



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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation

UpperCamelCase_									=   logging.get_logger(__name__)

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

UpperCamelCase_									=   {
    '''vocab_file''': {
        '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json''',
        '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json''',
        '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json''',
        '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json''',
        '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json''',
        '''microsoft/deberta-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt''',
        '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt''',
        '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt''',
        '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt''',
        '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt''',
        '''microsoft/deberta-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'''
        ),
    },
}

UpperCamelCase_									=   {
    '''microsoft/deberta-base''': 512,
    '''microsoft/deberta-large''': 512,
    '''microsoft/deberta-xlarge''': 512,
    '''microsoft/deberta-base-mnli''': 512,
    '''microsoft/deberta-large-mnli''': 512,
    '''microsoft/deberta-xlarge-mnli''': 512,
}

UpperCamelCase_									=   {
    '''microsoft/deberta-base''': {'''do_lower_case''': False},
    '''microsoft/deberta-large''': {'''do_lower_case''': False},
}







def       lowerCamelCase_  (    ):



    '''simple docstring'''




    UpperCAmelCase_     :       int  		=					(
        list(range(ord("""!"""    )   ,		ord("""~"""    ) + 1    )    ) + list(range(ord("""¡"""    )   ,		ord("""¬"""    ) + 1    )    ) + list(range(ord("""®"""    )   ,		ord("""ÿ"""    ) + 1    )    )
    )
    UpperCAmelCase_     :       Any  		=					bs[:]
    UpperCAmelCase_     :       List[Any]  		=					0
    for b in range(2**8    ):
        if b not in bs:
            bs.append(_a    )
            cs.append(2**8 + n    )
            n += 1
    UpperCAmelCase_     :       Tuple  		=					[chr(_a    ) for n in cs]
    return dict(zip(_a   ,		_a    )    )







def       lowerCamelCase_  (    _a       :						Optional[Any]    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Optional[int]  		=					set()
    UpperCAmelCase_     :       Dict  		=					word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)    )
        UpperCAmelCase_     :       List[str]  		=					char
    return pairs







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : Dict       =						VOCAB_FILES_NAMES
   A__      : str       =						PRETRAINED_VOCAB_FILES_MAP
   A__      : int       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__      : Optional[int]       =						["input_ids", "attention_mask", "token_type_ids"]


   def __init__(    self:     Dict  ,lowerCamelCase_:     str  ,lowerCamelCase_:     Optional[Any]  ,lowerCamelCase_:     Optional[Any]="replace"  ,lowerCamelCase_:     List[str]="[CLS]"  ,lowerCamelCase_:     Any="[SEP]"  ,lowerCamelCase_:     Any="[SEP]"  ,lowerCamelCase_:     str="[CLS]"  ,lowerCamelCase_:     List[Any]="[UNK]"  ,lowerCamelCase_:     Union[str, Any]="[PAD]"  ,lowerCamelCase_:     List[str]="[MASK]"  ,lowerCamelCase_:     str=False  ,lowerCamelCase_:     Any=False  ,**lowerCamelCase_:     List[Any]  ,)	->      Optional[Any]:
       UpperCAmelCase_     :       List[str]  		=					AddedToken(lowerCamelCase_  ,lstrip=lowerCamelCase_  ,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_  ,lowerCamelCase_							) else bos_token
       UpperCAmelCase_     :       Optional[int]  		=					AddedToken(lowerCamelCase_  ,lstrip=lowerCamelCase_  ,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_  ,lowerCamelCase_							) else eos_token
       UpperCAmelCase_     :       Tuple  		=					AddedToken(lowerCamelCase_  ,lstrip=lowerCamelCase_  ,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_  ,lowerCamelCase_							) else sep_token
       UpperCAmelCase_     :       Any  		=					AddedToken(lowerCamelCase_  ,lstrip=lowerCamelCase_  ,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_  ,lowerCamelCase_							) else cls_token
       UpperCAmelCase_     :       Any  		=					AddedToken(lowerCamelCase_  ,lstrip=lowerCamelCase_  ,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_  ,lowerCamelCase_							) else unk_token
       UpperCAmelCase_     :       Optional[Any]  		=					AddedToken(lowerCamelCase_  ,lstrip=lowerCamelCase_  ,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_  ,lowerCamelCase_							) else pad_token

