






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


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

transformers_logging.set_verbosity_info()

def    _lowerCamelCase		(  __A			:							List[Any]       )					->    Union[str, Any]:
    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		(  __A			:							Dict						,						__A			:							Optional[Any]						,						__A			:							Any       )					->    str:
    return max(metric_fn(__A						,						__A       ) for gt in ground_truths       )

def    _lowerCamelCase		(  __A			:							Dict						,						__A			:							Optional[int]						,						__A			:							int       )					->    Tuple:
    _UpperCAmelCase :					Optional[Any]	     = [line.strip() for line in open(__A						,						'''r'''       ).readlines()]
    _UpperCAmelCase :					Union[str, Any]	     = []

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

    _UpperCAmelCase :					Tuple	     = 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	     = 100.0 * em / total
    _UpperCAmelCase :					Any	     = 100.0 * fa / total

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

def    _lowerCamelCase		(  __A			:							Optional[Any]						,						__A			:							Tuple						,						__A			:							List[str]       )					->    Tuple:
    _UpperCAmelCase :					Dict	     = args.k
    _UpperCAmelCase :					Dict	     = [line.strip() for line in open(__A						,						'''r'''       ).readlines()]
    _UpperCAmelCase :					str	     = [line.strip() for line in open(__A						,						'''r'''       ).readlines()]

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

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

def    _lowerCamelCase		(  __A			:							Any						,						__A			:							List[Any]						,						__A			:							List[str]       )					->    int:
    def strip_title(__A			:							Dict       ):
        if title.startswith('''"'''       ):
            _UpperCAmelCase :					List[Any]	     = title[1:]
        if title.endswith('''"'''       ):
            _UpperCAmelCase :					int	     = title[:-1]
        return title

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

    _UpperCAmelCase :					List[str]	     = rag_model.rag.question_encoder(__A       )
    _UpperCAmelCase :					Optional[Any]	     = question_enc_outputs[0]

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

def    _lowerCamelCase		(  __A			:							int						,						__A			:							Tuple						,						__A			:							List[str]       )					->    Tuple:
    with torch.no_grad():
        _UpperCAmelCase :					str	     = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
            __A						,						return_tensors='''pt'''						,						padding=__A						,						truncation=__A       )

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

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

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











from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_        =			logging.get_logger(__name__)

lowerCAmelCase_        =			{
    """google/mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"""
}



class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''
  a_							:				List[Any]					    =MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
  a_							:				Union[str, Any]					    ="""mobilebert"""







  def __init__(					self					:    int					, UpperCamelCase					:    int=3_05_22					, UpperCamelCase					:    Any=5_12					, UpperCamelCase					:    Any=24					, UpperCamelCase					:    List[str]=4					, UpperCamelCase					:    Any=5_12					, UpperCamelCase					:    str="relu"					, UpperCamelCase					:    Dict=0.0					, UpperCamelCase					:    int=0.1					, UpperCamelCase					:    Optional[Any]=5_12					, UpperCamelCase					:    str=2					, UpperCamelCase					:    Tuple=0.02					, UpperCamelCase					:    Union[str, Any]=1e-1_2					, UpperCamelCase					:    Optional[Any]=0					, UpperCamelCase					:    Union[str, Any]=1_28					, UpperCamelCase					:    Tuple=True					, UpperCamelCase					:    int=True					, UpperCamelCase					:    List[Any]=1_28					, UpperCamelCase					:    Dict=False					, UpperCamelCase					:    List[str]=True					, UpperCamelCase					:    Optional[Any]=4					, UpperCamelCase					:    int="no_norm"					, UpperCamelCase					:    Tuple=True					, UpperCamelCase					:    str=None					, **UpperCamelCase					:    str					, ):


    '''simple docstring'''





    super().__init__(pad_token_id=UpperCamelCase					, **UpperCamelCase							)

    _snake_case      : Union[str, Any]														=		vocab_size
    _snake_case      : Any														=		hidden_size
    _snake_case      : Tuple														=		num_hidden_layers
    _snake_case      : Any														=		num_attention_heads
    _snake_case      : Optional[Any]														=		hidden_act
    _snake_case      : List[Any]														=		intermediate_size
    _snake_case      : List[Any]														=		hidden_dropout_prob
    _snake_case      : Optional[int]														=		attention_probs_dropout_prob
    _snake_case      : int														=		max_position_embeddings
    _snake_case      : Optional[int]														=		type_vocab_size
    _snake_case      : Any														=		initializer_range
    _snake_case      : Optional[int]														=		layer_norm_eps
    _snake_case      : Tuple														=		embedding_size
    _snake_case      : int														=		trigram_input
    _snake_case      : Optional[int]														=		use_bottleneck
    _snake_case      : List[Any]														=		intra_bottleneck_size
    _snake_case      : Any														=		use_bottleneck_attention
    _snake_case      : str														=		key_query_shared_bottleneck
    _snake_case      : List[str]														=		num_feedforward_networks
    _snake_case      : int														=		normalization_type
    _snake_case      : str														=		classifier_activation

    if self.use_bottleneck:
      _snake_case      : Any														=		intra_bottleneck_size
    else:
      _snake_case      : Union[str, Any]														=		hidden_size

    _snake_case      : Dict														=		classifier_dropout




class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''







  @property
  def UpperCamelCase_       (					self					:    List[Any]							):


    '''simple docstring'''





    if self.task == "multiple-choice":
      _snake_case      : str														=		{0: 'batch', 1: 'choice', 2: 'sequence'}
    else:
      _snake_case      : Dict														=		{0: 'batch', 1: 'sequence'}
    return OrderedDict(
        [
            ('input_ids', dynamic_axis),
            ('attention_mask', dynamic_axis),
            ('token_type_ids', dynamic_axis),
        ]							)




