
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:
					A     :	List[str]    = None


A     :	List[str]    = logging.get_logger(__name__)


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

A     :	Optional[Any]    = {
    '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',
    },
}

A     :	List[str]    = {
    'facebook/mbart-large-en-ro': 1_0_2_4,
    'facebook/mbart-large-cc25': 1_0_2_4,
}

# fmt: off
A     :	Optional[int]    = ['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 			__A( a			):
				snake_case_						   =    VOCAB_FILES_NAMES
				snake_case_						   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				snake_case_						   =    PRETRAINED_VOCAB_FILES_MAP
				snake_case_						   =    ['''input_ids''', '''attention_mask''']
				snake_case_						   =    MBartTokenizer

				snake_case_						   =    []
				snake_case_						   =    []




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


								'''simple docstring'''




								__a										=    AddedToken(_snake_case		,     lstrip=_snake_case		,     rstrip=_snake_case				) if isinstance(_snake_case		,     _snake_case				) else mask_token

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

								__a										=    vocab_file
								__a										=    False if not self.vocab_file else True

								__a										=    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}				)
								__a										=    {
								    lang_code: self.convert_tokens_to_ids(_snake_case				) for lang_code in FAIRSEQ_LANGUAGE_CODES
								}

								__a										=    src_lang if src_lang is not None else '''en_XX'''
								__a										=    self.convert_tokens_to_ids(self._src_lang				)
								__a										=    tgt_lang
								self.set_src_lang_special_tokens(self._src_lang				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								return self._src_lang




				@src_lang.setter
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      None:


								'''simple docstring'''




								__a										=    new_src_lang
								self.set_src_lang_special_tokens(self._src_lang				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = 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 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None				)    ->      List[int]:


								'''simple docstring'''




								__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]




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case		,     **_snake_case				)    ->      Any:


								'''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'''				)
								__a										=    src_lang
								__a										=    self(_snake_case		,     add_special_tokens=_snake_case		,     return_tensors=_snake_case		,     **_snake_case				)
								__a										=    self.convert_tokens_to_ids(_snake_case				)
								__a										=    tgt_lang_id
								return inputs




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = "en_XX"		,     _snake_case = None		,     _snake_case = "ro_RO"		,     **_snake_case		,     )    ->      BatchEncoding:


								'''simple docstring'''




								__a										=    src_lang
								__a										=    tgt_lang
								return super().prepare_seqaseq_batch(_snake_case		,     _snake_case		,     **_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								return self.set_src_lang_special_tokens(self.src_lang				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								return self.set_tgt_lang_special_tokens(self.tgt_lang				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      None:


								'''simple docstring'''




								__a										=    self.convert_tokens_to_ids(_snake_case				)
								__a										=    []
								__a										=    [self.eos_token_id, self.cur_lang_code]

								__a										=    self.convert_ids_to_tokens(self.prefix_tokens				)
								__a										=    self.convert_ids_to_tokens(self.suffix_tokens				)

								__a										=    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 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      None:


								'''simple docstring'''




								__a										=    self.convert_tokens_to_ids(_snake_case				)
								__a										=    []
								__a										=    [self.eos_token_id, self.cur_lang_code]

								__a										=    self.convert_ids_to_tokens(self.prefix_tokens				)
								__a										=    self.convert_ids_to_tokens(self.suffix_tokens				)

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

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

								return (out_vocab_file,)





import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase__ :   List[Any]					=							logging.get_logger(__name__)


lowerCamelCase__ :   List[str]					=							{
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

lowerCamelCase__ :   Union[str, Any]					=							{
    'vocab_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
        ),
    },
    'tokenizer_config_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
        ),
    },
    'merges_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
        ),
    },
}

lowerCamelCase__ :   Optional[Any]					=							'</w>'
lowerCamelCase__ :   Union[str, Any]					=							'@@ '

