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

import regex as re

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


_lowercase							:      Dict      							=logging.get_logger(__name__)


_lowercase							:      List[Any]      							={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

_lowercase							:      List[str]      							={
    '''vocab_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
    },
}

_lowercase							:      Any      							={
    '''allenai/longformer-base-4096''': 4_0_9_6,
    '''allenai/longformer-large-4096''': 4_0_9_6,
    '''allenai/longformer-large-4096-finetuned-triviaqa''': 4_0_9_6,
    '''allenai/longformer-base-4096-extra.pos.embd.only''': 4_0_9_6,
    '''allenai/longformer-large-4096-extra.pos.embd.only''': 4_0_9_6,
}



@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def A__  (			)				->		Dict:
       A						:     List[Any]         =(
           list(range(ord('!'					),		ord('~'					) + 1					)					) + list(range(ord('¡'					),		ord('¬'					) + 1					)					) + list(range(ord('®'					),		ord('ÿ'					) + 1					)					)
       )
       A						:     List[Any]         =bs[:]
       A						:     Optional[Any]         =0
       for b in range(2**8					):
              if b not in bs:
                     bs.append(lowercase					)
                     cs.append(2**8 + n					)
                     n += 1
       A						:     str         =[chr(lowercase					) for n in cs]
       return dict(zip(lowercase,		lowercase					)					)



def A__  (			lowercase:     List[Any]					)				->		Tuple:
       A						:     Optional[Any]         =set()
       A						:     Dict         =word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)					)
              A						:     Tuple         =char
       return pairs



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Dict            =				VOCAB_FILES_NAMES
  lowercase		:     List[str]            =				PRETRAINED_VOCAB_FILES_MAP
  lowercase		:     List[str]            =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowercase		:     Optional[Any]            =				["input_ids", "attention_mask"]







  def __init__(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					List[str]="replace"      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]="<s>"      ,	SCREAMING_SNAKE_CASE__    :					List[Any]="</s>"      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]="</s>"      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]="<s>"      ,	SCREAMING_SNAKE_CASE__    :					str="<unk>"      ,	SCREAMING_SNAKE_CASE__    :					str="<pad>"      ,	SCREAMING_SNAKE_CASE__    :					Any="<mask>"      ,	SCREAMING_SNAKE_CASE__    :					str=False      ,	**SCREAMING_SNAKE_CASE__    :					Tuple      ,	)    ->				Optional[Any]:
         A						:     List[str]         =AddedToken(SCREAMING_SNAKE_CASE__      ,	lstrip=SCREAMING_SNAKE_CASE__      ,	rstrip=SCREAMING_SNAKE_CASE__     ) if isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     ) else bos_token
         A						:     Tuple         =AddedToken(SCREAMING_SNAKE_CASE__      ,	lstrip=SCREAMING_SNAKE_CASE__      ,	rstrip=SCREAMING_SNAKE_CASE__     ) if isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     ) else eos_token
         A						:     int         =AddedToken(SCREAMING_SNAKE_CASE__      ,	lstrip=SCREAMING_SNAKE_CASE__      ,	rstrip=SCREAMING_SNAKE_CASE__     ) if isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     ) else sep_token
         A						:     Dict         =AddedToken(SCREAMING_SNAKE_CASE__      ,	lstrip=SCREAMING_SNAKE_CASE__      ,	rstrip=SCREAMING_SNAKE_CASE__     ) if isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     ) else cls_token
         A						:     Union[str, Any]         =AddedToken(SCREAMING_SNAKE_CASE__      ,	lstrip=SCREAMING_SNAKE_CASE__      ,	rstrip=SCREAMING_SNAKE_CASE__     ) if isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     ) else unk_token
         A						:     Tuple         =AddedToken(SCREAMING_SNAKE_CASE__      ,	lstrip=SCREAMING_SNAKE_CASE__      ,	rstrip=SCREAMING_SNAKE_CASE__     ) if isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     ) else pad_token

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

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

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

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







  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				List[Any]:
         return len(self.encoder     )







