'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase						:    Union[str, Any]     					=					logging.get_logger(__name__)

_UpperCAmelCase						:    Dict     					=					{
    "uw-madison/yoso-4096": "https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json",
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}







class lowercase_					(		SCREAMING_SNAKE_CASE__ ):







   """simple docstring"""

   __lowerCAmelCase    						=      "yoso"
   def __init__(			self			: int,   UpperCamelCase__			: Optional[int]=5_02_65,   UpperCamelCase__			: Dict=7_68,   UpperCamelCase__			: List[Any]=12,   UpperCamelCase__			: List[Any]=12,   UpperCamelCase__			: Any=30_72,   UpperCamelCase__			: Union[str, Any]="gelu",   UpperCamelCase__			: Any=0.1,   UpperCamelCase__			: Optional[int]=0.1,   UpperCamelCase__			: Optional[Any]=40_96,   UpperCamelCase__			: List[Any]=1,   UpperCamelCase__			: List[str]=0.02,   UpperCamelCase__			: List[str]=1e-12,   UpperCamelCase__			: str="absolute",   UpperCamelCase__			: Any=True,   UpperCamelCase__			: Optional[Any]=9,   UpperCamelCase__			: Any=64,   UpperCamelCase__			: Dict=None,   UpperCamelCase__			: Optional[int]=True,   UpperCamelCase__			: List[Any]=True,   UpperCamelCase__			: List[str]=1,   UpperCamelCase__			: Optional[int]=0,   UpperCamelCase__			: Union[str, Any]=2,   **UpperCamelCase__			: List[str],   )     -> int:
        super().__init__(pad_token_id=UpperCamelCase__,   bos_token_id=UpperCamelCase__,   eos_token_id=UpperCamelCase__,   **UpperCamelCase__    )

        _A								=     vocab_size
        _A								=     max_position_embeddings
        _A								=     hidden_size
        _A								=     num_hidden_layers
        _A								=     num_attention_heads
        _A								=     intermediate_size
        _A								=     hidden_act
        _A								=     hidden_dropout_prob
        _A								=     attention_probs_dropout_prob
        _A								=     initializer_range
        _A								=     type_vocab_size
        _A								=     layer_norm_eps
        _A								=     position_embedding_type
        _A								=     use_expectation
        _A								=     hash_code_len
        _A								=     num_hash
        _A								=     conv_window
        _A								=     use_fast_hash
        _A								=     lsh_backward




'''simple docstring'''






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 lowercase_					(		_UpperCamelCase ):







   """simple docstring"""

   def __init__(			self			: Any,   UpperCamelCase__			: NestedDataStructureLike[PathLike],   UpperCamelCase__			: Optional[NamedSplit] = None,   UpperCamelCase__			: Optional[Features] = None,   UpperCamelCase__			: str = None,   UpperCamelCase__			: bool = False,   UpperCamelCase__			: bool = False,   UpperCamelCase__			: Optional[int] = None,   **UpperCamelCase__			: Optional[Any],   )     -> Tuple:
        super().__init__(
            UpperCamelCase__,   split=UpperCamelCase__,   features=UpperCamelCase__,   cache_dir=UpperCamelCase__,   keep_in_memory=UpperCamelCase__,   streaming=UpperCamelCase__,   num_proc=UpperCamelCase__,   **UpperCamelCase__,   )
        _A								=     path_or_paths if isinstance(UpperCamelCase__,   UpperCamelCase__    ) else {self.split: path_or_paths}
        _A								=     Csv(
            cache_dir=UpperCamelCase__,   data_files=UpperCamelCase__,   features=UpperCamelCase__,   **UpperCamelCase__,   )

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

             self.builder.download_and_prepare(
                 download_config=UpperCamelCase__,   download_mode=UpperCamelCase__,   verification_mode=UpperCamelCase__,   base_path=UpperCamelCase__,   num_proc=self.num_proc,   )
             _A								=     self.builder.as_dataset(
                 split=self.split,   verification_mode=UpperCamelCase__,   in_memory=self.keep_in_memory    )
        return dataset







class lowercase_					:







   """simple docstring"""

   def __init__(			self			: Optional[Any],   UpperCamelCase__			: Dataset,   UpperCamelCase__			: Union[PathLike, BinaryIO],   UpperCamelCase__			: Optional[int] = None,   UpperCamelCase__			: Optional[int] = None,   **UpperCamelCase__			: Dict,   )     -> Any:
        if num_proc is not None and num_proc <= 0:
             raise ValueError(f'num_proc {num_proc} must be an integer > 0.'    )

        _A								=     dataset
        _A								=     path_or_buf
        _A								=     batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        _A								=     num_proc
        _A								=     'utf-8'
        _A								=     to_csv_kwargs
   def   __UpperCAmelCase      (			self			: List[Any]    )     -> int:
        _A								=     self.to_csv_kwargs.pop('path_or_buf',   UpperCamelCase__    )
        _A								=     self.to_csv_kwargs.pop('header',   UpperCamelCase__    )
        _A								=     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:
                  _A								=     self._write(file_obj=UpperCamelCase__,   header=UpperCamelCase__,   index=UpperCamelCase__,   **self.to_csv_kwargs    )
        else:
             _A								=     self._write(file_obj=self.path_or_buf,   header=UpperCamelCase__,   index=UpperCamelCase__,   **self.to_csv_kwargs    )
        return written
   def   __UpperCAmelCase      (			self			: str,   UpperCamelCase__			: Optional[int]    )     -> str:
        _A		,					_A		,					_A		,					_A								=     args

        _A								=     query_table(
            table=self.dataset.data,   key=slice(UpperCamelCase__,   offset + self.batch_size    ),   indices=self.dataset._indices,   )
        _A								=     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   __UpperCAmelCase      (			self			: Union[str, Any],   UpperCamelCase__			: BinaryIO,   UpperCamelCase__			: List[str],   UpperCamelCase__			: Dict,   **UpperCamelCase__			: Optional[int]    )     -> int:
        _A								=     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',   ):
                  _A								=     self._batch_csv((offset, header, index, to_csv_kwargs)    )
                  written += file_obj.write(UpperCamelCase__    )

        else:
             _A		,					_A								=     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



