





import torch

from transformers import AutoModel






class 					lowercase__ (			torch.nn.Module       ):

   '''simple docstring'''


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






       """simple docstring"""
       super(__magic_name__,	self							).__init__()

       UpperCamelCase__							:       int      =   AutoModel.from_pretrained(__magic_name__,	return_dict=__magic_name__							)
       UpperCamelCase__							:       List[str]      =   torch.nn.CosineSimilarity(3,	1E-08							)
       UpperCamelCase__							:       Tuple      =   torch.nn.Softmax(dim=1							)




   def    UpperCamelCase__						(					self,	**__magic_name__							)				->   List[str]:






       """simple docstring"""
       return self.bert(**__magic_name__							).last_hidden_state




   def    UpperCamelCase__						(					self,	__magic_name__							)				->   List[Any]:






       """simple docstring"""
       return token_embeddings.sum(2,	keepdim=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__=1							)				->   str:






       """simple docstring"""
       return self.softmax(T * self.cos(__magic_name__,	__magic_name__							)							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   W_supports['''sizes'''].tolist()
       UpperCamelCase__							:       Dict      =   W_supports['''start_token_id'''].item()
       UpperCamelCase__							:       List[Any]      =   W_supports['''end_token_id'''].item()

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

       UpperCamelCase__							:       List[Any]      =   self.BERT(**__magic_name__							)
       UpperCamelCase__							:       Optional[Any]      =   self.BERT(**__magic_name__							)

       UpperCamelCase__							:       Any      =   None
       UpperCamelCase__							:       Any      =   None

       UpperCamelCase__							:       str      =   W_supports['''input_ids'''] == start_token_id
       UpperCamelCase__							:       Optional[Any]      =   W_supports['''input_ids'''] == end_token_id

       for i, size in enumerate(__magic_name__							):
           if i == 0:
               UpperCamelCase__							:       Union[str, Any]      =   0
           else:
               UpperCamelCase__							:       Union[str, Any]      =   support_sizes[i - 1]

           UpperCamelCase__							:       str      =   S[s : s + size][start_token_masks[s : s + size]]
           UpperCamelCase__							:       str      =   S[s : s + size][end_token_masks[s : s + size]]

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

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

       return p_starts, p_ends








import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


UpperCAmelCase_  							=				logging.getLogger(__name__)






@dataclass
@add_start_docstrings(TrainingArguments.__doc__       )
class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							bool     	=	field(default=__lowerCamelCase    ,       metadata={"help": "Whether to use SortishSampler or not."}       )
   a		:							bool     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}       )
   a		:							Optional[int]     	=	field(
       default=__lowerCamelCase    ,       metadata={
           "help": (
               "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
               "to the `max_length` value of the model configuration."
           )
       }    ,       )
   a		:							Optional[int]     	=	field(
       default=__lowerCamelCase    ,       metadata={
           "help": (
               "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
               "to the `num_beams` value of the model configuration."
           )
       }    ,       )
   a		:							Optional[Union[str, Path, GenerationConfig]]     	=	field(
       default=__lowerCamelCase    ,       metadata={
           "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
       }    ,       )




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   super().to_dict()
       for k, v in d.items():
           if isinstance(__magic_name__,	__magic_name__							):
               UpperCamelCase__							:       Tuple      =   v.to_dict()
       return d

