





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


snake_case_				=			datasets.utils.logging.get_logger(__name__)

@dataclass
class 		SCREAMING_SNAKE_CASE__				(       datasets.BuilderConfig  ):
      A_						:				int							=    10_000
      A_						:				Optional[List[str]]							=    None
      A_						:				Optional[datasets.Features]							=    None






class 		SCREAMING_SNAKE_CASE__				(       datasets.ArrowBasedBuilder  ):
      A_						:				Optional[Any]							=    ParquetConfig


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



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


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


      def    a (self       :      str				,			a__       :      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
            __snake_case							       =   table_cast(a__				,			self.info.features.arrow_schema      )
         return pa_table


      def    a (self       :      Any				,			a__       :      Dict      ):





         """simple docstring"""



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








import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin

@require_jieba
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				List[str]							=    CpmAntTokenizer
      A_						:				Optional[int]							=    False


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         super().setUp()

         __snake_case							       =   [
             '''<d>''',
             '''</d>''',
             '''<s>''',
             '''</s>''',
             '''</_>''',
             '''<unk>''',
             '''<pad>''',
             '''</n>''',
             '''我''',
             '''是''',
             '''C''',
             '''P''',
             '''M''',
             '''A''',
             '''n''',
             '''t''',
         ]
         __snake_case							       =   os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES['''vocab_file''']      )
         with open(self.vocab_file				,			'''w'''				,			encoding='''utf-8'''      ) as vocab_writer:
            vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]      )      )


      @tooslow
      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   CpmAntTokenizer.from_pretrained('''openbmb/cpm-ant-10b'''      )
         __snake_case							       =   '''今天天气真好！'''
         __snake_case							       =   ['''今天''', '''天气''', '''真''', '''好''', '''！''']
         __snake_case							       =   tokenizer.tokenize(a__      )
         self.assertListEqual(a__				,			a__      )
         __snake_case							       =   '''今天天气真好！'''
         __snake_case							       =   [tokenizer.bos_token] + tokens

         __snake_case							       =   [6, 9802, 1_4962, 2082, 831, 244]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(a__      )				,			a__      )

         __snake_case							       =   tokenizer.decode(a__      )
         self.assertEqual(a__				,			a__      )

