



"""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


_lowerCAmelCase					:List[Any]  =      logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()





def 				lowerCamelCase_		(UpperCamelCase__	:	Optional[int]							):
    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_		(UpperCamelCase__	:	List[str]  , UpperCamelCase__	:	str  , UpperCamelCase__	:	List[str]							):
    return max(metric_fn(UpperCamelCase__  , UpperCamelCase__							) for gt in ground_truths							)





def 				lowerCamelCase_		(UpperCamelCase__	:	int  , UpperCamelCase__	:	List[str]  , UpperCamelCase__	:	int							):
    _UpperCAmelCase							:						List[str]       							=					[line.strip() for line in open(UpperCamelCase__  , '''r'''							).readlines()]
    _UpperCAmelCase							:						Dict       							=					[]

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

    _UpperCAmelCase							:						List[str]       							=					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__							)

    _UpperCAmelCase							:						List[Any]       							=					100.0 * em / total
    _UpperCAmelCase							:						Tuple       							=					100.0 * fa / total

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





def 				lowerCamelCase_		(UpperCamelCase__	:	Tuple  , UpperCamelCase__	:	Any  , UpperCamelCase__	:	Optional[int]							):
    _UpperCAmelCase							:						Tuple       							=					args.k
    _UpperCAmelCase							:						List[Any]       							=					[line.strip() for line in open(UpperCamelCase__  , '''r'''							).readlines()]
    _UpperCAmelCase							:						int       							=					[line.strip() for line in open(UpperCamelCase__  , '''r'''							).readlines()]

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

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





def 				lowerCamelCase_		(UpperCamelCase__	:	Optional[Any]  , UpperCamelCase__	:	str  , UpperCamelCase__	:	List[Any]							):
    def strip_title(UpperCamelCase__	:	Dict							):
        if title.startswith('''"'''							):
            _UpperCAmelCase							:						Any       							=					title[1:]
        if title.endswith('''"'''							):
            _UpperCAmelCase							:						Dict       							=					title[:-1]
        return title

    _UpperCAmelCase							:						List[Any]       							=					rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
        UpperCamelCase__  , return_tensors='''pt'''  , padding=UpperCamelCase__  , truncation=UpperCamelCase__  , )['''input_ids'''].to(args.device							)

    _UpperCAmelCase							:						int       							=					rag_model.rag.question_encoder(UpperCamelCase__							)
    _UpperCAmelCase							:						Dict       							=					question_enc_outputs[0]

    _UpperCAmelCase							:						Tuple       							=					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'''  , )
    _UpperCAmelCase							:						int       							=					rag_model.retriever.index.get_doc_dicts(result.doc_ids							)
    _UpperCAmelCase							:						Optional[int]       							=					[]
    for docs in all_docs:
        _UpperCAmelCase							:						Union[str, Any]       							=					[strip_title(UpperCamelCase__							) for title in docs['''title''']]
        provenance_strings.append('''\t'''.join(UpperCamelCase__							)							)
    return provenance_strings





def 				lowerCamelCase_		(UpperCamelCase__	:	Tuple  , UpperCamelCase__	:	Dict  , UpperCamelCase__	:	Dict							):
    with torch.no_grad():
        _UpperCAmelCase							:						Any       							=					rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
            UpperCamelCase__  , return_tensors='''pt'''  , padding=UpperCamelCase__  , truncation=UpperCamelCase__							)

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





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

    _UpperCAmelCase							:						Tuple       							=					(
        [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__							)

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







"""simple docstring"""
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


_lowerCAmelCase					:Dict  =      logging.get_logger(__name__)

_lowerCAmelCase					:Tuple  =      {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_lowerCAmelCase					:Any  =      {
    'vocab_file': {
        'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt',
    },
    'tokenizer_file': {
        'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json',
    },
}

_lowerCAmelCase					:str  =      {
    'microsoft/mpnet-base': 512,
}

_lowerCAmelCase					:Union[str, Any]  =      {
    'microsoft/mpnet-base': {'do_lower_case': True},
}
class 							_UpperCAmelCase							(				a   ):





     '''simple docstring'''



     a__             =VOCAB_FILES_NAMES
     a__             =PRETRAINED_VOCAB_FILES_MAP
     a__             =PRETRAINED_INIT_CONFIGURATION
     a__             =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     a__             =MPNetTokenizer
     a__             =['''input_ids''', '''attention_mask''']





     def __init__(      self ,			A=None ,			A=None ,			A=True ,			A="<s>" ,			A="</s>" ,			A="</s>" ,			A="<s>" ,			A="[UNK]" ,			A="<pad>" ,			A="<mask>" ,			A=True ,			A=None ,			**A ,			)							->      Dict:
         super().__init__(
             A ,			tokenizer_file=A ,			do_lower_case=A ,			bos_token=A ,			eos_token=A ,			sep_token=A ,			cls_token=A ,			unk_token=A ,			pad_token=A ,			mask_token=A ,			tokenize_chinese_chars=A ,			strip_accents=A ,			**A ,			)

         _UpperCAmelCase							:						List[str]       							=					json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
         if (
             pre_tok_state.get('''lowercase''' ,			A	) != do_lower_case
             or pre_tok_state.get('''strip_accents''' ,			A	) != strip_accents
         ):
             _UpperCAmelCase							:						Optional[int]       							=					getattr(A ,			pre_tok_state.pop('''type'''	)	)
             _UpperCAmelCase							:						List[Any]       							=					do_lower_case
             _UpperCAmelCase							:						List[Any]       							=					strip_accents
             _UpperCAmelCase							:						Tuple       							=					pre_tok_class(**A	)

         _UpperCAmelCase							:						Union[str, Any]       							=					do_lower_case





     @property
     def 				__lowerCAmelCase			(      self	)							->      str:
         if self._mask_token is None:
             if self.verbose:
                 logger.error('''Using mask_token, but it is not set yet.'''	)
             return None
         return str(self._mask_token	)





     @mask_token.setter
     def 				__lowerCAmelCase			(      self ,			A	)							->      str:
         _UpperCAmelCase							:						Optional[int]       							=					AddedToken(A ,			lstrip=A ,			rstrip=A	) if isinstance(A ,			A	) else value
         _UpperCAmelCase							:						List[Any]       							=					value





     def 				__lowerCAmelCase			(      self ,			A ,			A=None	)							->      str:
         _UpperCAmelCase							:						List[str]       							=					[self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
             return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





     def 				__lowerCAmelCase			(      self ,			A ,			A = None	)							->      List[int]:
         _UpperCAmelCase							:						int       							=					[self.sep_token_id]
         _UpperCAmelCase							:						int       							=					[self.cls_token_id]

         if token_ids_a is None:
             return len(cls + token_ids_a + sep	) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]





     def 				__lowerCAmelCase			(      self ,			A ,			A = None	)							->      Tuple[str]:
         _UpperCAmelCase							:						str       							=					self._tokenizer.model.save(A ,			name=A	)
         return tuple(A	)


