



"""simple docstring"""

import argparse
import json

from tqdm import tqdm
def  __snake_case							(	)     ->       Any:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							str							=     argparse.ArgumentParser()

				# Required parameters
				parser.add_argument(
				    '--src_path'							,		type=__A							,		default='biencoder-nq-dev.json'							,		help='Path to raw DPR training data'							,		)
				parser.add_argument(
				    '--evaluation_set'							,		type=__A							,		help='where to store parsed evaluation_set file'							,		)
				parser.add_argument(
				    '--gold_data_path'							,		type=__A							,		help='where to store parsed gold_data_path file'							,		)
				SCREAMING_SNAKE_CASE						:							Tuple							=     parser.parse_args()

				with open(args.src_path							,		'r'     ) as src_file, open(args.evaluation_set							,		'w'     ) as eval_file, open(
				    args.gold_data_path							,		'w'     ) as gold_file:
								SCREAMING_SNAKE_CASE						:							Tuple							=     json.load(__A     )
								for dpr_record in tqdm(__A     ):
												SCREAMING_SNAKE_CASE						:							Tuple							=     dpr_record['question']
												SCREAMING_SNAKE_CASE						:							Optional[int]							=     [context['title'] for context in dpr_record['positive_ctxs']]
												eval_file.write(question + '\n'     )
												gold_file.write('\t'.join(__A     ) + '\n'     )


if __name__ == "__main__":
							main()










"""simple docstring"""

from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class     lowerCAmelCase__						(	_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Dict  						= [r'''h\.\d+\.attn\.bias''', r'''h\.\d+\.attn\.masked_bias''']





	@register_to_config
	def __init__(      self				:       Any     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       Optional[int] = None     ,       _SCREAMING_SNAKE_CASE				:       int = 50_257     ,       _SCREAMING_SNAKE_CASE				:       int = 1_024     ,       _SCREAMING_SNAKE_CASE				:       int = 768     ,       _SCREAMING_SNAKE_CASE				:       int = 12     ,       _SCREAMING_SNAKE_CASE				:       int = 12     ,       _SCREAMING_SNAKE_CASE				:       Optional[int] = None     ,       _SCREAMING_SNAKE_CASE				:       str = "gelu_new"     ,       _SCREAMING_SNAKE_CASE				:       float = 0.1     ,       _SCREAMING_SNAKE_CASE				:       float = 0.1     ,       _SCREAMING_SNAKE_CASE				:       float = 0.1     ,       _SCREAMING_SNAKE_CASE				:       float = 1E-5     ,       _SCREAMING_SNAKE_CASE				:       float = 0.0_2     ,       _SCREAMING_SNAKE_CASE				:       bool = True     ,       _SCREAMING_SNAKE_CASE				:       bool = True     ,       _SCREAMING_SNAKE_CASE				:       bool = False     ,       _SCREAMING_SNAKE_CASE				:       bool = False     ,       )						->	Tuple:





					"""simple docstring"""


					super().__init__()

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     prefix_length

					if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
									raise ValueError(
									    f"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
									    f""" `n_embd`: {n_embd} are not equal."""    )

					SCREAMING_SNAKE_CASE						:							int							=     prefix_inner_dim
					SCREAMING_SNAKE_CASE						:							Dict							=     prefix_hidden_dim

					SCREAMING_SNAKE_CASE						:							int							=     (
					    nn.Linear(self.prefix_inner_dim     ,       self.prefix_hidden_dim    )
					    if self.prefix_hidden_dim is not None
					    else nn.Identity()
					)
					SCREAMING_SNAKE_CASE						:							List[Any]							=     (
					    nn.Linear(self.prefix_hidden_dim     ,       _SCREAMING_SNAKE_CASE    ) if self.prefix_hidden_dim is not None else nn.Identity()
					)

