






'''simple docstring'''


import torch

from transformers import AutoModel
class    _SCREAMING_SNAKE_CASE							(		torch.nn.Module	):


			'''simple docstring'''





			def __init__(   self							:						Union[str, Any] ,      lowercase							:						Union[str, Any]="sayef/fsner-bert-base-uncased"			)							->    Dict:

									'''simple docstring'''







									super(lowercase ,      self			).__init__()

									UpperCamelCase__         = AutoModel.from_pretrained(lowercase ,      return_dict=lowercase			)
									UpperCamelCase__         = torch.nn.CosineSimilarity(3 ,      1e-08			)
									UpperCamelCase__         = torch.nn.Softmax(dim=1			)






			def 				A      (   self							:						Any ,      **lowercase							:						str			)							->    Dict:

									'''simple docstring'''







									return self.bert(**lowercase			).last_hidden_state






			def 				A      (   self							:						Union[str, Any] ,      lowercase							:						Optional[int]			)							->    Tuple:

									'''simple docstring'''







									return token_embeddings.sum(2 ,      keepdim=lowercase			)






			def 				A      (   self							:						List[Any] ,      lowercase							:						Any ,      lowercase							:						Union[str, Any] ,      lowercase							:						int=1			)							->    Union[str, Any]:

									'''simple docstring'''







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







			def 				A      (   self							:						Any ,      lowercase							:						Optional[int] ,      lowercase							:						Any			)							->    Dict:

									'''simple docstring'''







									UpperCamelCase__         = W_supports["""sizes"""].tolist()
									UpperCamelCase__         = W_supports["""start_token_id"""].item()
									UpperCamelCase__         = W_supports["""end_token_id"""].item()

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

									UpperCamelCase__         = self.BERT(**lowercase			)
									UpperCamelCase__         = self.BERT(**lowercase			)

									UpperCamelCase__         = None
									UpperCamelCase__         = None

									UpperCamelCase__         = W_supports["""input_ids"""] == start_token_id
									UpperCamelCase__         = W_supports["""input_ids"""] == end_token_id

									for i, size in enumerate(lowercase			):
															if i == 0:
																					UpperCamelCase__         = 0
															else:
																					UpperCamelCase__         = support_sizes[i - 1]

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

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

															if p_starts is not None:
																					UpperCamelCase__         = torch.vstack((p_starts, p_start)			)
																					UpperCamelCase__         = torch.vstack((p_ends, p_end)			)
															else:
																					UpperCamelCase__         = p_start
																					UpperCamelCase__         = p_end

									return p_starts, p_ends








'''simple docstring'''


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

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE	)
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							str					     =					field(default="audio-classification"       ,metadata={"include_in_asdict_even_if_is_default": True}	)
			__a							:							ClassVar[Features]					     =					Features({"audio": Audio()}	)
			__a							:							ClassVar[Features]					     =					Features({"labels": ClassLabel}	)
			__a							:							str					     =					"audio"
			__a							:							str					     =					"labels"






			def 				A      (   self							:						List[Any] ,      lowercase							:						List[Any]			)							->    Any:

									'''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] ,      lowercase			):
															raise ValueError(f"Column {self.label_column} is not a ClassLabel."			)
									UpperCamelCase__         = copy.deepcopy(self			)
									UpperCamelCase__         = self.label_schema.copy()
									UpperCamelCase__         = features[self.label_column]
									UpperCamelCase__         = label_schema
									return task_template







			@property
			def 				A      (   self							:						int			)							->    Dict[str, str]:

									'''simple docstring'''







									return {
									    self.audio_column: "audio",
									    self.label_column: "labels",
									}
