

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 (	__a      :			Features	)			->     Optional[int]:

					"""simple docstring"""

					_a	:    Dict         =		np.inf

					def set_batch_size(__a      :			FeatureType	) -> None:
										nonlocal batch_size
										if isinstance(__A		,__A	):
															_a	:    List[Any]         =		min(__A		,config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS	)
										elif isinstance(__A		,__A	):
															_a	:    List[Any]         =		min(__A		,config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS	)
										elif isinstance(__A		,__A	) and feature.dtype == "binary":
															_a	:    List[Any]         =		min(__A		,config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS	)

					_visit(__A		,__A	)

					return None if batch_size is np.inf else batch_size

class 				UpperCAmelCase_		(			lowercase__   ):
							"""simple docstring"""







							def __init__(			self       ,							_a       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = False       ,							_a = False       ,							_a = None       ,							**_a       ,							)    ->    Dict:
												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__       ,							)
												_a	:    List[Any]         =		path_or_paths if isinstance(SCREAMING_SNAKE_CASE__       ,							SCREAMING_SNAKE_CASE__       ) else {self.split: path_or_paths}
												_a	:    int         =		_PACKAGED_DATASETS_MODULES['parquet'][1]
												_a	:    Optional[Any]         =		Parquet(
												    cache_dir=SCREAMING_SNAKE_CASE__       ,							data_files=SCREAMING_SNAKE_CASE__       ,							features=SCREAMING_SNAKE_CASE__       ,							hash=SCREAMING_SNAKE_CASE__       ,							**SCREAMING_SNAKE_CASE__       ,							)




							def 				__lowercase				(			self       )    ->    Any:
												# Build iterable dataset
												if self.streaming:
																	_a	:    Any         =		self.builder.as_streaming_dataset(split=self.split       )
												# Build regular (map-style) dataset
												else:
																	_a	:    Union[str, Any]         =		None
																	_a	:    List[str]         =		None
																	_a	:    Any         =		None
																	_a	:    Union[str, 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       ,							)
																	_a	:    List[Any]         =		self.builder.as_dataset(
																	    split=self.split       ,							verification_mode=SCREAMING_SNAKE_CASE__       ,							in_memory=self.keep_in_memory       )
												return dataset



class 				UpperCAmelCase_		:
							"""simple docstring"""







							def __init__(			self       ,							_a       ,							_a       ,							_a = None       ,							**_a       ,							)    ->    int:
												_a	:    int         =		dataset
												_a	:    str         =		path_or_buf
												_a	:    int         =		batch_size or get_writer_batch_size(dataset.features       )
												_a	:    Union[str, Any]         =		parquet_writer_kwargs


							def 				__lowercase				(			self       )    ->    int:
												_a	:    Tuple         =		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:
																						_a	:    List[str]         =		self._write(file_obj=SCREAMING_SNAKE_CASE__       ,							batch_size=SCREAMING_SNAKE_CASE__       ,							**self.parquet_writer_kwargs       )
												else:
																	_a	:    Union[str, Any]         =		self._write(file_obj=self.path_or_buf       ,							batch_size=SCREAMING_SNAKE_CASE__       ,							**self.parquet_writer_kwargs       )
												return written




							def 				__lowercase				(			self       ,							_a       ,							_a       ,							**_a       )    ->    int:
												_a	:    Tuple         =		0
												_a	:    Dict         =		parquet_writer_kwargs.pop('''path_or_buf'''       ,							SCREAMING_SNAKE_CASE__       )
												_a	:    str         =		self.dataset.features.arrow_schema

												_a	:    Dict         =		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'''       ,							):
																	_a	:    List[str]         =		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







from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase_      : str  					=       logging.get_logger(__name__)

UpperCAmelCase_      : List[str]  					=       {
    'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json',
    'YituTech/conv-bert-medium-small': (
        'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'
    ),
    'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Optional[int]        =						'''convbert'''
      def __init__(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=3_0_5_2_2     ,							SCREAMING_SNAKE_CASE__				:       Dict=7_6_8     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=1_2     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=1_2     ,							SCREAMING_SNAKE_CASE__				:       str=3_0_7_2     ,							SCREAMING_SNAKE_CASE__				:       Dict="gelu"     ,							SCREAMING_SNAKE_CASE__				:       Dict=0.1     ,							SCREAMING_SNAKE_CASE__				:       Tuple=0.1     ,							SCREAMING_SNAKE_CASE__				:       List[str]=5_1_2     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=2     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=0.02     ,							SCREAMING_SNAKE_CASE__				:       Any=1E-12     ,							SCREAMING_SNAKE_CASE__				:       int=1     ,							SCREAMING_SNAKE_CASE__				:       int=0     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=2     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=7_6_8     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=2     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=9     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=1     ,							SCREAMING_SNAKE_CASE__				:       Dict=None     ,							**SCREAMING_SNAKE_CASE__				:       List[Any]     ,							)		->			Any:
             super().__init__(
                 pad_token_id=SCREAMING_SNAKE_CASE__     ,							bos_token_id=SCREAMING_SNAKE_CASE__     ,							eos_token_id=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     ,							)

             a_					:		Tuple							=  vocab_size
             a_					:		List[str]							=  hidden_size
             a_					:		List[str]							=  num_hidden_layers
             a_					:		Dict							=  num_attention_heads
             a_					:		Optional[int]							=  intermediate_size
             a_					:		int							=  hidden_act
             a_					:		Dict							=  hidden_dropout_prob
             a_					:		int							=  attention_probs_dropout_prob
             a_					:		str							=  max_position_embeddings
             a_					:		List[str]							=  type_vocab_size
             a_					:		List[str]							=  initializer_range
             a_					:		Tuple							=  layer_norm_eps
             a_					:		Optional[int]							=  embedding_size
             a_					:		List[Any]							=  head_ratio
             a_					:		List[Any]							=  conv_kernel_size
             a_					:		Tuple							=  num_groups
             a_					:		Tuple							=  classifier_dropout
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Mapping[str, Mapping[int, str]]:
             if self.task == "multiple-choice":
                    a_					:		Tuple							=  {0: 'batch', 1: 'choice', 2: 'sequence'}
             else:
                    a_					:		List[str]							=  {0: 'batch', 1: 'sequence'}
             return OrderedDict(
                 [
                     ('input_ids', dynamic_axis),
                     ('attention_mask', dynamic_axis),
                     ('token_type_ids', dynamic_axis),
                 ]     )


