






'''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


__UpperCamelCase   :							List[str]         =   datasets.utils.logging.get_logger(__name__)
@dataclass
class 				__SCREAMING_SNAKE_CASE							(				datasets.BuilderConfig  ):
      __a	       =10_000
      __a	       =None
      __a	       =None







class 				__SCREAMING_SNAKE_CASE							(				datasets.ArrowBasedBuilder  ):
      __a	       =ParquetConfig




      def 			__UpperCamelCase				(	self   )				->Any:



         '''simple docstring'''
         return datasets.DatasetInfo(features=self.config.features   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->Any:



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




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->int:



         '''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					  =     table_cast(lowerCamelCase			, self.info.features.arrow_schema   )
         return pa_table






      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->Dict:



         '''simple docstring'''
         __a					  =     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(lowerCamelCase   )   ):
            with open(lowerCamelCase			, 'rb'   ) as f:
               __a					  =     pq.ParquetFile(lowerCamelCase   )
               try:
                  for batch_idx, record_batch in enumerate(
                      parquet_file.iter_batches(batch_size=self.config.batch_size			, columns=self.config.columns   )   ):
                     __a					  =     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(lowerCamelCase   )
               except ValueError as e:
                  logger.error(F"""Failed to read file '{file}' with error {type(lowerCamelCase   )}: {e}"""   )
                  raise




"""simple docstring"""







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


a_							=    logging.get_logger(__name__)


a_							=    """▁"""

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

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

a_							=    {
    """google/reformer-crime-and-punishment""": 52_42_88,
}







class 					A_(SCREAMING_SNAKE_CASE_      ):







			"""simple docstring"""



			a_       :			Optional[int]												=			VOCAB_FILES_NAMES
			a_       :			int												=			PRETRAINED_VOCAB_FILES_MAP
			a_       :			str												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			a_       :			Optional[Any]												=			["""input_ids""", """attention_mask"""]
			def __init__(	self				,   A				,   A="</s>"				,   A="<unk>"				,   A=[]				,   A = None				,   **A				,   ):
						_lowerCamelCase					:					Any	       =    {} if sp_model_kwargs is None else sp_model_kwargs

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

						_lowerCamelCase					:					Dict	       =    vocab_file
						_lowerCamelCase					:					List[Any]	       =    spm.SentencePieceProcessor(**self.sp_model_kwargs							)
						self.sp_model.Load(A							)
			@property
			def    _lowerCAmelCase		(	self							):
						return self.sp_model.get_piece_size()
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					int	       =    {self.convert_ids_to_tokens(A							): i for i in range(self.vocab_size							)}
						vocab.update(self.added_tokens_encoder							)
						return vocab
			def __getstate__(	self							):
						_lowerCamelCase					:					Optional[int]	       =    self.__dict__.copy()
						_lowerCamelCase					:					Union[str, Any]	       =    None
						return state
			def __setstate__(	self				,   A							):
						_lowerCamelCase					:					int	       =    d

						# for backward compatibility
						if not hasattr(self				,   'sp_model_kwargs'							):
									_lowerCamelCase					:					Union[str, Any]	       =    {}

						_lowerCamelCase					:					Dict	       =    spm.SentencePieceProcessor(**self.sp_model_kwargs							)
						self.sp_model.Load(self.vocab_file							)
			def    _lowerCAmelCase		(	self				,   A							):
						return self.sp_model.encode(A				,   out_type=A							)
			def    _lowerCAmelCase		(	self				,   A							):
						return self.sp_model.piece_to_id(A							)
			def    _lowerCAmelCase		(	self				,   A							):
						if index < self.sp_model.get_piece_size():
									_lowerCamelCase					:					Dict	       =    self.sp_model.IdToPiece(A							)
						return token
			def    _lowerCAmelCase		(	self				,   A							):
						_lowerCamelCase					:					Tuple	       =    []
						_lowerCamelCase					:					Optional[int]	       =    ''
						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(A							) + token
												_lowerCamelCase					:					Dict	       =    []
									else:
												current_sub_tokens.append(A							)
						out_string += self.sp_model.decode(A							)
						return out_string.strip()

			def    _lowerCAmelCase		(	self				,   A				,   A = None							):
						if not os.path.isdir(A							):
									logger.error(F"Vocabulary path ({save_directory}) should be a directory"							)
									return
						_lowerCamelCase					:					Any	       =    os.path.join(
						    A				,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

						if os.path.abspath(self.vocab_file							) != os.path.abspath(A							) and os.path.isfile(self.vocab_file							):
									copyfile(self.vocab_file				,   A							)
						elif not os.path.isfile(self.vocab_file							):
									with open(A				,   'wb'							) as fi:
												_lowerCamelCase					:					List[str]	       =    self.sp_model.serialized_model_proto()
												fi.write(A							)

						return (out_vocab_file,)





