

import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


lowerCAmelCase__							=			logging.get_logger(__name__)

lowerCAmelCase__							=			{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase__							=			{
    "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"
        ),
    },
    "tokenizer_file": {
        "allenai/longformer-base-4096": (
            "https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json"
        ),
    },
}

lowerCAmelCase__							=			{
    "allenai/longformer-base-4096": 4096,
    "allenai/longformer-large-4096": 4096,
    "allenai/longformer-large-4096-finetuned-triviaqa": 4096,
    "allenai/longformer-base-4096-extra.pos.embd.only": 4096,
    "allenai/longformer-large-4096-extra.pos.embd.only": 4096,
}






class     __magic_name__     (							_snake_case     ):
 UpperCAmelCase				     =    VOCAB_FILES_NAMES
 UpperCAmelCase				     =    PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase				     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase				     =    ["""input_ids""", """attention_mask"""]
 UpperCAmelCase				     =    LongformerTokenizer


 def __init__(    self		:			Union[str, Any]      ,    lowerCAmelCase__		:			List[str]=None      ,    lowerCAmelCase__		:			Optional[Any]=None      ,    lowerCAmelCase__		:			Optional[Any]=None      ,    lowerCAmelCase__		:			Dict="replace"      ,    lowerCAmelCase__		:			Any="<s>"      ,    lowerCAmelCase__		:			Any="</s>"      ,    lowerCAmelCase__		:			Optional[Any]="</s>"      ,    lowerCAmelCase__		:			Dict="<s>"      ,    lowerCAmelCase__		:			Optional[int]="<unk>"      ,    lowerCAmelCase__		:			Any="<pad>"      ,    lowerCAmelCase__		:			Dict="<mask>"      ,    lowerCAmelCase__		:			Tuple=False      ,    lowerCAmelCase__		:			Union[str, Any]=True      ,    **lowerCAmelCase__		:			Tuple      ,    )     ->   Dict:
   super().__init__(
       lowerCAmelCase__      ,    lowerCAmelCase__      ,    tokenizer_file=lowerCAmelCase__      ,    errors=lowerCAmelCase__      ,    bos_token=lowerCAmelCase__      ,    eos_token=lowerCAmelCase__      ,    sep_token=lowerCAmelCase__      ,    cls_token=lowerCAmelCase__      ,    unk_token=lowerCAmelCase__      ,    pad_token=lowerCAmelCase__      ,    mask_token=lowerCAmelCase__      ,    add_prefix_space=lowerCAmelCase__      ,    trim_offsets=lowerCAmelCase__      ,    **lowerCAmelCase__      ,    )

   UpperCAmelCase          =	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
   if pre_tok_state.get("add_prefix_space"      ,    lowerCAmelCase__     ) != add_prefix_space:
     UpperCAmelCase          =	getattr(lowerCAmelCase__      ,    pre_tok_state.pop("type"     )     )
     UpperCAmelCase          =	add_prefix_space
     UpperCAmelCase          =	pre_tok_class(**lowerCAmelCase__     )

   UpperCAmelCase          =	add_prefix_space

   UpperCAmelCase          =	"post_processor"
   UpperCAmelCase          =	getattr(self.backend_tokenizer      ,    lowerCAmelCase__      ,    lowerCAmelCase__     )
   if tokenizer_component_instance:
     UpperCAmelCase          =	json.loads(tokenizer_component_instance.__getstate__()     )

     # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
     if "sep" in state:
       UpperCAmelCase          =	tuple(state["sep"]     )
     if "cls" in state:
       UpperCAmelCase          =	tuple(state["cls"]     )

     UpperCAmelCase          =	False

     if state.get("add_prefix_space"      ,    lowerCAmelCase__     ) != add_prefix_space:
       UpperCAmelCase          =	add_prefix_space
       UpperCAmelCase          =	True

     if state.get("trim_offsets"      ,    lowerCAmelCase__     ) != trim_offsets:
       UpperCAmelCase          =	trim_offsets
       UpperCAmelCase          =	True

     if changes_to_apply:
       UpperCAmelCase          =	getattr(lowerCAmelCase__      ,    state.pop("type"     )     )
       UpperCAmelCase          =	component_class(**lowerCAmelCase__     )
       setattr(self.backend_tokenizer      ,    lowerCAmelCase__      ,    lowerCAmelCase__     )


 @property
 def _UpperCamelCase						(    self		:			Any     )     ->   str:
   if self._mask_token is None:
     if self.verbose:
       logger.error("Using mask_token, but it is not set yet."     )
     return None
   return str(self._mask_token     )


