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

transformers_logging.set_verbosity_info()
def 					a__ ( a :					int		):

  """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__ ( a :					Dict							,  a :					Dict							,  a :					Union[str, Any]		):

  """simple docstring"""







  return max(metric_fn(a							,  a		) for gt in ground_truths		)
def 					a__ ( a :					List[Any]							,  a :					Optional[int]							,  a :					Optional[int]		):

  """simple docstring"""







  _snake_case  :    Tuple            = [line.strip() for line in open(a							,  "r"		).readlines()]
  _snake_case  :    Tuple            = []

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

  _snake_case  :    int            = 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		)

  _snake_case  :    Any            = 100.0 * em / total
  _snake_case  :    Union[str, Any]            = 100.0 * fa / total

  logger.info(f'F1: {fa:.2f}'		)
  logger.info(f'EM: {em:.2f}'		)
def 					a__ ( a :					str							,  a :					List[str]							,  a :					Optional[int]		):

  """simple docstring"""







  _snake_case  :    Dict            = args.k
  _snake_case  :    Optional[int]            = [line.strip() for line in open(a							,  "r"		).readlines()]
  _snake_case  :    Optional[Any]            = [line.strip() for line in open(a							,  "r"		).readlines()]

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

  _snake_case  :    List[str]            = 100.0 * em / total
  logger.info(f'Precision@{k}: {em: .2f}'		)
def 					a__ ( a :					Dict							,  a :					Optional[Any]							,  a :					Tuple		):

  """simple docstring"""







  def strip_title(a :					Optional[Any]		):
    if title.startswith("\""		):
      _snake_case  :    List[Any]            = title[1:]
    if title.endswith("\""		):
      _snake_case  :    Union[str, Any]            = title[:-1]
    return title

  _snake_case  :    List[str]            = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
      a							,  return_tensors="pt"							,  padding=a							,  truncation=a							,  )["input_ids"].to(args.device		)

  _snake_case  :    str            = rag_model.rag.question_encoder(a		)
  _snake_case  :    str            = question_enc_outputs[0]

  _snake_case  :    Any            = 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"							,  )
  _snake_case  :    Optional[Any]            = rag_model.retriever.index.get_doc_dicts(result.doc_ids		)
  _snake_case  :    str            = []
  for docs in all_docs:
    _snake_case  :    str            = [strip_title(a		) for title in docs["title"]]
    provenance_strings.append("\t".join(a		)		)
  return provenance_strings
def 					a__ ( a :					Dict							,  a :					Any							,  a :					Optional[Any]		):

  """simple docstring"""







  with torch.no_grad():
    _snake_case  :    Union[str, Any]            = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
        a							,  return_tensors="pt"							,  padding=a							,  truncation=a		)

    _snake_case  :    str            = inputs_dict.input_ids.to(args.device		)
    _snake_case  :    List[Any]            = inputs_dict.attention_mask.to(args.device		)
    _snake_case  :    int            = 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]]							,  )
    _snake_case  :    List[Any]            = 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 					a__ ( ):

  """simple docstring"""







  _snake_case  :    Union[str, Any]            = 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."							,  )
  _snake_case  :    Optional[int]            = parser.parse_args()
  _snake_case  :    int            = torch.device("cuda" if torch.cuda.is_available() else "cpu"		)
  return args
def 					a__ ( a :					Optional[Any]		):

  """simple docstring"""







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

  _snake_case  :    List[Any]            = (
      [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		)

  _snake_case  :    str            = get_scores if args.eval_mode == "e2e" else get_precision_at_k
  _snake_case  :    Union[str, Any]            = 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"		):
      _snake_case  :    str            = RagRetriever.from_pretrained(a							,  **a		)
      _snake_case  :    Any            = model_class.from_pretrained(a							,  retriever=a							,  **a		)
      model.retriever.init_retrieval()
    else:
      _snake_case  :    Tuple            = 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:
      _snake_case  :    Any            = []
      for line in tqdm(a		):
        questions.append(line.strip()		)
        if len(a		) == args.eval_batch_size:
          _snake_case  :    Tuple            = evaluate_batch_fn(a							,  a							,  a		)
          preds_file.write("\n".join(a		) + "\n"		)
          preds_file.flush()
          _snake_case  :    Optional[Any]            = []
      if len(a		) > 0:
        _snake_case  :    Optional[Any]            = 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__":
    _a   :      Dict       =				get_args()
    main(args)



"""simple docstring"""
import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    SummarizationPipeline,
    TFPreTrainedModel,
    pipeline,
)
from transformers.testing_utils import get_gpu_count, is_pipeline_test, require_tf, require_torch, slow, torch_device
from transformers.tokenization_utils import TruncationStrategy

from .test_pipelines_common import ANY


_a   :      List[Any]       =				-1 if torch_device == """cpu""" else 0






@is_pipeline_test
class   _UpperCAmelCase					(       unittest.TestCase):
    __lowercase				:			List[str]	=       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    __lowercase				:			str	=       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    int            = SummarizationPipeline(model=snake_case_    ,     tokenizer=snake_case_  )
      return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    str            = summarizer.model

