



"""simple docstring"""






import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


_lowercase					:					Optional[Any] 		=		logging.getLogger(__name__)






def 	snake_case__  (						__lowerCamelCase							:       Any      ):
			"""simple docstring"""
			random.seed(__lowerCamelCase      )
			np.random.seed(__lowerCamelCase      )
			torch.manual_seed(__lowerCamelCase      )
			torch.cuda.manual_seed_all(__lowerCamelCase      )






def 	snake_case__  (						__lowerCamelCase							:       Any				,						__lowerCamelCase							:       str				,						__lowerCamelCase							:       Tuple      ):
			"""simple docstring"""
			model.eval()
			lowerCamelCase__						:						Union[str, Any]						      =next(model.parameters()      ).device
			lowerCamelCase__						:						Union[str, Any]						      =1
			lowerCamelCase__						:						int						      =torch.zeros((eval_batch_size, context_len)				,						dtype=torch.long				,						device=__lowerCamelCase      )
			lowerCamelCase__						:						Tuple						      =DataLoader(__lowerCamelCase				,						shuffle=__lowerCamelCase				,						batch_size=__lowerCamelCase      )
			lowerCamelCase__						:						Optional[Any]						      =torch.zeros(1				,						device=__lowerCamelCase      )
			lowerCamelCase__						:						Optional[Any]						      =0
			for batch in eval_dataloader:
						batch.to(__lowerCamelCase      )
						# pad
						context.zero_()
						for i in range(__lowerCamelCase      ):
									lowerCamelCase__						:						Optional[int]						      =batch[i]
						lowerCamelCase__						:						Tuple						      =model(__lowerCamelCase				,						labels=__lowerCamelCase      )
						eval_loss += outputs[0].sum().item()
						nb_eval_examples += batch.size(0      )
			lowerCamelCase__						:						Union[str, Any]						      =eval_loss / nb_eval_examples
			lowerCamelCase__						:						Union[str, Any]						      =torch.exp(__lowerCamelCase      )
			model.train()
			return perplexity






def 	snake_case__  (						__lowerCamelCase							:       List[Any]="gpt2"      ):
			"""simple docstring"""
			lowerCamelCase__						:						Tuple						      =GPTaLMHeadModel.from_pretrained(__lowerCamelCase				,						output_hidden_states=__lowerCamelCase      )
			torch.save(model.state_dict()				,						model_name + '''local.pt'''      )
			return model






def 	snake_case__  (						__lowerCamelCase							:       Any				,						__lowerCamelCase							:       List[Any]				,						__lowerCamelCase							:       int      ):
			"""simple docstring"""
			lowerCamelCase__						:						Union[str, Any]						      =copy.deepcopy(__lowerCamelCase      )
			model.to(__lowerCamelCase      )

			lowerCamelCase__						:						List[Any]						      =['''bias''', '''LayerNorm.weight''']
			lowerCamelCase__						:						Optional[int]						      =[
			    {
			        '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay      )],
			        '''weight_decay''': 0.0,
			    },
			    {'''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay      )], '''weight_decay''': 0.0},
			]
			lowerCamelCase__						:						List[Any]						      =AdamW(__lowerCamelCase				,						lr=5e-5				,						eps=1e-8      )
			lowerCamelCase__						:						Union[str, Any]						      =get_linear_schedule_with_warmup(__lowerCamelCase				,						0				,						__lowerCamelCase      )
			torch.cuda.empty_cache()
			return model, lm_optimizer, lm_scheduler






def 	snake_case__  (						__lowerCamelCase							:       int				,						__lowerCamelCase							:       Optional[Any]				,						__lowerCamelCase							:       Any				,						__lowerCamelCase							:       Tuple      ):
			"""simple docstring"""
			lowerCamelCase__						:						List[str]						      =np.array(__lowerCamelCase      ).mean()
			lowerCamelCase__						:						Optional[int]						      =np.array(__lowerCamelCase      ).std()
			lowerCamelCase__						:						List[str]						      =(real_perps - avg) / std
			lowerCamelCase__						:						Optional[int]						      =list(zip(__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase      )      )
			joblib.dump(__lowerCamelCase				,						__lowerCamelCase      )






def 	snake_case__  (						__lowerCamelCase							:       Any				,						__lowerCamelCase							:       Any				,						__lowerCamelCase							:       Optional[int]				,						__lowerCamelCase							:       Optional[int]				,						__lowerCamelCase							:       Optional[int]				,						__lowerCamelCase							:       int				,						__lowerCamelCase							:       List[Any]				,						__lowerCamelCase							:       str="dev.jbl"				,						__lowerCamelCase							:       List[str]=recopy_gpta				,						):
			"""simple docstring"""
			lowerCamelCase__						:						List[Any]						      =[]
			lowerCamelCase__						:						Any						      =[]
			lowerCamelCase__						:						Tuple						      =[]

