





'''simple docstring'''






import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A_		:  Optional[Any]		 =						logging.get_logger(__name__)

A_		:  str		 =						{"vocab_file": "vocab.json"}

A_		:  List[str]		 =						{
    "vocab_file": {
        "mgp-str": "https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json",
    }
}

A_		:  Any		 =						{"mgp-str": 27}
class 					__snake_case  (					__SCREAMING_SNAKE_CASE							):






   '''simple docstring'''





   lowerCamelCase__    							=      VOCAB_FILES_NAMES
   lowerCamelCase__    							=      PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__    							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

   def __init__(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE="[GO]"	,		__SCREAMING_SNAKE_CASE="[GO]"	,		__SCREAMING_SNAKE_CASE="[s]"	,		__SCREAMING_SNAKE_CASE="[GO]"	,		**__SCREAMING_SNAKE_CASE       ):
    super().__init__(
        unk_token=__SCREAMING_SNAKE_CASE	,		bos_token=__SCREAMING_SNAKE_CASE	,		eos_token=__SCREAMING_SNAKE_CASE	,		pad_token=__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE	,		)

    with open(__SCREAMING_SNAKE_CASE	,		encoding="""utf-8"""       ) as vocab_handle:
     snake_case__     :					List[Any]    =  json.load(__SCREAMING_SNAKE_CASE       )
    snake_case__     :					int    =  {v: k for k, v in self.vocab.items()}

   @property
   def 		__UpperCamelCase						(							self       ):
    return len(self.vocab       )

   def 		__UpperCamelCase						(							self       ):
    return dict(self.vocab	,		**self.added_tokens_encoder       )

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE       ):
    snake_case__     :					Tuple    =  []
    for s in text:
     char_tokens.extend(__SCREAMING_SNAKE_CASE       )
    return char_tokens

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE       ):
    return self.vocab.get(__SCREAMING_SNAKE_CASE	,		self.vocab.get(self.unk_token       )       )

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE       ):
    return self.decoder.get(__SCREAMING_SNAKE_CASE       )


   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE = None       ):
    if not os.path.isdir(__SCREAMING_SNAKE_CASE       ):
     logger.error("""Vocabulary path ({}) should be a directory""".format(__SCREAMING_SNAKE_CASE       )       )
     return
    snake_case__     :					int    =  os.path.join(
        __SCREAMING_SNAKE_CASE	,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )

    with open(__SCREAMING_SNAKE_CASE	,		"""w"""	,		encoding="""utf-8"""       ) as f:
     f.write(json.dumps(self.vocab	,		indent=2	,		sort_keys=__SCREAMING_SNAKE_CASE	,		ensure_ascii=__SCREAMING_SNAKE_CASE       ) + """\n"""       )

    return (vocab_file,)









from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


a_      :      Any    =     logging.get_logger(__name__)


a_      :      str    =     TypeVar('DatasetType', Dataset, IterableDataset)






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = "first_exhausted"							,  ):
 from .arrow_dataset import Dataset
 from .iterable_dataset import IterableDataset

 if not datasets:
  raise ValueError('''Unable to interleave an empty list of datasets.'''       )
 for i, dataset in enumerate(__UpperCAmelCase       ):
  if not isinstance(__UpperCAmelCase							,  (Dataset, IterableDataset)       ):
   if isinstance(__UpperCAmelCase							,  (DatasetDict, IterableDatasetDict)       ):
    if not dataset:
     raise ValueError(
         f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
         '''is an empty dataset dictionary.'''       )
    raise ValueError(
        f"Dataset at position {i} has at least one split: {list(__UpperCAmelCase       )}\n"
        f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__UpperCAmelCase       )       )}']"       )
   raise ValueError(
       f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__UpperCAmelCase       ).__name__}."       )
  if i == 0:
   a__							,			a__												= (
       (Dataset, IterableDataset) if isinstance(__UpperCAmelCase							,  __UpperCAmelCase       ) else (IterableDataset, Dataset)
   )
  elif not isinstance(__UpperCAmelCase							,  __UpperCAmelCase       ):
   raise ValueError(
       f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."       )
 if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
  raise ValueError(f"{stopping_strategy} is not supported. Please enter a valid stopping_strategy."       )
 if dataset_type is Dataset:
  return _interleave_map_style_datasets(
      __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  info=__UpperCAmelCase							,  split=__UpperCAmelCase							,  stopping_strategy=__UpperCAmelCase       )
 else:
  return _interleave_iterable_datasets(
      __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  info=__UpperCAmelCase							,  split=__UpperCAmelCase							,  stopping_strategy=__UpperCAmelCase       )






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = 0							,  ):

 if not dsets:
  raise ValueError('''Unable to concatenate an empty list of datasets.'''       )
 for i, dataset in enumerate(__UpperCAmelCase       ):
  if not isinstance(__UpperCAmelCase							,  (Dataset, IterableDataset)       ):
   if isinstance(__UpperCAmelCase							,  (DatasetDict, IterableDatasetDict)       ):
    if not dataset:
     raise ValueError(
         f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
         '''is an empty dataset dictionary.'''       )
    raise ValueError(
        f"Dataset at position {i} has at least one split: {list(__UpperCAmelCase       )}\n"
        f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__UpperCAmelCase       )       )}']"       )
   raise ValueError(
       f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__UpperCAmelCase       ).__name__}."       )
  if i == 0:
   a__							,			a__												= (
       (Dataset, IterableDataset) if isinstance(__UpperCAmelCase							,  __UpperCAmelCase       ) else (IterableDataset, Dataset)
   )
  elif not isinstance(__UpperCAmelCase							,  __UpperCAmelCase       ):
   raise ValueError(
       f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."       )
 if dataset_type is Dataset:
  return _concatenate_map_style_datasets(__UpperCAmelCase							,  info=__UpperCAmelCase							,  split=__UpperCAmelCase							,  axis=__UpperCAmelCase       )
 else:
  return _concatenate_iterable_datasets(__UpperCAmelCase							,  info=__UpperCAmelCase							,  split=__UpperCAmelCase							,  axis=__UpperCAmelCase       )