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Optional[Any]							)					-> Dict:
      SCREAMING_SNAKE_CASE_							=			set()
      SCREAMING_SNAKE_CASE_							=			word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)							)
            SCREAMING_SNAKE_CASE_							=			char
      return pairs


# Speech2Text2 has no max input length
lowerCamelCase__ :   Any					=							{'facebook/s2t-wav2vec2-large-en-de': 1_024}





class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 lowercase_				    =    VOCAB_FILES_NAMES
 lowercase_				    =    PRETRAINED_VOCAB_FILES_MAP
 lowercase_				    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_				    =    ["input_ids", "attention_mask"]



 def __init__(							self  :		List[str]				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		List[Any]="<s>"				,				_lowerCAmelCase  :		Any="<pad>"				,				_lowerCAmelCase  :		List[str]="</s>"				,				_lowerCAmelCase  :		int="<unk>"				,				_lowerCAmelCase  :		Optional[Any]=False				,				_lowerCAmelCase  :		Dict=None				,				**_lowerCAmelCase  :		Tuple				,				):
       super().__init__(
           unk_token=_lowerCAmelCase				,				bos_token=_lowerCAmelCase				,				eos_token=_lowerCAmelCase				,				pad_token=_lowerCAmelCase				,				do_lower_case=_lowerCAmelCase				,				**_lowerCAmelCase				,				)

       SCREAMING_SNAKE_CASE_							=			do_lower_case

       with open(_lowerCAmelCase				,				encoding='utf-8'						) as vocab_handle:
             SCREAMING_SNAKE_CASE_							=			json.load(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			{v: k for k, v in self.encoder.items()}

       if merges_file is None:
             logger.info(F"No merges files provided. {self.__class__.__name__} can only be used for decoding."						)

             SCREAMING_SNAKE_CASE_							=			None
             SCREAMING_SNAKE_CASE_							=			None
       else:
             with open(_lowerCAmelCase				,				encoding='utf-8'						) as merges_handle:
                   SCREAMING_SNAKE_CASE_							=			merges_handle.read().split('\n'						)[:-1]

             SCREAMING_SNAKE_CASE_							=			[tuple(merge.split()[:2]						) for merge in merges]
             SCREAMING_SNAKE_CASE_							=			dict(zip(_lowerCAmelCase				,				range(len(_lowerCAmelCase						)						)						)						)
             SCREAMING_SNAKE_CASE_							=			{}



 @property
 def       lowerCAmelCase_  (							self  :		List[str]						):
       return len(self.decoder						)



 def       lowerCAmelCase_  (							self  :		Tuple						):
       return dict(self.encoder				,				**self.added_tokens_encoder						)



 def       lowerCAmelCase_  (							self  :		Tuple				,				_lowerCAmelCase  :		Tuple						):
       SCREAMING_SNAKE_CASE_							=			tuple(token[:-1]						) + (token[-1] + BPE_TOKEN_MERGES,)
       if token in self.cache:
             return self.cache[token]
       SCREAMING_SNAKE_CASE_							=			get_pairs(_lowerCAmelCase						)

       if not pairs:
             return token

       while True:
             SCREAMING_SNAKE_CASE_							=			min(_lowerCAmelCase				,				key=lambda _lowerCAmelCase						: self.bpe_ranks.get(_lowerCAmelCase				,				float('inf'						)						)						)
             if bigram not in self.bpe_ranks:
                   break
             SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			bigram
             SCREAMING_SNAKE_CASE_							=			[]
             SCREAMING_SNAKE_CASE_							=			0
             while i < len(_lowerCAmelCase						):
                   try:
                         SCREAMING_SNAKE_CASE_							=			word.index(_lowerCAmelCase				,				_lowerCAmelCase						)
                   except ValueError:
                         new_word.extend(word[i:]						)
                         break
                   else:
                         new_word.extend(word[i:j]						)
                         SCREAMING_SNAKE_CASE_							=			j

