


'''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 						a__					(					a_   ):





			'''simple docstring'''

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

			def 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		Union[str, Any]:
						# 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=lowerCAmelCase_			,    download_mode=lowerCAmelCase_			,    verification_mode=lowerCAmelCase_			,    base_path=lowerCAmelCase_			,    num_proc=self.num_proc			,    )
									__A=	self.builder.as_dataset(
									    split=self.split			,    verification_mode=lowerCAmelCase_			,    in_memory=self.keep_in_memory	)
						return dataset


class 						a__					:





			'''simple docstring'''

			def __init__(  self							:			Tuple			,    lowerCAmelCase_							:			Dataset			,    lowerCAmelCase_							:			Union[PathLike, BinaryIO]			,    lowerCAmelCase_							:			Optional[int] = None			,    lowerCAmelCase_							:			Optional[int] = None			,    **lowerCAmelCase_							:			List[Any]			,    )      ->		Union[str, 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_json_kwargs
			def 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		int:
						__A=	self.to_json_kwargs.pop('path_or_buf'			,    lowerCAmelCase_	)
						__A=	self.to_json_kwargs.pop('orient'			,    'records'	)
						__A=	self.to_json_kwargs.pop('lines'			,    True if orient == 'records' else False	)
						__A=	self.to_json_kwargs.pop('index'			,    False if orient in ['split', 'table'] else True	)
						__A=	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:
												__A=	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.'	)
									__A=	self._write(
									    file_obj=self.path_or_buf			,    orient=lowerCAmelCase_			,    lines=lowerCAmelCase_			,    index=lowerCAmelCase_			,    **self.to_json_kwargs	)
						return written
			def 					lowerCAmelCase     (  self							:			Any			,    lowerCAmelCase_							:			List[Any]	)      ->		Optional[int]:
						__A,	__A,	__A,	__A,	__A=	args

						__A=	query_table(
						    table=self.dataset.data			,    key=slice(lowerCAmelCase_			,    offset + self.batch_size	)			,    indices=self.dataset._indices			,    )
						__A=	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 					lowerCAmelCase     (  self							:			List[Any]			,    lowerCAmelCase_							:			BinaryIO			,    lowerCAmelCase_							:			List[Any]			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			Optional[Any]			,    **lowerCAmelCase_							:			Tuple			,    )      ->		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 json from Arrow format'			,    ):
												__A=	self._batch_json((offset, orient, lines, index, to_json_kwargs)	)
												written += file_obj.write(lowerCAmelCase_	)
						else:
									__A,	__A=	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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__  			=				logging.get_logger(__name__)

UpperCAmelCase__  			=				{
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}
class 						a__					(					a_   ):





			'''simple docstring'''

			A						:				Union[str, Any]					      =			'''roberta'''
			def __init__(  self							:			Any			,    lowerCAmelCase_							:			int=50_265			,    lowerCAmelCase_							:			int=768			,    lowerCAmelCase_							:			Optional[Any]=12			,    lowerCAmelCase_							:			Tuple=12			,    lowerCAmelCase_							:			Optional[int]=3_072			,    lowerCAmelCase_							:			List[str]="gelu"			,    lowerCAmelCase_							:			List[str]=0.1			,    lowerCAmelCase_							:			Tuple=0.1			,    lowerCAmelCase_							:			List[Any]=512			,    lowerCAmelCase_							:			Union[str, Any]=2			,    lowerCAmelCase_							:			List[Any]=0.02			,    lowerCAmelCase_							:			Optional[int]=1E-12			,    lowerCAmelCase_							:			str=1			,    lowerCAmelCase_							:			Dict=0			,    lowerCAmelCase_							:			Any=2			,    lowerCAmelCase_							:			List[str]="absolute"			,    lowerCAmelCase_							:			Any=True			,    lowerCAmelCase_							:			Dict=None			,    **lowerCAmelCase_							:			Any			,    )      ->		int:
						super().__init__(pad_token_id=lowerCAmelCase_			,    bos_token_id=lowerCAmelCase_			,    eos_token_id=lowerCAmelCase_			,    **lowerCAmelCase_	)

						__A=	vocab_size
						__A=	hidden_size
						__A=	num_hidden_layers
						__A=	num_attention_heads
						__A=	hidden_act
						__A=	intermediate_size
						__A=	hidden_dropout_prob
						__A=	attention_probs_dropout_prob
						__A=	max_position_embeddings
						__A=	type_vocab_size
						__A=	initializer_range
						__A=	layer_norm_eps
						__A=	position_embedding_type
						__A=	use_cache
						__A=	classifier_dropout


class 						a__					(					a_   ):





			'''simple docstring'''

			@property
			def 					lowerCAmelCase     (  self							:			Any	)      ->		Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
									__A=	{0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
									__A=	{0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						    ]	)



