






"""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_			: Optional[int]				=			logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()






def        lowerCamelCase_  (						_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        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				):
 return max(metric_fn(_lowerCamelCase			,   _lowerCamelCase				) for gt in ground_truths				)






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				):
 lowerCamelCase__     :			str     				=						[line.strip() for line in open(_lowerCamelCase			,   'r'				).readlines()]
 lowerCamelCase__     :			List[Any]     				=						[]

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

 lowerCamelCase__     :			Optional[int]     				=						0
 for prediction, ground_truths in zip(_lowerCamelCase			,   _lowerCamelCase				):
  total += 1
  em += metric_max_over_ground_truths(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				)
  fa += metric_max_over_ground_truths(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				)

 lowerCamelCase__     :			List[str]     				=						100.0 * em / total
 lowerCamelCase__     :			List[str]     				=						100.0 * fa / total

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






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				):
 lowerCamelCase__     :			int     				=						args.k
 lowerCamelCase__     :			Union[str, Any]     				=						[line.strip() for line in open(_lowerCamelCase			,   'r'				).readlines()]
 lowerCamelCase__     :			Any     				=						[line.strip() for line in open(_lowerCamelCase			,   'r'				).readlines()]

 lowerCamelCase__     :			Dict     				=						0
 for hypo, reference in zip(_lowerCamelCase			,   _lowerCamelCase				):
  lowerCamelCase__     :			str     				=						set(hypo.split('\t'				)[:k]				)
  lowerCamelCase__     :			List[Any]     				=						set(reference.split('\t'				)				)
  total += 1
  em += len(hypo_provenance & ref_provenance				) / k

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






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				):
 def strip_title(_lowerCamelCase				):
  if title.startswith('"'				):
   lowerCamelCase__     :			List[str]     				=						title[1:]
  if title.endswith('"'				):
   lowerCamelCase__     :			Any     				=						title[:-1]
  return title

 lowerCamelCase__     :			Dict     				=						rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
     _lowerCamelCase			,   return_tensors='pt'			,   padding=_lowerCamelCase			,   truncation=_lowerCamelCase			,   )['input_ids'].to(args.device				)

 lowerCamelCase__     :			List[str]     				=						rag_model.rag.question_encoder(_lowerCamelCase				)
 lowerCamelCase__     :			int     				=						question_enc_outputs[0]

 lowerCamelCase__     :			List[str]     				=						rag_model.retriever(
     _lowerCamelCase			,   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'			,   )
 lowerCamelCase__     :			Tuple     				=						rag_model.retriever.index.get_doc_dicts(result.doc_ids				)
 lowerCamelCase__     :			Dict     				=						[]
 for docs in all_docs:
  lowerCamelCase__     :			Optional[int]     				=						[strip_title(_lowerCamelCase				) for title in docs['title']]
  provenance_strings.append('\t'.join(_lowerCamelCase				)				)
 return provenance_strings






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				):
 with torch.no_grad():
  lowerCamelCase__     :			Any     				=						rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
      _lowerCamelCase			,   return_tensors='pt'			,   padding=_lowerCamelCase			,   truncation=_lowerCamelCase				)

  lowerCamelCase__     :			List[Any]     				=						inputs_dict.input_ids.to(args.device				)
  lowerCamelCase__     :			List[Any]     				=						inputs_dict.attention_mask.to(args.device				)
  lowerCamelCase__     :			Optional[Any]     				=						rag_model.generate(  # rag_model overwrites generate
      _lowerCamelCase			,   attention_mask=_lowerCamelCase			,   num_beams=args.num_beams			,   min_length=args.min_length			,   max_length=args.max_length			,   early_stopping=_lowerCamelCase			,   num_return_sequences=1			,   bad_words_ids=[[0, 0]]			,   )
  lowerCamelCase__     :			Tuple     				=						rag_model.retriever.generator_tokenizer.batch_decode(_lowerCamelCase			,   skip_special_tokens=_lowerCamelCase				)

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

  return answers






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






def        lowerCamelCase_  (						_lowerCamelCase				):
 lowerCamelCase__     :			Optional[int]     				=						{}
 if args.model_type is None:
  lowerCamelCase__     :			Optional[Any]     				=						infer_model_type(args.model_name_or_path				)
  assert args.model_type is not None
 if args.model_type.startswith('rag'				):
  lowerCamelCase__     :			Tuple     				=						RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
  lowerCamelCase__     :			Optional[int]     				=						args.n_docs
  if args.index_name is not None:
   lowerCamelCase__     :			Dict     				=						args.index_name
  if args.index_path is not None:
   lowerCamelCase__     :			Tuple     				=						args.index_path
 else:
  lowerCamelCase__     :			Any     				=						BartForConditionalGeneration

 lowerCamelCase__     :			Dict     				=						(
     [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'			,   _lowerCamelCase				)

 lowerCamelCase__     :			Any     				=						get_scores if args.eval_mode == 'e2e' else get_precision_at_k
 lowerCamelCase__     :			Optional[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(_lowerCamelCase			,   args.predictions_path			,   args.gold_data_path				)
   continue

  logger.info('***** Running evaluation for {} *****'.format(_lowerCamelCase				)				)
  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'				):
   lowerCamelCase__     :			Optional[int]     				=						RagRetriever.from_pretrained(_lowerCamelCase			,   **_lowerCamelCase				)
   lowerCamelCase__     :			Dict     				=						model_class.from_pretrained(_lowerCamelCase			,   retriever=_lowerCamelCase			,   **_lowerCamelCase				)
   model.retriever.init_retrieval()
  else:
   lowerCamelCase__     :			str     				=						model_class.from_pretrained(_lowerCamelCase			,   **_lowerCamelCase				)
  model.to(args.device				)

