

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:
		snake_case    :						Union[str, Any]  =						None


snake_case    :						str  =						logging.get_logger(__name__)


snake_case    :						List[str]  =						'▁'

snake_case    :						List[str]  =						{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

snake_case    :						Tuple  =						{
    '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'
    },
}

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




class 							lowerCAmelCase__  (	UpperCamelCase						):
					__A     :    int           =						VOCAB_FILES_NAMES
					__A     :    Union[str, Any]           =						PRETRAINED_VOCAB_FILES_MAP
					__A     :    List[Any]           =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__A     :    Dict           =						PegasusTokenizer
					__A     :    Optional[int]           =						['input_ids', 'attention_mask']







					def __init__(       self      :     Optional[int]			,		_A      :     Any=None			,		_A      :     List[Any]=None			,		_A      :     Union[str, Any]="<pad>"			,		_A      :     Optional[int]="</s>"			,		_A      :     Dict="<unk>"			,		_A      :     str="<mask_2>"			,		_A      :     Optional[Any]="<mask_1>"			,		_A      :     Optional[int]=None			,		_A      :     Tuple=103			,		**_A      :     Any			,		):
											A__	:							List[Any]						    = 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_)}')

																	A__	:							List[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}.')
																	A__	:							Union[str, Any]						    = additional_special_tokens_extended
											else:
																	A__	:							int						    = [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_			,		)
											A__	:							Union[str, Any]						    = vocab_file
											A__	:							List[str]						    = False if not self.vocab_file else True







					def  _lowercase			(       self      :     List[Any]			,		_A      :     List[Any]):
											A__	:							Union[str, 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  _lowercase			(       self      :     int			,		_A      :     List			,		_A      :     Optional[List] = None			,		_A      :     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  _lowercase			(       self      :     Optional[int]			,		_A      :     List[str]			,		_A      :     Tuple=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  _lowercase			(       self      :     str			,		_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(lowercase_):
																	logger.error(F'Vocabulary path ({save_directory}) should be a directory')
																	return
											A__	:							Tuple						    = 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,)




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 							lowerCAmelCase__  (	UpperCamelCase						):







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







					def  _lowercase			(       self      :     Dict):
											# Build iterable dataset
											if self.streaming:
																	A__	:							Union[str, Any]						    = self.builder.as_streaming_dataset(split=self.split)
											# Build regular (map-style) dataset
											else:
																	A__	:							Optional[Any]						    = None
																	A__	:							Any						    = None
																	A__	:							Tuple						    = None
																	A__	:							Tuple						    = None

																	self.builder.download_and_prepare(
																	    download_config=_A			,		download_mode=_A			,		verification_mode=_A			,		base_path=_A			,		num_proc=self.num_proc			,		)
																	A__	:							Any						    = self.builder.as_dataset(
																	    split=self.split			,		verification_mode=_A			,		in_memory=self.keep_in_memory)
											return dataset




class 							lowerCAmelCase__  :







					def __init__(       self      :     Union[str, Any]			,		_A      :     Dataset			,		_A      :     Union[PathLike, BinaryIO]			,		_A      :     Optional[int] = None			,		_A      :     Optional[int] = None			,		**_A      :     Any			,		):
											if num_proc is not None and num_proc <= 0:
																	raise ValueError(F'num_proc {num_proc} must be an integer > 0.')

											A__	:							Tuple						    = dataset
											A__	:							Tuple						    = path_or_buf
											A__	:							int						    = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
											A__	:							Tuple						    = num_proc
											A__	:							Any						    = "utf-8"
											A__	:							Optional[int]						    = to_json_kwargs







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

											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=_A) as buffer:
																							A__	:							int						    = self._write(file_obj=_A			,		orient=_A			,		lines=_A			,		index=_A			,		**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__	:							Optional[Any]						    = self._write(
																	    file_obj=self.path_or_buf			,		orient=_A			,		lines=_A			,		index=_A			,		**self.to_json_kwargs)
											return written







					def  _lowercase			(       self      :     List[Any]			,		_A      :     int):
											A__    ,       A__    ,       A__    ,       A__    ,       A__	:							Union[str, Any]						    = args

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







					def  _lowercase			(       self      :     List[str]			,		_A      :     BinaryIO			,		_A      :     str			,		_A      :     List[Any]			,		_A      :     Optional[Any]			,		**_A      :     Optional[Any]			,		):
											A__	:							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"			,		):
																							A__	:							List[str]						    = self._batch_json((offset, orient, lines, index, to_json_kwargs))
																							written += file_obj.write(_A)
											else:
																	A__    ,       A__	:							Union[str, 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			,		_A			,		_A)]			,		)			,		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(_A)

											return written