

import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


A__        =			logging.getLogger(__name__)







class    UpperCamelCase_			:


  """simple docstring"""



  def __init__(	self    :				Optional[int]	)					-> List[Any]:
      SCREAMING_SNAKE_CASE__					     =						False



  def      _UpperCamelCase    (	self    :				Union[str, Any]     ,  A_    :				Optional[Any]     ,  A_    :				Optional[Any]     ,  A_    :				Optional[Any]     ,  A_    :				str	)					-> Optional[Any]:
      if not self.initialized:
          SCREAMING_SNAKE_CASE__					     =						RagRetriever(
              A_     ,  question_encoder_tokenizer=A_     ,  generator_tokenizer=A_     ,  index=A_     ,  init_retrieval=A_     ,  )
          SCREAMING_SNAKE_CASE__					     =						True



  def      _UpperCamelCase    (	self    :				int	)					-> Optional[int]:
      self.retriever.index.init_index()



  def      _UpperCamelCase    (	self    :				Dict	)					-> Optional[Any]:
      # delete the old self.retriever object before assigning the new index
      del self.retriever
      SCREAMING_SNAKE_CASE__					     =						False







  def      _UpperCamelCase    (	self    :				Optional[int]     ,  A_    :				Union[str, Any]     ,  A_    :				Any	)					-> Union[str, Any]:
      SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						self.retriever._main_retrieve(A_     ,  A_	)
      SCREAMING_SNAKE_CASE__					     =						self.retriever.index.get_doc_dicts(A_	)
      return doc_ids, retrieved_doc_embeds, doc_dicts







class    UpperCamelCase_			(  _snake_case			):


  """simple docstring"""



  def __init__(	self    :				Any     ,  A_    :				Any     ,  A_    :				Optional[Any]     ,  A_    :				Optional[Any]     ,  A_    :				Optional[Any]     ,  A_    :				Tuple=None	)					-> List[Any]:
      if index is not None and index.is_initialized() and len(A_	) > 0:
          raise ValueError(
              "When using Ray for distributed fine-tuning, "
              "you'll need to provide the paths instead, "
              "as the dataset and the index are loaded "
              "separately. More info in examples/rag/use_own_knowledge_dataset.py "	)

      super().__init__(
          A_     ,  question_encoder_tokenizer=A_     ,  generator_tokenizer=A_     ,  index=A_     ,  init_retrieval=A_     ,  )

      SCREAMING_SNAKE_CASE__					     =						retrieval_workers
      SCREAMING_SNAKE_CASE__					     =						question_encoder_tokenizer
      SCREAMING_SNAKE_CASE__					     =						generator_tokenizer
      if len(self.retrieval_workers	) > 0:
          ray.get(
              [
                  worker.create_rag_retriever.remote(A_     ,  A_     ,  A_     ,  A_	)
                  for worker in self.retrieval_workers
              ]	)



  def      _UpperCamelCase    (	self    :				Dict	)					-> Union[str, Any]:
      logger.info("initializing retrieval"	)

      if len(self.retrieval_workers	) > 0:
          ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]	)
      else:
          # Non-distributed training. Load index into this same process.
          self.index.init_index()



  def      _UpperCamelCase    (	self    :				Any     ,  A_    :				Any     ,  A_    :				int	)					-> Tuple:
      if len(self.retrieval_workers	) > 0:
          # Select a random retrieval actor.
          SCREAMING_SNAKE_CASE__					     =						self.retrieval_workers[random.randint(0     ,  len(self.retrieval_workers	) - 1	)]
          SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						ray.get(
              random_worker.retrieve.remote(A_     ,  A_	)	)
      else:
          SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						self._main_retrieve(A_     ,  A_	)
          SCREAMING_SNAKE_CASE__					     =						self.index.get_doc_dicts(A_	)
      return retrieved_doc_embeds, doc_ids, doc_dicts



  @classmethod
  def      _UpperCamelCase    (	cls    :				Tuple     ,  A_    :				List[str]     ,  A_    :				Tuple=None     ,  **A_    :				int	)					-> List[str]:
      return super(A_     ,  cls	).get_tokenizers(A_     ,  A_     ,  **A_	)



