
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    ,    _UpperCamelCase    ,    _UpperCamelCase = None    ,    _UpperCamelCase = None    ,    _UpperCamelCase = None    ,    _UpperCamelCase = False    ,    _UpperCamelCase = False    ,    _UpperCamelCase = None    ,    **_UpperCamelCase    ,    )    ->				List[str]:
							super().__init__(
							    _UpperCamelCase    ,    split=_UpperCamelCase    ,    features=_UpperCamelCase    ,    cache_dir=_UpperCamelCase    ,    keep_in_memory=_UpperCamelCase    ,    streaming=_UpperCamelCase    ,    num_proc=_UpperCamelCase    ,    **_UpperCamelCase    ,    )
							lowerCAmelCase_      =     path_or_paths if isinstance(_UpperCamelCase    ,    _UpperCamelCase					) else {self.split: path_or_paths}
							lowerCAmelCase_      =     Csv(
							    cache_dir=_UpperCamelCase    ,    data_files=_UpperCamelCase    ,    features=_UpperCamelCase    ,    **_UpperCamelCase    ,    )




				def 					__a						( self					)    ->				List[str]:
							# Build iterable dataset
							if self.streaming:
										lowerCAmelCase_      =     self.builder.as_streaming_dataset(split=self.split					)
							# Build regular (map-style) dataset
							else:
										lowerCAmelCase_      =     None
										lowerCAmelCase_      =     None
										lowerCAmelCase_      =     None
										lowerCAmelCase_      =     None

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





class      _lowerCAmelCase :




				def __init__( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase = None    ,    _UpperCamelCase = None    ,    **_UpperCamelCase    ,    )    ->				Optional[int]:
							if num_proc is not None and num_proc <= 0:
										raise ValueError(f"""num_proc {num_proc} must be an integer > 0."""					)

							lowerCAmelCase_      =     dataset
							lowerCAmelCase_      =     path_or_buf
							lowerCAmelCase_      =     batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
							lowerCAmelCase_      =     num_proc
							lowerCAmelCase_      =     "utf-8"
							lowerCAmelCase_      =     to_csv_kwargs




				def 					__a						( self					)    ->				int:
							lowerCAmelCase_      =     self.to_csv_kwargs.pop("path_or_buf"    ,    _UpperCamelCase					)
							lowerCAmelCase_      =     self.to_csv_kwargs.pop("header"    ,    _UpperCamelCase					)
							lowerCAmelCase_      =     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:
													lowerCAmelCase_      =     self._write(file_obj=_UpperCamelCase    ,    header=_UpperCamelCase    ,    index=_UpperCamelCase    ,    **self.to_csv_kwargs					)
							else:
										lowerCAmelCase_      =     self._write(file_obj=self.path_or_buf    ,    header=_UpperCamelCase    ,    index=_UpperCamelCase    ,    **self.to_csv_kwargs					)
							return written




				def 					__a						( self    ,    _UpperCamelCase					)    ->				Optional[Any]:
							lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_      =     args

							lowerCAmelCase_      =     query_table(
							    table=self.dataset.data    ,    key=slice(_UpperCamelCase    ,    offset + self.batch_size					)    ,    indices=self.dataset._indices    ,    )
							lowerCAmelCase_      =     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 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    **_UpperCamelCase					)    ->				int:
							lowerCAmelCase_      =     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"    ,    ):
													lowerCAmelCase_      =     self._batch_csv((offset, header, index, to_csv_kwargs)					)
													written += file_obj.write(_UpperCamelCase					)

							else:
										lowerCAmelCase_     ,  lowerCAmelCase_      =     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 TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


_A       =					{
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A       =					["XLMProphetNetTokenizer"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A       =					[
			    "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "XLMProphetNetDecoder",
			    "XLMProphetNetEncoder",
			    "XLMProphetNetForCausalLM",
			    "XLMProphetNetForConditionalGeneration",
			    "XLMProphetNetModel",
			    "XLMProphetNetPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm_prophetnet import (
						    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMProphetNetDecoder,
						    XLMProphetNetEncoder,
						    XLMProphetNetForCausalLM,
						    XLMProphetNetForConditionalGeneration,
						    XLMProphetNetModel,
						    XLMProphetNetPreTrainedModel,
						)

else:
			import sys

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



