"""simple docstring"""







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

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


lowerCamelCase      : Dict       =logging.get_logger(__name__)

lowerCamelCase      : str       ={
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

lowerCamelCase      : Dict       ={
    '''vocab_file''': {
        '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json''',
    },
    '''merges_file''': {'''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'''},
}

lowerCamelCase      : Dict       ={
    '''microsoft/biogpt''': 10_24,
}




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Optional[int]						)    ->     int:







     '''simple docstring'''


     __A					:					Any								=		set()
     __A					:					List[Any]								=		word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)						)
          __A					:					int								=		char
     return pairs






class 							__snake_case(					A_	):


    '''simple docstring'''





    _UpperCAmelCase       =			VOCAB_FILES_NAMES
    _UpperCAmelCase       =			PRETRAINED_VOCAB_FILES_MAP
    _UpperCAmelCase       =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _UpperCAmelCase       =			["input_ids", "attention_mask"]
    def __init__(  self					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase="<unk>"					,       __lowerCamelCase="<s>"					,       __lowerCamelCase="</s>"					,       __lowerCamelCase="</s>"					,       __lowerCamelCase="<pad>"					,       **__lowerCamelCase					,       ):




         '''simple docstring'''



         super().__init__(
             bos_token=__lowerCamelCase					,       eos_token=__lowerCamelCase					,       sep_token=__lowerCamelCase					,       unk_token=__lowerCamelCase					,       pad_token=__lowerCamelCase					,       **__lowerCamelCase					,       )

         try:
              import sacremoses
         except ImportError:
              raise ImportError(
                  'You need to install sacremoses to use BioGptTokenizer. '
                  'See https://pypi.org/project/sacremoses/ for installation.'	)

         __A					:					List[str]								=		'en'
         __A					:					Tuple								=		sacremoses
         # cache of sm.MosesTokenizer instance
         __A					:					List[Any]								=		{}
         __A					:					Optional[Any]								=		{}
         with open(__lowerCamelCase					,       encoding='utf-8'	) as vocab_handle:
              __A					:					Union[str, Any]								=		json.load(__lowerCamelCase	)
         __A					:					Any								=		{v: k for k, v in self.encoder.items()}
         with open(__lowerCamelCase					,       encoding='utf-8'	) as merges_handle:
              __A					:					List[str]								=		merges_handle.read().split('\n'	)[:-1]
         __A					:					Optional[Any]								=		[tuple(merge.split()[:2]	) for merge in merges]
         __A					:					Union[str, Any]								=		dict(zip(__lowerCamelCase					,       range(len(__lowerCamelCase	)	)	)	)
         __A					:					Optional[Any]								=		{}
    @property
    def 						_a							(  self	):




         '''simple docstring'''



         return len(self.encoder	)
    def 						_a							(  self	):




         '''simple docstring'''



         return dict(self.encoder					,       **self.added_tokens_encoder	)
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         if lang not in self.cache_moses_tokenizer:
              __A					:					int								=		self.sm.MosesTokenizer(lang=__lowerCamelCase	)
              __A					:					List[str]								=		moses_tokenizer
         return self.cache_moses_tokenizer[lang].tokenize(
             __lowerCamelCase					,       aggressive_dash_splits=__lowerCamelCase					,       return_str=__lowerCamelCase					,       escape=__lowerCamelCase	)
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         if lang not in self.cache_moses_detokenizer:
              __A					:					Dict								=		self.sm.MosesDetokenizer(lang=__lowerCamelCase	)
              __A					:					Any								=		moses_detokenizer
         return self.cache_moses_detokenizer[lang].detokenize(__lowerCamelCase	)
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					Optional[Any]								=		tuple(token[:-1]	) + (token[-1] + '</w>',)
         if token in self.cache:
              return self.cache[token]
         __A					:					List[Any]								=		get_pairs(__lowerCamelCase	)

         if not pairs:
              return token + "</w>"

