




"""simple docstring"""






import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader





def 							UpperCAmelCase		(		snake_case			:					Optional[int]    ):
					_lowerCAmelCase:Optional[int]									=  np.inf

					def set_batch_size(snake_case			:					Optional[Any]    ) -> None:
										nonlocal batch_size
										if isinstance(__UpperCamelCase      ,       __UpperCamelCase    ):
															_lowerCAmelCase:Optional[int]									=  min(__UpperCamelCase      ,       config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS    )
										elif isinstance(__UpperCamelCase      ,       __UpperCamelCase    ):
															_lowerCAmelCase:int									=  min(__UpperCamelCase      ,       config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS    )
										elif isinstance(__UpperCamelCase      ,       __UpperCamelCase    ) and feature.dtype == "binary":
															_lowerCAmelCase:int									=  min(__UpperCamelCase      ,       config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS    )

					_visit(__UpperCamelCase      ,       __UpperCamelCase    )

					return None if batch_size is np.inf else batch_size




class   a__     (       UpperCamelCase_  ):





						def __init__(							self      :	str     ,a__      :	Union[str, Any]     ,a__      :	Union[str, Any] = None     ,a__      :	Any = None     ,a__      :	Tuple = None     ,a__      :	Tuple = False     ,a__      :	List[Any] = False     ,a__      :	Optional[int] = None     ,**a__      :	Optional[int]     ,)						->       Any:




											"""simple docstring"""







											super().__init__(
											    _SCREAMING_SNAKE_CASE     ,split=_SCREAMING_SNAKE_CASE     ,features=_SCREAMING_SNAKE_CASE     ,cache_dir=_SCREAMING_SNAKE_CASE     ,keep_in_memory=_SCREAMING_SNAKE_CASE     ,streaming=_SCREAMING_SNAKE_CASE     ,num_proc=_SCREAMING_SNAKE_CASE     ,**_SCREAMING_SNAKE_CASE     ,)
											_lowerCAmelCase:Optional[Any]									=  path_or_paths if isinstance(_SCREAMING_SNAKE_CASE     ,_SCREAMING_SNAKE_CASE) else {self.split: path_or_paths}
											_lowerCAmelCase:Any									=  _PACKAGED_DATASETS_MODULES['''parquet'''][1]
											_lowerCAmelCase:str									=  Parquet(
											    cache_dir=_SCREAMING_SNAKE_CASE     ,data_files=_SCREAMING_SNAKE_CASE     ,features=_SCREAMING_SNAKE_CASE     ,hash=_SCREAMING_SNAKE_CASE     ,**_SCREAMING_SNAKE_CASE     ,)







						def       __UpperCamelCase     (							self      :	str)						->       Tuple:




											"""simple docstring"""







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

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




class   a__     :





						def __init__(							self      :	int     ,a__      :	Tuple     ,a__      :	Optional[int]     ,a__      :	Optional[int] = None     ,**a__      :	Union[str, Any]     ,)						->       Union[str, Any]:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  dataset
											_lowerCAmelCase:List[str]									=  path_or_buf
											_lowerCAmelCase:Optional[int]									=  batch_size or get_writer_batch_size(dataset.features)
											_lowerCAmelCase:Optional[int]									=  parquet_writer_kwargs





						def       __UpperCamelCase     (							self      :	Optional[int])						->       int:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

											if isinstance(self.path_or_buf     ,(str, bytes, os.PathLike)):
																with open(self.path_or_buf     ,'''wb+''') as buffer:
																					_lowerCAmelCase:Any									=  self._write(file_obj=_SCREAMING_SNAKE_CASE     ,batch_size=_SCREAMING_SNAKE_CASE     ,**self.parquet_writer_kwargs)
											else:
																_lowerCAmelCase:Any									=  self._write(file_obj=self.path_or_buf     ,batch_size=_SCREAMING_SNAKE_CASE     ,**self.parquet_writer_kwargs)
											return written







						def       __UpperCamelCase     (							self      :	Tuple     ,a__      :	Optional[int]     ,a__      :	str     ,**a__      :	List[str])						->       int:




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  0
											_lowerCAmelCase:Optional[Any]									=  parquet_writer_kwargs.pop('''path_or_buf'''     ,_SCREAMING_SNAKE_CASE)
											_lowerCAmelCase:List[str]									=  self.dataset.features.arrow_schema

											_lowerCAmelCase:Union[str, Any]									=  pq.ParquetWriter(_SCREAMING_SNAKE_CASE     ,schema=_SCREAMING_SNAKE_CASE     ,**_SCREAMING_SNAKE_CASE)

											for offset in logging.tqdm(
											    range(0     ,len(self.dataset)     ,_SCREAMING_SNAKE_CASE)     ,unit='''ba'''     ,disable=not logging.is_progress_bar_enabled()     ,desc='''Creating parquet from Arrow format'''     ,):
																_lowerCAmelCase:Optional[Any]									=  query_table(
																    table=self.dataset._data     ,key=slice(_SCREAMING_SNAKE_CASE     ,offset + batch_size)     ,indices=self.dataset._indices if self.dataset._indices is not None else None     ,)
																writer.write_table(_SCREAMING_SNAKE_CASE)
																written += batch.nbytes
											writer.close()
											return written












"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__						=			{
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__						=			['''ConvBertTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__						=			[
				    '''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''ConvBertForMaskedLM''',
				    '''ConvBertForMultipleChoice''',
				    '''ConvBertForQuestionAnswering''',
				    '''ConvBertForSequenceClassification''',
				    '''ConvBertForTokenClassification''',
				    '''ConvBertLayer''',
				    '''ConvBertModel''',
				    '''ConvBertPreTrainedModel''',
				    '''load_tf_weights_in_convbert''',
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__						=			[
				    '''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFConvBertForMaskedLM''',
				    '''TFConvBertForMultipleChoice''',
				    '''TFConvBertForQuestionAnswering''',
				    '''TFConvBertForSequenceClassification''',
				    '''TFConvBertForTokenClassification''',
				    '''TFConvBertLayer''',
				    '''TFConvBertModel''',
				    '''TFConvBertPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
				from .tokenization_convbert import ConvBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_convbert_fast import ConvBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_convbert import (
								    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ConvBertForMaskedLM,
								    ConvBertForMultipleChoice,
								    ConvBertForQuestionAnswering,
								    ConvBertForSequenceClassification,
								    ConvBertForTokenClassification,
								    ConvBertLayer,
								    ConvBertModel,
								    ConvBertPreTrainedModel,
								    load_tf_weights_in_convbert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_convbert import (
								    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFConvBertForMaskedLM,
								    TFConvBertForMultipleChoice,
								    TFConvBertForQuestionAnswering,
								    TFConvBertForSequenceClassification,
								    TFConvBertForTokenClassification,
								    TFConvBertLayer,
								    TFConvBertModel,
								    TFConvBertPreTrainedModel,
								)


else:
				import sys

				UpperCamelCase__						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