      _snake_case  :    Optional[int]            = summarizer("(CNN)The Palestinian Authority officially became"  )
      self.assertEqual(snake_case_    ,     [{"summary_text": ANY(snake_case_  )}]  )

      _snake_case  :    List[str]            = summarizer(
          "(CNN)The Palestinian Authority officially became "    ,     num_beams=2    ,     min_length=2    ,     max_length=5    ,     )
      self.assertEqual(snake_case_    ,     [{"summary_text": ANY(snake_case_  )}]  )

      # Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
      _snake_case  :    Tuple            = [
          "SwitchTransformersConfig",
          "T5Config",
          "LongT5Config",
          "LEDConfig",
          "PegasusXConfig",
          "FSMTConfig",
          "M2M100Config",
          "ProphetNetConfig",  # positional embeddings up to a fixed maximum size (otherwise clamping the values)
      ]
      if model.config.__class__.__name__ not in model_can_handle_longer_seq:
        # Too long and exception is expected.
        # For TF models, if the weights are initialized in GPU context, we won't get expected index error from
        # the embedding layer.
        if not (
            isinstance(snake_case_    ,     snake_case_  )
            and get_gpu_count() > 0
            and len(summarizer.model.trainable_weights  ) > 0
        ):
          with self.assertRaises(snake_case_  ):
            _snake_case  :    Tuple            = summarizer("This " * 10_00  )
      _snake_case  :    Any            = summarizer("This " * 10_00    ,     truncation=TruncationStrategy.ONLY_FIRST  )
    @require_torch
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[Any]            = pipeline(task="summarization"    ,     model="sshleifer/tiny-mbart"    ,     framework="pt"  )
      _snake_case  :    List[Any]            = summarizer("This is a small test"  )
      self.assertEqual(
          snake_case_    ,     [
              {
                  "summary_text": "เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"
              }
          ]    ,     )
    @require_tf
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Dict            = pipeline(task="summarization"    ,     model="sshleifer/tiny-mbart"    ,     framework="tf"  )
      _snake_case  :    Union[str, Any]            = summarizer("This is a small test"  )
      self.assertEqual(
          snake_case_    ,     [
              {
                  "summary_text": "เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"
              }
          ]    ,     )


    @require_torch
    @slow
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Tuple            = pipeline(task="summarization"    ,     device=snake_case_  )
      _snake_case  :    Any            = (
          " (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on"
          " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The"
          " formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based."
          " The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its"
          " jurisdiction over alleged crimes committed \"in the occupied Palestinian territory, including East"
          " Jerusalem, since June 13, 2014.\" Later that month, the ICC opened a preliminary examination into the"
          " situation in Palestinian territories, paving the way for possible war crimes investigations against"
          " Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and"
          " the United States, neither of which is an ICC member, opposed the Palestinians' efforts to join the"
          " body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony, said it was a"
          " move toward greater justice. \"As Palestine formally becomes a State Party to the Rome Statute today, the"
          " world is also a step closer to ending a long era of impunity and injustice,\" he said, according to an"
          " ICC news release. \"Indeed, today brings us closer to our shared goals of justice and peace.\" Judge"
          " Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the"
          " Palestinians. \"As the Rome Statute today enters into force for the State of Palestine, Palestine"
          " acquires all the rights as well as responsibilities that come with being a State Party to the Statute."
          " These are substantive commitments, which cannot be taken lightly,\" she said. Rights group Human Rights"
          " Watch welcomed the development. \"Governments seeking to penalize Palestine for joining the ICC should"
          " immediately end their pressure, and countries that support universal acceptance of the court's treaty"
          " should speak out to welcome its membership,\" said Balkees Jarrah, international justice counsel for the"
          " group. \"What's objectionable is the attempts to undermine international justice, not Palestine's"
          " decision to join a treaty to which over 100 countries around the world are members.\" In January, when"
          " the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an"
          " outrage, saying the court was overstepping its boundaries. The United States also said it \"strongly\""
          " disagreed with the court's decision. \"As we have said repeatedly, we do not believe that Palestine is a"
          " state and therefore we do not believe that it is eligible to join the ICC,\" the State Department said in"
          " a statement. It urged the warring sides to resolve their differences through direct negotiations. \"We"
          " will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,\""
          " it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the"
          " territories as \"Palestine.\" While a preliminary examination is not a formal investigation, it allows the"
          " court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou"
          " Bensouda said her office would \"conduct its analysis in full independence and impartiality.\" The war"
          " between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry"
          " will include alleged war crimes committed since June. The International Criminal Court was set up in"
          " 2002 to prosecute genocide, crimes against humanity and war crimes. CNN's Vasco Cotovio, Kareem Khadder"
          " and Faith Karimi contributed to this report."
      )
      _snake_case  :    int            = (
          " The Palestinian Authority becomes the 123rd member of the International Criminal Court . The move gives"
          " the court jurisdiction over alleged crimes in Palestinian territories . Israel and the United States"
          " opposed the Palestinians' efforts to join the court . Rights group Human Rights Watch welcomes the move,"
          " says governments seeking to penalize Palestine should end pressure ."
      )
      _snake_case  :    Union[str, Any]            = summarizer(snake_case_  )
      self.assertEqual(result[0]["summary_text"]    ,     snake_case_  )

