
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_albert import AlbertTokenizer
else:
					SCREAMING_SNAKE_CASE							:List[str]							 =				None

SCREAMING_SNAKE_CASE							:List[Any]							 =				logging.get_logger(__name__)
SCREAMING_SNAKE_CASE							:str							 =				{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE							:int							 =				{
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json',
    },
}

SCREAMING_SNAKE_CASE							:Dict							 =				{
    'albert-base-v1': 512,
    'albert-large-v1': 512,
    'albert-xlarge-v1': 512,
    'albert-xxlarge-v1': 512,
    'albert-base-v2': 512,
    'albert-large-v2': 512,
    'albert-xlarge-v2': 512,
    'albert-xxlarge-v2': 512,
}

SCREAMING_SNAKE_CASE							:Dict							 =				'▁'





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     VOCAB_FILES_NAMES
	snake_case_					    =     PRETRAINED_VOCAB_FILES_MAP
	snake_case_					    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_					    =     AlbertTokenizer






	def __init__(       self					: Dict							,A					: Optional[int]=None							,A					: Dict=None							,A					: int=True							,A					: Any=True							,A					: List[str]=False							,A					: str="[CLS]"							,A					: Any="[SEP]"							,A					: Dict="<unk>"							,A					: int="[SEP]"							,A					: int="<pad>"							,A					: str="[CLS]"							,A					: Tuple="[MASK]"							,**A					: Optional[int]							,):
					# Mask token behave like a normal word, i.e. include the space before it and
					# is included in the raw text, there should be a match in a non-normalized sentence.
					__A   			= (
					    AddedToken(__lowerCamelCase							,lstrip=__lowerCamelCase							,rstrip=__lowerCamelCase							,normalized=__lowerCamelCase )
					    if isinstance(__lowerCamelCase							,__lowerCamelCase )
					    else mask_token
					)

					super().__init__(
					    __lowerCamelCase							,tokenizer_file=__lowerCamelCase							,do_lower_case=__lowerCamelCase							,remove_space=__lowerCamelCase							,keep_accents=__lowerCamelCase							,bos_token=__lowerCamelCase							,eos_token=__lowerCamelCase							,unk_token=__lowerCamelCase							,sep_token=__lowerCamelCase							,pad_token=__lowerCamelCase							,cls_token=__lowerCamelCase							,mask_token=__lowerCamelCase							,**__lowerCamelCase							,)

					__A   			= do_lower_case
					__A   			= remove_space
					__A   			= keep_accents
					__A   			= vocab_file
					__A   			= False if not self.vocab_file else True






	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Optional[Any]							,A					: int = None ):
					__A   			= [self.sep_token_id]
					__A   			= [self.cls_token_id]
					if token_ids_a is None:
									return cls + token_ids_a + sep
					return cls + token_ids_a + sep + token_ids_a + sep






	def 				UpperCamelCase_  (       self					: str							,A					: int							,A					: str = 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 ) * [0] + len(token_ids_a + sep ) * [1]







	def 				UpperCamelCase_  (       self					: Optional[int]							,A					: Optional[Any]							,A					: Tuple = None ):
					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(__lowerCamelCase ):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
									return
					__A   			= os.path.join(
					    __lowerCamelCase							,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

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

					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__      		= logging.get_logger(__name__)


lowerCAmelCase__      		= {
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

lowerCAmelCase__      		= {
    '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__      		= '</w>'
lowerCAmelCase__      		= '@@ '
def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[Any]						):
       _A      :       Optional[int]				    = set()
       _A      :       Optional[Any]				    = word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)						)
              _A      :       List[Any]				    = char
       return pairs


# Speech2Text2 has no max input length
lowerCAmelCase__      		= {'facebook/s2t-wav2vec2-large-en-de': 10_24}







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __SCREAMING_SNAKE_CASE  			= ["input_ids", "attention_mask"]


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase="<s>" ,						__lowerCamelCase="<pad>" ,						__lowerCamelCase="</s>" ,						__lowerCamelCase="<unk>" ,						__lowerCamelCase=False ,						__lowerCamelCase=None ,						**__lowerCamelCase ,						)			->				Optional[Any]:
            super().__init__(
                unk_token=__lowerCamelCase ,						bos_token=__lowerCamelCase ,						eos_token=__lowerCamelCase ,						pad_token=__lowerCamelCase ,						do_lower_case=__lowerCamelCase ,						**__lowerCamelCase ,						)

            _A      :       Dict				    = do_lower_case

            with open(__lowerCamelCase ,						encoding="utf-8") as vocab_handle:
                   _A      :       Optional[int]				    = json.load(__lowerCamelCase)
            _A      :       Optional[Any]				    = {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.")

