


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_pegasus import PegasusTokenizer
else:
      lowercase_	   =     None


lowercase_	   =     logging.get_logger(__name__)


lowercase_	   =     '▁'

lowercase_	   =     {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowercase_	   =     {
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

lowercase_	   =     {
    'google/pegasus-xsum': 5_1_2,
}


class 	A_ (	_a   ):




      '''simple docstring'''


      __snake_case 	=       VOCAB_FILES_NAMES
      __snake_case 	=       PRETRAINED_VOCAB_FILES_MAP
      __snake_case 	=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __snake_case 	=       PegasusTokenizer
      __snake_case 	=       ["""input_ids""", """attention_mask"""]




      def __init__(    self:	Any						,				a:	List[Any]=None						,				a:	str=None						,				a:	str="<pad>"						,				a:	Optional[int]="</s>"						,				a:	Dict="<unk>"						,				a:	Optional[int]="<mask_2>"						,				a:	Union[str, Any]="<mask_1>"						,				a:	Dict=None						,				a:	Optional[int]=103						,				**a:	List[Any]						,				):
       __lowerCamelCase		:							Union[str, Any]		 =						offset

       if additional_special_tokens is not None:
        if not isinstance(snake_case_						,				snake_case_    ):
         raise TypeError(
             F'additional_special_tokens should be of type {type(snake_case_    )}, but is'
             F' {type(snake_case_    )}'    )

        __lowerCamelCase		:							Any		 =						(
            ([mask_token_sent] + additional_special_tokens)
            if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
            else additional_special_tokens
        )
        # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
        additional_special_tokens_extended += [
            F'<unk_{i}>' for i in range(len(snake_case_    )						,				self.offset - 1    )
        ]

        if len(set(snake_case_    )    ) != len(snake_case_    ):
         raise ValueError(
             'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
             F' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'    )
        __lowerCamelCase		:							int		 =						additional_special_tokens_extended
       else:
        __lowerCamelCase		:							List[Any]		 =						[mask_token_sent] if mask_token_sent is not None else []
        additional_special_tokens += [F'<unk_{i}>' for i in range(2						,				self.offset    )]

       super().__init__(
           snake_case_						,				tokenizer_file=snake_case_						,				pad_token=snake_case_						,				eos_token=snake_case_						,				unk_token=snake_case_						,				mask_token=snake_case_						,				mask_token_sent=snake_case_						,				offset=snake_case_						,				additional_special_tokens=snake_case_						,				**snake_case_						,				)
       __lowerCamelCase		:							Dict		 =						vocab_file
       __lowerCamelCase		:							Optional[int]		 =						False if not self.vocab_file else True




      def 					_snake_case     (    self:	Any						,				a:	Dict    ):
       __lowerCamelCase		:							Dict		 =						set(self.all_special_ids    )  # call it once instead of inside list comp
       all_special_ids.remove(self.unk_token_id    )  # <unk> is only sometimes special

       if all_special_ids != set(range(len(self.additional_special_tokens    ) + 3    )    ):
        raise ValueError(
            'There should be 3 special tokens: mask_token, pad_token, and eos_token +'
            F' {len(self.additional_special_tokens    )} additional_special_tokens, but got {all_special_ids}'    )

       return [1 if x in all_special_ids else 0 for x in seq]




      def 					_snake_case     (    self:	Optional[Any]						,				a:	List						,				a:	Optional[List] = None						,				a:	bool = False    ):
       if already_has_special_tokens:
        return self._special_token_mask(snake_case_    )
       elif token_ids_a is None:
        return self._special_token_mask(snake_case_    ) + [1]
       else:
        return self._special_token_mask(token_ids_a + token_ids_a    ) + [1]




      def 					_snake_case     (    self:	Dict						,				a:	Optional[int]						,				a:	Dict=None    ):
       if token_ids_a is None:
        return token_ids_a + [self.eos_token_id]
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return token_ids_a + token_ids_a + [self.eos_token_id]