					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     GPTaConfig(
					    vocab_size=_SCREAMING_SNAKE_CASE     ,       n_positions=_SCREAMING_SNAKE_CASE     ,       n_embd=_SCREAMING_SNAKE_CASE     ,       n_layer=_SCREAMING_SNAKE_CASE     ,       n_head=_SCREAMING_SNAKE_CASE     ,       n_inner=_SCREAMING_SNAKE_CASE     ,       activation_function=_SCREAMING_SNAKE_CASE     ,       resid_pdrop=_SCREAMING_SNAKE_CASE     ,       embd_pdrop=_SCREAMING_SNAKE_CASE     ,       attn_pdrop=_SCREAMING_SNAKE_CASE     ,       layer_norm_epsilon=_SCREAMING_SNAKE_CASE     ,       initializer_range=_SCREAMING_SNAKE_CASE     ,       scale_attn_weights=_SCREAMING_SNAKE_CASE     ,       use_cache=_SCREAMING_SNAKE_CASE     ,       scale_attn_by_inverse_layer_idx=_SCREAMING_SNAKE_CASE     ,       reorder_and_upcast_attn=_SCREAMING_SNAKE_CASE     ,       )
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     GPTaLMHeadModel(_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Any     ,       _SCREAMING_SNAKE_CASE				:       torch.Tensor     ,       _SCREAMING_SNAKE_CASE				:       torch.Tensor     ,       _SCREAMING_SNAKE_CASE				:       Optional[torch.Tensor] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[torch.Tensor] = None     ,       )						->	Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     self.transformer.transformer.wte(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							str							=     self.encode_prefix(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Dict							=     self.decode_prefix(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     torch.cat((prefix_embeds, embedding_text)     ,       dim=1    )

					if labels is not None:
									SCREAMING_SNAKE_CASE						:							Dict							=     self.get_dummy_token(input_ids.shape[0]     ,       input_ids.device    )
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     torch.cat((dummy_token, input_ids)     ,       dim=1    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     self.transformer(inputs_embeds=_SCREAMING_SNAKE_CASE     ,       labels=_SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE    )
					if self.prefix_hidden_dim is not None:
									return out, hidden
					else:
									return out





	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       torch.device    )						->	torch.Tensor:





					"""simple docstring"""


					return torch.zeros(_SCREAMING_SNAKE_CASE     ,       self.prefix_length     ,       dtype=torch.intaa     ,       device=_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]    )						->	str:





					"""simple docstring"""


					return self.encode_prefix(_SCREAMING_SNAKE_CASE    )





	@torch.no_grad()
	def 			_lowerCAmelCase     (      self				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       List[str]    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     torch.split(_SCREAMING_SNAKE_CASE     ,       1     ,       dim=0    )
					SCREAMING_SNAKE_CASE						:							int							=     []
					SCREAMING_SNAKE_CASE						:							str							=     []
					for feature in features:
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.decode_prefix(feature.to(_SCREAMING_SNAKE_CASE    )    )  # back to the clip feature
									# Only support beam search for now
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							List[str]							=     self.generate_beam(
									    input_embeds=_SCREAMING_SNAKE_CASE     ,       device=_SCREAMING_SNAKE_CASE     ,       eos_token_id=_SCREAMING_SNAKE_CASE    )
									generated_tokens.append(output_tokens[0]    )
									generated_seq_lengths.append(seq_lengths[0]    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     torch.stack(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Dict							=     torch.stack(_SCREAMING_SNAKE_CASE    )
					return generated_tokens, generated_seq_lengths







	@torch.no_grad()
	def 			_lowerCAmelCase     (      self				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       int=None     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=None     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=None     ,       _SCREAMING_SNAKE_CASE				:       int = 5     ,       _SCREAMING_SNAKE_CASE				:       int = 67     ,       _SCREAMING_SNAKE_CASE				:       float = 1.0     ,       _SCREAMING_SNAKE_CASE				:       Optional[int] = None     ,       )						->	Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							str							=     eos_token_id
					SCREAMING_SNAKE_CASE						:							Tuple							=     None
					SCREAMING_SNAKE_CASE						:							int							=     None
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     torch.ones(_SCREAMING_SNAKE_CASE     ,       device=_SCREAMING_SNAKE_CASE     ,       dtype=torch.int    )
					SCREAMING_SNAKE_CASE						:							Dict							=     torch.zeros(_SCREAMING_SNAKE_CASE     ,       device=_SCREAMING_SNAKE_CASE     ,       dtype=torch.bool    )

