



"""simple docstring"""

import torch

from transformers import AutoModel







class    __lowercase     (					torch.nn.Module):







						"""simple docstring"""







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

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







						def 						__UpperCamelCase							(self   ,		**lowercase__       ):
											return self.bert(**lowercase__       ).last_hidden_state







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											return token_embeddings.sum(2   ,		keepdim=lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__=1       ):
											return self.softmax(T * self.cos(lowercase__   ,		lowercase__       )       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		Tuple 					=					W_supports["""sizes"""].tolist()
											snake_case_					:		Optional[Any] 					=					W_supports["""start_token_id"""].item()
											snake_case_					:		Dict 					=					W_supports["""end_token_id"""].item()

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

											snake_case_					:		int 					=					self.BERT(**lowercase__       )
											snake_case_					:		Union[str, Any] 					=					self.BERT(**lowercase__       )

											snake_case_					:		Union[str, Any] 					=					None
											snake_case_					:		List[Any] 					=					None

											snake_case_					:		Optional[int] 					=					W_supports["""input_ids"""] == start_token_id
											snake_case_					:		List[Any] 					=					W_supports["""input_ids"""] == end_token_id

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

																snake_case_					:		Dict 					=					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_					:		List[str] 					=					torch.matmul(q[i]   ,		s_start.T       ).sum(1       ).softmax(0       )
																snake_case_					:		str 					=					torch.matmul(q[i]   ,		s_end.T       ).sum(1       ).softmax(0       )

																if p_starts is not None:
																					snake_case_					:		Union[str, Any] 					=					torch.vstack((p_starts, p_start)       )
																					snake_case_					:		str 					=					torch.vstack((p_ends, p_end)       )
																else:
																					snake_case_					:		str 					=					p_start
																					snake_case_					:		List[str] 					=					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, Features, Value
from .base import TaskTemplate







@dataclass(frozen=_UpperCAmelCase)
class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							str							       =		field(default="""automatic-speech-recognition"""					,    metadata={"""include_in_asdict_even_if_is_default""": True})
						_A		:							ClassVar[Features]							       =		Features({"""audio""": Audio()})
						_A		:							ClassVar[Features]							       =		Features({"""transcription""": Value("""string""")})
						_A		:							str							       =		"audio"
						_A		:							str							       =		"transcription"







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											if self.audio_column not in features:
																raise ValueError(f'Column {self.audio_column} is not present in features.'       )
											if not isinstance(features[self.audio_column]   ,		lowercase__       ):
																raise ValueError(f'Column {self.audio_column} is not an Audio type.'       )
											snake_case_					:		Optional[int] 					=					copy.deepcopy(self       )
											snake_case_					:		Tuple 					=					self.input_schema.copy()
											snake_case_					:		List[str] 					=					features[self.audio_column]
											snake_case_					:		Any 					=					input_schema
											return task_template







						@property
						def 						__UpperCamelCase							(self       ):
											return {self.audio_column: "audio", self.transcription_column: "transcription"}




