

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

transformers_logging.set_verbosity_info()




def   _A  (  UpperCamelCase_					:	Union[str, Any])     ->						Optional[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   _A  (  UpperCamelCase_					:	Any,      UpperCamelCase_					:	Union[str, Any],      UpperCamelCase_					:	Dict)     ->						Tuple:
   '''simple docstring'''


   return max(metric_fn(lowercase__,      lowercase__) for gt in ground_truths)




def   _A  (  UpperCamelCase_					:	Any,      UpperCamelCase_					:	int,      UpperCamelCase_					:	Optional[int])     ->						Any:
   '''simple docstring'''


   __lowercase								=						[line.strip() for line in open(lowercase__,      "r").readlines()]
   __lowercase								=						[]

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

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

   __lowercase								=						100.0 * em / total
   __lowercase								=						100.0 * fa / total

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




def   _A  (  UpperCamelCase_					:	Any,      UpperCamelCase_					:	List[str],      UpperCamelCase_					:	List[Any])     ->						Any:
   '''simple docstring'''


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

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

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




def   _A  (  UpperCamelCase_					:	Union[str, Any],      UpperCamelCase_					:	Dict,      UpperCamelCase_					:	Union[str, Any])     ->						Dict:
   '''simple docstring'''


   def strip_title(UpperCamelCase_					:	Optional[Any]):
      if title.startswith("\""):
         __lowercase								=						title[1:]
      if title.endswith("\""):
         __lowercase								=						title[:-1]
      return title

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

   __lowercase								=						rag_model.rag.question_encoder(lowercase__)
   __lowercase								=						question_enc_outputs[0]

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




def   _A  (  UpperCamelCase_					:	Union[str, Any],      UpperCamelCase_					:	Dict,      UpperCamelCase_					:	Optional[int])     ->						Optional[int]:
   '''simple docstring'''


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

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

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

      return answers




def   _A  (  )     ->						Dict:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_					:	List[Any])     ->						Union[str, Any]:
   '''simple docstring'''


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

   __lowercase								=						(
       [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",      lowercase__)

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

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

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

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


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







"""simple docstring"""





import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
   # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
   # setup instructions, if using on-demand hardware

   # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
   # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
   # Throw an error if user passes both BYO and on-demand cluster args
   # Otherwise, use default values
   _a							=							argparse.ArgumentParser()
   parser.add_argument('--user', type=str, default='ubuntu')
   parser.add_argument('--host', type=str, default='localhost')
   parser.add_argument('--key_path', type=str, default=None)
   parser.add_argument('--instance', type=str, default='V100:1')
   parser.add_argument('--provider', type=str, default='cheapest')
   parser.add_argument('--use_spot', type=bool, default=False)
   parser.add_argument('--example', type=str, default='pytorch/text-generation/run_generation.py')
   _a				,						_a							=							parser.parse_known_args()
   if args.host != "localhost":
      if args.instance != "V100:1" or args.provider != "cheapest":
         raise ValueError('Cannot specify both BYO and on-demand cluster args')
      _a							=							rh.cluster(
          name='rh-cluster', ips=[args.host], ssh_creds={'ssh_user': args.user, 'ssh_private_key': args.key_path}
      )
   else:
      _a							=							rh.cluster(
          name='rh-cluster', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
      )
   _a							=							args.example.rsplit('/', 1)[0]

   # Set up remote environment
   cluster.install_packages(['pip:./'])  # Installs transformers from local source
   # Note transformers is copied into the home directory on the remote machine, so we can install from there
   cluster.run([F"pip install -r transformers/examples/{example_dir}/requirements.txt"])
   cluster.run(['pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'])

   # Run example. You can bypass the CLI wrapper and paste your own code here.
   cluster.run([F"python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"])

   # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
   # from my_script... import train
   # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
   # launch_train_gpu = rh.function(fn=train,
   #                                system=gpu,
   #                                reqs=reqs,
   #                                name='train_bert_glue')
   #
   # We can pass in arguments just like we would to a function:
   # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
   #                  stream_logs=True)




