


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        A     (		lowerCamelCase_	):







     '''simple docstring'''







     def __init__(				self    :							int			,				_UpperCamelCase    :							Any			,				_UpperCamelCase    :							int = None			,				_UpperCamelCase    :							Optional[Any] = None			,				_UpperCamelCase    :							Optional[Any] = None			,				_UpperCamelCase    :							str = False			,				_UpperCamelCase    :							int = False			,				_UpperCamelCase    :							int = None			,				_UpperCamelCase    :							Optional[Any] = None			,				**_UpperCamelCase    :							Optional[int]			,				):
      super().__init__(
          _UpperCamelCase			,				split=_UpperCamelCase			,				features=_UpperCamelCase			,				cache_dir=_UpperCamelCase			,				keep_in_memory=_UpperCamelCase			,				streaming=_UpperCamelCase			,				num_proc=_UpperCamelCase			,				**_UpperCamelCase			,				)
      _lowercase:       Optional[Any]						=				field
      _lowercase:       Optional[Any]						=				path_or_paths if isinstance(_UpperCamelCase			,				_UpperCamelCase) else {self.split: path_or_paths}
      _lowercase:       List[str]						=				Json(
          cache_dir=_UpperCamelCase			,				data_files=_UpperCamelCase			,				features=_UpperCamelCase			,				field=_UpperCamelCase			,				**_UpperCamelCase			,				)






     def 					UpperCAmelCase__       (				self    :							Optional[Any]):
      # Build iterable dataset
      if self.streaming:
       _lowercase:       str						=				self.builder.as_streaming_dataset(split=self.split)
      # Build regular (map-style) dataset
      else:
       _lowercase:       Optional[int]						=				None
       _lowercase:       Optional[int]						=				None
       _lowercase:       Any						=				None
       _lowercase:       Dict						=				None

       self.builder.download_and_prepare(
           download_config=_UpperCamelCase			,				download_mode=_UpperCamelCase			,				verification_mode=_UpperCamelCase			,				base_path=_UpperCamelCase			,				num_proc=self.num_proc			,				)
       _lowercase:       List[Any]						=				self.builder.as_dataset(
           split=self.split			,				verification_mode=_UpperCamelCase			,				in_memory=self.keep_in_memory)
      return dataset






class        A     :







     '''simple docstring'''







     def __init__(				self    :							Dict			,				_UpperCamelCase    :							Tuple			,				_UpperCamelCase    :							Union[str, Any]			,				_UpperCamelCase    :							Union[str, Any] = None			,				_UpperCamelCase    :							Tuple = None			,				**_UpperCamelCase    :							List[str]			,				):
      if num_proc is not None and num_proc <= 0:
       raise ValueError(f"num_proc {num_proc} must be an integer > 0.")

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

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

      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=_UpperCamelCase) as buffer:
        _lowercase:       str						=				self._write(file_obj=_UpperCamelCase			,				orient=_UpperCamelCase			,				lines=_UpperCamelCase			,				index=_UpperCamelCase			,				**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.")
       _lowercase:       List[Any]						=				self._write(
           file_obj=self.path_or_buf			,				orient=_UpperCamelCase			,				lines=_UpperCamelCase			,				index=_UpperCamelCase			,				**self.to_json_kwargs)
      return written

     def 					UpperCAmelCase__       (				self    :							List[str]			,				_UpperCamelCase    :							Tuple):
      _lowercase					,	_lowercase					,	_lowercase					,	_lowercase					,	_lowercase:       List[Any]						=				args

      _lowercase:       int						=				query_table(
          table=self.dataset.data			,				key=slice(_UpperCamelCase			,				offset + self.batch_size)			,				indices=self.dataset._indices			,				)
      _lowercase:       Union[str, Any]						=				batch.to_pandas().to_json(
          path_or_buf=_UpperCamelCase			,				orient=_UpperCamelCase			,				lines=_UpperCamelCase			,				index=_UpperCamelCase			,				**_UpperCamelCase)
      if not json_str.endswith("\n"):
       json_str += "\n"
      return json_str.encode(self.encoding)






     def 					UpperCAmelCase__       (				self    :							str			,				_UpperCamelCase    :							int			,				_UpperCamelCase    :							int			,				_UpperCamelCase    :							Dict			,				_UpperCamelCase    :							Any			,				**_UpperCamelCase    :							Optional[Any]			,				):
      _lowercase:       Optional[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 json from Arrow format"			,				):
        _lowercase:       str						=				self._batch_json((offset, orient, lines, index, to_json_kwargs))
        written += file_obj.write(_UpperCamelCase)
      else:
       _lowercase					,	_lowercase:       str						=				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			,				_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 json from Arrow format"			,				):
         written += file_obj.write(_UpperCamelCase)

      return written



"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_    							=					{
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """NllbMoeForConditionalGeneration""",
						    """NllbMoeModel""",
						    """NllbMoePreTrainedModel""",
						    """NllbMoeTop2Router""",
						    """NllbMoeSparseMLP""",
						]


if TYPE_CHECKING:
						from .configuration_nllb_moe import (
						    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    NllbMoeConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nllb_moe import (
												    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NllbMoeForConditionalGeneration,
												    NllbMoeModel,
												    NllbMoePreTrainedModel,
												    NllbMoeSparseMLP,
												    NllbMoeTopaRouter,
												)


else:
						import sys

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



