






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


__snake_case												=  logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()


def   _A   (				SCREAMING_SNAKE_CASE__  :	Dict       ):
    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   (				SCREAMING_SNAKE_CASE__  :	Any     ,  SCREAMING_SNAKE_CASE__  :	Optional[Any]     ,  SCREAMING_SNAKE_CASE__  :	List[Any]       ):
    return max(metric_fn(A_     ,  A_       ) for gt in ground_truths       )


def   _A   (				SCREAMING_SNAKE_CASE__  :	Optional[Any]     ,  SCREAMING_SNAKE_CASE__  :	Tuple     ,  SCREAMING_SNAKE_CASE__  :	Tuple       ):
    UpperCamelCase       :Any								=   [line.strip() for line in open(A_     ,  '''r'''       ).readlines()]
    UpperCamelCase       :str								=   []

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

    UpperCamelCase       :Any								=   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_       )

    UpperCamelCase       :Any								=   1_00.0 * em / total
    UpperCamelCase       :List[Any]								=   1_00.0 * fa / total

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


def   _A   (				SCREAMING_SNAKE_CASE__  :	Dict     ,  SCREAMING_SNAKE_CASE__  :	Union[str, Any]     ,  SCREAMING_SNAKE_CASE__  :	Tuple       ):
    UpperCamelCase       :Optional[int]								=   args.k
    UpperCamelCase       :Union[str, Any]								=   [line.strip() for line in open(A_     ,  '''r'''       ).readlines()]
    UpperCamelCase       :Union[str, Any]								=   [line.strip() for line in open(A_     ,  '''r'''       ).readlines()]

    UpperCamelCase       :Optional[int]								=   0
    for hypo, reference in zip(A_     ,  A_       ):
        UpperCamelCase       :List[str]								=   set(hypo.split('''\t'''       )[:k]       )
        UpperCamelCase       :int								=   set(reference.split('''\t'''       )       )
        total += 1
        em += len(hypo_provenance & ref_provenance       ) / k

    UpperCamelCase       :str								=   1_00.0 * em / total
    logger.info(F'''Precision@{k}: {em: .2f}'''       )


def   _A   (				SCREAMING_SNAKE_CASE__  :	Union[str, Any]     ,  SCREAMING_SNAKE_CASE__  :	str     ,  SCREAMING_SNAKE_CASE__  :	Dict       ):
    def strip_title(SCREAMING_SNAKE_CASE__  :	Optional[int]       ):
        if title.startswith('''"'''       ):
            UpperCamelCase       :List[str]								=   title[1:]
        if title.endswith('''"'''       ):
            UpperCamelCase       :Optional[Any]								=   title[:-1]
        return title

    UpperCamelCase       :str								=   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
        A_     ,  return_tensors='''pt'''     ,  padding=A_     ,  truncation=A_     ,  )['''input_ids'''].to(args.device       )

    UpperCamelCase       :Optional[int]								=   rag_model.rag.question_encoder(A_       )
    UpperCamelCase       :str								=   question_enc_outputs[0]

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


def   _A   (				SCREAMING_SNAKE_CASE__  :	Dict     ,  SCREAMING_SNAKE_CASE__  :	Optional[Any]     ,  SCREAMING_SNAKE_CASE__  :	str       ):
    with torch.no_grad():
        UpperCamelCase       :Tuple								=   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
            A_     ,  return_tensors='''pt'''     ,  padding=A_     ,  truncation=A_       )

        UpperCamelCase       :Dict								=   inputs_dict.input_ids.to(args.device       )
        UpperCamelCase       :Tuple								=   inputs_dict.attention_mask.to(args.device       )
        UpperCamelCase       :List[str]								=   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]]     ,  )
        UpperCamelCase       :Dict								=   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   (				):
    UpperCamelCase       :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.'''     ,  )
    UpperCamelCase       :List[str]								=   parser.parse_args()
    UpperCamelCase       :Tuple								=   torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''       )
    return args


def   _A   (				SCREAMING_SNAKE_CASE__  :	int       ):
    UpperCamelCase       :int								=   {}
    if args.model_type is None:
        UpperCamelCase       :Optional[int]								=   infer_model_type(args.model_name_or_path       )
        assert args.model_type is not None
    if args.model_type.startswith('''rag'''       ):
        UpperCamelCase       :Dict								=   RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
        UpperCamelCase       :int								=   args.n_docs
        if args.index_name is not None:
            UpperCamelCase       :List[str]								=   args.index_name
        if args.index_path is not None:
            UpperCamelCase       :Optional[int]								=   args.index_path
    else:
        UpperCamelCase       :Dict								=   BartForConditionalGeneration