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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]     )    ->				List[Any]:
         if token in self.cache:
                return self.cache[token]
         A						:     List[Any]         =tuple(SCREAMING_SNAKE_CASE__     )
         A						:     Any         =get_pairs(SCREAMING_SNAKE_CASE__     )

         if not pairs:
                return token

         while True:
                A						:     int         =min(SCREAMING_SNAKE_CASE__      ,	key=lambda SCREAMING_SNAKE_CASE__     : self.bpe_ranks.get(SCREAMING_SNAKE_CASE__      ,	float('inf'     )     )     )
                if bigram not in self.bpe_ranks:
                       break
                A						:     Optional[Any]         =bigram
                A						:     Dict         =[]
                A						:     Optional[Any]         =0
                while i < len(SCREAMING_SNAKE_CASE__     ):
                       try:
                              A						:     Tuple         =word.index(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )
                       except ValueError:
                              new_word.extend(word[i:]     )
                              break
                       else:
                              new_word.extend(word[i:j]     )
                              A						:     List[str]         =j

                       if word[i] == first and i < len(SCREAMING_SNAKE_CASE__     ) - 1 and word[i + 1] == second:
                              new_word.append(first + second     )
                              i += 2
                       else:
                              new_word.append(word[i]     )
                              i += 1
                A						:     Optional[Any]         =tuple(SCREAMING_SNAKE_CASE__     )
                A						:     str         =new_word
                if len(SCREAMING_SNAKE_CASE__     ) == 1:
                       break
                else:
                       A						:     str         =get_pairs(SCREAMING_SNAKE_CASE__     )
         A						:     int         =' '.join(SCREAMING_SNAKE_CASE__     )
         A						:     str         =word
         return word







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]     )    ->				Dict:
         A						:     Tuple         =[]
         for token in re.findall(self.pat      ,	SCREAMING_SNAKE_CASE__     ):
                A						:     Optional[int]         =''.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(SCREAMING_SNAKE_CASE__     ).split(' '     )     )
         return bpe_tokens







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					int     )    ->				Any:
         return self.encoder.get(SCREAMING_SNAKE_CASE__      ,	self.encoder.get(self.unk_token     )     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]     )    ->				Any:
         return self.decoder.get(SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]     )    ->				Tuple:
         A						:     Optional[int]         =''.join(SCREAMING_SNAKE_CASE__     )
         A						:     str         =bytearray([self.byte_decoder[c] for c in text]     ).decode('utf-8'      ,	errors=self.errors     )
         return text







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

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

         A						:     Optional[int]         =0
         with open(SCREAMING_SNAKE_CASE__      ,	'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 SCREAMING_SNAKE_CASE__     : 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						:     Dict         =token_index
                       writer.write(' '.join(SCREAMING_SNAKE_CASE__     ) + '\n'     )
                       index += 1

         return vocab_file, merge_file







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					List[int]      ,	SCREAMING_SNAKE_CASE__    :					Optional[List[int]] = None     )    ->				List[int]:
         if token_ids_a is None:
                return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         A						:     str         =[self.cls_token_id]
         A						:     Union[str, Any]         =[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep







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

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					List[int]      ,	SCREAMING_SNAKE_CASE__    :					Optional[List[int]] = None     )    ->				List[int]:
         A						:     List[Any]         =[self.sep_token_id]
         A						:     Optional[int]         =[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    :					str      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					str=False      ,	**SCREAMING_SNAKE_CASE__    :					int     )    ->				Optional[int]:
         A						:     Optional[int]         =kwargs.pop('add_prefix_space'      ,	self.add_prefix_space     )
         if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE__     ) > 0 and not text[0].isspace()):
                A						:     List[Any]         =' ' + text
         return (text, kwargs)







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
    from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


_lowercase							:      Optional[int]      							=logging.get_logger(__name__)

_lowercase							:      List[str]      							={
    '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json''',
    '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json''',
    '''microsoft/deberta-v2-xlarge-mnli''': (
        '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'''
    ),
    '''microsoft/deberta-v2-xxlarge-mnli''': (
        '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'''
    ),
}



