import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowerCamelCase    :				Optional[int]							     =	logging.get_logger(__name__)


__lowerCamelCase    :				List[Any]							     =	'''▁'''

__lowerCamelCase    :				Optional[int]							     =	{'''vocab_file''': '''spiece.model'''}

__lowerCamelCase    :				str							     =	{
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

__lowerCamelCase    :				Tuple							     =	{
    '''google/reformer-crime-and-punishment''': 52_4288,
}







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							VOCAB_FILES_NAMES
   lowerCAmelCase_														=							PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase_														=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase_														=							["input_ids", "attention_mask"]




   def __init__(					self    :       str ,			_lowercase    :       Optional[int] ,			_lowercase    :       str="</s>" ,			_lowercase    :       Dict="<unk>" ,			_lowercase    :       Tuple=[] ,			_lowercase    :       Optional[Dict[str, Any]] = None ,			**_lowercase    :       List[Any] ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         eos_token=_lowercase ,			unk_token=_lowercase ,			additional_special_tokens=_lowercase ,			sp_model_kwargs=self.sp_model_kwargs ,			**_lowercase ,			)

     SCREAMING_SNAKE_CASE__    							=   vocab_file
     SCREAMING_SNAKE_CASE__    							=   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
     self.sp_model.Load(_lowercase   )




   @property
   def 			__a		(					self    :       str   ):



     """simple docstring"""






     return self.sp_model.get_piece_size()




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   {self.convert_ids_to_tokens(_lowercase   ): i for i in range(self.vocab_size   )}
     vocab.update(self.added_tokens_encoder   )
     return vocab




   def __getstate__(					self    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.__dict__.copy()
     SCREAMING_SNAKE_CASE__    							=   None
     return state




   def __setstate__(					self    :       str ,			_lowercase    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   d

     # for backward compatibility
     if not hasattr(self ,			"""sp_model_kwargs"""   ):
       SCREAMING_SNAKE_CASE__    							=   {}

     SCREAMING_SNAKE_CASE__    							=   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
     self.sp_model.Load(self.vocab_file   )




   def 			__a		(					self    :       Optional[Any] ,			_lowercase    :       str   ):



     """simple docstring"""






     return self.sp_model.encode(_lowercase ,			out_type=_lowercase   )




   def 			__a		(					self    :       Dict ,			_lowercase    :       Dict   ):



     """simple docstring"""






     return self.sp_model.piece_to_id(_lowercase   )




   def 			__a		(					self    :       str ,			_lowercase    :       int   ):



     """simple docstring"""






     if index < self.sp_model.get_piece_size():
       SCREAMING_SNAKE_CASE__    							=   self.sp_model.IdToPiece(_lowercase   )
     return token




   def 			__a		(					self    :       Tuple ,			_lowercase    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   []
     SCREAMING_SNAKE_CASE__    							=   """"""
     for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
         out_string += self.sp_model.decode(_lowercase   ) + token
         SCREAMING_SNAKE_CASE__    							=   []
       else:
         current_sub_tokens.append(_lowercase   )
     out_string += self.sp_model.decode(_lowercase   )
     return out_string.strip()




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       str ,			_lowercase    :       Optional[str] = None   ):



     """simple docstring"""






     if not os.path.isdir(_lowercase   ):
       logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
       return
     SCREAMING_SNAKE_CASE__    							=   os.path.join(
         _lowercase ,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

     if os.path.abspath(self.vocab_file   ) != os.path.abspath(_lowercase   ) and os.path.isfile(self.vocab_file   ):
       copyfile(self.vocab_file ,			_lowercase   )
     elif not os.path.isfile(self.vocab_file   ):
       with open(_lowercase ,			"""wb"""   ) as fi:
         SCREAMING_SNAKE_CASE__    							=   self.sp_model.serialized_model_proto()
         fi.write(_lowercase   )

     return (out_vocab_file,)

import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


__lowerCamelCase    :				Union[str, Any]							     =	datasets.utils.logging.get_logger(__name__)







@dataclass
class __snake_case       (		datasets.BuilderConfig	):
   lowerCAmelCase_														=							1_00_00
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							None







class __snake_case       (		datasets.ArrowBasedBuilder	):
   lowerCAmelCase_														=							ParquetConfig




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     return datasets.DatasetInfo(features=self.config.features   )




   def 			__a		(					self    :       Dict ,			_lowercase    :       List[str]   ):



     """simple docstring"""






     if not self.config.data_files:
       raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""   )
     SCREAMING_SNAKE_CASE__    							=   dl_manager.download_and_extract(self.config.data_files   )
     if isinstance(_lowercase ,			(str, list, tuple)   ):
       SCREAMING_SNAKE_CASE__    							=   data_files
       if isinstance(_lowercase ,			_lowercase   ):
         SCREAMING_SNAKE_CASE__    							=   [files]
       # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
       SCREAMING_SNAKE_CASE__    							=   [dl_manager.iter_files(_lowercase   ) for file in files]
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,			gen_kwargs={"""files""": files}   )]
     SCREAMING_SNAKE_CASE__    							=   []
     for split_name, files in data_files.items():
       if isinstance(_lowercase ,			_lowercase   ):
         SCREAMING_SNAKE_CASE__    							=   [files]
       # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
       SCREAMING_SNAKE_CASE__    							=   [dl_manager.iter_files(_lowercase   ) for file in files]
       # Infer features is they are stoed in the arrow schema
       if self.info.features is None:
         for file in itertools.chain.from_iterable(_lowercase   ):
           with open(_lowercase ,			"""rb"""   ) as f:
             SCREAMING_SNAKE_CASE__    							=   datasets.Features.from_arrow_schema(pq.read_schema(_lowercase   )   )
           break
       splits.append(datasets.SplitGenerator(name=_lowercase ,			gen_kwargs={"""files""": files}   )   )
     return splits




   def 			__a		(					self    :       Optional[Any] ,			_lowercase    :       pa.Table   ):



     """simple docstring"""






     if self.info.features is not None:
       # more expensive cast to support nested features with keys in a different order
       # allows str <-> int/float or str to Audio for example
       SCREAMING_SNAKE_CASE__    							=   table_cast(_lowercase ,			self.info.features.arrow_schema   )
     return pa_table




   def 			__a		(					self    :       Dict ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.info.features.arrow_schema if self.info.features is not None else None
     if self.info.features is not None and self.config.columns is not None:
       if sorted(field.name for field in schema   ) != sorted(self.config.columns   ):
         raise ValueError(
             f"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"""   )
     for file_idx, file in enumerate(itertools.chain.from_iterable(_lowercase   )   ):
       with open(_lowercase ,			"""rb"""   ) as f:
         SCREAMING_SNAKE_CASE__    							=   pq.ParquetFile(_lowercase   )
         try:
           for batch_idx, record_batch in enumerate(
               parquet_file.iter_batches(batch_size=self.config.batch_size ,			columns=self.config.columns   )   ):
             SCREAMING_SNAKE_CASE__    							=   pa.Table.from_batches([record_batch]   )
             # Uncomment for debugging (will print the Arrow table size and elements)
             # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
             # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
             yield f"""{file_idx}_{batch_idx}""", self._cast_table(_lowercase   )
         except ValueError as e:
           logger.error(f"""Failed to read file '{file}' with error {type(_lowercase   )}: {e}"""   )
           raise
