


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate



@dataclass(frozen=_a)
class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     str							=					field(default="image-classification",     metadata={"include_in_asdict_even_if_is_default": True})
       __UpperCamelCase      :     ClassVar[Features]							=					Features({"image": Image()})
       __UpperCamelCase      :     ClassVar[Features]							=					Features({"labels": ClassLabel})
       __UpperCamelCase      :     str							=					"image"
       __UpperCamelCase      :     str							=					"labels"






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              if self.label_column not in features:
                     raise ValueError(f"""Column {self.label_column} is not present in features."""						)
              if not isinstance(features[self.label_column] ,					__SCREAMING_SNAKE_CASE						):
                     raise ValueError(f"""Column {self.label_column} is not a ClassLabel."""						)
              UpperCamelCase    :	str   	=		copy.deepcopy(self						)
              UpperCamelCase    :	Optional[Any]   	=		self.label_schema.copy()
              UpperCamelCase    :	Union[str, Any]   	=		features[self.label_column]
              UpperCamelCase    :	Union[str, Any]   	=		label_schema
              return task_template






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return {
                  self.image_column: "image",
                  self.label_column: "labels",
              }





import torch

from transformers import AutoModel



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






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




              """simple docstring"""




              super(__SCREAMING_SNAKE_CASE ,					self						).__init__()

              UpperCamelCase    :	List[str]   	=		AutoModel.from_pretrained(__SCREAMING_SNAKE_CASE ,					return_dict=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[int]   	=		torch.nn.CosineSimilarity(3 ,					1e-08						)
              UpperCamelCase    :	List[Any]   	=		torch.nn.Softmax(dim=1						)






       def  _lowercase   (     self ,					**__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              return self.bert(**__SCREAMING_SNAKE_CASE						).last_hidden_state






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              return token_embeddings.sum(2 ,					keepdim=__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=1						):




              """simple docstring"""




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






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		W_supports['''sizes'''].tolist()
              UpperCamelCase    :	Optional[int]   	=		W_supports['''start_token_id'''].item()
              UpperCamelCase    :	Any   	=		W_supports['''end_token_id'''].item()

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

              UpperCamelCase    :	Union[str, Any]   	=		self.BERT(**__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[str]   	=		self.BERT(**__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	List[Any]   	=		None
              UpperCamelCase    :	Any   	=		None

              UpperCamelCase    :	Optional[Any]   	=		W_supports['''input_ids'''] == start_token_id
              UpperCamelCase    :	Any   	=		W_supports['''input_ids'''] == end_token_id

              for i, size in enumerate(__SCREAMING_SNAKE_CASE						):
                     if i == 0:
                            UpperCamelCase    :	Optional[int]   	=		0
                     else:
                            UpperCamelCase    :	Tuple   	=		support_sizes[i - 1]

                     UpperCamelCase    :	Tuple   	=		S[s : s + size][start_token_masks[s : s + size]]
                     UpperCamelCase    :	List[str]   	=		S[s : s + size][end_token_masks[s : s + size]]

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

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

              return p_starts, p_ends

