import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader



class        lowerCAmelCase_	(						a__							):
       def __init__(    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ = None,    SCREAMING_SNAKE_CASE_ = None,    SCREAMING_SNAKE_CASE_ = None,    SCREAMING_SNAKE_CASE_ = False,    SCREAMING_SNAKE_CASE_ = False,    SCREAMING_SNAKE_CASE_ = None,    **SCREAMING_SNAKE_CASE_,    )  ->	Dict:
          super().__init__(
              UpperCamelCase_,    split=UpperCamelCase_,    features=UpperCamelCase_,    cache_dir=UpperCamelCase_,    keep_in_memory=UpperCamelCase_,    streaming=UpperCamelCase_,    num_proc=UpperCamelCase_,    **UpperCamelCase_,    )
          UpperCamelCase					:						List[Any]					=			path_or_paths if isinstance(UpperCamelCase_,    UpperCamelCase_ ) else {self.split: path_or_paths}
          UpperCamelCase					:						Union[str, Any]					=			Csv(
              cache_dir=UpperCamelCase_,    data_files=UpperCamelCase_,    features=UpperCamelCase_,    **UpperCamelCase_,    )






       def       snake_case_ (    self )  ->	Any:
          # Build iterable dataset
          if self.streaming:
             UpperCamelCase					:						List[Any]					=			self.builder.as_streaming_dataset(split=self.split )
          # Build regular (map-style) dataset
          else:
             UpperCamelCase					:						Any					=			None
             UpperCamelCase					:						List[Any]					=			None
             UpperCamelCase					:						Optional[int]					=			None
             UpperCamelCase					:						Tuple					=			None

             self.builder.download_and_prepare(
                 download_config=UpperCamelCase_,    download_mode=UpperCamelCase_,    verification_mode=UpperCamelCase_,    base_path=UpperCamelCase_,    num_proc=self.num_proc,    )
             UpperCamelCase					:						str					=			self.builder.as_dataset(
                 split=self.split,    verification_mode=UpperCamelCase_,    in_memory=self.keep_in_memory )
          return dataset






class        lowerCAmelCase_	:
       def __init__(    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ = None,    SCREAMING_SNAKE_CASE_ = None,    **SCREAMING_SNAKE_CASE_,    )  ->	List[str]:
          if num_proc is not None and num_proc <= 0:
             raise ValueError(F"""num_proc {num_proc} must be an integer > 0.""" )

          UpperCamelCase					:						Dict					=			dataset
          UpperCamelCase					:						Any					=			path_or_buf
          UpperCamelCase					:						Union[str, Any]					=			batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
          UpperCamelCase					:						int					=			num_proc
          UpperCamelCase					:						List[str]					=			'utf-8'
          UpperCamelCase					:						str					=			to_csv_kwargs
       def       snake_case_ (    self )  ->	str:
          UpperCamelCase					:						Dict					=			self.to_csv_kwargs.pop('path_or_buf',    UpperCamelCase_ )
          UpperCamelCase					:						Union[str, Any]					=			self.to_csv_kwargs.pop('header',    UpperCamelCase_ )
          UpperCamelCase					:						List[str]					=			self.to_csv_kwargs.pop('index',    UpperCamelCase_ )

          if isinstance(self.path_or_buf,    (str, bytes, os.PathLike) ):
             with open(self.path_or_buf,    'wb+' ) as buffer:
                UpperCamelCase					:						List[Any]					=			self._write(file_obj=UpperCamelCase_,    header=UpperCamelCase_,    index=UpperCamelCase_,    **self.to_csv_kwargs )
          else:
             UpperCamelCase					:						List[Any]					=			self._write(file_obj=self.path_or_buf,    header=UpperCamelCase_,    index=UpperCamelCase_,    **self.to_csv_kwargs )
          return written
       def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_ )  ->	Tuple:
          UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase					:						Union[str, Any]					=			args

          UpperCamelCase					:						List[str]					=			query_table(
              table=self.dataset.data,    key=slice(UpperCamelCase_,    offset + self.batch_size ),    indices=self.dataset._indices,    )
          UpperCamelCase					:						Any					=			batch.to_pandas().to_csv(
              path_or_buf=UpperCamelCase_,    header=header if (offset == 0) else False,    index=UpperCamelCase_,    **UpperCamelCase_ )
          return csv_str.encode(self.encoding )






       def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_ )  ->	Tuple:
          UpperCamelCase					:						List[Any]					=			0

          if self.num_proc is None or self.num_proc == 1:
             for offset in logging.tqdm(
                 range(0,    len(self.dataset ),    self.batch_size ),    unit='ba',    disable=not logging.is_progress_bar_enabled(),    desc='Creating CSV from Arrow format',    ):
                UpperCamelCase					:						Any					=			self._batch_csv((offset, header, index, to_csv_kwargs) )
                written += file_obj.write(UpperCamelCase_ )

          else:
             UpperCamelCase			,  UpperCamelCase					:						str					=			len(self.dataset ), self.batch_size
             with multiprocessing.Pool(self.num_proc ) as pool:
                for csv_str in logging.tqdm(
                    pool.imap(
                        self._batch_csv,    [(offset, header, index, to_csv_kwargs) for offset in range(0,    UpperCamelCase_,    UpperCamelCase_ )],    ),    total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size,    unit='ba',    disable=not logging.is_progress_bar_enabled(),    desc='Creating CSV from Arrow format',    ):
                   written += file_obj.write(UpperCamelCase_ )

          return written








from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


__UpperCAmelCase      = logging.get_logger(__name__)

__UpperCAmelCase      = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__UpperCAmelCase      = {
    '''vocab_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/vocab.json'''},
    '''merges_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/merges.txt'''},
    '''tokenizer_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/tokenizer.json'''},
}

__UpperCAmelCase      = {
    '''openai-gpt''': 512,
}



class        lowerCAmelCase_	(						a__							):
       UpperCAmelCase__       :      Union[str, Any]       =						VOCAB_FILES_NAMES
       UpperCAmelCase__       :      int       =						PRETRAINED_VOCAB_FILES_MAP
       UpperCAmelCase__       :      Optional[int]       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCAmelCase__       :      Optional[int]       =						["input_ids", "attention_mask"]
       UpperCAmelCase__       :      Tuple       =						OpenAIGPTTokenizer
       def __init__(    self,    SCREAMING_SNAKE_CASE_=None,    SCREAMING_SNAKE_CASE_=None,    SCREAMING_SNAKE_CASE_=None,    SCREAMING_SNAKE_CASE_="<unk>",    **SCREAMING_SNAKE_CASE_ )  ->	List[str]:
          super().__init__(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    tokenizer_file=SCREAMING_SNAKE_CASE_,    unk_token=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_ )
       @property
       def       snake_case_ (    self )  ->	Dict:
          return True






       def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ = None )  ->	Tuple[str]:
          UpperCamelCase					:						List[Any]					=			self._tokenizer.model.save(SCREAMING_SNAKE_CASE_,    name=SCREAMING_SNAKE_CASE_ )
          return tuple(SCREAMING_SNAKE_CASE_ )



