code: 
  
  '''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
  
  
  lowerCamelCase     =      logging.getLogger(__name__)
  logging.basicConfig(level=logging.INFO)
  
  transformers_logging.set_verbosity_info()
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				str							):
  
  
  
  
  
    '''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     UpperCAmelCase__       (    _A   :				Optional[Any]			,   _A   :				str			,   _A   :				str							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    return max(metric_fn(_A			,   _A							) for gt in ground_truths							)
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				List[str]			,   _A   :				Optional[Any]			,   _A   :				Dict							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=[line.strip() for line in open(_A			,   '''r'''							).readlines()]
    a__						=[]
  
    if args.gold_data_mode == "qa":
      a__						=pd.read_csv(_A			,   sep='''\t'''			,   header=_A							)
      for answer_list in data[1]:
        a__						=ast.literal_eval(_A							)
        answers.append(_A							)
    else:
      a__						=[line.strip() for line in open(_A			,   '''r'''							).readlines()]
      a__						=[[reference] for reference in references]
  
    a__						=a__						=a__						=0
    for prediction, ground_truths in zip(_A			,   _A							):
      total += 1
      em += metric_max_over_ground_truths(_A			,   _A			,   _A							)
      fa += metric_max_over_ground_truths(_A			,   _A			,   _A							)
  
    a__						=100.0 * em / total
    a__						=100.0 * fa / total
  
    logger.info(F"""F1: {fa:.2f}"""							)
    logger.info(F"""EM: {em:.2f}"""							)
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				List[str]			,   _A   :				Any			,   _A   :				Optional[int]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=args.k
    a__						=[line.strip() for line in open(_A			,   '''r'''							).readlines()]
    a__						=[line.strip() for line in open(_A			,   '''r'''							).readlines()]
  
    a__						=a__						=0
    for hypo, reference in zip(_A			,   _A							):
      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     UpperCAmelCase__       (    _A   :				List[Any]			,   _A   :				str			,   _A   :				List[Any]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def strip_title(_A   :				List[str]							):
      if title.startswith('''"'''							):
        a__						=title[1:]
      if title.endswith('''"'''							):
        a__						=title[:-1]
      return title
  
    a__						=rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
        _A			,   return_tensors='''pt'''			,   padding=_A			,   truncation=_A			,   )['''input_ids'''].to(args.device							)
  
    a__						=rag_model.rag.question_encoder(_A							)
    a__						=question_enc_outputs[0]
  
    a__						=rag_model.retriever(
        _A			,   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(_A							) for title in docs['''title''']]
      provenance_strings.append('''\t'''.join(_A							)							)
    return provenance_strings
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Any			,   _A   :				Union[str, Any]			,   _A   :				Dict							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    with torch.no_grad():
      a__						=rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
          _A			,   return_tensors='''pt'''			,   padding=_A			,   truncation=_A							)
  
      a__						=inputs_dict.input_ids.to(args.device							)
      a__						=inputs_dict.attention_mask.to(args.device							)
      a__						=rag_model.generate(  # rag_model overwrites generate
          _A			,   attention_mask=_A			,   num_beams=args.num_beams			,   min_length=args.min_length			,   max_length=args.max_length			,   early_stopping=_A			,   num_return_sequences=1			,   bad_words_ids=[[0, 0]]			,   )
      a__						=rag_model.retriever.generator_tokenizer.batch_decode(_A			,   skip_special_tokens=_A							)
  
      if args.print_predictions:
        for q, a in zip(_A			,   _A							):
          logger.info('''Q: {} - A: {}'''.format(_A			,   _A							)							)
  
