



'''simple docstring'''





from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


_UpperCamelCase											=						logging.get_logger(__name__)






class 					lowerCamelCase_    :
  """simple docstring"""




  a_												=42
  a_												=None




  @staticmethod
  def 						_lowercase     ( )					->					Dict:
    raise NotImplementedError




  def 						_lowercase     ( self      :	int			,	_a      :	Optional[Any]			,	_a      :	int			,	_a      :	str			,	**_a      :	List[Any]   )					->					List[str]:
    raise NotImplementedError




  def 						_lowercase     ( self      :	Optional[int]			,	_a      :	Any   )					->					Optional[Any]:
    raise NotImplementedError




  def 						_lowercase     ( self      :	Optional[Any]   )					->					Tuple:
    if not self.is_available():
      raise RuntimeError(
          f'You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.'   )




  @classmethod
  def 						_lowercase     ( cls      :	Dict   )					->					Dict:
    return f'`pip install {cls.pip_package or cls.name}`'






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												="""optuna"""




  @staticmethod
  def 						_lowercase     ( )					->					Union[str, Any]:
    return is_optuna_available()




  def 						_lowercase     ( self      :	List[str]			,	_a      :	Optional[int]			,	_a      :	int			,	_a      :	str			,	**_a      :	str   )					->					Optional[Any]:
    return run_hp_search_optuna(_a			,	_a			,	_a			,	**_a   )




  def 						_lowercase     ( self      :	List[str]			,	_a      :	Union[str, Any]   )					->					Optional[int]:
    return default_hp_space_optuna(_a   )






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												="""ray"""
  a_												="""'ray[tune]'"""




  @staticmethod
  def 						_lowercase     ( )					->					List[str]:
    return is_ray_available()




  def 						_lowercase     ( self      :	Union[str, Any]			,	_a      :	List[str]			,	_a      :	int			,	_a      :	str			,	**_a      :	Optional[int]   )					->					str:
    return run_hp_search_ray(_a			,	_a			,	_a			,	**_a   )




  def 						_lowercase     ( self      :	int			,	_a      :	List[str]   )					->					str:
    return default_hp_space_ray(_a   )






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												="""sigopt"""




  @staticmethod
  def 						_lowercase     ( )					->					str:
    return is_sigopt_available()




  def 						_lowercase     ( self      :	Optional[int]			,	_a      :	Any			,	_a      :	int			,	_a      :	str			,	**_a      :	Union[str, Any]   )					->					List[Any]:
    return run_hp_search_sigopt(_a			,	_a			,	_a			,	**_a   )




  def 						_lowercase     ( self      :	Union[str, Any]			,	_a      :	Tuple   )					->					Any:
    return default_hp_space_sigopt(_a   )






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												="""wandb"""




  @staticmethod
  def 						_lowercase     ( )					->					str:
    return is_wandb_available()




  def 						_lowercase     ( self      :	Union[str, Any]			,	_a      :	str			,	_a      :	int			,	_a      :	str			,	**_a      :	List[str]   )					->					List[Any]:
    return run_hp_search_wandb(_a			,	_a			,	_a			,	**_a   )




  def 						_lowercase     ( self      :	Tuple			,	_a      :	Optional[Any]   )					->					List[str]:
    return default_hp_space_wandb(_a   )




_UpperCamelCase											=						{
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}

def        a_     (							)      ->      str:
  __lowerCamelCase       :       str           = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
  if len(_lowerCAmelCase       ) > 0:
    __lowerCamelCase       :       int           = available_backends[0].name
    if len(_lowerCAmelCase       ) > 1:
      logger.info(
          F'{len(_lowerCAmelCase       )} hyperparameter search backends available. Using {name} as the default.'       )
    return name
  raise RuntimeError(
      'No hyperparameter search backend available.\n'
      + '\n'.join(
          F' - To install {backend.name} run {backend.pip_install()}'
          for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()       )       )











'''simple docstring'''





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

from tokenizers import processors

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


if is_sentencepiece_available():
   from .tokenization_mbart import MBartTokenizer
else:
   _UpperCamelCase											=						None


_UpperCamelCase											=						logging.get_logger(__name__)


_UpperCamelCase											=						{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase											=						{
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

_UpperCamelCase											=						{
    'facebook/mbart-large-en-ro': 1024,
    'facebook/mbart-large-cc25': 1024,
}

# fmt: off
_UpperCamelCase											=						['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												=VOCAB_FILES_NAMES
  a_												=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_												=PRETRAINED_VOCAB_FILES_MAP
  a_												=["""input_ids""", """attention_mask"""]
  a_												=MBartTokenizer

  a_												=[]
  a_												=[]




  def __init__( self      :	Optional[Any]			,	_a      :	Optional[int]=None			,	_a      :	Any=None			,	_a      :	Any="<s>"			,	_a      :	Optional[Any]="</s>"			,	_a      :	List[str]="</s>"			,	_a      :	List[Any]="<s>"			,	_a      :	Union[str, Any]="<unk>"			,	_a      :	str="<pad>"			,	_a      :	Any="<mask>"			,	_a      :	Optional[Any]=None			,	_a      :	str=None			,	_a      :	Tuple=None			,	**_a      :	Dict			,	)					->					List[str]:
    # Mask token behave like a normal word, i.e. include the space before it
    __lowerCamelCase       :       List[Any]           = AddedToken(_a			,	lstrip=_a			,	rstrip=_a   ) if isinstance(_a			,	_a   ) else mask_token

    super().__init__(
        vocab_file=_a			,	tokenizer_file=_a			,	bos_token=_a			,	eos_token=_a			,	sep_token=_a			,	cls_token=_a			,	unk_token=_a			,	pad_token=_a			,	mask_token=_a			,	src_lang=_a			,	tgt_lang=_a			,	additional_special_tokens=_a			,	**_a			,	)