			# Initialize the transformer model
			lowerCamelCase__						:						List[Any]						      =copy.deepcopy(__lowerCamelCase      )
			orig_model.to(device='''cpu'''      )
			torch.cuda.empty_cache()

			# Compute perplexity of initial transformer model for comparison
			model.train()
			lowerCamelCase__      ,  lowerCamelCase__      ,  lowerCamelCase__						:						str						      =recopy_model(__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase      )

			for step in tqdm(range(__lowerCamelCase      )      ):
						lowerCamelCase__						:						Dict						      =torch.zeros((1, context_len)				,						dtype=torch.long				,						device=__lowerCamelCase      )
						lowerCamelCase__						:						List[Any]						      =random.choice(__lowerCamelCase      )
						lowerCamelCase__						:						Optional[Any]						      =random.randint(0				,						len(story[0]      ) - context_len - 1      )
						lowerCamelCase__						:						int						      =story[0][start : start + context_len]
						lm_optimizer.zero_grad()
						lowerCamelCase__						:						Dict						      =model(__lowerCamelCase				,						labels=__lowerCamelCase      )
						lowerCamelCase__						:						List[Any]						      =outputs[0]
						lowerCamelCase__						:						Dict						      =compute_perplexity(__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase      )
						model.train()
						lm_loss.backward()
						# Do LM backprop
						torch.nn.utils.clip_grad_norm_(model.parameters()				,						3.0      )
						lm_optimizer.step()
						lm_scheduler.step()  # Update learning rate schedule

						# Compute perplexity after back-propagating on the selected context
						lowerCamelCase__						:						str						      =compute_perplexity(__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase      )

						# Periodically save the stored (X, IG(X)) pairs
						if step % 1000 == 0 and step > 1:
									intermittent_save(__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase      )

						# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
						lowerCamelCase__      ,  lowerCamelCase__      ,  lowerCamelCase__						:						Union[str, Any]						      =recopy_model(__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase      )

						past_perps.append(past_perp.item()      )
						real_perps.append(orig_perp - real_perp.item()      )
						contexts.append(np.array(context.cpu()      )      )

			intermittent_save(__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase      )






def 	snake_case__  (						__lowerCamelCase							:       Any				,						__lowerCamelCase							:       Union[str, Any]="data/tokenized_stories_train_wikitext103.jbl"				,						__lowerCamelCase							:       Tuple=100				,						__lowerCamelCase							:       Union[str, Any]=1026				,						__lowerCamelCase							:       Optional[Any]=True      ):
			"""simple docstring"""
			lowerCamelCase__						:						Dict						      =joblib.load(__lowerCamelCase      )
			print('''data loaded'''      )
			lowerCamelCase__						:						Union[str, Any]						      =[]
			if trim:
						for i, example in enumerate(__lowerCamelCase      ):
									if len(example[0]      ) > min_len:
												lowerCamelCase__						:						str						      =random.randint(0				,						len(example[0]      ) - context_len - 1      )
												objective_set.append(example[0, start : start + context_len]      )
									if len(__lowerCamelCase      ) >= number:
												break
						lowerCamelCase__						:						Optional[Any]						      =[]
						for j in range(i + 1				,						len(__lowerCamelCase      )      ):
									if len(data[j][0]      ) > min_len:
												train_data.append(data[j]      )
			else:
						lowerCamelCase__						:						int						      =data[0:number]
						lowerCamelCase__						:						Union[str, Any]						      =data[number:]

			joblib.dump(__lowerCamelCase				,						'''objective_set.jbl'''      )
			print('''objective set saved'''      )
			return train_data, objective_set






