'''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


__snake_case   :      Optional[Any]						    =  logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()





def      _UpperCAmelCase    (							_UpperCamelCase    :	int   )	->      List[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      _UpperCAmelCase    (							_UpperCamelCase    :	List[Any],   _UpperCamelCase    :	Any,   _UpperCamelCase    :	str   )	->      Optional[Any]:
    return max(metric_fn(_UpperCamelCase,   _UpperCamelCase   ) for gt in ground_truths   )





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

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

    A_     =   A_     =   A_     =   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   )

    A_     =   1_0_0.0 * em / total
    A_     =   1_0_0.0 * fa / total

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





def      _UpperCAmelCase    (							_UpperCamelCase    :	int,   _UpperCamelCase    :	int,   _UpperCamelCase    :	str   )	->      Dict:
    A_     =   args.k
    A_     =   [line.strip() for line in open(_UpperCamelCase,   '''r'''   ).readlines()]
    A_     =   [line.strip() for line in open(_UpperCamelCase,   '''r'''   ).readlines()]

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

    A_     =   1_0_0.0 * em / total
    logger.info(F'''Precision@{k}: {em: .2f}'''   )





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

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

    A_     =   rag_model.rag.question_encoder(_UpperCamelCase   )
    A_     =   question_enc_outputs[0]

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





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

        A_     =   inputs_dict.input_ids.to(args.device   )
        A_     =   inputs_dict.attention_mask.to(args.device   )
        A_     =   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]],   )
        A_     =   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      _UpperCAmelCase    (							)	->      Any:
    A_     =   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.''',   )
    A_     =   parser.parse_args()
    A_     =   torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''   )
    return args





def      _UpperCAmelCase    (							_UpperCamelCase    :	Any   )	->      Tuple:
    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''',   _UpperCamelCase   )

    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(_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'''   ):
            A_     =   RagRetriever.from_pretrained(_UpperCamelCase,   **_UpperCamelCase   )
            A_     =   model_class.from_pretrained(_UpperCamelCase,   retriever=_UpperCamelCase,   **_UpperCamelCase   )
            model.retriever.init_retrieval()
        else:
            A_     =   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:
            A_     =   []
            for line in tqdm(_UpperCamelCase   ):
                questions.append(line.strip()   )
                if len(_UpperCamelCase   ) == args.eval_batch_size:
                    A_     =   evaluate_batch_fn(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )
                    preds_file.write('''\n'''.join(_UpperCamelCase   ) + '''\n'''   )
                    preds_file.flush()
                    A_     =   []
            if len(_UpperCamelCase   ) > 0:
                A_     =   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__":
  __snake_case   :      List[Any]						    =  get_args()
  main(args)







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case__     :			int      			=  {
    '''configuration_markuplm''': ['''MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MarkupLMConfig'''],
    '''feature_extraction_markuplm''': ['''MarkupLMFeatureExtractor'''],
    '''processing_markuplm''': ['''MarkupLMProcessor'''],
    '''tokenization_markuplm''': ['''MarkupLMTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__     :			List[str]      			=  ['''MarkupLMTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__     :			Dict      			=  [
     '''MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MarkupLMForQuestionAnswering''',
     '''MarkupLMForSequenceClassification''',
     '''MarkupLMForTokenClassification''',
     '''MarkupLMModel''',
     '''MarkupLMPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
 from .feature_extraction_markuplm import MarkupLMFeatureExtractor
 from .processing_markuplm import MarkupLMProcessor
 from .tokenization_markuplm import MarkupLMTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_markuplm_fast import MarkupLMTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_markuplm import (
      MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      MarkupLMForQuestionAnswering,
      MarkupLMForSequenceClassification,
      MarkupLMForTokenClassification,
      MarkupLMModel,
      MarkupLMPreTrainedModel,
  )


else:
 import sys

 snake_case__     :			Optional[Any]      			=  _LazyModule(__name__, globals()['''__file__'''], _import_structure)






