



"""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       SCREAMING_SNAKE_CASE__  (					lowerCAmelCase__ ):

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = False      ,     _SCREAMING_SNAKE_CASE = False      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = None      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      int:





    '''simple docstring'''




    super().__init__(
        lowercase_      ,     split=lowercase_      ,     features=lowercase_      ,     cache_dir=lowercase_      ,     keep_in_memory=lowercase_      ,     streaming=lowercase_      ,     num_proc=lowercase_      ,     **lowercase_      ,     )
    UpperCAmelCase	:					Dict	=	field
    UpperCAmelCase	:					Optional[int]	=	path_or_paths if isinstance(lowercase_      ,     lowercase_						) else {self.split: path_or_paths}
    UpperCAmelCase	:					List[Any]	=	Json(
        cache_dir=lowercase_      ,     data_files=lowercase_      ,     features=lowercase_      ,     field=lowercase_      ,     **lowercase_      ,     )




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




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

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



class       SCREAMING_SNAKE_CASE__  :

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = None      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      List[Any]:





    '''simple docstring'''




    if num_proc is not None and num_proc <= 0:
     raise ValueError(F"num_proc {num_proc} must be an integer > 0."						)

    UpperCAmelCase	:					Any	=	dataset
    UpperCAmelCase	:					str	=	path_or_buf
    UpperCAmelCase	:					List[Any]	=	batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
    UpperCAmelCase	:					int	=	num_proc
    UpperCAmelCase	:					Union[str, Any]	=	'''utf-8'''
    UpperCAmelCase	:					List[Any]	=	to_json_kwargs

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	self.to_json_kwargs.pop("""path_or_buf"""      ,     lowercase_						)
    UpperCAmelCase	:					List[Any]	=	self.to_json_kwargs.pop("""orient"""      ,     """records"""						)
    UpperCAmelCase	:					str	=	self.to_json_kwargs.pop("""lines"""      ,     True if orient == """records""" else False						)
    UpperCAmelCase	:					Union[str, Any]	=	self.to_json_kwargs.pop("""index"""      ,     False if orient in ["""split""", """table"""] else True						)
    UpperCAmelCase	:					List[str]	=	self.to_json_kwargs.pop("""compression"""      ,     lowercase_						)

    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=lowercase_						) as buffer:
      UpperCAmelCase	:					str	=	self._write(file_obj=lowercase_      ,     orient=lowercase_      ,     lines=lowercase_      ,     index=lowercase_      ,     **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."""						)
     UpperCAmelCase	:					List[str]	=	self._write(
         file_obj=self.path_or_buf      ,     orient=lowercase_      ,     lines=lowercase_      ,     index=lowercase_      ,     **self.to_json_kwargs						)
    return written

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Optional[int]:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	args

    UpperCAmelCase	:					Dict	=	query_table(
        table=self.dataset.data      ,     key=slice(lowercase_      ,     offset + self.batch_size						)      ,     indices=self.dataset._indices      ,     )
    UpperCAmelCase	:					Optional[int]	=	batch.to_pandas().to_json(
        path_or_buf=lowercase_      ,     orient=lowercase_      ,     lines=lowercase_      ,     index=lowercase_      ,     **lowercase_						)
    if not json_str.endswith("""\n"""						):
     json_str += "\n"
    return json_str.encode(self.encoding						)




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      str:





    '''simple docstring'''




    UpperCAmelCase	:					List[Any]	=	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"""      ,     ):
      UpperCAmelCase	:					List[Any]	=	self._batch_json((offset, orient, lines, index, to_json_kwargs)						)
      written += file_obj.write(lowercase_						)
    else:
     UpperCAmelCase	:					Any	=	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      ,     lowercase_      ,     lowercase_						)]      ,     )      ,     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(lowercase_						)

    return written





"""simple docstring"""

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__    :  int							    =	None


lowercase__    :  Any							    =	logging.get_logger(__name__)


lowercase__    :  List[str]							    =	'''▁'''

lowercase__    :  Optional[int]							    =	{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase__    :  str							    =	{
    '''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__    :  List[Any]							    =	{
    '''google/pegasus-xsum''': 5_12,
}




class        _UpperCAmelCase       (			lowerCAmelCase__):
 _lowerCAmelCase   :			List[str]							=		VOCAB_FILES_NAMES
 _lowerCAmelCase   :			List[str]							=		PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase   :			Any							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase   :			Tuple							=		PegasusTokenizer
 _lowerCAmelCase   :			str							=		["""input_ids""", """attention_mask"""]

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

     if additional_special_tokens is not None:
         if not isinstance(lowercase_  ,     lowercase_				):
             raise TypeError(
                 f"additional_special_tokens should be of type {type(lowercase_				)}, but is"
                 f" {type(lowercase_				)}"				)

         snake_case_ :     str				=	(
             ([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(lowercase_				)  ,     self.offset - 1				)
         ]

         if len(set(lowercase_				)				) != len(lowercase_				):
             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}."				)
         snake_case_ :     Union[str, Any]				=	additional_special_tokens_extended
     else:
         snake_case_ :     Dict				=	[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__(
         lowercase_  ,     tokenizer_file=lowercase_  ,     pad_token=lowercase_  ,     eos_token=lowercase_  ,     unk_token=lowercase_  ,     mask_token=lowercase_  ,     mask_token_sent=lowercase_  ,     offset=lowercase_  ,     additional_special_tokens=lowercase_  ,     **lowercase_  ,     )
     snake_case_ :     List[Any]				=	vocab_file
     snake_case_ :     List[Any]				=	False if not self.vocab_file else True

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

 def 						_snake_case					(     self		:       List[Any]  ,     lowercase_		:       Optional[int]  ,     lowercase_		:       str=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]  ,     lowercase_		:       str  ,     lowercase_		:       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(lowercase_				):
         logger.error(f"Vocabulary path ({save_directory}) should be a directory"				)
         return
     snake_case_ :     Dict				=	os.path.join(
         lowercase_  ,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

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

     return (out_vocab_file,)