         while True:
              __A					:					Optional[Any]								=		min(__lowerCamelCase					,       key=lambda __lowerCamelCase	: self.bpe_ranks.get(__lowerCamelCase					,       float('inf'	)	)	)
              if bigram not in self.bpe_ranks:
                   break
              __A						,		__A					:					int								=		bigram
              __A					:					Tuple								=		[]
              __A					:					str								=		0
              while i < len(__lowerCamelCase	):
                   try:
                        __A					:					Optional[int]								=		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[Any]								=		tuple(__lowerCamelCase	)
              __A					:					Optional[int]								=		new_word
              if len(__lowerCamelCase	) == 1:
                   break
              else:
                   __A					:					List[Any]								=		get_pairs(__lowerCamelCase	)
         __A					:					List[str]								=		' '.join(__lowerCamelCase	)
         if word == "\n  </w>":
              __A					:					int								=		'\n</w>'
         __A					:					Tuple								=		word
         return word
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase=False	):




         '''simple docstring'''



         if bypass_tokenizer:
              __A					:					Dict								=		text.split()
         else:
              __A					:					List[Any]								=		self.moses_tokenize(__lowerCamelCase					,       self.lang	)

         __A					:					Any								=		[]
         for token in text:
              if token:
                   split_tokens.extend(list(self.bpe(__lowerCamelCase	).split(' '	)	)	)

         return split_tokens
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         return self.encoder.get(__lowerCamelCase					,       self.encoder.get(self.unk_token	)	)
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         return self.decoder.get(__lowerCamelCase					,       self.unk_token	)
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					Union[str, Any]								=		[t.replace(' '					,       ''	).replace('</w>'					,       ' '	) for t in tokens]
         __A					:					int								=		''.join(__lowerCamelCase	).split()
         # detokenize
         __A					:					Optional[Any]								=		self.moses_detokenize(__lowerCamelCase					,       self.lang	)
         return text
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase = None	):




         '''simple docstring'''



         if token_ids_a is None:
              return [self.sep_token_id] + token_ids_a
         __A					:					Optional[Any]								=		[self.sep_token_id]
         return sep + token_ids_a + sep + token_ids_a
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase = None					,       __lowerCamelCase = False	):




         '''simple docstring'''



         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=__lowerCamelCase					,       token_ids_a=__lowerCamelCase					,       already_has_special_tokens=__lowerCamelCase	)
         # no bos used in fairseq
         if token_ids_a is not None:
              return [1] + ([0] * len(__lowerCamelCase	)) + [1] + ([0] * len(__lowerCamelCase	))
         return [1] + ([0] * len(__lowerCamelCase	))
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase = None	):




         '''simple docstring'''



         __A					:					str								=		[self.sep_token_id]

         # no bos used in fairseq
         if token_ids_a is None:
              return len(token_ids_a + sep	) * [0]
         return len(token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase = None	):




         '''simple docstring'''



         if not os.path.isdir(__lowerCamelCase	):
              logger.error(F'Vocabulary path ({save_directory}) should be a directory'	)
              return
         __A					:					List[Any]								=		os.path.join(
             __lowerCamelCase					,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)
         __A					:					str								=		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					:					int								=		0
         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 {merge_file}: BPE merge indices are not consecutive.'
                            ' Please check that the tokenizer is not corrupted!'	)
                        __A					:					int								=		token_index
                   writer.write(' '.join(__lowerCamelCase	) + '\n'	)
                   index += 1

         return vocab_file, merge_file
    def __getstate__(  self	):




         '''simple docstring'''



         __A					:					int								=		self.__dict__.copy()
         __A					:					List[Any]								=		None
         return state

    def __setstate__(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					Dict								=		d

         try:
              import sacremoses
         except ImportError:
              raise ImportError(
                  'You need to install sacremoses to use XLMTokenizer. '
                  'See https://pypi.org/project/sacremoses/ for installation.'	)

         __A					:					Dict								=		sacremoses




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase      : Dict       ={'''configuration_sew_d''': ['''SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWDConfig''']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase      : List[str]       =[
           '''SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''SEWDForCTC''',
           '''SEWDForSequenceClassification''',
           '''SEWDModel''',
           '''SEWDPreTrainedModel''',
       ]

if TYPE_CHECKING:
       from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_sew_d import (
                  SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SEWDForCTC,
                  SEWDForSequenceClassification,
                  SEWDModel,
                  SEWDPreTrainedModel,
              )


else:
       import sys

       lowerCamelCase      : str       =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