    __lowerCamelCase       :       Optional[Any]           = vocab_file
    __lowerCamelCase       :       List[str]           = False if not self.vocab_file else True

    __lowerCamelCase       :       str           = FAIRSEQ_LANGUAGE_CODES.copy()

    if additional_special_tokens is not None:
      # Only add those special tokens if they are not already there.
      _additional_special_tokens.extend(
          [t for t in additional_special_tokens if t not in _additional_special_tokens]   )

    self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}   )
    __lowerCamelCase       :       Optional[Any]           = {
        lang_code: self.convert_tokens_to_ids(_a   ) for lang_code in FAIRSEQ_LANGUAGE_CODES
    }

    __lowerCamelCase       :       Optional[Any]           = src_lang if src_lang is not None else 'en_XX'
    __lowerCamelCase       :       int           = self.convert_tokens_to_ids(self._src_lang   )
    __lowerCamelCase       :       List[str]           = tgt_lang
    self.set_src_lang_special_tokens(self._src_lang   )




  @property
  def 						_lowercase     ( self      :	List[Any]   )					->					str:
    return self._src_lang




  @src_lang.setter
  def 						_lowercase     ( self      :	Union[str, Any]			,	_a      :	str   )					->					None:
    __lowerCamelCase       :       Union[str, Any]           = new_src_lang
    self.set_src_lang_special_tokens(self._src_lang   )




  def 						_lowercase     ( self      :	List[Any]			,	_a      :	List[int]			,	_a      :	Optional[List[int]] = None   )					->					List[int]:
    if token_ids_a is None:
      return self.prefix_tokens + token_ids_a + self.suffix_tokens
    # We don't expect to process pairs, but leave the pair logic for API consistency
    return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




  def 						_lowercase     ( self      :	int			,	_a      :	List[int]			,	_a      :	Optional[List[int]] = None   )					->					List[int]:
    __lowerCamelCase       :       Optional[int]           = [self.sep_token_id]
    __lowerCamelCase       :       Union[str, 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 						_lowercase     ( self      :	Optional[Any]			,	_a      :	Optional[Any]			,	_a      :	str			,	_a      :	Optional[str]			,	_a      :	Optional[str]			,	**_a      :	Optional[int]   )					->					Any:
    if src_lang is None or tgt_lang is None:
      raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'   )
    __lowerCamelCase       :       Optional[Any]           = src_lang
    __lowerCamelCase       :       Dict           = self(_a			,	add_special_tokens=_a			,	return_tensors=_a			,	**_a   )
    __lowerCamelCase       :       Tuple           = self.convert_tokens_to_ids(_a   )
    __lowerCamelCase       :       Optional[Any]           = tgt_lang_id
    return inputs




  def 						_lowercase     ( self      :	Any			,	_a      :	List[str]			,	_a      :	str = "en_XX"			,	_a      :	Optional[List[str]] = None			,	_a      :	str = "ro_RO"			,	**_a      :	Tuple			,	)					->					BatchEncoding:
    __lowerCamelCase       :       List[Any]           = src_lang
    __lowerCamelCase       :       str           = tgt_lang
    return super().prepare_seqaseq_batch(_a			,	_a			,	**_a   )




  def 						_lowercase     ( self      :	List[Any]   )					->					Any:
    return self.set_src_lang_special_tokens(self.src_lang   )




  def 						_lowercase     ( self      :	Dict   )					->					Optional[Any]:
    return self.set_tgt_lang_special_tokens(self.tgt_lang   )




  def 						_lowercase     ( self      :	Tuple			,	_a      :	List[str]   )					->					None:
    __lowerCamelCase       :       Tuple           = self.convert_tokens_to_ids(_a   )
    __lowerCamelCase       :       Optional[Any]           = []
    __lowerCamelCase       :       List[str]           = [self.eos_token_id, self.cur_lang_code]

    __lowerCamelCase       :       Dict           = self.convert_ids_to_tokens(self.prefix_tokens   )
    __lowerCamelCase       :       Optional[Any]           = self.convert_ids_to_tokens(self.suffix_tokens   )

    __lowerCamelCase       :       Union[str, Any]           = processors.TemplateProcessing(
        single=prefix_tokens_str + ['$A'] + suffix_tokens_str			,	pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str			,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str			,	self.prefix_tokens + self.suffix_tokens   )   )			,	)




  def 						_lowercase     ( self      :	Optional[Any]			,	_a      :	str   )					->					None:
    __lowerCamelCase       :       Union[str, Any]           = self.convert_tokens_to_ids(_a   )
    __lowerCamelCase       :       int           = []
    __lowerCamelCase       :       List[str]           = [self.eos_token_id, self.cur_lang_code]

    __lowerCamelCase       :       int           = self.convert_ids_to_tokens(self.prefix_tokens   )
    __lowerCamelCase       :       Any           = self.convert_ids_to_tokens(self.suffix_tokens   )

    __lowerCamelCase       :       Optional[int]           = processors.TemplateProcessing(
        single=prefix_tokens_str + ['$A'] + suffix_tokens_str			,	pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str			,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str			,	self.prefix_tokens + self.suffix_tokens   )   )			,	)




  def 						_lowercase     ( self      :	Any			,	_a      :	str			,	_a      :	Optional[str] = None   )					->					Tuple[str]:
    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(_a   ):
      logger.error(f'Vocabulary path ({save_directory}) should be a directory.'   )
      return
    __lowerCamelCase       :       List[str]           = os.path.join(
        _a			,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

    if os.path.abspath(self.vocab_file   ) != os.path.abspath(_a   ):
      copyfile(self.vocab_file			,	_a   )

    return (out_vocab_file,)






