   def __init__(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   **lowercase_,   )							->   Optional[int]:






     """simple docstring"""






     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]:






     """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	)
     a__,				a__  =self._dataset, self._name
     a__  =dataset_module_factory(lowercase_	)
     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=lowercase_,   cache_dir=self._cache_dir,   base_path=dataset_module.builder_kwargs.get('''base_path'''	),   )	)
     else:
       builders.append(
           builder_cls(
               config_name=lowercase_,   data_dir=self._data_dir,   beam_options=lowercase_,   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=lowercase_,   )
       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(lowercase_	).name + '''.py'''

       a__  =os.path.join(lowercase_,   lowercase_	)
       if os.path.isfile(lowercase_	):
         a__  =os.path.dirname(lowercase_	)
       elif os.path.isfile(lowercase_	):
         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(lowercase_,   config.DATASETDICT_INFOS_FILENAME	)
       copyfile(lowercase_,   lowercase_	)
       print(F"""Dataset Infos file saved at {user_dataset_infos_path}"""	)



from dataclasses import dataclass

lowerCamelCase									= '''0.1'''
lowerCamelCase									= '''Lucia Harcekova'''







@dataclass
class 						__magic_name__      :

   '''simple docstring'''




   lowerCamelCase__			:     int
   lowerCamelCase__			:     int
   lowerCamelCase__			:     str



   def __repr__(    self	)							->   str:






     """simple docstring"""






     return F"""({self.offset}, {self.length}, {self.indicator})"""







class 						__magic_name__      :

   '''simple docstring'''




   def __init__(    self,   lowercase_ = 13,   lowercase_ = 6	)							->   None:






     """simple docstring"""






     a__  =window_size
     a__  =lookahead_buffer_size
     a__  =self.window_size - self.lookahead_buffer_size



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   list[Token]:






     """simple docstring"""






     a__  =[]
     a__  =''''''

     # while there are still characters in text to compress
     while text:
       # find the next encoding phrase
       # - triplet with offset, length, indicator (the next encoding character)
       a__  =self._find_encoding_token(lowercase_,   lowercase_	)

       # update the search buffer:
       # - add new characters from text into it
       # - check if size exceed the max search buffer size, if so, drop the
       #   oldest elements
       search_buffer += text[: token.length + 1]
       if len(lowercase_	) > self.search_buffer_size:
         a__  =search_buffer[-self.search_buffer_size :]

       # update the text
       a__  =text[token.length + 1 :]

       # append the token to output
       output.append(lowercase_	)

     return output



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   str:






     """simple docstring"""






     a__  =''''''

     for token in tokens:
       for _ in range(token.length	):
         output += output[-token.offset]
       output += token.indicator

     return output



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   Token:






     """simple docstring"""






     if not text:
       raise ValueError('''We need some text to work with.'''	)

     # Initialise result parameters to default values
     a__,				a__  =0, 0

     if not search_buffer:
       return Token(lowercase_,   lowercase_,   text[length]	)

     for i, character in enumerate(lowercase_	):
       a__  =len(lowercase_	) - i
       if character == text[0]:
         a__  =self._match_length_from_index(lowercase_,   lowercase_,   0,   lowercase_	)
         # if the found length is bigger than the current or if it's equal,
         # which means it's offset is smaller: update offset and length
         if found_length >= length:
           a__,				a__  =found_offset, found_length

     return Token(lowercase_,   lowercase_,   text[length]	)



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)							->   int:






     """simple docstring"""






     if not text or text[text_index] != window[window_index]:
       return 0
     return 1 + self._match_length_from_index(
         lowercase_,   window + text[text_index],   text_index + 1,   window_index + 1	)


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       # Initialize compressor class
       lowerCamelCase									= LZaaCompressor(window_size=13, lookahead_buffer_size=6)

       # Example
       lowerCamelCase									= '''cabracadabrarrarrad'''
       lowerCamelCase									= lzaa_compressor.compress(TEXT)
       print(lzaa_compressor.compress('''ababcbababaa'''))
       lowerCamelCase									= lzaa_compressor.decompress(compressed_text)
       assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
