





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     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							int   		=		VOCAB_FILES_NAMES
   a						:							Dict   		=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a						:							List[Any]   		=		PRETRAINED_VOCAB_FILES_MAP
   a						:							Optional[int]   		=		["input_ids", "attention_mask"]
   a						:							Dict   		=		MBartTokenizer

   a						:							List[int]   		=		[]
   a						:							List[int]   		=		[]




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






       """simple docstring"""
       # Mask token behave like a normal word, i.e. include the space before it
       UpperCamelCase__							:				Tuple          =      AddedToken(__magic_name__,		lstrip=__magic_name__,		rstrip=__magic_name__							) if isinstance(__magic_name__,		__magic_name__							) else mask_token

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

       UpperCamelCase__							:				Tuple          =      vocab_file
       UpperCamelCase__							:				int          =      False if not self.vocab_file else True

       UpperCamelCase__							:				Any          =      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}							)
       UpperCamelCase__							:				Union[str, Any]          =      {
           lang_code: self.convert_tokens_to_ids(__magic_name__							) for lang_code in FAIRSEQ_LANGUAGE_CODES
       }

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




   @property
   def       UpperCamelCase__  (    self							) ->      str:






       """simple docstring"""
       return self._src_lang




   @src_lang.setter
   def       UpperCamelCase__  (    self,		__magic_name__							) ->      None:






       """simple docstring"""
       UpperCamelCase__							:				str          =      new_src_lang
       self.set_src_lang_special_tokens(self._src_lang							)




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






       """simple docstring"""
       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       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None							) ->      List[int]:






       """simple docstring"""
       UpperCamelCase__							:				Any          =      [self.sep_token_id]
       UpperCamelCase__							:				int          =      [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       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		**__magic_name__							) ->      int:






       """simple docstring"""
       if src_lang is None or tgt_lang is None:
           raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''							)
       UpperCamelCase__							:				Union[str, Any]          =      src_lang
       UpperCamelCase__							:				Tuple          =      self(__magic_name__,		add_special_tokens=__magic_name__,		return_tensors=__magic_name__,		**__magic_name__							)
       UpperCamelCase__							:				List[str]          =      self.convert_tokens_to_ids(__magic_name__							)
       UpperCamelCase__							:				Any          =      tgt_lang_id
       return inputs




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = "en_XX",		__magic_name__ = None,		__magic_name__ = "ro_RO",		**__magic_name__,		) ->      BatchEncoding:






       """simple docstring"""
       UpperCamelCase__							:				str          =      src_lang
       UpperCamelCase__							:				Optional[int]          =      tgt_lang
       return super().prepare_seqaseq_batch(__magic_name__,		__magic_name__,		**__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      Union[str, Any]:






       """simple docstring"""
       return self.set_src_lang_special_tokens(self.src_lang							)




   def       UpperCamelCase__  (    self							) ->      Optional[int]:






       """simple docstring"""
       return self.set_tgt_lang_special_tokens(self.tgt_lang							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      None:






       """simple docstring"""
       UpperCamelCase__							:				List[Any]          =      self.convert_tokens_to_ids(__magic_name__							)
       UpperCamelCase__							:				Optional[int]          =      []
       UpperCamelCase__							:				List[Any]          =      [self.eos_token_id, self.cur_lang_code]

       UpperCamelCase__							:				Tuple          =      self.convert_ids_to_tokens(self.prefix_tokens							)
       UpperCamelCase__							:				Union[str, Any]          =      self.convert_ids_to_tokens(self.suffix_tokens							)

       UpperCamelCase__							:				List[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       UpperCamelCase__  (    self,		__magic_name__							) ->      None:






       """simple docstring"""
       UpperCamelCase__							:				str          =      self.convert_tokens_to_ids(__magic_name__							)
       UpperCamelCase__							:				Dict          =      []
       UpperCamelCase__							:				Dict          =      [self.eos_token_id, self.cur_lang_code]

       UpperCamelCase__							:				List[Any]          =      self.convert_ids_to_tokens(self.prefix_tokens							)
       UpperCamelCase__							:				str          =      self.convert_ids_to_tokens(self.suffix_tokens							)

       UpperCamelCase__							:				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       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = 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(__magic_name__							):
           logger.error(f"Vocabulary path ({save_directory}) should be a directory."							)
           return
       UpperCamelCase__							:				str          =      os.path.join(
           __magic_name__,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

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

       return (out_vocab_file,)








from math import factorial




def 		lowerCAmelCase_    (  __UpperCAmelCase:			int		,   __UpperCAmelCase:			int      )      ->	int:

    # If either of the conditions are true, the function is being asked
    # to calculate a factorial of a negative number, which is not possible
    if n < k or k < 0:
        raise ValueError('''Please enter positive integers for n and k where n >= k'''      )
    return factorial(__UpperCAmelCase      ) // (factorial(__UpperCAmelCase      ) * factorial(n - k      ))


if __name__ == "__main__":
  print(
      'The number of five-card hands possible from a standard',
      F'''fifty-two card deck is: {combinations(52, 5)}\n''',
  )

  print(
      'If a class of 40 students must be arranged into groups of',
      F'''4 for group projects, there are {combinations(40, 4)} ways''',
      'to arrange them.\n',
  )

  print(
      'If 10 teams are competing in a Formula One race, there',
      F'''are {combinations(10, 3)} ways that first, second and''',
      'third place can be awarded.',
  )