					if input_embeds is not None:
									SCREAMING_SNAKE_CASE						:							List[Any]							=     input_embeds
					else:
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.transformer.transformer.wte(_SCREAMING_SNAKE_CASE    )

					for i in range(_SCREAMING_SNAKE_CASE    ):
									SCREAMING_SNAKE_CASE						:							Tuple							=     self.transformer(inputs_embeds=_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							int							=     outputs.logits
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     logits.softmax(-1    ).log()

									if scores is None:
													SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Tuple							=     logits.topk(_SCREAMING_SNAKE_CASE     ,       -1    )
													SCREAMING_SNAKE_CASE						:							Optional[Any]							=     generated.expand(_SCREAMING_SNAKE_CASE     ,       *generated.shape[1:]    )
													SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							List[str]							=     next_tokens.permute(1     ,       0    ), scores.squeeze(0    )
													if tokens is None:
																	SCREAMING_SNAKE_CASE						:							Optional[Any]							=     next_tokens
													else:
																	SCREAMING_SNAKE_CASE						:							List[Any]							=     tokens.expand(_SCREAMING_SNAKE_CASE     ,       *tokens.shape[1:]    )
																	SCREAMING_SNAKE_CASE						:							List[str]							=     torch.cat((tokens, next_tokens)     ,       dim=1    )
									else:
													SCREAMING_SNAKE_CASE						:							List[str]							=     -float(np.inf    )
													SCREAMING_SNAKE_CASE						:							int							=     0
													SCREAMING_SNAKE_CASE						:							Tuple							=     scores[:, None] + logits
													seq_lengths[~is_stopped] += 1
													SCREAMING_SNAKE_CASE						:							Any							=     scores_sum / seq_lengths[:, None]
													SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Dict							=     scores_sum_average.view(-1    ).topk(_SCREAMING_SNAKE_CASE     ,       -1    )
													SCREAMING_SNAKE_CASE						:							Optional[Any]							=     next_tokens // scores_sum.shape[1]
													SCREAMING_SNAKE_CASE						:							Dict							=     seq_lengths[next_tokens_source]
													SCREAMING_SNAKE_CASE						:							List[str]							=     next_tokens % scores_sum.shape[1]
													SCREAMING_SNAKE_CASE						:							int							=     next_tokens.unsqueeze(1    )
													SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     tokens[next_tokens_source]
													SCREAMING_SNAKE_CASE						:							List[Any]							=     torch.cat((tokens, next_tokens)     ,       dim=1    )
													SCREAMING_SNAKE_CASE						:							Any							=     generated[next_tokens_source]
													SCREAMING_SNAKE_CASE						:							Any							=     scores_sum_average * seq_lengths
													SCREAMING_SNAKE_CASE						:							Tuple							=     is_stopped[next_tokens_source]

									SCREAMING_SNAKE_CASE						:							str							=     self.transformer.transformer.wte(next_tokens.squeeze()    ).view(generated.shape[0]     ,       1     ,       -1    )
									SCREAMING_SNAKE_CASE						:							Tuple							=     torch.cat((generated, next_token_embed)     ,       dim=1    )
									SCREAMING_SNAKE_CASE						:							str							=     is_stopped + next_tokens.eq(_SCREAMING_SNAKE_CASE    ).squeeze()
									if is_stopped.all():
													break

					SCREAMING_SNAKE_CASE						:							Tuple							=     scores / seq_lengths
					SCREAMING_SNAKE_CASE						:							Tuple							=     scores.argsort(descending=_SCREAMING_SNAKE_CASE    )
					# tokens tensors are already padded to max_seq_length
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     [tokens[i] for i in order]
					SCREAMING_SNAKE_CASE						:							List[str]							=     torch.stack(_SCREAMING_SNAKE_CASE     ,       dim=0    )
					SCREAMING_SNAKE_CASE						:							int							=     torch.tensor([seq_lengths[i] for i in order]     ,       dtype=seq_lengths.dtype    )
					return output_texts, seq_lengths