  @classmethod
  def      _UpperCamelCase    (	cls    :				str     ,  A_    :				int     ,  A_    :				Tuple     ,  A_    :				Dict=None     ,  **A_    :				str	)					-> Union[str, Any]:
      SCREAMING_SNAKE_CASE__					     =						kwargs.pop("config"     ,  A_	) or RagConfig.from_pretrained(A_     ,  **A_	)
      SCREAMING_SNAKE_CASE__					     =						RagTokenizer.from_pretrained(A_     ,  config=A_	)
      SCREAMING_SNAKE_CASE__					     =						rag_tokenizer.question_encoder
      SCREAMING_SNAKE_CASE__					     =						rag_tokenizer.generator

      if indexed_dataset is not None:
          SCREAMING_SNAKE_CASE__					     =						"custom"
          SCREAMING_SNAKE_CASE__					     =						CustomHFIndex(config.retrieval_vector_size     ,  A_	)
      else:
          SCREAMING_SNAKE_CASE__					     =						cls._build_index(A_	)

      return cls(
          A_     ,  question_encoder_tokenizer=A_     ,  generator_tokenizer=A_     ,  retrieval_workers=A_     ,  index=A_     ,  )







  def      _UpperCamelCase    (	self    :				List[str]	)					-> int:
      logger.info("re-loading the new dataset with embeddings"	)
      # access from the training loop

      ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]	)

      # build the index object again
      SCREAMING_SNAKE_CASE__					     =						self._build_index(self.config	)

      ray.get(
          [
              worker.create_rag_retriever.remote(
                  self.config     ,  self.question_encoder_tokenizer     ,  self.generator_tokenizer     ,  A_	)
              for worker in self.retrieval_workers
          ]	)





import argparse
import json
import re
import string
import sys
from collections import Counter





def 							__a       (					__A	):

    def remove_articles(__A	):
        return re.sub(r"\b(a|an|the)\b" ,       " " ,       __A	)

    def white_space_fix(__A	):
        return " ".join(text.split()	)

    def remove_punc(__A	):
        SCREAMING_SNAKE_CASE__					     =						set(string.punctuation	)
        return "".join(ch for ch in text if ch not in exclude	)

    def lower(__A	):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(__A	)	)	)	)





def 							__a       (					__A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						normalize_answer(__A	).split()
    SCREAMING_SNAKE_CASE__					     =						normalize_answer(__A	).split()
    SCREAMING_SNAKE_CASE__					     =						Counter(__A	) & Counter(__A	)
    SCREAMING_SNAKE_CASE__					     =						sum(common.values()	)
    if num_same == 0:
        return 0
    SCREAMING_SNAKE_CASE__					     =						1.0 * num_same / len(__A	)
    SCREAMING_SNAKE_CASE__					     =						1.0 * num_same / len(__A	)
    SCREAMING_SNAKE_CASE__					     =						(2 * precision * recall) / (precision + recall)
    return fa





def 							__a       (					__A ,       __A	):
    return normalize_answer(__A	) == normalize_answer(__A	)





def 							__a       (					__A ,       __A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						[]
    for ground_truth in ground_truths:
        SCREAMING_SNAKE_CASE__					     =						metric_fn(__A ,       __A	)
        scores_for_ground_truths.append(__A	)
    return max(__A	)





def 							__a       (					__A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						SCREAMING_SNAKE_CASE__					     =						SCREAMING_SNAKE_CASE__					     =						0
    SCREAMING_SNAKE_CASE__					     =						[]
    for passage in dataset:
        for qa in passage["qas"]:
            total += 1
            if qa["id"] not in predictions:
                SCREAMING_SNAKE_CASE__					     =						f"Unanswered question {qa['id']} will receive score 0."
                print(__A ,       file=sys.stderr	)
                continue

            SCREAMING_SNAKE_CASE__					     =						[x["text"] for x in qa["answers"]]
            SCREAMING_SNAKE_CASE__					     =						predictions[qa["id"]]

            SCREAMING_SNAKE_CASE__					     =						metric_max_over_ground_truths(__A ,       __A ,       __A	)
            if int(_exact_match	) == 1:
                correct_ids.append(qa["id"]	)
            exact_match += _exact_match

            fa += metric_max_over_ground_truths(__A ,       __A ,       __A	)

    SCREAMING_SNAKE_CASE__					     =						exact_match / total
    SCREAMING_SNAKE_CASE__					     =						fa / total

    return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
  A__        =			"1.0"
  A__        =			argparse.ArgumentParser("Official evaluation script for ReCoRD v1.0.")
  parser.add_argument("data_file", help="The dataset file in JSON format.")
  parser.add_argument("pred_file", help="The model prediction file in JSON format.")
  parser.add_argument("--output_correct_ids", action="store_true", help="Output the correctly answered query IDs.")
  A__        =			parser.parse_args()

  with open(args.data_file) as data_file:
    A__        =			json.load(data_file)
    if dataset_json["version"] != expected_version:
      print(
          f'''Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json['version']}''',
          file=sys.stderr,
      )
    A__        =			dataset_json["data"]

  with open(args.pred_file) as pred_file:
    A__        =			json.load(pred_file)

  A__       ,    A__        =			evaluate(dataset, predictions)

  if args.output_correct_ids:
    print(f'''Output {len(correct_ids)} correctly answered question IDs.''')
    with open("correct_ids.json", "w") as f:
      json.dump(correct_ids, f)


