"""simple docstring"""


import torch

from accelerate import Accelerator, DistributedType




class 				UpperCamelCase      :







       def __enter__(				self)   ->   Dict:
            if self.enabled:
                 snake_case_       =      self.model.no_sync()
                 self.model_sync_obj.__enter__()

            return self







       def __exit__(				self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)   ->   int:
            if self.enabled:
                 # Average all models on exit
                 self._sync_and_avg_model_params()
                 self.model_sync_obj.__exit__(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)







       def __init__(				self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__ = True)   ->   Tuple:
            if accelerator.distributed_type not in [
                DistributedType.NO,
                DistributedType.MULTI_CPU,
                DistributedType.MULTI_GPU,
            ]:
                 raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)')
            snake_case_       =      enabled and accelerator.distributed_type != DistributedType.NO
            snake_case_       =      0
            if self.enabled:
                 snake_case_       =      accelerator
                 snake_case_       =      model
                 snake_case_       =      local_sgd_steps







       def 	a_		(				self)   ->   Dict:
            self.num_steps += 1
            if not self.enabled:
                 return

            if self.num_steps % self.local_sgd_steps == 0:
                 self._sync_and_avg_model_params()







       def 	a_		(				self)   ->   Any:
            self.accelerator.wait_for_everyone()
            with self.accelerator.autocast():
                 for param in self.model.parameters():
                      snake_case_       =      self.accelerator.reduce(param.data,							reduction='mean')


"""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


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

transformers_logging.set_verbosity_info()







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







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase      )       ->   Optional[Any]:
     snake_case_       =      [line.strip() for line in open(UpperCAmelCase		,      'r'      ).readlines()]
     snake_case_       =      []

     if args.gold_data_mode == "qa":
          snake_case_       =      pd.read_csv(UpperCAmelCase		,      sep='\t'		,      header=UpperCAmelCase      )
          for answer_list in data[1]:
               snake_case_       =      ast.literal_eval(UpperCAmelCase      )
               answers.append(UpperCAmelCase      )
     else:
          snake_case_       =      [line.strip() for line in open(UpperCAmelCase		,      'r'      ).readlines()]
          snake_case_       =      [[reference] for reference in references]

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

     snake_case_       =      100.0 * em / total
     snake_case_       =      100.0 * fa / total

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







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase      )       ->   Optional[Any]:
     snake_case_       =      args.k
     snake_case_       =      [line.strip() for line in open(UpperCAmelCase		,      'r'      ).readlines()]
     snake_case_       =      [line.strip() for line in open(UpperCAmelCase		,      'r'      ).readlines()]

     snake_case_       =      snake_case_       =      0
     for hypo, reference in zip(UpperCAmelCase		,      UpperCAmelCase      ):
          snake_case_       =      set(hypo.split('\t'      )[:k]      )
          snake_case_       =      set(reference.split('\t'      )      )
          total += 1
          em += len(hypo_provenance & ref_provenance      ) / k

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







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase      )       ->   str:
     def strip_title(UpperCAmelCase      ):
          if title.startswith('"'      ):
               snake_case_       =      title[1:]
          if title.endswith('"'      ):
               snake_case_       =      title[:-1]
          return title

     snake_case_       =      rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
         UpperCAmelCase		,      return_tensors='pt'		,      padding=UpperCAmelCase		,      truncation=UpperCAmelCase		,      )['input_ids'].to(args.device      )

     snake_case_       =      rag_model.rag.question_encoder(UpperCAmelCase      )
     snake_case_       =      question_enc_outputs[0]

     snake_case_       =      rag_model.retriever(
         UpperCAmelCase		,      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'		,      )
     snake_case_       =      rag_model.retriever.index.get_doc_dicts(result.doc_ids      )
     snake_case_       =      []
     for docs in all_docs:
          snake_case_       =      [strip_title(UpperCAmelCase      ) for title in docs['title']]
          provenance_strings.append('\t'.join(UpperCAmelCase      )      )
     return provenance_strings







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase      )       ->   str:
     with torch.no_grad():
          snake_case_       =      rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
              UpperCAmelCase		,      return_tensors='pt'		,      padding=UpperCAmelCase		,      truncation=UpperCAmelCase      )

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

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

          return answers







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







def       UpperCAmelCase					(	UpperCAmelCase      )       ->   str:
     snake_case_       =      {}
     if args.model_type is None:
          snake_case_       =      infer_model_type(args.model_name_or_path      )
          assert args.model_type is not None
     if args.model_type.startswith('rag'      ):
          snake_case_       =      RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
          snake_case_       =      args.n_docs
          if args.index_name is not None:
               snake_case_       =      args.index_name
          if args.index_path is not None:
               snake_case_       =      args.index_path
     else:
          snake_case_       =      BartForConditionalGeneration

     snake_case_       =      (
         [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'		,      UpperCAmelCase      )

     snake_case_       =      get_scores if args.eval_mode == 'e2e' else get_precision_at_k
     snake_case_       =      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(UpperCAmelCase		,      args.predictions_path		,      args.gold_data_path      )
               continue

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

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

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


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

