





'''simple docstring'''




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


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


@dataclass
class 					SCREAMING_SNAKE_CASE       (datasets.BuilderConfig				):
     lowerCAmelCase						     =			1_0000
     lowerCAmelCase						     =			None
     lowerCAmelCase						     =			None




class 					SCREAMING_SNAKE_CASE       (datasets.ArrowBasedBuilder				):
     lowerCAmelCase						     =			ParquetConfig


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''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}')
          __A		:					Any						=			dl_manager.download_and_extract(self.config.data_files)
          if isinstance(__SCREAMING_SNAKE_CASE		,      (str, list, tuple)):
               __A		:					Optional[Any]						=			data_files
               if isinstance(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE):
                    __A		:					Dict						=			[files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               __A		:					Optional[Any]						=			[dl_manager.iter_files(__SCREAMING_SNAKE_CASE) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN		,      gen_kwargs={'files': files})]
          __A		:					Optional[Any]						=			[]
          for split_name, files in data_files.items():
               if isinstance(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE):
                    __A		:					Union[str, Any]						=			[files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               __A		:					Optional[Any]						=			[dl_manager.iter_files(__SCREAMING_SNAKE_CASE) 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(__SCREAMING_SNAKE_CASE):
                         with open(__SCREAMING_SNAKE_CASE		,      'rb') as f:
                              __A		:					Union[str, Any]						=			datasets.Features.from_arrow_schema(pq.read_schema(__SCREAMING_SNAKE_CASE))
                         break
               splits.append(datasets.SplitGenerator(name=__SCREAMING_SNAKE_CASE		,      gen_kwargs={'files': files}))
          return splits


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''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
               __A		:					str						=			table_cast(__SCREAMING_SNAKE_CASE		,      self.info.features.arrow_schema)
          return pa_table


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					List[str]						=			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(__SCREAMING_SNAKE_CASE)):
               with open(__SCREAMING_SNAKE_CASE		,      'rb') as f:
                    __A		:					Union[str, Any]						=			pq.ParquetFile(__SCREAMING_SNAKE_CASE)
                    try:
                         for batch_idx, record_batch in enumerate(
                             parquet_file.iter_batches(batch_size=self.config.batch_size		,      columns=self.config.columns)):
                              __A		:					Tuple						=			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(__SCREAMING_SNAKE_CASE)
                    except ValueError as e:
                         logger.error(F'Failed to read file \'{file}\' with error {type(__SCREAMING_SNAKE_CASE)}: {e}')
                         raise

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  =logging.get_logger(__name__)


lowerCamelCase  ="▁"

lowerCamelCase  ={"vocab_file": "spiece.model"}

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

lowerCamelCase  ={
    "google/reformer-crime-and-punishment": 5_2_4_2_8_8,
}




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE_		 =	PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE_		 =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE_		 =	['''input_ids''', '''attention_mask''']






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE="</s>"    ,    __SCREAMING_SNAKE_CASE="<unk>"    ,    __SCREAMING_SNAKE_CASE=[]    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> None:




           """simple docstring"""







           UpperCamelCase__     : int											=					{} if sp_model_kwargs is None else sp_model_kwargs

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

           UpperCamelCase__     : Dict											=					vocab_file
           UpperCamelCase__     : Any											=					spm.SentencePieceProcessor(**self.sp_model_kwargs	)
           self.sp_model.Load(__SCREAMING_SNAKE_CASE	)






     @property
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           return self.sp_model.get_piece_size()






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict[str, int]:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					{self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE	): i for i in range(self.vocab_size	)}
           vocab.update(self.added_tokens_encoder	)
           return vocab






     def __getstate__(				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					self.__dict__.copy()
           UpperCamelCase__     : int											=					None
           return state






     def __setstate__(				self    ,    __SCREAMING_SNAKE_CASE	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : int											=					d

           # for backward compatibility
           if not hasattr(self    ,    '''sp_model_kwargs'''	):
                 UpperCamelCase__     : int											=					{}

           UpperCamelCase__     : Optional[Any]											=					spm.SentencePieceProcessor(**self.sp_model_kwargs	)
           self.sp_model.Load(self.vocab_file	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> List[str]:




           """simple docstring"""







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






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> List[str]:




           """simple docstring"""







           return self.sp_model.piece_to_id(__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> List[Any]:




           """simple docstring"""







           if index < self.sp_model.get_piece_size():
                 UpperCamelCase__     : str											=					self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE	)
           return token






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					[]
           UpperCamelCase__     : Union[str, Any]											=					''''''
           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(__SCREAMING_SNAKE_CASE	) + token
                       UpperCamelCase__     : str											=					[]
                 else:
                       current_sub_tokens.append(__SCREAMING_SNAKE_CASE	)
           out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE	)
           return out_string.strip()






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None	)	-> Tuple[str]:




           """simple docstring"""







           if not os.path.isdir(__SCREAMING_SNAKE_CASE	):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
                 return
           UpperCamelCase__     : Optional[int]											=					os.path.join(
               __SCREAMING_SNAKE_CASE    ,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

           if os.path.abspath(self.vocab_file	) != os.path.abspath(__SCREAMING_SNAKE_CASE	) and os.path.isfile(self.vocab_file	):
                 copyfile(self.vocab_file    ,    __SCREAMING_SNAKE_CASE	)
           elif not os.path.isfile(self.vocab_file	):
                 with open(__SCREAMING_SNAKE_CASE    ,    '''wb'''	) as fi:
                       UpperCamelCase__     : int											=					self.sp_model.serialized_model_proto()
                       fi.write(__SCREAMING_SNAKE_CASE	)

           return (out_vocab_file,)


