


'''simple docstring'''

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


a_						:       Any      			=				logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()





def __snake_case      (   UpperCAmelCase_		:		int	):
 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 __snake_case      (   UpperCAmelCase_		:		List[Any]      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		str	):
 return max(metric_fn(SCREAMING_SNAKE_CASE_      ,       SCREAMING_SNAKE_CASE_	) for gt in ground_truths	)





def __snake_case      (   UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		Tuple	):
 lowerCamelCase_  				=						[line.strip() for line in open(SCREAMING_SNAKE_CASE_      ,       "r"	).readlines()]
 lowerCamelCase_  				=						[]

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

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

 lowerCamelCase_  				=						100.0 * em / total
 lowerCamelCase_  				=						100.0 * fa / total

 logger.info(F'''F1: {fa:.2f}'''	)
 logger.info(F'''EM: {em:.2f}'''	)





def __snake_case      (   UpperCAmelCase_		:		Dict      ,       UpperCAmelCase_		:		Optional[int]      ,       UpperCAmelCase_		:		Optional[Any]	):
 lowerCamelCase_  				=						args.k
 lowerCamelCase_  				=						[line.strip() for line in open(SCREAMING_SNAKE_CASE_      ,       "r"	).readlines()]
 lowerCamelCase_  				=						[line.strip() for line in open(SCREAMING_SNAKE_CASE_      ,       "r"	).readlines()]

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

 lowerCamelCase_  				=						100.0 * em / total
 logger.info(F'''Precision@{k}: {em: .2f}'''	)





def __snake_case      (   UpperCAmelCase_		:		str      ,       UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		Union[str, Any]	):
 def strip_title(UpperCAmelCase_		:		Any	):
  if title.startswith("\""	):
   lowerCamelCase_  				=						title[1:]
  if title.endswith("\""	):
   lowerCamelCase_  				=						title[:-1]
  return title

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

 lowerCamelCase_  				=						rag_model.rag.question_encoder(SCREAMING_SNAKE_CASE_	)
 lowerCamelCase_  				=						question_enc_outputs[0]

 lowerCamelCase_  				=						rag_model.retriever(
     SCREAMING_SNAKE_CASE_      ,       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"      ,       )
 lowerCamelCase_  				=						rag_model.retriever.index.get_doc_dicts(result.doc_ids	)
 lowerCamelCase_  				=						[]
 for docs in all_docs:
  lowerCamelCase_  				=						[strip_title(SCREAMING_SNAKE_CASE_	) for title in docs["title"]]
  provenance_strings.append("\t".join(SCREAMING_SNAKE_CASE_	)	)
 return provenance_strings





def __snake_case      (   UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		Optional[Any]	):
 with torch.no_grad():
  lowerCamelCase_  				=						rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
      SCREAMING_SNAKE_CASE_      ,       return_tensors="pt"      ,       padding=SCREAMING_SNAKE_CASE_      ,       truncation=SCREAMING_SNAKE_CASE_	)

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

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

  return answers





def __snake_case      (   ):
 lowerCamelCase_  				=						argparse.ArgumentParser()
 parser.add_argument(
     "--model_type"      ,       choices=["rag_sequence", "rag_token", "bart"]      ,       type=SCREAMING_SNAKE_CASE_      ,       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=SCREAMING_SNAKE_CASE_      ,       choices=["exact", "compressed", "legacy"]      ,       type=SCREAMING_SNAKE_CASE_      ,       help="RAG model retriever type"      ,       )
 parser.add_argument(
     "--index_path"      ,       default=SCREAMING_SNAKE_CASE_      ,       type=SCREAMING_SNAKE_CASE_      ,       help="Path to the retrieval index"      ,       )
 parser.add_argument("--n_docs"      ,       default=5      ,       type=SCREAMING_SNAKE_CASE_      ,       help="Number of retrieved docs"	)
 parser.add_argument(
     "--model_name_or_path"      ,       default=SCREAMING_SNAKE_CASE_      ,       type=SCREAMING_SNAKE_CASE_      ,       required=SCREAMING_SNAKE_CASE_      ,       help="Path to pretrained checkpoints or model identifier from huggingface.co/models"      ,       )
 parser.add_argument(
     "--eval_mode"      ,       choices=["e2e", "retrieval"]      ,       default="e2e"      ,       type=SCREAMING_SNAKE_CASE_      ,       help=(
         "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
         " precision@k."
     )      ,       )
 parser.add_argument("--k"      ,       default=1      ,       type=SCREAMING_SNAKE_CASE_      ,       help="k for the precision@k calculation"	)
 parser.add_argument(
     "--evaluation_set"      ,       default=SCREAMING_SNAKE_CASE_      ,       type=SCREAMING_SNAKE_CASE_      ,       required=SCREAMING_SNAKE_CASE_      ,       help="Path to a file containing evaluation samples"      ,       )
 parser.add_argument(
     "--gold_data_path"      ,       default=SCREAMING_SNAKE_CASE_      ,       type=SCREAMING_SNAKE_CASE_      ,       required=SCREAMING_SNAKE_CASE_      ,       help="Path to a tab-separated file with gold samples"      ,       )
 parser.add_argument(
     "--gold_data_mode"      ,       default="qa"      ,       type=SCREAMING_SNAKE_CASE_      ,       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=SCREAMING_SNAKE_CASE_      ,       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=SCREAMING_SNAKE_CASE_      ,       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=SCREAMING_SNAKE_CASE_      ,       help="Number of beams to be used when generating answers"      ,       )
 parser.add_argument("--min_length"      ,       default=1      ,       type=SCREAMING_SNAKE_CASE_      ,       help="Min length of the generated answers"	)
 parser.add_argument("--max_length"      ,       default=50      ,       type=SCREAMING_SNAKE_CASE_      ,       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."      ,       )
 lowerCamelCase_  				=						parser.parse_args()
 lowerCamelCase_  				=						torch.device("cuda" if torch.cuda.is_available() else "cpu"	)
 return args





