



import torch

from transformers import AutoModel


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






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






								"""simple docstring"""


								super(_snake_case				,					self					).__init__()

								A__       =    AutoModel.from_pretrained(_snake_case				,					return_dict=_snake_case					)
								A__       =    torch.nn.CosineSimilarity(3				,					1E-08					)
								A__       =    torch.nn.Softmax(dim=1					)



					def     _a							(     self					:       Any				,					**_snake_case					:       Dict					):






								"""simple docstring"""


								return self.bert(**_snake_case					).last_hidden_state



					def     _a							(     self					:       Optional[int]				,					_snake_case					:       int					):






								"""simple docstring"""


								return token_embeddings.sum(2				,					keepdim=_snake_case					)



					def     _a							(     self					:       str				,					_snake_case					:       Tuple				,					_snake_case					:       Tuple				,					_snake_case					:       List[Any]=1					):






								"""simple docstring"""


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



					def     _a							(     self					:       Any				,					_snake_case					:       int				,					_snake_case					:       Union[str, Any]					):






								"""simple docstring"""


								A__       =    W_supports['sizes'].tolist()
								A__       =    W_supports['start_token_id'].item()
								A__       =    W_supports['end_token_id'].item()

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

								A__       =    self.BERT(**_snake_case					)
								A__       =    self.BERT(**_snake_case					)

								A__       =    None
								A__       =    None

								A__       =    W_supports['input_ids'] == start_token_id
								A__       =    W_supports['input_ids'] == end_token_id

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

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

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

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

								return p_starts, p_ends









from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       =      {
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		List[str]			       =    "roberta"



					def __init__(     self					:       List[str]				,					_snake_case					:       Union[str, Any]=5_02_65				,					_snake_case					:       List[Any]=7_68				,					_snake_case					:       List[str]=12				,					_snake_case					:       List[str]=12				,					_snake_case					:       Any=30_72				,					_snake_case					:       Union[str, Any]="gelu"				,					_snake_case					:       int=0.1				,					_snake_case					:       Union[str, Any]=0.1				,					_snake_case					:       Tuple=5_12				,					_snake_case					:       Union[str, Any]=2				,					_snake_case					:       Any=0.02				,					_snake_case					:       Any=1E-12				,					_snake_case					:       List[Any]=1				,					_snake_case					:       int=0				,					_snake_case					:       Any=2				,					_snake_case					:       Optional[Any]="absolute"				,					_snake_case					:       int=True				,					_snake_case					:       Any=None				,					**_snake_case					:       Any				,					):






								"""simple docstring"""


								super().__init__(pad_token_id=_snake_case				,					bos_token_id=_snake_case				,					eos_token_id=_snake_case				,					**_snake_case					)

								A__       =    vocab_size
								A__       =    hidden_size
								A__       =    num_hidden_layers
								A__       =    num_attention_heads
								A__       =    hidden_act
								A__       =    intermediate_size
								A__       =    hidden_dropout_prob
								A__       =    attention_probs_dropout_prob
								A__       =    max_position_embeddings
								A__       =    type_vocab_size
								A__       =    initializer_range
								A__       =    layer_norm_eps
								A__       =    position_embedding_type
								A__       =    use_cache
								A__       =    classifier_dropout







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					@property
					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								if self.task == "multiple-choice":
											A__       =    {0: 'batch', 1: 'choice', 2: 'sequence'}
								else:
											A__       =    {0: 'batch', 1: 'sequence'}
								return OrderedDict(
								    [
								        ('input_ids', dynamic_axis),
								        ('attention_mask', dynamic_axis),
								    ]					)