       # Mask token behave like a normal word, i.e. include the space before it
       UpperCAmelCase_     :       List[Any]  		=					AddedToken(lowerCamelCase_  ,lstrip=lowerCamelCase_  ,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_  ,lowerCamelCase_							) else mask_token

       super().__init__(
           errors=lowerCamelCase_  ,bos_token=lowerCamelCase_  ,eos_token=lowerCamelCase_  ,unk_token=lowerCamelCase_  ,sep_token=lowerCamelCase_  ,cls_token=lowerCamelCase_  ,pad_token=lowerCamelCase_  ,mask_token=lowerCamelCase_  ,add_prefix_space=lowerCamelCase_  ,add_bos_token=lowerCamelCase_  ,**lowerCamelCase_  ,)
       UpperCAmelCase_     :       str  		=					add_bos_token

       with open(lowerCamelCase_  ,encoding="""utf-8"""							) as vocab_handle:
           UpperCAmelCase_     :       List[Any]  		=					json.load(lowerCamelCase_							)
       UpperCAmelCase_     :       Optional[int]  		=					{v: k for k, v in self.encoder.items()}
       UpperCAmelCase_     :       int  		=					errors  # how to handle errors in decoding
       UpperCAmelCase_     :       List[Any]  		=					bytes_to_unicode()
       UpperCAmelCase_     :       Tuple  		=					{v: k for k, v in self.byte_encoder.items()}
       with open(lowerCamelCase_  ,encoding="""utf-8"""							) as merges_handle:
           UpperCAmelCase_     :       List[str]  		=					merges_handle.read().split("""\n"""							)[1:-1]
       UpperCAmelCase_     :       str  		=					[tuple(merge.split()							) for merge in bpe_merges]
       UpperCAmelCase_     :       Optional[int]  		=					dict(zip(lowerCamelCase_  ,range(len(lowerCamelCase_							)							)							)							)
       UpperCAmelCase_     :       Tuple  		=					{}
       UpperCAmelCase_     :       List[str]  		=					add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       UpperCAmelCase_     :       Union[str, Any]  		=					re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""							)


   @property
   # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
   def       A__    (    self:     str							)	->      Optional[Any]:
       return len(self.encoder							)


   def       A__    (    self:     Tuple							)	->      Tuple:
       return dict(self.encoder  ,**self.added_tokens_encoder							)


   def       A__    (    self:     Dict  ,lowerCamelCase_:     Union[str, Any]							)	->      int:
       if token in self.cache:
           return self.cache[token]
       UpperCAmelCase_     :       Optional[int]  		=					tuple(lowerCamelCase_							)
       UpperCAmelCase_     :       Union[str, Any]  		=					get_pairs(lowerCamelCase_							)

       if not pairs:
           return token

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


   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     List[int]  ,lowerCamelCase_:     Optional[List[int]] = None							)	->      List[int]:
       if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       UpperCAmelCase_     :       Dict  		=					[self.cls_token_id]
       UpperCAmelCase_     :       Dict  		=					[self.sep_token_id]
       return cls + token_ids_a + sep + token_ids_a + sep


   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     List[int]  ,lowerCamelCase_:     Optional[List[int]] = None  ,lowerCamelCase_:     bool = False							)	->      List[int]:
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=lowerCamelCase_  ,token_ids_a=lowerCamelCase_  ,already_has_special_tokens=lowerCamelCase_							)

       if token_ids_a is None:
           return [1] + ([0] * len(lowerCamelCase_							)) + [1]
       return [1] + ([0] * len(lowerCamelCase_							)) + [1] + ([0] * len(lowerCamelCase_							)) + [1]


   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     List[int]  ,lowerCamelCase_:     Optional[List[int]] = None							)	->      List[int]:
       UpperCAmelCase_     :       List[Any]  		=					[self.sep_token_id]
       UpperCAmelCase_     :       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							) * [0] + len(token_ids_a + sep							) * [1]


   def       A__    (    self:     int  ,lowerCamelCase_:     str							)	->      str:
       UpperCAmelCase_     :       Tuple  		=					[]
       for token in re.findall(self.pat  ,lowerCamelCase_							):
           UpperCAmelCase_     :       List[Any]  		=					"""""".join(
               self.byte_encoder[b] for b in token.encode("""utf-8"""							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
           bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase_							).split(""" """							)							)
       return bpe_tokens