def 	snake_case__  (						__lowerCamelCase							:       int				,						__lowerCamelCase							:       Tuple				,						__lowerCamelCase							:       List[Any]				,						__lowerCamelCase							:       Optional[int]				,						__lowerCamelCase							:       Tuple=50				,						__lowerCamelCase							:       List[Any]="secondary_learner.pt"      ):
			"""simple docstring"""
			lowerCamelCase__						:						Union[str, Any]						      =torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''      )
			# We will use the first 512 pairs from our dataset as a test set for
			# our secondary learner and the rest to train
			lowerCamelCase__						:						Optional[int]						      =train_dataset[:512]
			lowerCamelCase__						:						str						      =train_dataset[512:]
			lowerCamelCase__						:						Optional[Any]						      =DataLoader(__lowerCamelCase				,						shuffle=__lowerCamelCase				,						batch_size=__lowerCamelCase      )
			lowerCamelCase__						:						Tuple						      =DataLoader(__lowerCamelCase				,						shuffle=__lowerCamelCase				,						batch_size=__lowerCamelCase      )

			# secondary learner model set up
			lowerCamelCase__						:						Optional[int]						      =nn.MSELoss()
			lowerCamelCase__						:						Any						      =nn.MSELoss(reduction='''sum'''      )
			secondary_learner.to(__lowerCamelCase      )
			lowerCamelCase__						:						List[Any]						      =torch.optim.Adam(secondary_learner.parameters()				,						lr=0.0_00_01      )
			secondary_learner.train()

			# TODO in original code this is written as number of actual batches seen
			# not number of items seen but other places it is number of items instead.
			# improve consistency! changed this to epochs for clarity
			lowerCamelCase__						:						Dict						      =float('''inf'''      )
			# Iterate through batches until we've used max_steps batches
			for epoch in range(int(__lowerCamelCase      )      ):
						lowerCamelCase__						:						Union[str, Any]						      =0.0
						secondary_learner.train()
						for step, batch in enumerate(__lowerCamelCase      ):
									lowerCamelCase__						:						Dict						      =batch[0].to(__lowerCamelCase      )
									lowerCamelCase__						:						str						      =batch[1].to(__lowerCamelCase      )
									lowerCamelCase__						:						Any						      =secondary_learner(__lowerCamelCase      )
									q_optimizer.zero_grad()
									lowerCamelCase__						:						Optional[Any]						      =loss(__lowerCamelCase				,						real_q.float()      )
									q_loss.backward()
									q_optimizer.step()
									tr_q_loss += q_loss.item()

									# model trains fairly quickly so we won't wait for a full epoch
									# eval is triggered at eval_freq and end of epochs
									if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(__lowerCamelCase      )):
												lowerCamelCase__						:						List[str]						      =tr_q_loss / (step + 1)

												secondary_learner.eval()
												lowerCamelCase__						:						Tuple						      =0.0
												lowerCamelCase__						:						Union[str, Any]						      =0.0
												lowerCamelCase__						:						Tuple						      =[]
												lowerCamelCase__						:						List[str]						      =[]
												# Compute performance of the secondary learner after this batch
												for stepa, batcha in enumerate(__lowerCamelCase      ):
															lowerCamelCase__						:						Tuple						      =batcha[0].to(__lowerCamelCase      )
															lowerCamelCase__						:						str						      =batcha[1].to(__lowerCamelCase      )
															lowerCamelCase__						:						Dict						      =secondary_learner(__lowerCamelCase      )
															q_lossa += test_loss(__lowerCamelCase				,						__lowerCamelCase      ).item()
															sum_qa += torch.sum(__lowerCamelCase      ).item()
															for ei, i in enumerate(predicted_qa.cpu().detach().numpy()      ):
																		predicted.append(i.item()      )
															for ei, i in enumerate(real_qa.cpu().detach().numpy()      ):
																		actual.append(i.item()      )

												q_lossa /= len(__lowerCamelCase      )
												print(
												    '''Epoch: '''				,						__lowerCamelCase				,						'''step: '''				,						__lowerCamelCase				,						'''Avg. q:'''				,						sum_qa / len(__lowerCamelCase      )				,						'''Train Loss: '''				,						__lowerCamelCase				,						'''Test Loss: '''				,						__lowerCamelCase				,						)
												if q_lossa < best_test_loss:
															joblib.dump((predicted, actual)				,						'''pred_vs_actual.jbl'''      )
															torch.save(secondary_learner.state_dict()				,						__lowerCamelCase      )
															lowerCamelCase__						:						Union[str, Any]						      =q_lossa

									secondary_learner.train()
			return secondary_learner

class 			__SCREAMING_SNAKE_CASE      ( nn.Module  ):


