

"""simple docstring"""
import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

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

class       lowerCamelCase	(       lowerCAmelCase__   ):


  '''simple docstring'''







  def __init__(self							,       _lowerCamelCase							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = False							,       _lowerCamelCase = False							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       **_lowerCamelCase							,       ):


        """simple docstring"""

        super().__init__(
            _lowerCamelCase							,       split=_lowerCamelCase							,       features=_lowerCamelCase							,       cache_dir=_lowerCamelCase							,       keep_in_memory=_lowerCamelCase							,       streaming=_lowerCamelCase							,       num_proc=_lowerCamelCase							,       **_lowerCamelCase							,       )
        UpperCAmelCase__			:							List[Any]							=    field
        UpperCAmelCase__			:							List[str]							=    path_or_paths if isinstance(_lowerCamelCase							,       _lowerCamelCase	) else {self.split: path_or_paths}
        UpperCAmelCase__			:							List[Any]							=    Json(
            cache_dir=_lowerCamelCase							,       data_files=_lowerCamelCase							,       features=_lowerCamelCase							,       field=_lowerCamelCase							,       **_lowerCamelCase							,       )




  def  _a      (self	):


        """simple docstring"""

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

              self.builder.download_and_prepare(
                  download_config=_lowerCamelCase							,       download_mode=_lowerCamelCase							,       verification_mode=_lowerCamelCase							,       base_path=_lowerCamelCase							,       num_proc=self.num_proc							,       )
              UpperCAmelCase__			:							List[str]							=    self.builder.as_dataset(
                  split=self.split							,       verification_mode=_lowerCamelCase							,       in_memory=self.keep_in_memory	)
        return dataset




class       lowerCamelCase	:


  '''simple docstring'''







  def __init__(self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       **_lowerCamelCase							,       ):


        """simple docstring"""

        if num_proc is not None and num_proc <= 0:
              raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""	)

        UpperCAmelCase__			:							Optional[int]							=    dataset
        UpperCAmelCase__			:							List[Any]							=    path_or_buf
        UpperCAmelCase__			:							Optional[Any]							=    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        UpperCAmelCase__			:							str							=    num_proc
        UpperCAmelCase__			:							Dict							=    """utf-8"""
        UpperCAmelCase__			:							Optional[Any]							=    to_json_kwargs



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							int							=    self.to_json_kwargs.pop("""path_or_buf"""							,       _lowerCamelCase	)
        UpperCAmelCase__			:							Union[str, Any]							=    self.to_json_kwargs.pop("""orient"""							,       """records"""	)
        UpperCAmelCase__			:							Union[str, Any]							=    self.to_json_kwargs.pop("""lines"""							,       True if orient == """records""" else False	)
        UpperCAmelCase__			:							Optional[int]							=    self.to_json_kwargs.pop("""index"""							,       False if orient in ["""split""", """table"""] else True	)
        UpperCAmelCase__			:							str							=    self.to_json_kwargs.pop("""compression"""							,       _lowerCamelCase	)

        if compression not in [None, "infer", "gzip", "bz2", "xz"]:
              raise NotImplementedError(F"""`datasets` currently does not support {compression} compression"""	)

        if isinstance(self.path_or_buf							,       (str, bytes, os.PathLike)	):
              with fsspec.open(self.path_or_buf							,       """wb"""							,       compression=_lowerCamelCase	) as buffer:
                    UpperCAmelCase__			:							int							=    self._write(file_obj=_lowerCamelCase							,       orient=_lowerCamelCase							,       lines=_lowerCamelCase							,       index=_lowerCamelCase							,       **self.to_json_kwargs	)
        else:
              if compression:
                    raise NotImplementedError(
                        F"""The compression parameter is not supported when writing to a buffer, but compression={compression}"""
                        """ was passed. Please provide a local path instead."""	)
              UpperCAmelCase__			:							Optional[Any]							=    self._write(
                  file_obj=self.path_or_buf							,       orient=_lowerCamelCase							,       lines=_lowerCamelCase							,       index=_lowerCamelCase							,       **self.to_json_kwargs	)
        return written



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__					,		UpperCAmelCase__					,		UpperCAmelCase__					,		UpperCAmelCase__					,		UpperCAmelCase__			:							str							=    args

        UpperCAmelCase__			:							Dict							=    query_table(
            table=self.dataset.data							,       key=slice(_lowerCamelCase							,       offset + self.batch_size	)							,       indices=self.dataset._indices							,       )
        UpperCAmelCase__			:							Dict							=    batch.to_pandas().to_json(
            path_or_buf=_lowerCamelCase							,       orient=_lowerCamelCase							,       lines=_lowerCamelCase							,       index=_lowerCamelCase							,       **_lowerCamelCase	)
        if not json_str.endswith("""\n"""	):
              json_str += "\n"
        return json_str.encode(self.encoding	)




  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       **_lowerCamelCase							,       ):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    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 json from Arrow format"""							,       ):
                    UpperCAmelCase__			:							Union[str, Any]							=    self._batch_json((offset, orient, lines, index, to_json_kwargs)	)
                    written += file_obj.write(_lowerCamelCase	)
        else:
              UpperCAmelCase__					,		UpperCAmelCase__			:							int							=    len(self.dataset	), self.batch_size
              with multiprocessing.Pool(self.num_proc	) as pool:
                    for json_str in logging.tqdm(
                        pool.imap(
                            self._batch_json							,       [(offset, orient, lines, index, to_json_kwargs) for offset in range(0							,       _lowerCamelCase							,       _lowerCamelCase	)]							,       )							,       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 json from Arrow format"""							,       ):
                          written += file_obj.write(_lowerCamelCase	)

        return written




"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A							=  {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A							=  [
        """SEW_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """SEWForCTC""",
        """SEWForSequenceClassification""",
        """SEWModel""",
        """SEWPreTrainedModel""",
    ]

if TYPE_CHECKING:
    from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_sew import (
            SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
            SEWForCTC,
            SEWForSequenceClassification,
            SEWModel,
            SEWPreTrainedModel,
        )


else:
    import sys

    _A							=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