                   if word[i] == first and i < len(_lowerCAmelCase						) - 1 and word[i + 1] == second:
                         new_word.append(first + second						)
                         i += 2
                   else:
                         new_word.append(word[i]						)
                         i += 1
             SCREAMING_SNAKE_CASE_							=			tuple(_lowerCAmelCase						)
             SCREAMING_SNAKE_CASE_							=			new_word
             if len(_lowerCAmelCase						) == 1:
                   break
             else:
                   SCREAMING_SNAKE_CASE_							=			get_pairs(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			' '.join(_lowerCAmelCase						)
       if word == "\n  " + BPE_TOKEN_MERGES:
             SCREAMING_SNAKE_CASE_							=			'\n' + BPE_TOKEN_MERGES

       if word.endswith(_lowerCAmelCase						):
             SCREAMING_SNAKE_CASE_							=			word.replace(_lowerCAmelCase				,				''						)

       SCREAMING_SNAKE_CASE_							=			word.replace(' '				,				_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			word
       return word



 def       lowerCAmelCase_  (							self  :		Optional[int]				,				_lowerCAmelCase  :		Optional[int]						):

       if self.bpe_ranks is None:
             raise ValueError(
                 'This tokenizer was instantiated without a `merges.txt` file, so'
                 ' that it can only be used for decoding, not for encoding.'
                 'Make sure to provide `merges.txt` file at instantiation to enable '
                 'encoding.'						)

       if self.do_lower_case:
             SCREAMING_SNAKE_CASE_							=			text.lower()

       SCREAMING_SNAKE_CASE_							=			text.split()

       SCREAMING_SNAKE_CASE_							=			[]
       for token in text:
             if token:
                   split_tokens.extend(list(self.bpe(_lowerCAmelCase						).split(' '						)						)						)

       return split_tokens



 def       lowerCAmelCase_  (							self  :		Union[str, Any]				,				_lowerCAmelCase  :		str						):
       return self.encoder.get(_lowerCAmelCase				,				self.encoder.get(self.unk_token						)						)



 def       lowerCAmelCase_  (							self  :		int				,				_lowerCAmelCase  :		int						):
       SCREAMING_SNAKE_CASE_							=			self.decoder.get(_lowerCAmelCase				,				self.unk_token						)
       return result



 def       lowerCAmelCase_  (							self  :		int				,				_lowerCAmelCase  :		List[str]						):
       SCREAMING_SNAKE_CASE_							=			' '.join(_lowerCAmelCase						)

       # make sure @@ tokens are concatenated
       SCREAMING_SNAKE_CASE_							=			''.join(string.split(_lowerCAmelCase						)						)

       return string




 def       lowerCAmelCase_  (							self  :		int				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Optional[str] = None						):
       if not os.path.isdir(_lowerCAmelCase						):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
             return
       SCREAMING_SNAKE_CASE_							=			os.path.join(
           _lowerCAmelCase				,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)
       SCREAMING_SNAKE_CASE_							=			os.path.join(
           _lowerCAmelCase				,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']						)

       with open(_lowerCAmelCase				,				'w'				,				encoding='utf-8'						) as f:
             f.write(json.dumps(self.encoder				,				indent=2				,				sort_keys=_lowerCAmelCase				,				ensure_ascii=_lowerCAmelCase						) + '\n'						)

       SCREAMING_SNAKE_CASE_							=			0
       if self.bpe_ranks is None:
             return (vocab_file,)

       with open(_lowerCAmelCase				,				'w'				,				encoding='utf-8'						) as writer:
             for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,				key=lambda _lowerCAmelCase						: kv[1]						):
                   if index != token_index:
                         logger.warning(
                             F"Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."
                             ' Please check that the tokenizer is not corrupted!'						)
                         SCREAMING_SNAKE_CASE_							=			token_index
                   writer.write(' '.join(_lowerCAmelCase						) + '\n'						)
                   index += 1

       return (vocab_file, merges_file)