                   _A      :       Optional[Any]				    = None
                   _A      :       Tuple				    = None
            else:
                   with open(__lowerCamelCase ,						encoding="utf-8") as merges_handle:
                          _A      :       Optional[int]				    = merges_handle.read().split("\n")[:-1]

                   _A      :       Union[str, Any]				    = [tuple(merge.split()[:2]) for merge in merges]
                   _A      :       Optional[int]				    = dict(zip(__lowerCamelCase ,						range(len(__lowerCamelCase))))
                   _A      :       List[Any]				    = {}


     @property
     def  _lowerCamelCase (						self)			->				int:
            return len(self.decoder)


     def  _lowerCamelCase (						self)			->				Dict:
            return dict(self.encoder ,						**self.added_tokens_encoder)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Dict:
            _A      :       Tuple				    = tuple(token[:-1]) + (token[-1] + BPE_TOKEN_MERGES,)
            if token in self.cache:
                   return self.cache[token]
            _A      :       int				    = get_pairs(__lowerCamelCase)

            if not pairs:
                   return token

            while True:
                   _A      :       Any				    = min(__lowerCamelCase ,						key=lambda __lowerCamelCase: self.bpe_ranks.get(__lowerCamelCase ,						float("inf")))
                   if bigram not in self.bpe_ranks:
                          break
                   _A    ,			_A      :       Optional[int]				    = bigram
                   _A      :       int				    = []
                   _A      :       str				    = 0
                   while i < len(__lowerCamelCase):
                          try:
                                 _A      :       str				    = word.index(__lowerCamelCase ,						__lowerCamelCase)
                          except ValueError:
                                 new_word.extend(word[i:])
                                 break
                          else:
                                 new_word.extend(word[i:j])
                                 _A      :       str				    = 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
                   _A      :       List[str]				    = tuple(__lowerCamelCase)
                   _A      :       List[str]				    = new_word
                   if len(__lowerCamelCase) == 1:
                          break
                   else:
                          _A      :       List[Any]				    = get_pairs(__lowerCamelCase)
            _A      :       Tuple				    = " ".join(__lowerCamelCase)
            if word == "\n  " + BPE_TOKEN_MERGES:
                   _A      :       List[str]				    = "\n" + BPE_TOKEN_MERGES

            if word.endswith(__lowerCamelCase):
                   _A      :       int				    = word.replace(__lowerCamelCase ,						"")

            _A      :       int				    = word.replace(" " ,						__lowerCamelCase)
            _A      :       Union[str, Any]				    = word
            return word


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Optional[Any]:

            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:
                   _A      :       List[Any]				    = text.lower()

            _A      :       Optional[int]				    = text.split()

            _A      :       List[str]				    = []
            for token in text:
                   if token:
                          split_tokens.extend(list(self.bpe(__lowerCamelCase).split(" ")))

            return split_tokens


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				int:
            return self.encoder.get(__lowerCamelCase ,						self.encoder.get(self.unk_token))


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				str:
            _A      :       List[str]				    = self.decoder.get(__lowerCamelCase ,						self.unk_token)
            return result


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				str:
            _A      :       str				    = " ".join(__lowerCamelCase)

            # make sure @@ tokens are concatenated
            _A      :       int				    = "".join(string.split(__lowerCamelCase))

            return string


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				Tuple[str]:
            if not os.path.isdir(__lowerCamelCase):
                   logger.error(F"Vocabulary path ({save_directory}) should be a directory")
                   return
            _A      :       Any				    = os.path.join(
                __lowerCamelCase ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
            _A      :       Any				    = 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")

            _A      :       Union[str, Any]				    = 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!")
                                 _A      :       Optional[int]				    = token_index
                          writer.write(" ".join(__lowerCamelCase) + "\n")
                          index += 1

            return (vocab_file, merges_file)