      return answers
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=argparse.ArgumentParser()
    parser.add_argument(
        '''--model_type'''			,   choices=['''rag_sequence''', '''rag_token''', '''bart''']			,   type=_A			,   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=_A			,   choices=['''exact''', '''compressed''', '''legacy''']			,   type=_A			,   help='''RAG model retriever type'''			,   )
    parser.add_argument(
        '''--index_path'''			,   default=_A			,   type=_A			,   help='''Path to the retrieval index'''			,   )
    parser.add_argument('''--n_docs'''			,   default=5			,   type=_A			,   help='''Number of retrieved docs'''							)
    parser.add_argument(
        '''--model_name_or_path'''			,   default=_A			,   type=_A			,   required=_A			,   help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''			,   )
    parser.add_argument(
        '''--eval_mode'''			,   choices=['''e2e''', '''retrieval''']			,   default='''e2e'''			,   type=_A			,   help=(
            '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
            ''' precision@k.'''
        )			,   )
    parser.add_argument('''--k'''			,   default=1			,   type=_A			,   help='''k for the precision@k calculation'''							)
    parser.add_argument(
        '''--evaluation_set'''			,   default=_A			,   type=_A			,   required=_A			,   help='''Path to a file containing evaluation samples'''			,   )
    parser.add_argument(
        '''--gold_data_path'''			,   default=_A			,   type=_A			,   required=_A			,   help='''Path to a tab-separated file with gold samples'''			,   )
    parser.add_argument(
        '''--gold_data_mode'''			,   default='''qa'''			,   type=_A			,   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=_A			,   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=_A			,   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=_A			,   help='''Number of beams to be used when generating answers'''			,   )
    parser.add_argument('''--min_length'''			,   default=1			,   type=_A			,   help='''Min length of the generated answers'''							)
    parser.add_argument('''--max_length'''			,   default=50			,   type=_A			,   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     UpperCAmelCase__       (    _A   :				Any							):
  
  
  
  
  
    '''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'''			,   _A							)
  
    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(_A			,   args.predictions_path			,   args.gold_data_path							)
        continue
  
      logger.info('''***** Running evaluation for {} *****'''.format(_A							)							)
      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(_A			,   **_A							)
        a__						=model_class.from_pretrained(_A			,   retriever=_A			,   **_A							)
        model.retriever.init_retrieval()
      else:
        a__						=model_class.from_pretrained(_A			,   **_A							)
      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(_A							):
          questions.append(line.strip()							)
          if len(_A							) == args.eval_batch_size:
            a__						=evaluate_batch_fn(_A			,   _A			,   _A							)
            preds_file.write('''\n'''.join(_A							) + '''\n'''							)
            preds_file.flush()
            a__						=[]
        if len(_A							) > 0:
          a__						=evaluate_batch_fn(_A			,   _A			,   _A							)
          preds_file.write('''\n'''.join(_A							)							)
          preds_file.flush()
  
        score_fn(_A			,   args.predictions_path			,   args.gold_data_path							)
  
  
  if __name__ == "__main__":
         lowerCamelCase     =      get_args()
         main(args)
  
style_context: 
  
  import os
  import random
  from pathlib import Path
  from typing import List
  
  import numpy as np
  import torch
  from torch.cuda.amp import GradScaler
  
  from .utils import (
      MODEL_NAME,
      OPTIMIZER_NAME,
      RNG_STATE_NAME,
      SCALER_NAME,
      SCHEDULER_NAME,
      get_pretty_name,
      is_tpu_available,
      is_xpu_available,
      save,
  )
  
  
  if is_tpu_available(check_device=False):
         import torch_xla.core.xla_model as xm
  
