"""simple docstring"""



import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


SCREAMING_SNAKE_CASE__:Any			=	logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__:Optional[int]			=	{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
SCREAMING_SNAKE_CASE__:Tuple			=	{
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json""",
    },
}

SCREAMING_SNAKE_CASE__:int			=	{
    """RUCAIBox/mvp""": 1024,
}






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			Tuple           =		VOCAB_FILES_NAMES
 _snake_case   :			Union[str, Any]           =		PRETRAINED_VOCAB_FILES_MAP
 _snake_case   :			Optional[Any]           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case   :			Any           =		["""input_ids""", """attention_mask"""]
 _snake_case   :			Tuple           =		MvpTokenizer


 def __init__(				self  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase="replace"  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="<unk>"  ,						lowerCamelCase="<pad>"  ,						lowerCamelCase="<mask>"  ,						lowerCamelCase=False  ,						lowerCamelCase=True  ,						**lowerCamelCase  ,						):
        super().__init__(
            lowerCamelCase  ,						lowerCamelCase  ,						tokenizer_file=lowerCamelCase  ,						errors=lowerCamelCase  ,						bos_token=lowerCamelCase  ,						eos_token=lowerCamelCase  ,						sep_token=lowerCamelCase  ,						cls_token=lowerCamelCase  ,						unk_token=lowerCamelCase  ,						pad_token=lowerCamelCase  ,						mask_token=lowerCamelCase  ,						add_prefix_space=lowerCamelCase  ,						trim_offsets=lowerCamelCase  ,						**lowerCamelCase  ,						)

        __a     				=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
        if pre_tok_state.get("add_prefix_space"  ,						lowerCamelCase		) != add_prefix_space:
               __a     				=  getattr(lowerCamelCase  ,						pre_tok_state.pop("type"		)		)
               __a     				=  add_prefix_space
               __a     				=  pre_tok_class(**lowerCamelCase		)

        __a     				=  add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        __a     				=  "post_processor"
        __a     				=  getattr(self.backend_tokenizer  ,						lowerCamelCase  ,						lowerCamelCase		)
        if tokenizer_component_instance:
               __a     				=  json.loads(tokenizer_component_instance.__getstate__()		)

               # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
               if "sep" in state:
                      __a     				=  tuple(state["sep"]		)
               if "cls" in state:
                      __a     				=  tuple(state["cls"]		)

               __a     				=  False

               if state.get("add_prefix_space"  ,						lowerCamelCase		) != add_prefix_space:
                      __a     				=  add_prefix_space
                      __a     				=  True

               if state.get("trim_offsets"  ,						lowerCamelCase		) != trim_offsets:
                      __a     				=  trim_offsets
                      __a     				=  True

               if changes_to_apply:
                      __a     				=  getattr(lowerCamelCase  ,						state.pop("type"		)		)
                      __a     				=  component_class(**lowerCamelCase		)
                      setattr(self.backend_tokenizer  ,						lowerCamelCase  ,						lowerCamelCase		)


 @property
 def 		a__ (				self		):
        if self._mask_token is None:
               if self.verbose:
                      logger.error("Using mask_token, but it is not set yet."		)
               return None
        return str(self._mask_token		)


 @mask_token.setter
 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  AddedToken(lowerCamelCase  ,						lstrip=lowerCamelCase  ,						rstrip=lowerCamelCase		) if isinstance(lowerCamelCase  ,						lowerCamelCase		) else value
        __a     				=  value


 def 		a__ (				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        __a     				=  kwargs.get("is_split_into_words"  ,						lowerCamelCase		)

        if is_split_into_words and not self.add_prefix_space:
               raise ValueError(
                   F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                   "to use it with pretokenized inputs."		)

        return super()._batch_encode_plus(*lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        __a     				=  kwargs.get("is_split_into_words"  ,						lowerCamelCase		)

        if is_split_into_words and not self.add_prefix_space:
               raise ValueError(
                   F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                   "to use it with pretokenized inputs."		)

        return super()._encode_plus(*lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        __a     				=  self._tokenizer.model.save(lowerCamelCase  ,						name=lowerCamelCase		)
        return tuple(lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase=None		):
        __a     				=  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
               return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        __a     				=  [self.sep_token_id]
        __a     				=  [self.cls_token_id]

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

"""simple docstring"""



import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


SCREAMING_SNAKE_CASE__:Union[str, Any]			=	logging.get_logger(__name__)






class 				snake_case__     :
 _snake_case   :			List[str]           =		None



@experimental
def 					_lowerCamelCase(					a ,  a ,  a ,  a ,  a ,  a ,  a							):
       if ParallelBackendConfig.backend_name is None:
              return _map_with_multiprocessing_pool(
                  a ,  a ,  a ,  a ,  a ,  a ,  a							)

       return _map_with_joblib(a ,  a ,  a ,  a ,  a ,  a ,  a							)



def 					_lowerCamelCase(					a ,  a ,  a ,  a ,  a ,  a ,  a							):
       __a     				=  num_proc if num_proc <= len(a							) else len(a							)
       __a     				=  []  # We organize the splits ourselve (contiguous splits)
       for index in range(a							):
              __a     				=  len(a							) // num_proc
              __a     				=  len(a							) % num_proc
              __a     				=  div * index + min(a ,  a							)
              __a     				=  start + div + (1 if index < mod else 0)
              split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)							)

       if len(a							) != sum(len(i[1]							) for i in split_kwds							):
              raise ValueError(
                  F"Error dividing inputs iterable among processes. "
                  F"Total number of objects {len(a							)}, "
                  F"length: {sum(len(i[1]							) for i in split_kwds							)}"							)

       logger.info(
           F"Spawning {num_proc} processes for {len(a							)} objects in slices of {[len(i[1]							) for i in split_kwds]}"							)
       __a				, __a     				=  None, None
       if not disable_tqdm:
              __a				, __a     				=  (RLock(),), tqdm.set_lock
       with Pool(a ,  initargs=a ,  initializer=a							) as pool:
              __a     				=  pool.map(a ,  a							)
       logger.info(F"Finished {num_proc} processes"							)
       __a     				=  [obj for proc_res in mapped for obj in proc_res]
       logger.info(F"Unpacked {len(a							)} objects"							)

       return mapped



def 					_lowerCamelCase(					a ,  a ,  a ,  a ,  a ,  a ,  a							):
       # progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
       # and it requires monkey-patching joblib internal classes which is subject to change
       import joblib

       with joblib.parallel_backend(ParallelBackendConfig.backend_name ,  n_jobs=a							):
              return joblib.Parallel()(
                  joblib.delayed(a							)((function, obj, types, None, True, None)							) for obj in iterable							)




@experimental
@contextlib.contextmanager
def 					_lowerCamelCase(					a							):
       __a     				=  backend_name

       if backend_name == "spark":
              from joblibspark import register_spark

              register_spark()

              # TODO: call create_cache_and_write_probe if "download" in steps
              # TODO: raise NotImplementedError when Dataset.map etc is called

       try:
              yield
       finally:
              __a     				=  None
