


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 : Any 			=				datasets.utils.logging.get_logger(__name__)



@dataclass
class    UpperCAmelCase_	(							datasets.BuilderConfig):
       '''simple docstring'''
       __UpperCamelCase      :     int							=					10000
       __UpperCamelCase      :     Optional[List[str]]							=					None
       __UpperCamelCase      :     Optional[datasets.Features]							=					None




class    UpperCAmelCase_	(							datasets.ArrowBasedBuilder):
       '''simple docstring'''
       __UpperCamelCase      :     str							=					ParquetConfig






       def  _lowercase   (     self						):




              """simple docstring"""




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






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """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_    :	Optional[Any]   	=		dl_manager.download_and_extract(self.config.data_files						)
              if isinstance(__SCREAMING_SNAKE_CASE ,					(str, list, tuple)						):
                     a_    :	Tuple   	=		data_files
                     if isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                            a_    :	Optional[int]   	=		[files]
                     # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
                     a_    :	Union[str, Any]   	=		[dl_manager.iter_files(__SCREAMING_SNAKE_CASE						) for file in files]
                     return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,					gen_kwargs={'''files''': files}						)]
              a_    :	int   	=		[]
              for split_name, files in data_files.items():
                     if isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                            a_    :	Any   	=		[files]
                     # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
                     a_    :	List[str]   	=		[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_    :	Optional[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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """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_    :	List[Any]   	=		table_cast(__SCREAMING_SNAKE_CASE ,					self.info.features.arrow_schema						)
              return pa_table






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              a_    :	List[Any]   	=		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_    :	List[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_    :	int   	=		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 json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


__UpperCAmelCase : Dict 			=				logging.get_logger(__name__)

__UpperCAmelCase : Optional[Any] 			=				{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__UpperCAmelCase : Dict 			=				{
    "vocab_file": {
        "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_char_small": (
            "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_chinese_char_base": (
            "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_discriminator": (
            "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_generator": (
            "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"
        ),
    }
}

__UpperCAmelCase : Tuple 			=				{
    "junnyu/roformer_chinese_small": 1536,
    "junnyu/roformer_chinese_base": 1536,
    "junnyu/roformer_chinese_char_small": 512,
    "junnyu/roformer_chinese_char_base": 512,
    "junnyu/roformer_small_discriminator": 128,
    "junnyu/roformer_small_generator": 128,
}


__UpperCAmelCase : Any 			=				{
    "junnyu/roformer_chinese_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_base": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_base": {"do_lower_case": True},
    "junnyu/roformer_small_discriminator": {"do_lower_case": True},
    "junnyu/roformer_small_generator": {"do_lower_case": True},
}



class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     str							=					VOCAB_FILES_NAMES
       __UpperCamelCase      :     Optional[int]							=					PRETRAINED_VOCAB_FILES_MAP
       __UpperCamelCase      :     Union[str, Any]							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __UpperCamelCase      :     int							=					PRETRAINED_INIT_CONFIGURATION
       __UpperCamelCase      :     Any							=					RoFormerTokenizer






       def __init__(     self ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE="[UNK]" ,					__SCREAMING_SNAKE_CASE="[SEP]" ,					__SCREAMING_SNAKE_CASE="[PAD]" ,					__SCREAMING_SNAKE_CASE="[CLS]" ,					__SCREAMING_SNAKE_CASE="[MASK]" ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=None ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              super().__init__(
                  __SCREAMING_SNAKE_CASE ,					tokenizer_file=__SCREAMING_SNAKE_CASE ,					do_lower_case=__SCREAMING_SNAKE_CASE ,					unk_token=__SCREAMING_SNAKE_CASE ,					sep_token=__SCREAMING_SNAKE_CASE ,					pad_token=__SCREAMING_SNAKE_CASE ,					cls_token=__SCREAMING_SNAKE_CASE ,					mask_token=__SCREAMING_SNAKE_CASE ,					tokenize_chinese_chars=__SCREAMING_SNAKE_CASE ,					strip_accents=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE ,					)

              UpperCamelCase    :	List[Any]   	=		json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
              if (
                  pre_tok_state.get('''lowercase''' ,					__SCREAMING_SNAKE_CASE						) != do_lower_case
                  or pre_tok_state.get('''strip_accents''' ,					__SCREAMING_SNAKE_CASE						) != strip_accents
              ):
                     UpperCamelCase    :	List[Any]   	=		getattr(__SCREAMING_SNAKE_CASE ,					pre_tok_state.pop('''type'''						)						)
                     UpperCamelCase    :	Optional[int]   	=		do_lower_case
                     UpperCamelCase    :	Optional[Any]   	=		strip_accents
                     UpperCamelCase    :	List[Any]   	=		pre_tok_class(**__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	List[Any]   	=		do_lower_case






       def __getstate__(     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		self.__dict__.copy()
              UpperCamelCase    :	Any   	=		BertPreTokenizer()
              return state






       def __setstate__(     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		d
              UpperCamelCase    :	List[str]   	=		self.__dict__['''_tokenizer'''].get_vocab()
              UpperCamelCase    :	Any   	=		PreTokenizer.custom(JiebaPreTokenizer(__SCREAMING_SNAKE_CASE						)						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=None						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		[self.cls_token_id] + token_ids_a + [self.sep_token_id]

              if token_ids_a:
                     output += token_ids_a + [self.sep_token_id]

              return output






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None						):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		[self.sep_token_id]
              UpperCamelCase    :	int   	=		[self.cls_token_id]
              if token_ids_a is None:
                     return len(cls + token_ids_a + sep						) * [0]
              return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None						):




              """simple docstring"""




              UpperCamelCase    :	List[Any]   	=		self._tokenizer.model.save(__SCREAMING_SNAKE_CASE ,					name=__SCREAMING_SNAKE_CASE						)
              return tuple(__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=False ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		BertPreTokenizer()
              return super().save_pretrained(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						)