					'''simple docstring'''





					def __init__(			self				:    str,    lowerCamelCase				:    Union[str, Any]	)->   Dict:
								super(lowerCamelCase,    self	).__init__()
								lowerCamelCase__						:						str						      =model.transformer.wte
								lowerCamelCase__						:						Tuple						      =copy.deepcopy(model.transformer.wte.weight	)
								lowerCamelCase__						:						Union[str, Any]						      =nn.Convad(self.embeddings.weight.size(1	),    256,    3,    padding=1	)
								lowerCamelCase__						:						Any						      =nn.Sequential(nn.Linear(256,    32	),    nn.Dropout(p=0.1	),    nn.Linear(32,    32	),    nn.Linear(32,    1	)	)



					def 						snake_case			(			self				:    Tuple,    lowerCamelCase				:    int	)->   Dict:
								lowerCamelCase__						:						Tuple						      =torch.max(self.conv(self.embeddings(lowerCamelCase	).squeeze(1	).transpose(1,    2	)	),    2	)[0]
								lowerCamelCase__						:						List[str]						      =self.fc(lowerCamelCase	)
								return qs.squeeze(1	)




					@classmethod
					def 						snake_case			(			cls				:    int,    lowerCamelCase				:    List[str],    lowerCamelCase				:    int	)->   Optional[int]:
								lowerCamelCase__						:						List[str]						      =cls(lowerCamelCase	)  # this calls __init__
								lowerCamelCase__						:						Union[str, Any]						      =torch.load(lowerCamelCase	)
								secondary_learner.load_state_dict(lowerCamelCase	)
								lowerCamelCase__						:						Tuple						      =model.transformer.wte
								lowerCamelCase__						:						Optional[int]						      =copy.deepcopy(model.transformer.wte.weight	)
								return secondary_learner





import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


_SCREAMING_SNAKE_CASE     =     logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()
def 					__lowercase						(    lowerCamelCase__       )					->Any:

  '''simple docstring'''




  if "token" in model_name_or_path:
    return "rag_token"
  if "sequence" in model_name_or_path:
    return "rag_sequence"
  if "bart" in model_name_or_path:
    return "bart"
  return None
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->List[str]:

  '''simple docstring'''




  return max(metric_fn(lowerCamelCase__			,					lowerCamelCase__       ) for gt in ground_truths       )
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->Optional[Any]:

  '''simple docstring'''




  a__  		=  [line.strip() for line in open(lowerCamelCase__			,					"r"       ).readlines()]
  a__  		=  []

  if args.gold_data_mode == "qa":
    a__  		=  pd.read_csv(lowerCamelCase__			,					sep="\t"			,					header=lowerCamelCase__       )
    for answer_list in data[1]:
      a__  		=  ast.literal_eval(lowerCamelCase__       )
      answers.append(lowerCamelCase__       )
  else:
    a__  		=  [line.strip() for line in open(lowerCamelCase__			,					"r"       ).readlines()]
    a__  		=  [[reference] for reference in references]

  a__  		=  a__  		=  a__  		=  0
  for prediction, ground_truths in zip(lowerCamelCase__			,					lowerCamelCase__       ):
    total += 1
    em += metric_max_over_ground_truths(lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )
    fa += metric_max_over_ground_truths(lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )

  a__  		=  100.0 * em / total
  a__  		=  100.0 * fa / total

  logger.info(f"F1: {fa:.2f}"       )
  logger.info(f"EM: {em:.2f}"       )
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->Tuple:

  '''simple docstring'''




  a__  		=  args.k
  a__  		=  [line.strip() for line in open(lowerCamelCase__			,					"r"       ).readlines()]
  a__  		=  [line.strip() for line in open(lowerCamelCase__			,					"r"       ).readlines()]

  a__  		=  a__  		=  0
  for hypo, reference in zip(lowerCamelCase__			,					lowerCamelCase__       ):
    a__  		=  set(hypo.split("\t"       )[:k]       )
    a__  		=  set(reference.split("\t"       )       )
    total += 1
    em += len(hypo_provenance & ref_provenance       ) / k

  a__  		=  100.0 * em / total
  logger.info(f"Precision@{k}: {em: .2f}"       )
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->str:

  '''simple docstring'''




  def strip_title(lowerCamelCase__       ):
    if title.startswith("\""       ):
      a__  		=  title[1:]
    if title.endswith("\""       ):
      a__  		=  title[:-1]
    return title

  a__  		=  rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
      lowerCamelCase__			,					return_tensors="pt"			,					padding=lowerCamelCase__			,					truncation=lowerCamelCase__			,					)["input_ids"].to(args.device       )

  a__  		=  rag_model.rag.question_encoder(lowerCamelCase__       )
  a__  		=  question_enc_outputs[0]

  a__  		=  rag_model.retriever(
      lowerCamelCase__			,					question_enc_pool_output.cpu().detach().to(torch.floataa       ).numpy()			,					prefix=rag_model.rag.generator.config.prefix			,					n_docs=rag_model.config.n_docs			,					return_tensors="pt"			,					)
  a__  		=  rag_model.retriever.index.get_doc_dicts(result.doc_ids       )
  a__  		=  []
  for docs in all_docs:
    a__  		=  [strip_title(lowerCamelCase__       ) for title in docs["title"]]
    provenance_strings.append("\t".join(lowerCamelCase__       )       )
  return provenance_strings
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->List[str]:

  '''simple docstring'''




  with torch.no_grad():
    a__  		=  rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
        lowerCamelCase__			,					return_tensors="pt"			,					padding=lowerCamelCase__			,					truncation=lowerCamelCase__       )

    a__  		=  inputs_dict.input_ids.to(args.device       )
    a__  		=  inputs_dict.attention_mask.to(args.device       )
    a__  		=  rag_model.generate(  # rag_model overwrites generate
        lowerCamelCase__			,					attention_mask=lowerCamelCase__			,					num_beams=args.num_beams			,					min_length=args.min_length			,					max_length=args.max_length			,					early_stopping=lowerCamelCase__			,					num_return_sequences=1			,					bad_words_ids=[[0, 0]]			,					)
    a__  		=  rag_model.retriever.generator_tokenizer.batch_decode(lowerCamelCase__			,					skip_special_tokens=lowerCamelCase__       )

    if args.print_predictions:
      for q, a in zip(lowerCamelCase__			,					lowerCamelCase__       ):
        logger.info("Q: {} - A: {}".format(lowerCamelCase__			,					lowerCamelCase__       )       )

    return answers
def 					__lowercase						(    )					->str:

  '''simple docstring'''




  a__  		=  argparse.ArgumentParser()
  parser.add_argument(
      "--model_type"			,					choices=["rag_sequence", "rag_token", "bart"]			,					type=lowerCamelCase__			,					help=(
          "RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"
          " model_name_or_path"
      )			,					)
  parser.add_argument(
      "--index_name"			,					default=lowerCamelCase__			,					choices=["exact", "compressed", "legacy"]			,					type=lowerCamelCase__			,					help="RAG model retriever type"			,					)
  parser.add_argument(
      "--index_path"			,					default=lowerCamelCase__			,					type=lowerCamelCase__			,					help="Path to the retrieval index"			,					)
  parser.add_argument("--n_docs"			,					default=5			,					type=lowerCamelCase__			,					help="Number of retrieved docs"       )
  parser.add_argument(
      "--model_name_or_path"			,					default=lowerCamelCase__			,					type=lowerCamelCase__			,					required=lowerCamelCase__			,					help="Path to pretrained checkpoints or model identifier from huggingface.co/models"			,					)
  parser.add_argument(
      "--eval_mode"			,					choices=["e2e", "retrieval"]			,					default="e2e"			,					type=lowerCamelCase__			,					help=(
          "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
          " precision@k."
      )			,					)
  parser.add_argument("--k"			,					default=1			,					type=lowerCamelCase__			,					help="k for the precision@k calculation"       )
  parser.add_argument(
      "--evaluation_set"			,					default=lowerCamelCase__			,					type=lowerCamelCase__			,					required=lowerCamelCase__			,					help="Path to a file containing evaluation samples"			,					)
  parser.add_argument(
      "--gold_data_path"			,					default=lowerCamelCase__			,					type=lowerCamelCase__			,					required=lowerCamelCase__			,					help="Path to a tab-separated file with gold samples"			,					)
  parser.add_argument(
      "--gold_data_mode"			,					default="qa"			,					type=lowerCamelCase__			,					choices=["qa", "ans"]			,					help=(
          "Format of the gold data file"
          "qa - a single line in the following format: question [tab] answer_list"
          "ans - a single line of the gold file contains the expected answer string"
      )			,					)
  parser.add_argument(
      "--predictions_path"			,					type=lowerCamelCase__			,					default="predictions.txt"			,					help="Name of the predictions file, to be stored in the checkpoints directory"			,					)
  parser.add_argument(
      "--eval_all_checkpoints"			,					action="store_true"			,					help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"			,					)
  parser.add_argument(
      "--eval_batch_size"			,					default=8			,					type=lowerCamelCase__			,					help="Batch size per GPU/CPU for evaluation."			,					)
  parser.add_argument(
      "--recalculate"			,					help="Recalculate predictions even if the prediction file exists"			,					action="store_true"			,					)
  parser.add_argument(
      "--num_beams"			,					default=4			,					type=lowerCamelCase__			,					help="Number of beams to be used when generating answers"			,					)
  parser.add_argument("--min_length"			,					default=1			,					type=lowerCamelCase__			,					help="Min length of the generated answers"       )
  parser.add_argument("--max_length"			,					default=50			,					type=lowerCamelCase__			,					help="Max length of the generated answers"       )