   def       A__    (    self:     List[Any]  ,lowerCamelCase_:     List[str]							)	->      Dict:
       return self.encoder.get(lowerCamelCase_  ,self.encoder.get(self.unk_token							)							)


   def       A__    (    self:     List[Any]  ,lowerCamelCase_:     Tuple							)	->      Tuple:
       return self.decoder.get(lowerCamelCase_							)


   def       A__    (    self:     Union[str, Any]  ,lowerCamelCase_:     List[str]							)	->      Dict:
       UpperCAmelCase_     :       Optional[int]  		=					"""""".join(lowerCamelCase_							)
       UpperCAmelCase_     :       Dict  		=					bytearray([self.byte_decoder[c] for c in text]							).decode("""utf-8"""  ,errors=self.errors							)
       return text


   def       A__    (    self:     List[str]  ,lowerCamelCase_:     str  ,lowerCamelCase_:     Optional[str] = None							)	->      Tuple[str]:
       if not os.path.isdir(lowerCamelCase_							):
           logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
           return
       UpperCAmelCase_     :       Dict  		=					os.path.join(
           lowerCamelCase_  ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
       UpperCAmelCase_     :       List[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"""							)

       UpperCAmelCase_     :       Tuple  		=					0
       with open(lowerCamelCase_  ,"""w"""  ,encoding="""utf-8"""							) as writer:
           writer.write("""#version: 0.2\n"""							)
           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!"""							)
                   UpperCAmelCase_     :       str  		=					token_index
               writer.write(""" """.join(lowerCamelCase_							) + """\n"""							)
               index += 1

       return vocab_file, merge_file


   def       A__    (    self:     Tuple  ,lowerCamelCase_:     int  ,lowerCamelCase_:     str=False  ,**lowerCamelCase_:     List[Any]							)	->      Union[str, Any]:
       UpperCAmelCase_     :       Optional[int]  		=					kwargs.pop("""add_prefix_space"""  ,self.add_prefix_space							)
       if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase_							) > 0 and not text[0].isspace()):
           UpperCAmelCase_     :       str  		=					""" """ + text
       return (text, kwargs)






   def       A__    (    self:     str  ,lowerCamelCase_:     "Conversation"							)	->      List[int]:
       UpperCAmelCase_     :       str  		=					[]
       for is_user, text in conversation.iter_texts():
           input_ids.extend(self.encode(lowerCamelCase_  ,add_special_tokens=lowerCamelCase_							) + [self.eos_token_id]							)
       if len(lowerCamelCase_							) > self.model_max_length:
           UpperCAmelCase_     :       int  		=					input_ids[-self.model_max_length :]
       return input_ids




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase							:							List[Any]    							=					{
    "configuration_mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig"],
    "tokenization_mpnet": ["MPNetTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase							:							Union[str, Any]    							=					["MPNetTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase							:							Union[str, Any]    							=					[
           "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MPNetForMaskedLM",
           "MPNetForMultipleChoice",
           "MPNetForQuestionAnswering",
           "MPNetForSequenceClassification",
           "MPNetForTokenClassification",
           "MPNetLayer",
           "MPNetModel",
           "MPNetPreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase							:							List[Any]    							=					[
           "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFMPNetEmbeddings",
           "TFMPNetForMaskedLM",
           "TFMPNetForMultipleChoice",
           "TFMPNetForQuestionAnswering",
           "TFMPNetForSequenceClassification",
           "TFMPNetForTokenClassification",
           "TFMPNetMainLayer",
           "TFMPNetModel",
           "TFMPNetPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
       from .tokenization_mpnet import MPNetTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mpnet_fast import MPNetTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mpnet import (
                  MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MPNetForMaskedLM,
                  MPNetForMultipleChoice,
                  MPNetForQuestionAnswering,
                  MPNetForSequenceClassification,
                  MPNetForTokenClassification,
                  MPNetLayer,
                  MPNetModel,
                  MPNetPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_mpnet import (
                  TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFMPNetEmbeddings,
                  TFMPNetForMaskedLM,
                  TFMPNetForMultipleChoice,
                  TFMPNetForQuestionAnswering,
                  TFMPNetForSequenceClassification,
                  TFMPNetForTokenClassification,
                  TFMPNetMainLayer,
                  TFMPNetModel,
                  TFMPNetPreTrainedModel,
              )

else:
       import sys

       __UpperCamelCase							:							Tuple    							=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


