'''simple docstring'''

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_bart import BartTokenizer


UpperCamelCase__     :	str				=			logging.get_logger(__name__)


UpperCamelCase__     :	Dict				=			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all BART models at https://huggingface.co/models?filter=bart
UpperCamelCase__     :	int				=			{
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

UpperCamelCase__     :	Dict				=			{
    'facebook/bart-base': 1_024,
    'facebook/bart-large': 1_024,
    'facebook/bart-large-mnli': 1_024,
    'facebook/bart-large-cnn': 1_024,
    'facebook/bart-large-xsum': 1_024,
    'yjernite/bart_eli5': 1_024,
}


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Tuple      							=   VOCAB_FILES_NAMES
      UpperCAmelCase_      :      int      							=   PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_      :      Dict      							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase_      :      str      							=   ['''input_ids''', '''attention_mask''']
      UpperCAmelCase_      :      Dict      							=   BartTokenizer




      def __init__(				self					,lowerCamelCase_=None					,lowerCamelCase_=None					,lowerCamelCase_=None					,lowerCamelCase_="replace"					,lowerCamelCase_="<s>"					,lowerCamelCase_="</s>"					,lowerCamelCase_="</s>"					,lowerCamelCase_="<s>"					,lowerCamelCase_="<unk>"					,lowerCamelCase_="<pad>"					,lowerCamelCase_="<mask>"					,lowerCamelCase_=False					,lowerCamelCase_=True					,**lowerCamelCase_					,)			->			str:
         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__							:				Any   				=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
         if pre_tok_state.get('''add_prefix_space'''					,lowerCamelCase_							) != add_prefix_space:
            UpperCAmelCase__							:				List[Any]   				=  getattr(lowerCamelCase_					,pre_tok_state.pop('''type'''							)							)
            UpperCAmelCase__							:				List[Any]   				=  add_prefix_space
            UpperCAmelCase__							:				int   				=  pre_tok_class(**lowerCamelCase_							)

         UpperCAmelCase__							:				List[str]   				=  add_prefix_space

         # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
         UpperCAmelCase__							:				Any   				=  '''post_processor'''
         UpperCAmelCase__							:				List[Any]   				=  getattr(self.backend_tokenizer					,lowerCamelCase_					,lowerCamelCase_							)
         if tokenizer_component_instance:
            UpperCAmelCase__							:				Tuple   				=  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__							:				Optional[int]   				=  tuple(state['''sep''']							)
            if "cls" in state:
               UpperCAmelCase__							:				List[str]   				=  tuple(state['''cls''']							)

            UpperCAmelCase__							:				str   				=  False

            if state.get('''add_prefix_space'''					,lowerCamelCase_							) != add_prefix_space:
               UpperCAmelCase__							:				Dict   				=  add_prefix_space
               UpperCAmelCase__							:				str   				=  True

            if state.get('''trim_offsets'''					,lowerCamelCase_							) != trim_offsets:
               UpperCAmelCase__							:				Tuple   				=  trim_offsets
               UpperCAmelCase__							:				Any   				=  True

            if changes_to_apply:
               UpperCAmelCase__							:				Optional[Any]   				=  getattr(lowerCamelCase_					,state.pop('''type'''							)							)
               UpperCAmelCase__							:				List[str]   				=  component_class(**lowerCamelCase_							)
               setattr(self.backend_tokenizer					,lowerCamelCase_					,lowerCamelCase_							)