  parser.add_argument(
      "--print_predictions"			,					action="store_true"			,					help="If True, prints predictions while evaluating."			,					)
  parser.add_argument(
      "--print_docs"			,					action="store_true"			,					help="If True, prints docs retried while generating."			,					)
  a__  		=  parser.parse_args()
  a__  		=  torch.device("cuda" if torch.cuda.is_available() else "cpu"       )
  return args
def 					__lowercase						(    lowerCamelCase__       )					->Dict:

  '''simple docstring'''




  a__  		=  {}
  if args.model_type is None:
    a__  		=  infer_model_type(args.model_name_or_path       )
    assert args.model_type is not None
  if args.model_type.startswith("rag"       ):
    a__  		=  RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
    a__  		=  args.n_docs
    if args.index_name is not None:
      a__  		=  args.index_name
    if args.index_path is not None:
      a__  		=  args.index_path
  else:
    a__  		=  BartForConditionalGeneration

  a__  		=  (
      [f.path for f in os.scandir(args.model_name_or_path       ) if f.is_dir()]
      if args.eval_all_checkpoints
      else [args.model_name_or_path]
  )

  logger.info("Evaluate the following checkpoints: %s"			,					lowerCamelCase__       )

  a__  		=  get_scores if args.eval_mode == "e2e" else get_precision_at_k
  a__  		=  evaluate_batch_eae if args.eval_mode == "e2e" else evaluate_batch_retrieval

  for checkpoint in checkpoints:
    if os.path.exists(args.predictions_path       ) and (not args.recalculate):
      logger.info("Calculating metrics based on an existing predictions file: {}".format(args.predictions_path       )       )
      score_fn(lowerCamelCase__			,					args.predictions_path			,					args.gold_data_path       )
      continue

    logger.info("***** Running evaluation for {} *****".format(lowerCamelCase__       )       )
    logger.info("  Batch size = %d"			,					args.eval_batch_size       )
    logger.info("  Predictions will be stored under {}".format(args.predictions_path       )       )

    if args.model_type.startswith("rag"       ):
      a__  		=  RagRetriever.from_pretrained(lowerCamelCase__			,					**lowerCamelCase__       )
      a__  		=  model_class.from_pretrained(lowerCamelCase__			,					retriever=lowerCamelCase__			,					**lowerCamelCase__       )
      model.retriever.init_retrieval()
    else:
      a__  		=  model_class.from_pretrained(lowerCamelCase__			,					**lowerCamelCase__       )
    model.to(args.device       )

    with open(args.evaluation_set			,					"r"       ) as eval_file, open(args.predictions_path			,					"w"       ) as preds_file:
      a__  		=  []
      for line in tqdm(lowerCamelCase__       ):
        questions.append(line.strip()       )
        if len(lowerCamelCase__       ) == args.eval_batch_size:
          a__  		=  evaluate_batch_fn(lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )
          preds_file.write("\n".join(lowerCamelCase__       ) + "\n"       )
          preds_file.flush()
          a__  		=  []
      if len(lowerCamelCase__       ) > 0:
        a__  		=  evaluate_batch_fn(lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )
        preds_file.write("\n".join(lowerCamelCase__       )       )
        preds_file.flush()

      score_fn(lowerCamelCase__			,					args.predictions_path			,					args.gold_data_path       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE     =     get_args()
     main(args)

