
'''simple docstring'''

import json
import os
from typing import Optional, Tuple

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


__snake_case:  Dict								=			logging.get_logger(__name__)

__snake_case:  Dict								=			{"vocab_file": "vocab.json"}

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

__snake_case:  int								=			{"mgp-str": 27}


class  _UpperCAmelCase ( __lowerCAmelCase    ):

     """simple docstring"""

     a_    	=					VOCAB_FILES_NAMES
     a_    	=					PRETRAINED_VOCAB_FILES_MAP
     a_    	=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_="[GO]"							,		lowerCAmelCase_="[GO]"							,		lowerCAmelCase_="[s]"							,		lowerCAmelCase_="[GO]"							,		**lowerCAmelCase_ ):

       '''simple docstring'''

       super().__init__(
           unk_token=_UpperCamelCase							,		bos_token=_UpperCamelCase							,		eos_token=_UpperCamelCase							,		pad_token=_UpperCamelCase							,		**_UpperCamelCase							,		)

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


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return len(self.vocab )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return dict(self.vocab							,		**self.added_tokens_encoder )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Tuple      =							[]
       for s in text:
         char_tokens.extend(_UpperCamelCase )
       return char_tokens


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       return self.vocab.get(_UpperCamelCase							,		self.vocab.get(self.unk_token ) )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       return self.decoder.get(_UpperCamelCase )






     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None ):

       '''simple docstring'''

       if not os.path.isdir(_UpperCamelCase ):
         logger.error("""Vocabulary path ({}) should be a directory""".format(_UpperCamelCase ) )
         return
       a_      :	Tuple      =							os.path.join(
           _UpperCamelCase							,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

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

       return (vocab_file,)

'''simple docstring'''

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


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


__snake_case:  Tuple								=			TypeVar("DatasetType", Dataset, IterableDataset)






def 	_snake_case						(      A_       :    List[DatasetType]     ,		A_       :    Optional[List[float]] = None     ,		A_       :    Optional[int] = None     ,		A_       :    Optional[DatasetInfo] = None     ,		A_       :    Optional[NamedSplit] = None     ,		A_       :    Literal["first_exhausted", "all_exhausted"] = "first_exhausted"     ,		):
  """simple docstring"""







  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(A_   ):
    if not isinstance(A_     ,		(Dataset, IterableDataset)   ):
      if isinstance(A_     ,		(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(A_   )}\n'''
            f'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A_   )   )}\']'''   )
      raise ValueError(
          f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A_   ).__name__}.'''   )
    if i == 0:
      a_      , a_      :	Union[str, Any]      =							(
          (Dataset, IterableDataset) if isinstance(A_     ,		A_   ) else (IterableDataset, Dataset)
      )
    elif not isinstance(A_     ,		A_   ):
      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(
        A_     ,		A_     ,		A_     ,		info=A_     ,		split=A_     ,		stopping_strategy=A_   )
  else:
    return _interleave_iterable_datasets(
        A_     ,		A_     ,		A_     ,		info=A_     ,		split=A_     ,		stopping_strategy=A_   )






def 	_snake_case						(      A_       :    List[DatasetType]     ,		A_       :    Optional[DatasetInfo] = None     ,		A_       :    Optional[NamedSplit] = None     ,		A_       :    int = 0     ,		):
  """simple docstring"""







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