      @property
      def 		lowerCAmelCase__					(				self							)			->			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 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			List[str]:
         UpperCAmelCase__							:				Tuple   				=  AddedToken(lowerCamelCase_					,lstrip=lowerCamelCase_					,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_					,lowerCamelCase_							) else value
         UpperCAmelCase__							:				List[Any]   				=  value




      def 		lowerCAmelCase__					(				self					,*lowerCamelCase_					,**lowerCamelCase_							)			->			BatchEncoding:
         UpperCAmelCase__							:				List[Any]   				=  kwargs.get('''is_split_into_words'''					,lowerCamelCase_							)

         if is_split_into_words and not self.add_prefix_space:
            raise ValueError(
                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 		lowerCAmelCase__					(				self					,*lowerCamelCase_					,**lowerCamelCase_							)			->			BatchEncoding:
         UpperCAmelCase__							:				str   				=  kwargs.get('''is_split_into_words'''					,lowerCamelCase_							)

         if is_split_into_words and not self.add_prefix_space:
            raise ValueError(
                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 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			Tuple[str]:
         UpperCAmelCase__							:				str   				=  self._tokenizer.model.save(lowerCamelCase_					,name=lowerCamelCase_							)
         return tuple(lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_=None							)			->			Any:
         UpperCAmelCase__							:				List[str]   				=  [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 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			List[int]:
         UpperCAmelCase__							:				str   				=  [self.sep_token_id]
         UpperCAmelCase__							:				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]





'''simple docstring'''

import torch

from transformers import AutoModel


class 				_lowercase	(      torch.nn.Module   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_="sayef/fsner-bert-base-uncased"							)			->			Dict:



         '''simple docstring'''



         super(lowerCamelCase_					,self							).__init__()

         UpperCAmelCase__							:				Union[str, Any]   				=  AutoModel.from_pretrained(lowerCamelCase_					,return_dict=lowerCamelCase_							)
         UpperCAmelCase__							:				str   				=  torch.nn.CosineSimilarity(3					,1e-08							)
         UpperCAmelCase__							:				int   				=  torch.nn.Softmax(dim=1							)




      def 		lowerCAmelCase__					(				self					,**lowerCamelCase_							)			->			Optional[Any]:



         '''simple docstring'''



         return self.bert(**lowerCamelCase_							).last_hidden_state




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



         return token_embeddings.sum(2					,keepdim=lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_=1							)			->			int:



         '''simple docstring'''



         return self.softmax(T * self.cos(lowerCamelCase_					,lowerCamelCase_							)							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  W_supports['''sizes'''].tolist()
         UpperCAmelCase__							:				Union[str, Any]   				=  W_supports['''start_token_id'''].item()
         UpperCAmelCase__							:				int   				=  W_supports['''end_token_id'''].item()

         del W_supports["sizes"]
         del W_supports["start_token_id"]
         del W_supports["end_token_id"]

         UpperCAmelCase__							:				Any   				=  self.BERT(**lowerCamelCase_							)
         UpperCAmelCase__							:				Any   				=  self.BERT(**lowerCamelCase_							)

         UpperCAmelCase__							:				Any   				=  None
         UpperCAmelCase__							:				Tuple   				=  None

         UpperCAmelCase__							:				Dict   				=  W_supports['''input_ids'''] == start_token_id
         UpperCAmelCase__							:				Any   				=  W_supports['''input_ids'''] == end_token_id

         for i, size in enumerate(lowerCamelCase_							):
            if i == 0:
               UpperCAmelCase__							:				int   				=  0
            else:
               UpperCAmelCase__							:				str   				=  support_sizes[i - 1]

            UpperCAmelCase__							:				Dict   				=  S[s : s + size][start_token_masks[s : s + size]]
            UpperCAmelCase__							:				Union[str, Any]   				=  S[s : s + size][end_token_masks[s : s + size]]

            UpperCAmelCase__							:				int   				=  torch.matmul(q[i]					,s_start.T							).sum(1							).softmax(0							)
            UpperCAmelCase__							:				int   				=  torch.matmul(q[i]					,s_end.T							).sum(1							).softmax(0							)

            if p_starts is not None:
               UpperCAmelCase__							:				str   				=  torch.vstack((p_starts, p_start)							)
               UpperCAmelCase__							:				Optional[int]   				=  torch.vstack((p_ends, p_end)							)
            else:
               UpperCAmelCase__							:				Union[str, Any]   				=  p_start
               UpperCAmelCase__							:				int   				=  p_end

         return p_starts, p_ends