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     int            =				"deberta-v2"







  def __init__(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					str=12_81_00      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=15_36      ,	SCREAMING_SNAKE_CASE__    :					Dict=24      ,	SCREAMING_SNAKE_CASE__    :					List[str]=24      ,	SCREAMING_SNAKE_CASE__    :					List[str]=61_44      ,	SCREAMING_SNAKE_CASE__    :					List[Any]="gelu"      ,	SCREAMING_SNAKE_CASE__    :					int=0.1      ,	SCREAMING_SNAKE_CASE__    :					Any=0.1      ,	SCREAMING_SNAKE_CASE__    :					List[str]=5_12      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0      ,	SCREAMING_SNAKE_CASE__    :					Tuple=0.0_2      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=1e-7      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=False      ,	SCREAMING_SNAKE_CASE__    :					Tuple=-1      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0      ,	SCREAMING_SNAKE_CASE__    :					Dict=True      ,	SCREAMING_SNAKE_CASE__    :					List[str]=None      ,	SCREAMING_SNAKE_CASE__    :					List[str]=0      ,	SCREAMING_SNAKE_CASE__    :					List[str]="gelu"      ,	**SCREAMING_SNAKE_CASE__    :					Dict      ,	)    ->				Dict:
         super().__init__(**SCREAMING_SNAKE_CASE__     )

         A						:     Dict         =hidden_size
         A						:     Optional[Any]         =num_hidden_layers
         A						:     Optional[int]         =num_attention_heads
         A						:     Optional[int]         =intermediate_size
         A						:     Any         =hidden_act
         A						:     Any         =hidden_dropout_prob
         A						:     Union[str, Any]         =attention_probs_dropout_prob
         A						:     Optional[Any]         =max_position_embeddings
         A						:     Tuple         =type_vocab_size
         A						:     Tuple         =initializer_range
         A						:     int         =relative_attention
         A						:     int         =max_relative_positions
         A						:     Optional[Any]         =pad_token_id
         A						:     Union[str, Any]         =position_biased_input

         # Backwards compatibility
         if type(SCREAMING_SNAKE_CASE__     ) == str:
                A						:     Any         =[x.strip() for x in pos_att_type.lower().split('|'     )]

         A						:     Any         =pos_att_type
         A						:     Tuple         =vocab_size
         A						:     Any         =layer_norm_eps

         A						:     Optional[Any]         =kwargs.get('pooler_hidden_size'      ,	SCREAMING_SNAKE_CASE__     )
         A						:     str         =pooler_dropout
         A						:     Any         =pooler_hidden_act







class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				Mapping[str, Mapping[int, str]]:
         if self.task == "multiple-choice":
                A						:     List[Any]         ={0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
                A						:     int         ={0: 'batch', 1: 'sequence'}
         if self._config.type_vocab_size > 0:
                return OrderedDict(
                    [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)]     )
         else:
                return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)]     )







  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				int:
         return 12







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]      ,	SCREAMING_SNAKE_CASE__    :					int = -1      ,	SCREAMING_SNAKE_CASE__    :					int = -1      ,	SCREAMING_SNAKE_CASE__    :					int = -1      ,	SCREAMING_SNAKE_CASE__    :					bool = False      ,	SCREAMING_SNAKE_CASE__    :					Optional["TensorType"] = None      ,	SCREAMING_SNAKE_CASE__    :					int = 3      ,	SCREAMING_SNAKE_CASE__    :					int = 40      ,	SCREAMING_SNAKE_CASE__    :					int = 40      ,	SCREAMING_SNAKE_CASE__    :					"PreTrainedTokenizerBase" = None      ,	)    ->				Mapping[str, Any]:
         A						:     str         =super().generate_dummy_inputs(preprocessor=SCREAMING_SNAKE_CASE__      ,	framework=SCREAMING_SNAKE_CASE__     )
         if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                del dummy_inputs["token_type_ids"]
         return dummy_inputs