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

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


if __name__ == "__main__":
      A_			: Union[str, Any]				=			get_args()
      main(args)








'''simple docstring'''



import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_SCREAMING_SNAKE_CASE    =			'''\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
'''

# TODO: Add description of the dataset here
# You can copy an official description
_SCREAMING_SNAKE_CASE    =			'''\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
'''

# TODO: Add a link to an official homepage for the dataset here
_SCREAMING_SNAKE_CASE    =			''''''

# TODO: Add the licence for the dataset here if you can find it
_SCREAMING_SNAKE_CASE    =			''''''

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_SCREAMING_SNAKE_CASE    =			{
    '''first_domain''': '''https://huggingface.co/great-new-dataset-first_domain.zip''',
    '''second_domain''': '''https://huggingface.co/great-new-dataset-second_domain.zip''',
}






class 							__lowercase				(					datasets.GeneratorBasedBuilder  ):







	'''simple docstring'''

	a			: Tuple      =				datasets.Version("1.1.0"  )

	# This is an example of a dataset with multiple configurations.
	# If you don't want/need to define several sub-sets in your dataset,
	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

	# If you need to make complex sub-parts in the datasets with configurable options
	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
	# BUILDER_CONFIG_CLASS = MyBuilderConfig

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('my_dataset', 'first_domain')
	# data = datasets.load_dataset('my_dataset', 'second_domain')
	a			: List[Any]      =				[
	    datasets.BuilderConfig(name="first_domain"       ,					version=lowerCAmelCase__       ,					description="This part of my dataset covers a first domain"  ),
	    datasets.BuilderConfig(name="second_domain"       ,					version=lowerCAmelCase__       ,					description="This part of my dataset covers a second domain"  ),
	]

	a			: Dict      =				"first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.


	def   _UpperCAmelCase   (self       )							->							List[str]:







				'''simple docstring'''





				if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
							__lowercase   =							datasets.Features(
							    {
							        '''sentence''': datasets.Value('''string'''       ),
							        '''option1''': datasets.Value('''string'''       ),
							        '''answer''': datasets.Value('''string'''       )
							        # These are the features of your dataset like images, labels ...
							    }       )
				else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
							__lowercase   =							datasets.Features(
							    {
							        '''sentence''': datasets.Value('''string'''       ),
							        '''option2''': datasets.Value('''string'''       ),
							        '''second_domain_answer''': datasets.Value('''string'''       )
							        # These are the features of your dataset like images, labels ...
							    }       )
				return datasets.DatasetInfo(
				    # This is the description that will appear on the datasets page.
				    description=_DESCRIPTION   ,features=_lowerCamelCase   ,homepage=_HOMEPAGE   ,license=_LICENSE   ,citation=_CITATION   ,)


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							Dict:







				'''simple docstring'''





				__lowercase   =							_URLS[self.config.name]
				__lowercase   =							dl_manager.download_and_extract(_lowerCamelCase       )
				return [
				    datasets.SplitGenerator(
				        name=datasets.Split.TRAIN   ,gen_kwargs={
				            '''filepath''': os.path.join(_lowerCamelCase   ,'''train.jsonl'''       ),
				            '''split''': '''train''',
				        }   ,),
				    datasets.SplitGenerator(
				        name=datasets.Split.VALIDATION   ,gen_kwargs={
				            '''filepath''': os.path.join(_lowerCamelCase   ,'''dev.jsonl'''       ),
				            '''split''': '''dev''',
				        }   ,),
				    datasets.SplitGenerator(
				        name=datasets.Split.TEST   ,gen_kwargs={
				            '''filepath''': os.path.join(_lowerCamelCase   ,'''test.jsonl'''       ),
				            '''split''': '''test'''
				        }   ,),
				]


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase       )							->							str:







				'''simple docstring'''





				with open(_lowerCamelCase   ,encoding='''utf-8'''       ) as f:
							for key, row in enumerate(_lowerCamelCase       ):
										__lowercase   =							json.loads(_lowerCamelCase       )
										if self.config.name == "first_domain":
													# Yields examples as (key, example) tuples
													yield key, {
													    "sentence": data["sentence"],
													    "option1": data["option1"],
													    "answer": "" if split == "test" else data["answer"],
													}
										else:
													yield key, {
													    "sentence": data["sentence"],
													    "option2": data["option2"],
													    "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
													}





