



'''simple docstring'''





import torch

from transformers import AutoModel







class     UpperCAmelCase_       (       torch.nn.Module	):
    """simple docstring"""







    def __init__(    self					:     Optional[Any] ,     snake_case_					:     Optional[Any]="sayef/fsner-bert-base-uncased"						):
          super(snake_case_ ,     self						).__init__()

          snake_case__  :    Any    						=      AutoModel.from_pretrained(snake_case_ ,     return_dict=snake_case_						)
          snake_case__  :    List[Any]    						=      torch.nn.CosineSimilarity(3 ,     1E-0_8						)
          snake_case__  :    Optional[Any]    						=      torch.nn.Softmax(dim=1						)




    def lowerCamelCase       (    self					:     Optional[int] ,     **snake_case_					:     str						):
          return self.bert(**snake_case_						).last_hidden_state




    def lowerCamelCase       (    self					:     Any ,     snake_case_					:     List[Any]						):
          return token_embeddings.sum(2 ,     keepdim=snake_case_						)




    def lowerCamelCase       (    self					:     Optional[Any] ,     snake_case_					:     int ,     snake_case_					:     Union[str, Any] ,     snake_case_					:     Union[str, Any]=1						):
          return self.softmax(T * self.cos(snake_case_ ,     snake_case_						)						)




    def lowerCamelCase       (    self					:     Any ,     snake_case_					:     Optional[Any] ,     snake_case_					:     int						):
          snake_case__  :    List[Any]    						=      W_supports["""sizes"""].tolist()
          snake_case__  :    Any    						=      W_supports["""start_token_id"""].item()
          snake_case__  :    Union[str, Any]    						=      W_supports["""end_token_id"""].item()

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

          snake_case__  :    Union[str, Any]    						=      self.BERT(**snake_case_						)
          snake_case__  :    Optional[Any]    						=      self.BERT(**snake_case_						)

          snake_case__  :    Dict    						=      None
          snake_case__  :    List[str]    						=      None

          snake_case__  :    List[str]    						=      W_supports["""input_ids"""] == start_token_id
          snake_case__  :    Dict    						=      W_supports["""input_ids"""] == end_token_id

          for i, size in enumerate(snake_case_						):
                if i == 0:
                      snake_case__  :    Optional[Any]    						=      0
                else:
                      snake_case__  :    Any    						=      support_sizes[i - 1]

                snake_case__  :    List[Any]    						=      S[s : s + size][start_token_masks[s : s + size]]
                snake_case__  :    Tuple    						=      S[s : s + size][end_token_masks[s : s + size]]

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

                if p_starts is not None:
                      snake_case__  :    Optional[int]    						=      torch.vstack((p_starts, p_start)						)
                      snake_case__  :    List[str]    						=      torch.vstack((p_ends, p_end)						)
                else:
                      snake_case__  :    List[str]    						=      p_start
                      snake_case__  :    Tuple    						=      p_end

          return p_starts, p_ends





'''simple docstring'''





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


__a          =		logging.getLogger(__name__)







@dataclass
@add_start_docstrings(TrainingArguments.__doc__	)
class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		field(default=_a		,		metadata={"help": "Whether to use SortishSampler or not."}	)
    lowercase					=		field(
        default=_a		,		metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}	)
    lowercase					=		field(
        default=_a		,		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."
            )
        }		,		)
    lowercase					=		field(
        default=_a		,		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."
            )
        }		,		)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
        }		,		)




    def lowerCamelCase       (    self					:     List[str]						):
          snake_case__  :    int    						=      super().to_dict()
          for k, v in d.items():
                if isinstance(snake_case_ ,     snake_case_						):
                      snake_case__  :    Optional[int]    						=      v.to_dict()
          return d
