



"""simple docstring"""





import os
import re
import warnings
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_ta import TaTokenizer
else:
  lowerCamelCase  			=		None


lowerCamelCase  			=		logging.get_logger(__name__)

lowerCamelCase  			=		{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase  			=		{
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
lowerCamelCase  			=		{
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}
class  lowercase__  (       SCREAMING_SNAKE_CASE ):







       '''simple docstring'''


       UpperCamelCase		=	VOCAB_FILES_NAMES
       UpperCamelCase		=	PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase		=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase		=	['''input_ids''', '''attention_mask''']
       UpperCamelCase		=	TaTokenizer

       UpperCamelCase		=	[]



       def __init__(				self     :    int						,							_UpperCAmelCase     :    List[str]=None						,							_UpperCAmelCase     :    str=None						,							_UpperCAmelCase     :    Tuple="</s>"						,							_UpperCAmelCase     :    str="<unk>"						,							_UpperCAmelCase     :    List[Any]="<pad>"						,							_UpperCAmelCase     :    List[str]=100						,							_UpperCAmelCase     :    str=None						,							**_UpperCAmelCase     :    List[str]						,							)					->		List[str]:


             '''simple docstring'''




             if extra_ids > 0 and additional_special_tokens is None:
                   UpperCAmelCase_     					=				[F"""<extra_id_{i}>""" for i in range(_UpperCAmelCase       )]
             elif extra_ids > 0 and additional_special_tokens is not None:
                   # Check that we have the right number of extra special tokens
                   UpperCAmelCase_     					=				len(set(filter(lambda _UpperCAmelCase       : bool("extra_id_" in str(_UpperCAmelCase       )       )						,							_UpperCAmelCase       )       )       )
                   if extra_tokens != extra_ids:
                         raise ValueError(
                             F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
                             " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
                             " tokens"       )

             super().__init__(
                 _UpperCAmelCase						,							tokenizer_file=_UpperCAmelCase						,							eos_token=_UpperCAmelCase						,							unk_token=_UpperCAmelCase						,							pad_token=_UpperCAmelCase						,							extra_ids=_UpperCAmelCase						,							additional_special_tokens=_UpperCAmelCase						,							**_UpperCAmelCase						,							)

             UpperCAmelCase_     					=				vocab_file
             UpperCAmelCase_     					=				False if not self.vocab_file else True
             UpperCAmelCase_     					=				extra_ids



       @staticmethod
       def 	lowercase__							(				_UpperCAmelCase     :    Optional[int]						,							_UpperCAmelCase     :    List[str]						,							_UpperCAmelCase     :    Any       )					->		List[Any]:


             '''simple docstring'''




             if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
                   UpperCAmelCase_     					=				TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
                   if init_max_model_length is not None and init_max_model_length != max_model_length:
                         return init_max_model_length
                   elif init_max_model_length is None:
                         warnings.warn(
                             "This tokenizer was incorrectly instantiated with a model max length of"
                             F""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
                             " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
                             " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
                             F""" {pretrained_model_name_or_path} automatically truncating your input to"""
                             F""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
                             F""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
                             " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
                             " instantiate this tokenizer with `model_max_length` set to your preferred value."						,							_UpperCAmelCase						,							)

             return max_model_length



       def 	lowercase__							(				self     :    Tuple						,							_UpperCAmelCase     :    str						,							_UpperCAmelCase     :    Optional[str] = None       )					->		Tuple[str]:


             '''simple docstring'''




             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(_UpperCAmelCase       ):
                   logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
                   return
             UpperCAmelCase_     					=				os.path.join(
                 _UpperCAmelCase						,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

             if os.path.abspath(self.vocab_file       ) != os.path.abspath(_UpperCAmelCase       ):
                   copyfile(self.vocab_file						,							_UpperCAmelCase       )
                   logger.info(F"""Copy vocab file to {out_vocab_file}"""       )

             return (out_vocab_file,)



       def 	lowercase__							(				self     :    Optional[Any]						,							_UpperCAmelCase     :    List[int]						,							_UpperCAmelCase     :    Optional[List[int]] = None       )					->		List[int]:


             '''simple docstring'''




             UpperCAmelCase_     					=				token_ids_a + [self.eos_token_id]
             if token_ids_a is None:
                   return self.prefix_tokens + token_ids_a
             else:
                   UpperCAmelCase_     					=				token_ids_a + [self.eos_token_id]
                   return self.prefix_tokens + token_ids_a + token_ids_a



       def 	lowercase__							(				self     :    Optional[Any]						,							_UpperCAmelCase     :    List[int]						,							_UpperCAmelCase     :    Optional[List[int]] = None       )					->		List[int]:


             '''simple docstring'''




             UpperCAmelCase_     					=				[self.eos_token_id]

             if token_ids_a is None:
                   return len(token_ids_a + eos       ) * [0]
             return len(token_ids_a + eos + token_ids_a + eos       ) * [0]



       def 	lowercase__							(				self     :    Optional[Any]       )					->		List[str]:


             '''simple docstring'''




             return list(
                 set(filter(lambda _UpperCAmelCase       : bool(re.search(r"<extra_id_\d+>"						,							_UpperCAmelCase       )       ) is not None						,							self.additional_special_tokens       )       )       )







       def 	lowercase__							(				self     :    int       )					->		Any:


             '''simple docstring'''




             return [self.convert_tokens_to_ids(_UpperCAmelCase       ) for token in self.get_sentinel_tokens()]









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       )





