"""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 lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:List[Any]        =		np.inf

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

    _visit(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

    return None if batch_size is np.inf else batch_size


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    def __init__(     self			,			__lowercase			,			__lowercase = None			,			__lowercase = None			,			__lowercase = None			,			__lowercase = False			,			__lowercase = False			,			__lowercase = None			,			**__lowercase			,			)	->     int:
        super().__init__(
            __lowercase			,			split=__lowercase			,			features=__lowercase			,			cache_dir=__lowercase			,			keep_in_memory=__lowercase			,			streaming=__lowercase			,			num_proc=__lowercase			,			**__lowercase			,			)
        __UpperCamelCase					:List[Any]        =		path_or_paths if isinstance(__lowercase			,			__lowercase) else {self.split: path_or_paths}
        __UpperCamelCase					:Dict        =		_PACKAGED_DATASETS_MODULES['''parquet'''][1]
        __UpperCamelCase					:Union[str, Any]        =		Parquet(
            cache_dir=__lowercase			,			data_files=__lowercase			,			features=__lowercase			,			hash=__lowercase			,			**__lowercase			,			)







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        # Build iterable dataset
        if self.streaming:
            __UpperCamelCase					:Optional[int]        =		self.builder.as_streaming_dataset(split=self.split)
        # Build regular (map-style) dataset
        else:
            __UpperCamelCase					:List[Any]        =		None
            __UpperCamelCase					:Optional[int]        =		None
            __UpperCamelCase					:str        =		None
            __UpperCamelCase					:int        =		None

            self.builder.download_and_prepare(
                download_config=__lowercase			,			download_mode=__lowercase			,			verification_mode=__lowercase			,			base_path=__lowercase			,			num_proc=self.num_proc			,			)
            __UpperCamelCase					:List[str]        =		self.builder.as_dataset(
                split=self.split			,			verification_mode=__lowercase			,			in_memory=self.keep_in_memory)
        return dataset


class      lowerCamelCase_					:






    '''simple docstring'''






    def __init__(     self			,			__lowercase			,			__lowercase			,			__lowercase = None			,			**__lowercase			,			)	->     Any:
        __UpperCamelCase					:List[Any]        =		dataset
        __UpperCamelCase					:Any        =		path_or_buf
        __UpperCamelCase					:Optional[Any]        =		batch_size or get_writer_batch_size(dataset.features)
        __UpperCamelCase					:Any        =		parquet_writer_kwargs







    def       UpperCamelCase__       (     self)	->     int:
        __UpperCamelCase					:Dict        =		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:
                __UpperCamelCase					:Union[str, Any]        =		self._write(file_obj=__lowercase			,			batch_size=__lowercase			,			**self.parquet_writer_kwargs)
        else:
            __UpperCamelCase					:Optional[Any]        =		self._write(file_obj=self.path_or_buf			,			batch_size=__lowercase			,			**self.parquet_writer_kwargs)
        return written







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

        __UpperCamelCase					:Optional[int]        =		pq.ParquetWriter(__lowercase			,			schema=__lowercase			,			**__lowercase)

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






from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


__lowercase										= logging.get_logger(__name__)

__lowercase										= {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__lowercase										= {
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

__lowercase										= {'''allegro/herbert-base-cased''': 514}
__lowercase										= {}


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						Tuple      =				VOCAB_FILES_NAMES
    a__     :						Dict      =				PRETRAINED_VOCAB_FILES_MAP
    a__     :						Tuple      =				PRETRAINED_INIT_CONFIGURATION
    a__     :						Optional[Any]      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__     :						Union[str, Any]      =				HerbertTokenizer







    def __init__(     self			,			__lowercase=None			,			__lowercase=None			,			__lowercase=None			,			__lowercase="<s>"			,			__lowercase="<unk>"			,			__lowercase="<pad>"			,			__lowercase="<mask>"			,			__lowercase="</s>"			,			**__lowercase			,			)	->     Optional[Any]:
        super().__init__(
            __lowercase			,			__lowercase			,			tokenizer_file=__lowercase			,			cls_token=__lowercase			,			unk_token=__lowercase			,			pad_token=__lowercase			,			mask_token=__lowercase			,			sep_token=__lowercase			,			**__lowercase			,			)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None)	->     List[int]:
        __UpperCamelCase					:List[str]        =		[self.cls_token_id]
        __UpperCamelCase					:Tuple        =		[self.sep_token_id]
        if token_ids_a is None:
            return cls + token_ids_a + sep

        return cls + token_ids_a + sep + token_ids_a + sep







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None			,			__lowercase = False)	->     List[int]:
        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=__lowercase			,			token_ids_a=__lowercase			,			already_has_special_tokens=__lowercase)

        if token_ids_a is None:
            return [1] + ([0] * len(__lowercase)) + [1]
        return [1] + ([0] * len(__lowercase)) + [1] + ([0] * len(__lowercase)) + [1]







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None)	->     List[int]:
        __UpperCamelCase					:Optional[Any]        =		[self.sep_token_id]
        __UpperCamelCase					:int        =		[self.cls_token_id]

        if token_ids_a is None:
            return len(cls + token_ids_a + sep) * [0]
        return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None)	->     Tuple[str]:
        __UpperCamelCase					:Optional[int]        =		self._tokenizer.model.save(__lowercase			,			name=__lowercase)
        return tuple(__lowercase)