def __snake_case      (   UpperCAmelCase_		:		Union[str, Any]	):
 lowerCamelCase_  				=						{}
 if args.model_type is None:
  lowerCamelCase_  				=						infer_model_type(args.model_name_or_path	)
  assert args.model_type is not None
 if args.model_type.startswith("rag"	):
  lowerCamelCase_  				=						RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
  lowerCamelCase_  				=						args.n_docs
  if args.index_name is not None:
   lowerCamelCase_  				=						args.index_name
  if args.index_path is not None:
   lowerCamelCase_  				=						args.index_path
 else:
  lowerCamelCase_  				=						BartForConditionalGeneration

 lowerCamelCase_  				=						(
     [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"      ,       SCREAMING_SNAKE_CASE_	)

 lowerCamelCase_  				=						get_scores if args.eval_mode == "e2e" else get_precision_at_k
 lowerCamelCase_  				=						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(SCREAMING_SNAKE_CASE_      ,       args.predictions_path      ,       args.gold_data_path	)
   continue

  logger.info("***** Running evaluation for {} *****".format(SCREAMING_SNAKE_CASE_	)	)
  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"	):
   lowerCamelCase_  				=						RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_      ,       **SCREAMING_SNAKE_CASE_	)
   lowerCamelCase_  				=						model_class.from_pretrained(SCREAMING_SNAKE_CASE_      ,       retriever=SCREAMING_SNAKE_CASE_      ,       **SCREAMING_SNAKE_CASE_	)
   model.retriever.init_retrieval()
  else:
   lowerCamelCase_  				=						model_class.from_pretrained(SCREAMING_SNAKE_CASE_      ,       **SCREAMING_SNAKE_CASE_	)
  model.to(args.device	)

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

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


if __name__ == "__main__":
     a_						:       Optional[Any]      			=				get_args()
     main(args)










'''simple docstring'''

import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
     import torch
     from torch import nn

     from transformers import (
         Adafactor,
         AdamW,
         get_constant_schedule,
         get_constant_schedule_with_warmup,
         get_cosine_schedule_with_warmup,
         get_cosine_with_hard_restarts_schedule_with_warmup,
         get_inverse_sqrt_schedule,
         get_linear_schedule_with_warmup,
         get_polynomial_decay_schedule_with_warmup,
     )





def __snake_case      (   UpperCAmelCase_		:		List[Any]      ,       UpperCAmelCase_		:		Union[str, Any]=10	):
 lowerCamelCase_  				=						[]
 for _ in range(UpperCAmelCase_	):
  lrs.append(scheduler.get_lr()[0]	)
  scheduler.step()
 return lrs





def __snake_case      (   UpperCAmelCase_		:		Tuple      ,       UpperCAmelCase_		:		List[Any]=10	):
 lowerCamelCase_  				=						[]
 for step in range(UpperCAmelCase_	):
  lrs.append(scheduler.get_lr()[0]	)
  scheduler.step()
  if step == num_steps // 2:
   with tempfile.TemporaryDirectory() as tmpdirname:
    lowerCamelCase_  				=						os.path.join(UpperCAmelCase_      ,       "schedule.bin"	)
    torch.save(scheduler.state_dict()      ,       UpperCAmelCase_	)

    lowerCamelCase_  				=						torch.load(UpperCAmelCase_	)
    scheduler.load_state_dict(UpperCAmelCase_	)
 return lrs