  from .logging import get_logger
  from .state import PartialState
  
  
  lowerCamelCase     =      get_logger(__name__)
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				str			,   _A   :				List[dict]			,   _A   :				list			,   _A   :				list			,   _A   :				int			,   _A   :				GradScaler = None			,   ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    for i, state in enumerate(_A							):
      a__						=F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
      a__						=os.path.join(_A			,   _A							)
      save(_A			,   _A							)
      logger.info(F"""Model weights saved in {output_model_file}"""							)
    # Optimizer states
    for i, opt in enumerate(_A							):
      a__						=opt.state_dict()
      a__						=F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
      a__						=os.path.join(_A			,   _A							)
      save(_A			,   _A							)
      logger.info(F"""Optimizer state saved in {output_optimizer_file}"""							)
    # Scheduler states
    for i, scheduler in enumerate(_A							):
      a__						=scheduler.state_dict()
      a__						=F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
      a__						=os.path.join(_A			,   _A							)
      save(_A			,   _A							)
      logger.info(F"""Scheduler state saved in {output_scheduler_file}"""							)
    # GradScaler state
    if scaler is not None:
      a__						=scaler.state_dict()
      a__						=os.path.join(_A			,   _A							)
      torch.save(_A			,   _A							)
      logger.info(F"""Gradient scaler state saved in {output_scaler_file}"""							)
    # Random number generator states
    a__						={}
    a__						=F"""{RNG_STATE_NAME}_{process_index}.pkl"""
    a__						=random.getstate()
    a__						=np.random.get_state()
    a__						=torch.get_rng_state()
    if is_xpu_available():
      a__						=torch.xpu.get_rng_state_all()
    else:
      a__						=torch.cuda.get_rng_state_all()
    if is_tpu_available():
      a__						=xm.get_rng_state()
    a__						=os.path.join(_A			,   _A							)
    torch.save(_A			,   _A							)
    logger.info(F"""Random states saved in {output_states_file}"""							)
    return output_dir
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Union[str, Any]			,   _A   :				int			,   _A   :				int			,   _A   :				Union[str, Any]			,   _A   :				Tuple			,   _A   :				Optional[int]=None			,   _A   :				List[Any]=None			,   **_A   :				Dict			,   ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    if map_location not in [None, "cpu", "on_device"]:
      raise TypeError(
          '''Unsupported optimizer map location passed, please choose one of `None`, `\'cpu\'`, or `\'on_device\'`'''							)
    if map_location is None:
      a__						='''cpu'''
    elif map_location == "on_device":
      a__						=PartialState().device
    # Model states
    for i, model in enumerate(_A							):
      a__						=F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
      a__						=os.path.join(_A			,   _A							)
      models[i].load_state_dict(torch.load(_A			,   map_location=_A							)			,   **_A							)
    logger.info('''All model weights loaded successfully'''							)
  
    # Optimizer states
    for i, opt in enumerate(_A							):
      a__						=F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
      a__						=os.path.join(_A			,   _A							)
      a__						=torch.load(_A			,   map_location=_A							)
      optimizers[i].load_state_dict(_A							)
    logger.info('''All optimizer states loaded successfully'''							)
  
    # Scheduler states
    for i, scheduler in enumerate(_A							):
      a__						=F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
      a__						=os.path.join(_A			,   _A							)
      scheduler.load_state_dict(torch.load(_A							)							)
    logger.info('''All scheduler states loaded successfully'''							)
  
    # GradScaler state
    if scaler is not None:
      a__						=os.path.join(_A			,   _A							)
      scaler.load_state_dict(torch.load(_A							)							)
      logger.info('''GradScaler state loaded successfully'''							)
  
    # Random states
    try:
      a__						=torch.load(os.path.join(_A			,   F"""{RNG_STATE_NAME}_{process_index}.pkl"""							)							)
      random.setstate(states['''random_state''']							)
      np.random.set_state(states['''numpy_random_seed''']							)
      torch.set_rng_state(states['''torch_manual_seed''']							)
      if is_xpu_available():
        torch.xpu.set_rng_state_all(states['''torch_xpu_manual_seed''']							)
      else:
        torch.cuda.set_rng_state_all(states['''torch_cuda_manual_seed''']							)
      if is_tpu_available():
        xm.set_rng_state(states['''xm_seed''']							)
      logger.info('''All random states loaded successfully'''							)
    except Exception:
      logger.info('''Could not load random states'''							)
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Optional[Any]			,   _A   :				Any			,   _A   :				int = 0							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=Path(_A							) / F"""custom_checkpoint_{index}.pkl"""
    logger.info(F"""Saving the state of {get_pretty_name(_A							)} to {save_location}"""							)
    torch.save(obj.state_dict()			,   _A							)
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				int			,   _A   :				Any			,   _A   :				int = 0							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=F"""{path}/custom_checkpoint_{index}.pkl"""
    logger.info(F"""Loading the state of {get_pretty_name(_A							)} from {load_location}"""							)
    obj.load_state_dict(torch.load(_A			,   map_location='''cpu'''							)							)
  