






import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets




@contextmanager
def    __A    (   ):

					'''simple docstring'''







					def filter_log(_lowercase       ):
										return False if "This IS expected if you are initializing" in record.msg else True

					_A									=       datasets.utils.logging.get_logger('''transformers.modeling_utils'''       )
					logger.addFilter(_lowercase       )
					try:
										yield
					finally:
										logger.removeFilter(_lowercase       )


__A          = '\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n'

__A          = '\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.\n'

__A          = '\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. \'en\').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bertscore = datasets.load_metric("bertscore")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")\n    >>> print([round(v, 2) for v in results["f1"]])\n    [1.0, 1.0]\n'





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,		_KWARGS_DESCRIPTION    )
class   SCREAMING_SNAKE_CASE (       datasets.Metric    ):





							"""simple docstring"""

							def 					__A       (     self:      Tuple      )			-> str:
												return datasets.MetricInfo(
												    description=_DESCRIPTION						,   citation=_CITATION						,   homepage='''https://github.com/Tiiiger/bert_score'''						,   inputs_description=_KWARGS_DESCRIPTION						,   features=datasets.Features(
												        {
												            '''predictions''': datasets.Value('''string'''						,   id='''sequence'''      ),
												            '''references''': datasets.Sequence(datasets.Value('''string'''						,   id='''sequence'''      )						,   id='''references'''      ),
												        }      )						,   codebase_urls=['''https://github.com/Tiiiger/bert_score''']						,   reference_urls=[
												        '''https://github.com/Tiiiger/bert_score''',
												        '''https://arxiv.org/abs/1904.09675''',
												    ]						,   )

							def 					__A       (     self:      int						,   __A:      str						,   __A:      str						,   __A:      Dict=None						,   __A:      Dict=None						,   __A:      Dict=None						,   __A:      List[str]=False						,   __A:      Optional[int]=False						,   __A:      Optional[Any]=None						,   __A:      Tuple=64						,   __A:      List[Any]=4						,   __A:      Optional[Any]=False						,   __A:      Any=False						,   __A:      List[Any]=None						,   __A:      Optional[int]=False						,   )			-> Optional[Any]:
												_A									=       bert_score.utils.get_hash
												_A									=       bert_score.BERTScorer

												if version.parse(bert_score.__version__      ) >= version.parse('''0.3.10'''      ):
																	_A									=       functools.partial(__A						,   use_fast_tokenizer=__A      )
																	_A									=       functools.partial(__A						,   use_fast_tokenizer=__A      )
												elif use_fast_tokenizer:
																	raise ImportWarning(
																	    '''To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn\'t match this condition.\n'''
																	    '''You can install it with `pip install "bert-score>=0.3.10"`.'''      )

												if model_type is None:
																	assert lang is not None, "either lang or model_type should be specified"
																	_A									=       bert_score.utils.langamodel[lang.lower()]

												if num_layers is None:
																	_A									=       bert_score.utils.modelalayers[model_type]

												_A									=       get_hash(
												    model=__A						,   num_layers=__A						,   idf=__A						,   rescale_with_baseline=__A						,   use_custom_baseline=baseline_path is not None						,   )

												with filter_logging_context():
																	if not hasattr(self						,   '''cached_bertscorer'''      ) or self.cached_bertscorer.hash != hashcode:
																						_A									=       scorer(
																						    model_type=__A						,   num_layers=__A						,   batch_size=__A						,   nthreads=__A						,   all_layers=__A						,   idf=__A						,   device=__A						,   lang=__A						,   rescale_with_baseline=__A						,   baseline_path=__A						,   )

												((_A)							,(_A)							,(_A))									=       self.cached_bertscorer.score(
												    cands=__A						,   refs=__A						,   verbose=__A						,   batch_size=__A						,   )
												_A									=       {
												    '''precision''': P.tolist(),
												    '''recall''': R.tolist(),
												    '''f1''': F.tolist(),
												    '''hashcode''': hashcode,
												}
												return output_dict

							def 					__A       (     self:      List[str]						,   __A:      str=None						,   __A:      Union[str, Any]=None						,   **__A:      Dict      )			-> Optional[int]:
												# References can be strings or lists of strings
												# Let's change strings to lists of strings with one element
												if references is not None:
																	_A									=       [[ref] if isinstance(__A						,   __A      ) else ref for ref in references]
												super().add_batch(predictions=__A						,   references=__A						,   **__A      )



							def 					__A       (     self:      int						,   __A:      int=None						,   __A:      List[str]=None						,   **__A:      Optional[int]      )			-> Optional[Any]:
												# References can be strings or lists of strings
												# Let's change strings to lists of strings with one element
												if isinstance(__A						,   __A      ):
																	_A									=       [reference]
												super().add(prediction=__A						,   reference=__A						,   **__A      )











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    (   _lowercase       ):

					'''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    (   _lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







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




def    __A    (   _lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       [line.strip() for line in open(_lowercase    ,	'''r'''       ).readlines()]
					_A									=       []

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

					_A									=       _A									=       _A									=       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       )

					_A									=       1_00.0 * em / total
					_A									=       1_00.0 * fa / total

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




def    __A    (   _lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       args.k
					_A									=       [line.strip() for line in open(_lowercase    ,	'''r'''       ).readlines()]
					_A									=       [line.strip() for line in open(_lowercase    ,	'''r'''       ).readlines()]

					_A									=       _A									=       0
					for hypo, reference in zip(_lowercase    ,	_lowercase       ):
										_A									=       set(hypo.split('''\t'''       )[:k]       )
										_A									=       set(reference.split('''\t'''       )       )
										total += 1
										em += len(hypo_provenance & ref_provenance       ) / k

					_A									=       1_00.0 * em / total
					logger.info(f"""Precision@{k}: {em: .2f}"""       )




def    __A    (   _lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







					def strip_title(_lowercase       ):
										if title.startswith('''"'''       ):
															_A									=       title[1:]
										if title.endswith('''"'''       ):
															_A									=       title[:-1]
										return title

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

					_A									=       rag_model.rag.question_encoder(_lowercase       )
					_A									=       question_enc_outputs[0]

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




def    __A    (   _lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







					with torch.no_grad():
										_A									=       rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
										    _lowercase    ,	return_tensors='''pt'''    ,	padding=_lowercase    ,	truncation=_lowercase       )

										_A									=       inputs_dict.input_ids.to(args.device       )
										_A									=       inputs_dict.attention_mask.to(args.device       )
										_A									=       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]]    ,	)
										_A									=       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    (   ):

					'''simple docstring'''







					_A									=       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.'''    ,	)
					_A									=       parser.parse_args()
					_A									=       torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''       )
					return args




def    __A    (   _lowercase       ):

					'''simple docstring'''







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

					_A									=       (
					    [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       )

					_A									=       get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
					_A									=       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'''       ):
															_A									=       RagRetriever.from_pretrained(_lowercase    ,	**_lowercase       )
															_A									=       model_class.from_pretrained(_lowercase    ,	retriever=_lowercase    ,	**_lowercase       )
															model.retriever.init_retrieval()
										else:
															_A									=       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:
															_A									=       []
															for line in tqdm(_lowercase       ):
																				questions.append(line.strip()       )
																				if len(_lowercase       ) == args.eval_batch_size:
																									_A									=       evaluate_batch_fn(_lowercase    ,	_lowercase    ,	_lowercase       )
																									preds_file.write('''\n'''.join(_lowercase       ) + '''\n'''       )
																									preds_file.flush()
																									_A									=       []
															if len(_lowercase       ) > 0:
																				_A									=       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)