@require_torch
class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   self.assertEqual(len(UpperCamelCase  )    ,						len(UpperCamelCase  )  )
   for a, b in zip(UpperCamelCase    ,						UpperCamelCase  ):
    self.assertAlmostEqual(UpperCamelCase    ,						UpperCamelCase    ,						delta=UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						torch.tensor([0.1, -0.2, -0.1]    ,						requires_grad=UpperCamelCase  )
   lowerCamelCase_  				=						torch.tensor([0.4, 0.2, -0.5]  )
   lowerCamelCase_  				=						nn.MSELoss()
   # No warmup, constant schedule, no gradient clipping
   lowerCamelCase_  				=						AdamW(params=[w]    ,						lr=2e-1    ,						weight_decay=0.0  )
   for _ in range(100  ):
    lowerCamelCase_  				=						criterion(UpperCamelCase    ,						UpperCamelCase  )
    loss.backward()
    optimizer.step()
    w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
    w.grad.zero_()
   self.assertListAlmostEqual(w.tolist()    ,						[0.4, 0.2, -0.5]    ,						tol=1e-2  )







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						torch.tensor([0.1, -0.2, -0.1]    ,						requires_grad=UpperCamelCase  )
   lowerCamelCase_  				=						torch.tensor([0.4, 0.2, -0.5]  )
   lowerCamelCase_  				=						nn.MSELoss()
   # No warmup, constant schedule, no gradient clipping
   lowerCamelCase_  				=						Adafactor(
       params=[w]    ,						lr=1e-2    ,						eps=(1e-30, 1e-3)    ,						clip_threshold=1.0    ,						decay_rate=-0.8    ,						betaa=UpperCamelCase    ,						weight_decay=0.0    ,						relative_step=UpperCamelCase    ,						scale_parameter=UpperCamelCase    ,						warmup_init=UpperCamelCase    ,						)
   for _ in range(1000  ):
    lowerCamelCase_  				=						criterion(UpperCamelCase    ,						UpperCamelCase  )
    loss.backward()
    optimizer.step()
    w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
    w.grad.zero_()
   self.assertListAlmostEqual(w.tolist()    ,						[0.4, 0.2, -0.5]    ,						tol=1e-2  )





@require_torch
class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  _lowerCamelCase						=						nn.Linear(50    ,		50    ) if is_torch_available() else None
  _lowerCamelCase						=						AdamW(m.parameters()    ,		lr=10.0    ) if is_torch_available() else None
  _lowerCamelCase						=						10






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase=None  ):







   """simple docstring"""
   self.assertEqual(len(UpperCamelCase  )    ,						len(UpperCamelCase  )  )
   for a, b in zip(UpperCamelCase    ,						UpperCamelCase  ):
    self.assertAlmostEqual(UpperCamelCase    ,						UpperCamelCase    ,						delta=UpperCamelCase    ,						msg=UpperCamelCase  )







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						{"num_warmup_steps": 2, "num_training_steps": 10}
   # schedulers doct format
   # function: (sched_args_dict, expected_learning_rates)
   lowerCamelCase_  				=						{
       get_constant_schedule: ({}, [10.0] * self.num_steps),
       get_constant_schedule_with_warmup: (
           {"num_warmup_steps": 4},
           [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
       ),
       get_linear_schedule_with_warmup: (
           {**common_kwargs},
           [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
       ),
       get_cosine_schedule_with_warmup: (
           {**common_kwargs},
           [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
       ),
       get_cosine_with_hard_restarts_schedule_with_warmup: (
           {**common_kwargs, "num_cycles": 2},
           [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
       ),
       get_polynomial_decay_schedule_with_warmup: (
           {**common_kwargs, "power": 2.0, "lr_end": 1e-7},
           [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
       ),
       get_inverse_sqrt_schedule: (
           {"num_warmup_steps": 2},
           [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
       ),
   }

   for scheduler_func, data in scheds.items():
    lowerCamelCase_				,lowerCamelCase_  				=						data

    lowerCamelCase_  				=						scheduler_func(self.optimizer    ,						**UpperCamelCase  )
    self.assertEqual(len([scheduler.get_lr()[0]]  )    ,						1  )
    lowerCamelCase_  				=						unwrap_schedule(UpperCamelCase    ,						self.num_steps  )
    self.assertListAlmostEqual(
        UpperCamelCase    ,						UpperCamelCase    ,						tol=1e-2    ,						msg=f'''failed for {scheduler_func} in normal scheduler'''    ,						)

    lowerCamelCase_  				=						scheduler_func(self.optimizer    ,						**UpperCamelCase  )
    if scheduler_func.__name__ != "get_constant_schedule":
     LambdaScheduleWrapper.wrap_scheduler(UpperCamelCase  )  # wrap to test picklability of the schedule
    lowerCamelCase_  				=						unwrap_and_save_reload_schedule(UpperCamelCase    ,						self.num_steps  )
    self.assertListEqual(UpperCamelCase    ,						UpperCamelCase    ,						msg=f'''failed for {scheduler_func} in save and reload'''  )





class   snake_case					:





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						fn






  def __call__(					self    ,						*UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   return self.fn(*UpperCamelCase    ,						**UpperCamelCase  )







  @classmethod
  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						list(map(self    ,						scheduler.lr_lambdas  )  )



