


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 							UpperCAmelCase_   (				)	->     List[Any]:







 """simple docstring"""


 _lowercase				 =(
     list(range(ord('''!'''		)	,		ord('''~'''		) + 1		)		) + list(range(ord('''¡'''		)	,		ord('''¬'''		) + 1		)		) + list(range(ord('''®'''		)	,		ord('''ÿ'''		) + 1		)		)
 )
 _lowercase				 =bs[:]
 _lowercase				 =0
 for b in range(2**8		):
  if b not in bs:
   bs.append(__snake_case		)
   cs.append(2**8 + n		)
   n += 1
 _lowercase				 =[chr(__snake_case		) for n in cs]
 return dict(zip(__snake_case	,		__snake_case		)		)
def 							UpperCAmelCase_   (				__snake_case		)	->     Any:







 """simple docstring"""


 _lowercase				 =set()
 _lowercase				 =word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)		)
  _lowercase				 =char
 return pairs







class  lowerCamelCase__  (			lowerCAmelCase):
     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''', '''token_type_ids''']





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

      # Mask token behave like a normal word, i.e. include the space before it
      _lowercase				 =AddedToken(UpperCAmelCase						,	lstrip=UpperCAmelCase						,	rstrip=UpperCAmelCase			) if isinstance(UpperCAmelCase						,	UpperCAmelCase			) else mask_token

      super().__init__(
          errors=UpperCAmelCase						,	bos_token=UpperCAmelCase						,	eos_token=UpperCAmelCase						,	unk_token=UpperCAmelCase						,	sep_token=UpperCAmelCase						,	cls_token=UpperCAmelCase						,	pad_token=UpperCAmelCase						,	mask_token=UpperCAmelCase						,	add_prefix_space=UpperCAmelCase						,	add_bos_token=UpperCAmelCase						,	**UpperCAmelCase						,	)
      _lowercase				 =add_bos_token

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

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      _lowercase				 =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			)	->  Any:
      return len(self.encoder			)





     def 	__A   (self			)	->  Optional[Any]:
      return dict(self.encoder						,	**self.added_tokens_encoder			)





     def 	__A   (self						,	UpperCAmelCase			)	->  int:
      if token in self.cache:
       return self.cache[token]
      _lowercase				 =tuple(UpperCAmelCase			)
      _lowercase				 =get_pairs(UpperCAmelCase			)

      if not pairs:
       return token

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

        if word[i] == first and i < len(UpperCAmelCase			) - 1 and word[i + 1] == second:
         new_word.append(first + second			)
         i += 2
        else:
         new_word.append(word[i]			)
         i += 1
       _lowercase				 =tuple(UpperCAmelCase			)
       _lowercase				 =new_word
       if len(UpperCAmelCase			) == 1:
        break
       else:
        _lowercase				 =get_pairs(UpperCAmelCase			)
      _lowercase				 =''' '''.join(UpperCAmelCase			)
      _lowercase				 =word
      return word





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase = None			)	->  List[int]:
      if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      _lowercase				 =[self.cls_token_id]
      _lowercase				 =[self.sep_token_id]
      return cls + token_ids_a + sep + token_ids_a + sep





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase = None						,	UpperCAmelCase = False			)	->  List[int]:
      if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=UpperCAmelCase						,	token_ids_a=UpperCAmelCase						,	already_has_special_tokens=UpperCAmelCase			)

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





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase = None			)	->  List[int]:
      _lowercase				 =[self.sep_token_id]
      _lowercase				 =[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						,	UpperCAmelCase			)	->  Union[str, Any]:
      _lowercase				 =[]
      for token in re.findall(self.pat						,	UpperCAmelCase			):
       _lowercase				 =''''''.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(UpperCAmelCase			).split(''' '''			)			)
      return bpe_tokens





     def 	__A   (self						,	UpperCAmelCase			)	->  Tuple:
      return self.encoder.get(UpperCAmelCase						,	self.encoder.get(self.unk_token			)			)





     def 	__A   (self						,	UpperCAmelCase			)	->  List[Any]:
      return self.decoder.get(UpperCAmelCase			)





     def 	__A   (self						,	UpperCAmelCase			)	->  Any:
      _lowercase				 =''''''.join(UpperCAmelCase			)
      _lowercase				 =bytearray([self.byte_decoder[c] for c in text]			).decode('''utf-8'''						,	errors=self.errors			)
      return text





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase = None			)	->  Tuple[str]:
      if not os.path.isdir(UpperCAmelCase			):
       logger.error(f"Vocabulary path ({save_directory}) should be a directory"			)
       return
      _lowercase				 =os.path.join(
          UpperCAmelCase						,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)
      _lowercase				 =os.path.join(
          UpperCAmelCase						,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']			)

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

      _lowercase				 =0
      with open(UpperCAmelCase						,	'''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 UpperCAmelCase			: 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!'''			)
         _lowercase				 =token_index
        writer.write(''' '''.join(UpperCAmelCase			) + '''\n'''			)
        index += 1

      return vocab_file, merge_file





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase=False						,	**UpperCAmelCase			)	->  List[Any]:
      _lowercase				 =kwargs.pop('''add_prefix_space'''						,	self.add_prefix_space			)
      if (is_split_into_words or add_prefix_space) and (len(UpperCAmelCase			) > 0 and not text[0].isspace()):
       _lowercase				 =''' ''' + text
      return (text, kwargs)





     def 	__A   (self						,	UpperCAmelCase			)	->  List[int]:
      _lowercase				 =[]
      for is_user, text in conversation.iter_texts():
       input_ids.extend(self.encode(UpperCAmelCase						,	add_special_tokens=UpperCAmelCase			) + [self.eos_token_id]			)
      if len(UpperCAmelCase			) > self.model_max_length:
       _lowercase				 =input_ids[-self.model_max_length :]
      return input_ids





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__        =				{
    '''configuration_flaubert''': ['''FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FlaubertConfig''', '''FlaubertOnnxConfig'''],
    '''tokenization_flaubert''': ['''FlaubertTokenizer'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__        =				[
      '''FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''FlaubertForMultipleChoice''',
      '''FlaubertForQuestionAnswering''',
      '''FlaubertForQuestionAnsweringSimple''',
      '''FlaubertForSequenceClassification''',
      '''FlaubertForTokenClassification''',
      '''FlaubertModel''',
      '''FlaubertWithLMHeadModel''',
      '''FlaubertPreTrainedModel''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__        =				[
      '''TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFFlaubertForMultipleChoice''',
      '''TFFlaubertForQuestionAnsweringSimple''',
      '''TFFlaubertForSequenceClassification''',
      '''TFFlaubertForTokenClassification''',
      '''TFFlaubertModel''',
      '''TFFlaubertPreTrainedModel''',
      '''TFFlaubertWithLMHeadModel''',
  ]


if TYPE_CHECKING:
  from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
  from .tokenization_flaubert import FlaubertTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flaubert import (
        FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaubertForMultipleChoice,
        FlaubertForQuestionAnswering,
        FlaubertForQuestionAnsweringSimple,
        FlaubertForSequenceClassification,
        FlaubertForTokenClassification,
        FlaubertModel,
        FlaubertPreTrainedModel,
        FlaubertWithLMHeadModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_flaubert import (
        TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFFlaubertForMultipleChoice,
        TFFlaubertForQuestionAnsweringSimple,
        TFFlaubertForSequenceClassification,
        TFFlaubertForTokenClassification,
        TFFlaubertModel,
        TFFlaubertPreTrainedModel,
        TFFlaubertWithLMHeadModel,
    )

else:
  import sys

  UpperCAmelCase__        =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