      def 					_snake_case     (    self:	Optional[Any]						,				a:	str						,				a:	Optional[str] = None    ):
       if not self.can_save_slow_tokenizer:
        raise ValueError(
            'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
            'tokenizer.'    )

       if not os.path.isdir(snake_case_    ):
        logger.error(F'Vocabulary path ({save_directory}) should be a directory'    )
        return
       __lowerCamelCase		:							Optional[int]		 =						os.path.join(
           snake_case_						,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

       if os.path.abspath(self.vocab_file    ) != os.path.abspath(snake_case_    ):
        copyfile(self.vocab_file						,				snake_case_    )

       return (out_vocab_file,)





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__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							List[str]		 =						np.inf

 def set_batch_size(SCREAMING_SNAKE_CASE__						) -> None:
  nonlocal batch_size
  if isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
   __lowerCamelCase		:							Union[str, Any]		 =						min(SCREAMING_SNAKE_CASE__      ,	config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS						)
  elif isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
   __lowerCamelCase		:							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":
   __lowerCamelCase		:							List[str]		 =						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 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      def __init__(    self:	Tuple						,				a:	NestedDataStructureLike[PathLike]						,				a:	Optional[NamedSplit] = None						,				a:	Optional[Features] = None						,				a:	str = None						,				a:	bool = False						,				a:	bool = False						,				a:	Optional[int] = None						,				**a:	Optional[Any]						,				):
       super().__init__(
           a						,				split=a						,				features=a						,				cache_dir=a						,				keep_in_memory=a						,				streaming=a						,				num_proc=a						,				**a						,				)
       __lowerCamelCase		:							List[Any]		 =						path_or_paths if isinstance(a						,				a    ) else {self.split: path_or_paths}
       __lowerCamelCase		:							Optional[Any]		 =						_PACKAGED_DATASETS_MODULES['parquet'][1]
       __lowerCamelCase		:							List[str]		 =						Parquet(
           cache_dir=a						,				data_files=a						,				features=a						,				hash=a						,				**a						,				)




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

        self.builder.download_and_prepare(
            download_config=a						,				download_mode=a						,				verification_mode=a						,				base_path=a						,				num_proc=self.num_proc						,				)
        __lowerCamelCase		:							Tuple		 =						self.builder.as_dataset(
            split=self.split						,				verification_mode=a						,				in_memory=self.keep_in_memory    )
       return dataset



class 	A_ :




      '''simple docstring'''


      def __init__(    self:	Optional[int]						,				a:	Dataset						,				a:	Union[PathLike, BinaryIO]						,				a:	Optional[int] = None						,				**a:	List[Any]						,				):
       __lowerCamelCase		:							Optional[int]		 =						dataset
       __lowerCamelCase		:							List[Any]		 =						path_or_buf
       __lowerCamelCase		:							List[str]		 =						batch_size or get_writer_batch_size(dataset.features    )
       __lowerCamelCase		:							List[Any]		 =						parquet_writer_kwargs




      def 					_snake_case     (    self:	Union[str, Any]    ):
       __lowerCamelCase		:							Optional[int]		 =						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		:							Optional[int]		 =						self._write(file_obj=a						,				batch_size=a						,				**self.parquet_writer_kwargs    )
       else:
        __lowerCamelCase		:							Any		 =						self._write(file_obj=self.path_or_buf						,				batch_size=a						,				**self.parquet_writer_kwargs    )
       return written




      def 					_snake_case     (    self:	Optional[int]						,				a:	BinaryIO						,				a:	int						,				**a:	str    ):
       __lowerCamelCase		:							Dict		 =						0
       __lowerCamelCase		:							Union[str, Any]		 =						parquet_writer_kwargs.pop('path_or_buf'						,				a    )
       __lowerCamelCase		:							str		 =						self.dataset.features.arrow_schema

       __lowerCamelCase		:							Any		 =						pq.ParquetWriter(a						,				schema=a						,				**a    )

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