 @mask_token.setter
 def _UpperCamelCase						(    self		:			Tuple      ,    lowerCAmelCase__		:			str     )     ->   Optional[Any]:
   UpperCAmelCase          =	AddedToken(lowerCAmelCase__      ,    lstrip=lowerCAmelCase__      ,    rstrip=lowerCAmelCase__     ) if isinstance(lowerCAmelCase__      ,    lowerCAmelCase__     ) else value
   UpperCAmelCase          =	value


 def _UpperCamelCase						(    self		:			List[Any]      ,    *lowerCAmelCase__		:			List[str]      ,    **lowerCAmelCase__		:			int     )     ->   BatchEncoding:
   UpperCAmelCase          =	kwargs.get("is_split_into_words"      ,    lowerCAmelCase__     )
   assert self.add_prefix_space or not is_split_into_words, (
       f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
       "to use it with pretokenized inputs."
   )

   return super()._batch_encode_plus(*lowerCAmelCase__      ,    **lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			Tuple      ,    *lowerCAmelCase__		:			Dict      ,    **lowerCAmelCase__		:			int     )     ->   BatchEncoding:
   UpperCAmelCase          =	kwargs.get("is_split_into_words"      ,    lowerCAmelCase__     )

   assert self.add_prefix_space or not is_split_into_words, (
       f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
       "to use it with pretokenized inputs."
   )

   return super()._encode_plus(*lowerCAmelCase__      ,    **lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			Union[str, Any]      ,    lowerCAmelCase__		:			str      ,    lowerCAmelCase__		:			Optional[str] = None     )     ->   Tuple[str]:
   UpperCAmelCase          =	self._tokenizer.model.save(lowerCAmelCase__      ,    name=lowerCAmelCase__     )
   return tuple(lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			Union[str, Any]      ,    lowerCAmelCase__		:			Optional[Any]      ,    lowerCAmelCase__		:			Optional[int]=None     )     ->   List[Any]:
   UpperCAmelCase          =	[self.bos_token_id] + token_ids_a + [self.eos_token_id]
   if token_ids_a is None:
     return output

   return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


 def _UpperCamelCase						(    self		:			Tuple      ,    lowerCAmelCase__		:			List[int]      ,    lowerCAmelCase__		:			Optional[List[int]] = None     )     ->   List[int]:
   UpperCAmelCase          =	[self.sep_token_id]
   UpperCAmelCase          =	[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]






from typing import Dict, Optional, Tuple

import torch
def   _lowerCAmelCase(						__A      ):
  UpperCAmelCase          =	boundaries[1] - boundaries[0]
  UpperCAmelCase          =	boundaries + step / 2
  UpperCAmelCase          =	torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1      )] ,							dim=0      )
  return bin_centers
def   _lowerCAmelCase(						__A ,							__A ,							):
  UpperCAmelCase          =	_calculate_bin_centers(__A      )
  return (
      torch.sum(aligned_distance_error_probs * bin_centers ,							dim=-1      ),
      bin_centers[-1],
  )
def   _lowerCAmelCase(						__A ,							__A = 31 ,							__A = 64 ,							**__A ,							):
  UpperCAmelCase          =	torch.linspace(0 ,							__A ,							steps=(no_bins - 1) ,							device=logits.device      )

  UpperCAmelCase          =	torch.nn.functional.softmax(__A ,							dim=-1      )
  UpperCAmelCase   ,						UpperCAmelCase          =	_calculate_expected_aligned_error(
      alignment_confidence_breaks=__A ,							aligned_distance_error_probs=__A ,							)

  return {
      "aligned_confidence_probs": aligned_confidence_probs,
      "predicted_aligned_error": predicted_aligned_error,
      "max_predicted_aligned_error": max_predicted_aligned_error,
  }





def   _lowerCAmelCase(						__A ,							__A = None ,							__A = 31 ,							__A = 64 ,							__A = 1E-8 ,							**__A ,							):
  if residue_weights is None:
    UpperCAmelCase          =	logits.new_ones(logits.shape[-2]      )

  UpperCAmelCase          =	torch.linspace(0 ,							__A ,							steps=(no_bins - 1) ,							device=logits.device      )

  UpperCAmelCase          =	_calculate_bin_centers(__A      )
  torch.sum(__A      )
  UpperCAmelCase          =	logits.shape[-2]
  UpperCAmelCase          =	max(__A ,							19      )

  UpperCAmelCase          =	1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

  UpperCAmelCase          =	torch.nn.functional.softmax(__A ,							dim=-1      )

  UpperCAmelCase          =	1.0 / (1 + (bin_centers**2) / (da**2))
  UpperCAmelCase          =	torch.sum(probs * tm_per_bin ,							dim=-1      )

  UpperCAmelCase          =	residue_weights / (eps + residue_weights.sum())
  UpperCAmelCase          =	torch.sum(predicted_tm_term * normed_residue_mask ,							dim=-1      )

  UpperCAmelCase          =	per_alignment * residue_weights

  UpperCAmelCase          =	(weighted == torch.max(__A      )).nonzero()[0]
  return per_alignment[tuple(__A      )]