    UpperCamelCase       :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'''     ,  A_       )

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








"""simple docstring"""





import torch

from transformers import AutoModel







class 				SCREAMING_SNAKE_CASE					(		torch.nn.Module       ):






					"""simple docstring"""



					def __init__(       self			:				Tuple   ,lowercase_			:				Dict="sayef/fsner-bert-base-uncased"		):
						super(lowercase_   ,self		).__init__()

						lowerCAmelCase__	:						int  				=	AutoModel.from_pretrained(lowercase_   ,return_dict=lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	torch.nn.CosineSimilarity(3   ,1E-08		)
						lowerCAmelCase__	:						List[str]  				=	torch.nn.Softmax(dim=1		)







					def        __lowerCAmelCase (       self			:				str   ,**lowercase_			:				int		):
						return self.bert(**lowercase_		).last_hidden_state







					def        __lowerCAmelCase (       self			:				List[Any]   ,lowercase_			:				Optional[int]		):
						return token_embeddings.sum(2   ,keepdim=lowercase_		)







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				int   ,lowercase_			:				str   ,lowercase_			:				Tuple=1		):
						return self.softmax(T * self.cos(lowercase_   ,lowercase_		)		)







					def        __lowerCAmelCase (       self			:				Optional[Any]   ,lowercase_			:				str   ,lowercase_			:				Union[str, Any]		):
						lowerCAmelCase__	:						List[Any]  				=	W_supports['''sizes'''].tolist()
						lowerCAmelCase__	:						Dict  				=	W_supports['''start_token_id'''].item()
						lowerCAmelCase__	:						Union[str, Any]  				=	W_supports['''end_token_id'''].item()

						del W_supports["sizes"]
						del W_supports["start_token_id"]
						del W_supports["end_token_id"]

						lowerCAmelCase__	:						Optional[Any]  				=	self.BERT(**lowercase_		)
						lowerCAmelCase__	:						int  				=	self.BERT(**lowercase_		)

						lowerCAmelCase__	:						List[str]  				=	None
						lowerCAmelCase__	:						Union[str, Any]  				=	None

						lowerCAmelCase__	:						int  				=	W_supports['''input_ids'''] == start_token_id
						lowerCAmelCase__	:						Optional[Any]  				=	W_supports['''input_ids'''] == end_token_id

						for i, size in enumerate(lowercase_		):
							if i == 0:
								lowerCAmelCase__	:						str  				=	0
							else:
								lowerCAmelCase__	:						List[Any]  				=	support_sizes[i - 1]

							lowerCAmelCase__	:						Optional[Any]  				=	S[s : s + size][start_token_masks[s : s + size]]
							lowerCAmelCase__	:						List[Any]  				=	S[s : s + size][end_token_masks[s : s + size]]

							lowerCAmelCase__	:						Union[str, Any]  				=	torch.matmul(q[i]   ,s_start.T		).sum(1		).softmax(0		)
							lowerCAmelCase__	:						Any  				=	torch.matmul(q[i]   ,s_end.T		).sum(1		).softmax(0		)

							if p_starts is not None:
								lowerCAmelCase__	:						List[Any]  				=	torch.vstack((p_starts, p_start)		)
								lowerCAmelCase__	:						List[Any]  				=	torch.vstack((p_ends, p_end)		)
							else:
								lowerCAmelCase__	:						Union[str, Any]  				=	p_start
								lowerCAmelCase__	:						str  				=	p_end

						return p_starts, p_ends




