




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

transformers_logging.set_verbosity_info()



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  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__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase      ):
  return max(metric_fn(__UpperCamelCase,	__UpperCamelCase      ) for gt in ground_truths      )



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase      ):
  lowercase			:List[str]							=  [line.strip() for line in open(__UpperCamelCase,	"r"      ).readlines()]
  lowercase			:Any							=  []

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

  lowercase			:Union[str, Any]							=  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      )

  lowercase			:Union[str, Any]							=  100.0 * em / total
  lowercase			:Optional[int]							=  100.0 * fa / total

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



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase      ):
  lowercase			:List[Any]							=  args.k
  lowercase			:Tuple							=  [line.strip() for line in open(__UpperCamelCase,	"r"      ).readlines()]
  lowercase			:int							=  [line.strip() for line in open(__UpperCamelCase,	"r"      ).readlines()]

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

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



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase      ):
  def strip_title(lowerCamelCase      ):
    if title.startswith("\""      ):
      lowercase			:int							=  title[1:]
    if title.endswith("\""      ):
      lowercase			:Optional[int]							=  title[:-1]
    return title

  lowercase			:Optional[Any]							=  rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
      __UpperCamelCase,	return_tensors="pt",	padding=__UpperCamelCase,	truncation=__UpperCamelCase,	)["input_ids"].to(args.device      )

  lowercase			:Dict							=  rag_model.rag.question_encoder(__UpperCamelCase      )
  lowercase			:int							=  question_enc_outputs[0]

  lowercase			:Any							=  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",	)
  lowercase			:Tuple							=  rag_model.retriever.index.get_doc_dicts(result.doc_ids      )
  lowercase			:Dict							=  []
  for docs in all_docs:
    lowercase			:Tuple							=  [strip_title(__UpperCamelCase      ) for title in docs["title"]]
    provenance_strings.append("\t".join(__UpperCamelCase      )      )
  return provenance_strings



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase      ):
  with torch.no_grad():
    lowercase			:Optional[int]							=  rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
        __UpperCamelCase,	return_tensors="pt",	padding=__UpperCamelCase,	truncation=__UpperCamelCase      )

    lowercase			:Union[str, Any]							=  inputs_dict.input_ids.to(args.device      )
    lowercase			:int							=  inputs_dict.attention_mask.to(args.device      )
    lowercase			:Optional[Any]							=  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]],	)
    lowercase			:int							=  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__     (   ):
  lowercase			:Optional[int]							=  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.",	)
  lowercase			:Dict							=  parser.parse_args()
  lowercase			:Optional[Any]							=  torch.device("cuda" if torch.cuda.is_available() else "cpu"      )
  return args



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

  lowercase			:Optional[int]							=  (
      [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      )

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




import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


A   :     Tuple       =				get_tests_dir("fixtures")


class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  def       __A   (							self       :				Union[str, Any]      )     ->		Union[str, Any]:
        # A mock response for an HTTP head request to emulate server down
        SCREAMING_SNAKE_CASE_      =     mock.Mock()
        SCREAMING_SNAKE_CASE_      =     500
        SCREAMING_SNAKE_CASE_      =     {}
        SCREAMING_SNAKE_CASE_      =     HTTPError
        SCREAMING_SNAKE_CASE_      =     {}

        # Download this model to make sure it's in the cache.
        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2"      )
        # Under the mock environment we get a 500 error when trying to reach the model.
        with mock.patch("requests.Session.request"     ,   return_value=__magic_name__      ) as mock_head:
              SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2"      )
              # This check we did call the fake head request
              mock_head.assert_called()







  def       __A   (							self       :				Optional[int]      )     ->		Tuple:
        # This test is for deprecated behavior and can be removed in v5
        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained(
            "https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json"      )



@is_staging_test
class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  @classmethod
  def       __A   (							cls       :				Any      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     TOKEN
        HfFolder.save_token(__magic_name__      )







  @classmethod
  def       __A   (							cls       :				int      )     ->		Optional[Any]:
        try:
              delete_repo(token=cls._token     ,   repo_id="test-feature-extractor"      )
        except HTTPError:
              pass

        try:
              delete_repo(token=cls._token     ,   repo_id="valid_org/test-feature-extractor-org"      )
        except HTTPError:
              pass

        try:
              delete_repo(token=cls._token     ,   repo_id="test-dynamic-feature-extractor"      )
        except HTTPError:
              pass







  def       __A   (							self       :				str      )     ->		List[Any]:
        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained(__magic_name__      )
        feature_extractor.push_to_hub("test-feature-extractor"     ,   use_auth_token=self._token      )

        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor'''      )
        for k, v in feature_extractor.__dict__.items():
              self.assertEqual(__magic_name__     ,   getattr(__magic_name__     ,   __magic_name__      )      )

        # Reset repo
        delete_repo(token=self._token     ,   repo_id="test-feature-extractor"      )

        # Push to hub via save_pretrained
        with tempfile.TemporaryDirectory() as tmp_dir:
              feature_extractor.save_pretrained(
                  __magic_name__     ,   repo_id="test-feature-extractor"     ,   push_to_hub=__magic_name__     ,   use_auth_token=self._token      )

        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor'''      )
        for k, v in feature_extractor.__dict__.items():
              self.assertEqual(__magic_name__     ,   getattr(__magic_name__     ,   __magic_name__      )      )







  def       __A   (							self       :				Dict      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained(__magic_name__      )
        feature_extractor.push_to_hub("valid_org/test-feature-extractor"     ,   use_auth_token=self._token      )

        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor"      )
        for k, v in feature_extractor.__dict__.items():
              self.assertEqual(__magic_name__     ,   getattr(__magic_name__     ,   __magic_name__      )      )

        # Reset repo
        delete_repo(token=self._token     ,   repo_id="valid_org/test-feature-extractor"      )

        # Push to hub via save_pretrained
        with tempfile.TemporaryDirectory() as tmp_dir:
              feature_extractor.save_pretrained(
                  __magic_name__     ,   repo_id="valid_org/test-feature-extractor-org"     ,   push_to_hub=__magic_name__     ,   use_auth_token=self._token      )

        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor-org"      )
        for k, v in feature_extractor.__dict__.items():
              self.assertEqual(__magic_name__     ,   getattr(__magic_name__     ,   __magic_name__      )      )







  def       __A   (							self       :				List[Any]      )     ->		Dict:
        CustomFeatureExtractor.register_for_auto_class()
        SCREAMING_SNAKE_CASE_      =     CustomFeatureExtractor.from_pretrained(__magic_name__      )

        feature_extractor.push_to_hub("test-dynamic-feature-extractor"     ,   use_auth_token=self._token      )

        # This has added the proper auto_map field to the config
        self.assertDictEqual(
            feature_extractor.auto_map     ,   {"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor"}     ,   )

        SCREAMING_SNAKE_CASE_      =     AutoFeatureExtractor.from_pretrained(
            F'''{USER}/test-dynamic-feature-extractor'''     ,   trust_remote_code=__magic_name__      )
        # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
        self.assertEqual(new_feature_extractor.__class__.__name__     ,   "CustomFeatureExtractor"      )


