


import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case            =   logging.get_logger(__name__)

__snake_case            =   {
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= """mvp"""
			lowercase						= ["""past_key_values"""]
			lowercase						= {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




			def __init__(						self							:		str       ,       __magic_name__							:		Any=5_0_2_6_7       ,       __magic_name__							:		Optional[int]=1_0_2_4       ,       __magic_name__							:		Dict=1_2       ,       __magic_name__							:		Union[str, Any]=4_0_9_6       ,       __magic_name__							:		Union[str, Any]=1_6       ,       __magic_name__							:		Any=1_2       ,       __magic_name__							:		List[str]=4_0_9_6       ,       __magic_name__							:		Tuple=1_6       ,       __magic_name__							:		Dict=0.0       ,       __magic_name__							:		Optional[int]=0.0       ,       __magic_name__							:		List[Any]="gelu"       ,       __magic_name__							:		str=1_0_2_4       ,       __magic_name__							:		Optional[int]=0.1       ,       __magic_name__							:		List[Any]=0.0       ,       __magic_name__							:		Optional[int]=0.0       ,       __magic_name__							:		Dict=0.02       ,       __magic_name__							:		Union[str, Any]=0.0       ,       __magic_name__							:		Union[str, Any]=False       ,       __magic_name__							:		List[str]=True       ,       __magic_name__							:		str=1       ,       __magic_name__							:		Tuple=0       ,       __magic_name__							:		Dict=2       ,       __magic_name__							:		int=True       ,       __magic_name__							:		Optional[Any]=2       ,       __magic_name__							:		List[str]=2       ,       __magic_name__							:		Dict=False       ,       __magic_name__							:		Dict=1_0_0       ,       __magic_name__							:		Dict=8_0_0       ,       **__magic_name__							:		Tuple       ,       ):







						"""simple docstring"""







						UpperCamelCase         =					vocab_size
						UpperCamelCase         =					max_position_embeddings
						UpperCamelCase         =					d_model
						UpperCamelCase         =					encoder_ffn_dim
						UpperCamelCase         =					encoder_layers
						UpperCamelCase         =					encoder_attention_heads
						UpperCamelCase         =					decoder_ffn_dim
						UpperCamelCase         =					decoder_layers
						UpperCamelCase         =					decoder_attention_heads
						UpperCamelCase         =					dropout
						UpperCamelCase         =					attention_dropout
						UpperCamelCase         =					activation_dropout
						UpperCamelCase         =					activation_function
						UpperCamelCase         =					init_std
						UpperCamelCase         =					encoder_layerdrop
						UpperCamelCase         =					decoder_layerdrop
						UpperCamelCase         =					classifier_dropout
						UpperCamelCase         =					use_cache
						UpperCamelCase         =					encoder_layers
						UpperCamelCase         =					scale_embedding  # scale factor will be sqrt(d_model) if True
						UpperCamelCase         =					use_prompt
						UpperCamelCase         =					prompt_length
						UpperCamelCase         =					prompt_mid_dim

						super().__init__(
						    pad_token_id=__magic_name__       ,       bos_token_id=__magic_name__       ,       eos_token_id=__magic_name__       ,       is_encoder_decoder=__magic_name__       ,       decoder_start_token_id=__magic_name__       ,       forced_eos_token_id=__magic_name__       ,       **__magic_name__       ,       )

						if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""       ,       __magic_name__						):
									UpperCamelCase         =					self.bos_token_id
									warnings.warn(
									    F'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
									    """The config can simply be saved and uploaded again to be fixed."""						)








import torch

from transformers import AutoModel




class    UpperCAmelCase					(       torch.nn.Module    ):




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







						"""simple docstring"""







						super(__magic_name__       ,       self						).__init__()

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




			def   lowerCamelCase_					(						self							:		Optional[int]       ,       **__magic_name__							:		List[Any]						):







						"""simple docstring"""







						return self.bert(**__magic_name__						).last_hidden_state




			def   lowerCamelCase_					(						self							:		Tuple       ,       __magic_name__							:		int						):







						"""simple docstring"""







						return token_embeddings.sum(2       ,       keepdim=__magic_name__						)




			def   lowerCamelCase_					(						self							:		Optional[Any]       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		Union[str, Any]       ,       __magic_name__							:		Optional[Any]=1						):







						"""simple docstring"""







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





			def   lowerCamelCase_					(						self							:		Union[str, Any]       ,       __magic_name__							:		List[Any]       ,       __magic_name__							:		List[str]						):







						"""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(**__magic_name__						)
						UpperCamelCase         =					self.BERT(**__magic_name__						)

						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(__magic_name__						):
									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




