


"""simple docstring"""



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_barthez import BarthezTokenizer
else:
  __snake_case   			=    None

__snake_case   			=    logging.get_logger(__name__)

__snake_case   			=    {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

__snake_case   			=    {
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
        ),
    },
}

__snake_case   			=    {
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

__snake_case   			=    """▁"""







class _lowerCAmelCase				(      snake_case_	):
   __UpperCAmelCase :       int							  =  VOCAB_FILES_NAMES
   __UpperCAmelCase :       str							  =  PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase :       Any							  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase :       str							  =  ['''input_ids''', '''attention_mask''']
   __UpperCAmelCase :       Optional[Any]							  =  BarthezTokenizer







   def __init__(	self						,	UpperCamelCase__=None						,	UpperCamelCase__=None						,	UpperCamelCase__="<s>"						,	UpperCamelCase__="</s>"						,	UpperCamelCase__="</s>"						,	UpperCamelCase__="<s>"						,	UpperCamelCase__="<unk>"						,	UpperCamelCase__="<pad>"						,	UpperCamelCase__="<mask>"						,	**UpperCamelCase__						,	)	->		str:

       '''simple docstring'''




       snake_case    :      List[str]      					=    AddedToken(UpperCamelCase__						,	lstrip=UpperCamelCase__						,	rstrip=UpperCamelCase__						) if isinstance(UpperCamelCase__						,	UpperCamelCase__						) else mask_token

       super().__init__(
           UpperCamelCase__						,	tokenizer_file=UpperCamelCase__						,	bos_token=UpperCamelCase__						,	eos_token=UpperCamelCase__						,	unk_token=UpperCamelCase__						,	sep_token=UpperCamelCase__						,	cls_token=UpperCamelCase__						,	pad_token=UpperCamelCase__						,	mask_token=UpperCamelCase__						,	**UpperCamelCase__						,	)

       snake_case    :      Tuple      					=    vocab_file
       snake_case    :      Tuple      					=    False if not self.vocab_file else True







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						)	->		List[int]:

       '''simple docstring'''




       if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       snake_case    :      List[str]      					=    [self.cls_token_id]
       snake_case    :      Dict      					=    [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						)	->		List[int]:

       '''simple docstring'''




       snake_case    :      Any      					=    [self.sep_token_id]
       snake_case    :      Any      					=    [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]







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = 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
       snake_case    :      str      					=    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__						)

       return (out_vocab_file,)









"""simple docstring"""



from typing import List

import numpy as np







def   __lowerCAmelCase						(						lowercase  :  dict )	->						int:


    """simple docstring"""





    snake_case    :      Union[str, Any]      					=    {key: len(lowercase ) for key, value in gen_kwargs.items() if isinstance(lowercase						,							lowercase )}
    if len(set(lists_lengths.values() ) ) > 1:
        raise RuntimeError(
            (
                "Sharding is ambiguous for this dataset: "
                + "we found several data sources lists of different lengths, and we don't know over which list we should parallelize:\n"
                + "\n".join(F'\t- key {key} has length {length}' for key, length in lists_lengths.items() )
                + "\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, "
                + "and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."
            ) )
    snake_case    :      int      					=    max(lists_lengths.values()						,							default=0 )
    return max(1						,							lowercase )







def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  int )	->						List[range]:


    """simple docstring"""





    snake_case    :      Union[str, Any]      					=    []
    for group_idx in range(lowercase ):
        snake_case    :      Union[str, Any]      					=    num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
        if num_shards_to_add == 0:
            break
        snake_case    :      int      					=    shards_indices_per_group[-1].stop if shards_indices_per_group else 0
        snake_case    :      Dict      					=    range(lowercase						,							start + num_shards_to_add )
        shards_indices_per_group.append(lowercase )
    return shards_indices_per_group







def   __lowerCAmelCase						(						lowercase  :  dict						,							lowercase  :  int )	->						List[dict]:


    """simple docstring"""





    snake_case    :      int      					=    _number_of_shards_in_gen_kwargs(lowercase )
    if num_shards == 1:
        return [dict(lowercase )]
    else:
        snake_case    :      Optional[int]      					=    _distribute_shards(num_shards=lowercase						,							max_num_jobs=lowercase )
        return [
            {
                key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                if isinstance(lowercase						,							lowercase )
                else value
                for key, value in gen_kwargs.items()
            }
            for group_idx in range(len(lowercase ) )
        ]







def   __lowerCAmelCase						(						lowercase  :  List[dict] )	->						dict:


    """simple docstring"""





    return {
        key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
        if isinstance(gen_kwargs_list[0][key]						,							lowercase )
        else gen_kwargs_list[0][key]
        for key in gen_kwargs_list[0]
    }







def   __lowerCAmelCase						(						lowercase  :  np.random.Generator						,							lowercase  :  dict )	->						dict:


    """simple docstring"""





    snake_case    :      Tuple      					=    {len(lowercase ) for value in gen_kwargs.values() if isinstance(lowercase						,							lowercase )}
    snake_case    :      str      					=    {}
    for size in list_sizes:
        snake_case    :      Optional[int]      					=    list(range(lowercase ) )
        rng.shuffle(indices_per_size[size] )
    # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
    snake_case    :      Dict      					=    dict(lowercase )
    for key, value in shuffled_kwargs.items():
        if isinstance(lowercase						,							lowercase ):
            snake_case    :      Dict      					=    [value[i] for i in indices_per_size[len(lowercase )]]
    return shuffled_kwargs





