
"""simple docstring"""
import torch

from transformers import AutoTokenizer




class    _snake_case  (						_lowercase       ):






							def __init__(					self						:			Any						, snake_case__						:			List[Any]    ):






												"""simple docstring"""



												__a:						Tuple		    =					AutoTokenizer.from_pretrained(snake_case__    )






							def 							__lowercase  (					self						:			List[str]						, snake_case__						:			List[str]    ):






												"""simple docstring"""



												if isinstance(snake_case__						, snake_case__    ) and all(isinstance(_x						, snake_case__    ) for _x in x    ):
																	__a:						Union[str, Any]		    =					None
																	for l in x:
																						__a:						Tuple		    =					self.tokenizer(
																						    snake_case__						, padding="""max_length"""						, max_length=3_8_4						, truncation=snake_case__						, return_tensors="""pt"""						, )
																						__a:						Optional[Any]		    =					torch.tensor([len(snake_case__    )]    )
																						if d is not None:
																											for k in d.keys():
																																__a:						List[Any]		    =					torch.cat((d[k], t[k])						, 0    )
																						else:
																											__a:						Union[str, Any]		    =					t

																	__a:						Optional[int]		    =					torch.tensor(self.tokenizer.convert_tokens_to_ids("""[E]"""    )    )
																	__a:						Optional[Any]		    =					torch.tensor(self.tokenizer.convert_tokens_to_ids("""[/E]"""    )    )

												elif isinstance(snake_case__						, snake_case__    ) and all(isinstance(_x						, snake_case__    ) for _x in x    ):
																	__a:						int		    =					self.tokenizer(
																	    snake_case__						, padding="""max_length"""						, max_length=3_8_4						, truncation=snake_case__						, return_tensors="""pt"""						, )

												else:
																	raise Exception(
																	    """Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"""
																	    """ strings` for supports are supported."""    )

												return d






							def 							__lowercase  (					self						:			Tuple						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			List[Any]						, snake_case__						:			Tuple						, snake_case__						:			List[str]=0.70    ):






												"""simple docstring"""



												__a:						List[Any]		    =					[]
												for idx in range(len(W_query["""input_ids"""]    )    ):
																	__a:						str		    =					range(p_start.shape[1]    )

																	__a:						Any		    =					[]
																	for start_id in start_indexes:
																						for end_id in end_indexes:
																											if start_id < end_id:
																																output.append(
																																    (
																																        start_id,
																																        end_id,
																																        p_start[idx][start_id].item(),
																																        p_end[idx][end_id].item(),
																																    )    )

																	output.sort(key=lambda snake_case__    : (tup[2] * tup[3])						, reverse=snake_case__    )
																	__a:						Union[str, Any]		    =					[]
																	for k in range(len(snake_case__    )    ):
																						if output[k][2] * output[k][3] >= thresh:
																											__a    ,				__a:						str		    =					output[k][0], output[k][1]
																											__a:						Optional[Any]		    =					self.tokenizer.decode(W_query["""input_ids"""][idx][c_start_pos:c_end_pos]    )
																											temp.append((decoded, output[k][2] * output[k][3])    )

																	final_outputs.append(snake_case__    )

												return final_outputs


"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class    _snake_case  (						_lowercase ,				_lowercase ,				_lowercase       ):






							@register_to_config
							def __init__(					self						:			str						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			float						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			str						, snake_case__						:			bool = False						, ):






												"""simple docstring"""



												super().__init__()

												__a:						Optional[Any]		    =					nn.Linear(snake_case__						, snake_case__						, bias=snake_case__    )

												__a:						Optional[Any]		    =					nn.Embedding(snake_case__						, snake_case__    )
												__a:						Any		    =					False

												__a:						Any		    =					nn.Dropout(p=snake_case__    )

												__a:						List[str]		    =					TaConfig(
												    d_model=snake_case__						, num_heads=snake_case__						, d_kv=snake_case__						, d_ff=snake_case__						, feed_forward_proj=snake_case__						, dropout_rate=snake_case__						, is_decoder=snake_case__						, is_encoder_decoder=snake_case__						, )
												__a:						Optional[int]		    =					nn.ModuleList()
												for lyr_num in range(snake_case__    ):
																	__a:						Union[str, Any]		    =					TaBlock(snake_case__    )
																	self.encoders.append(snake_case__    )

												__a:						List[str]		    =					TaLayerNorm(snake_case__    )
												__a:						Optional[int]		    =					nn.Dropout(p=snake_case__    )






							def 							__lowercase  (					self						:			str						, snake_case__						:			Optional[Any]						, snake_case__						:			int    ):






												"""simple docstring"""



												__a:						str		    =					self.input_proj(snake_case__    )

												# terminal relative positional encodings
												__a:						Optional[int]		    =					encoder_inputs.shape[1]
												__a:						List[Any]		    =					torch.arange(snake_case__						, device=encoder_inputs.device    )

												__a:						Union[str, Any]		    =					encoder_inputs_mask.sum(-1    )
												__a:						str		    =					torch.roll(input_positions.unsqueeze(0    )						, tuple(seq_lens.tolist()    )						, dims=0    )
												x += self.position_encoding(snake_case__    )

												__a:						Any		    =					self.dropout_pre(snake_case__    )

												# inverted the attention mask
												__a:						Any		    =					encoder_inputs.size()
												__a:						str		    =					self.get_extended_attention_mask(snake_case__						, snake_case__    )

												for lyr in self.encoders:
																	__a:						Any		    =					lyr(snake_case__						, snake_case__    )[0]
												__a:						int		    =					self.layer_norm(snake_case__    )

												return self.dropout_post(snake_case__    ), encoder_inputs_mask
