




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

transformers_logging.set_verbosity_info()






def  snake_case(    __magic_name__   )			->							Dict:





     '''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  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__   )			->							Optional[int]:





     '''simple docstring'''


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






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__   )			->							Union[str, Any]:





     '''simple docstring'''


     lowercase :       str       =			[line.strip() for line in open(_UpperCAmelCase			,		'''r'''   ).readlines()]
     lowercase :       Union[str, Any]       =			[]

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

     lowercase :       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   )

     lowercase :       int       =			1_0_0.0 * em / total
     lowercase :       Any       =			1_0_0.0 * fa / total

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






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__   )			->							List[Any]:





     '''simple docstring'''


     lowercase :       str       =			args.k
     lowercase :       Any       =			[line.strip() for line in open(_UpperCAmelCase			,		'''r'''   ).readlines()]
     lowercase :       Optional[int]       =			[line.strip() for line in open(_UpperCAmelCase			,		'''r'''   ).readlines()]

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

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






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__   )			->							Any:





     '''simple docstring'''


     def strip_title(__magic_name__   ):
          if title.startswith('''"'''   ):
               lowercase :       Tuple       =			title[1:]
          if title.endswith('''"'''   ):
               lowercase :       List[str]       =			title[:-1]
          return title

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

     lowercase :       int       =			rag_model.rag.question_encoder(_UpperCAmelCase   )
     lowercase :       Optional[Any]       =			question_enc_outputs[0]

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






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__   )			->							Any:





     '''simple docstring'''


     with torch.no_grad():
          lowercase :       str       =			rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
              _UpperCAmelCase			,		return_tensors='''pt'''			,		padding=_UpperCAmelCase			,		truncation=_UpperCAmelCase   )

          lowercase :       Optional[int]       =			inputs_dict.input_ids.to(args.device   )
          lowercase :       Optional[Any]       =			inputs_dict.attention_mask.to(args.device   )
          lowercase :       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]]			,		)
          lowercase :       Optional[Any]       =			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  snake_case(    )			->							int:





     '''simple docstring'''


     lowercase :       Tuple       =			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.'''			,		)
     lowercase :       Any       =			parser.parse_args()
     lowercase :       List[str]       =			torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''   )
     return args






def  snake_case(    __magic_name__   )			->							Tuple:





     '''simple docstring'''


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

     lowercase :       List[Any]       =			(
         [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   )

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





import os
from collections.abc import Iterator






def  snake_case(    __magic_name__ = "."   )			->							Iterator[str]:





     '''simple docstring'''


     for dir_path, dir_names, filenames in os.walk(__magic_name__   ):
          lowercase :       Tuple       =			[d for d in dir_names if d != '''scripts''' and d[0] not in '''._''']
          for filename in filenames:
               if filename == "__init__.py":
                    continue
               if os.path.splitext(__magic_name__   )[1] in (".py", ".ipynb"):
                    yield os.path.join(__magic_name__			,		__magic_name__   ).lstrip('''./'''   )






def  snake_case(    __magic_name__   )			->							Dict:





     '''simple docstring'''


     return F"""{i * '  '}*""" if i else "\n##"






def  snake_case(    __magic_name__			,		__magic_name__   )			->							str:





     '''simple docstring'''


     lowercase :       Dict       =			old_path.split(os.sep   )
     for i, new_part in enumerate(new_path.split(os.sep   )   ):
          if (i + 1 > len(__magic_name__   ) or old_parts[i] != new_part) and new_part:
               print(F"""{md_prefix(__magic_name__   )} {new_part.replace('_'			,		' '   ).title()}"""   )
     return new_path






def  snake_case(    __magic_name__ = "."   )			->							None:





     '''simple docstring'''


     lowercase :       str       =			''''''
     for filepath in sorted(good_file_paths(__magic_name__   )   ):
          lowercase      ,	lowercase :       Optional[int]       =			os.path.split(__magic_name__   )
          if filepath != old_path:
               lowercase :       str       =			print_path(__magic_name__			,		__magic_name__   )
          lowercase :       Optional[int]       =			(filepath.count(os.sep   ) + 1) if filepath else 0
          lowercase :       Optional[Any]       =			F"""{filepath}/{filename}""".replace(''' '''			,		'''%20'''   )
          lowercase :       List[str]       =			os.path.splitext(filename.replace('''_'''			,		''' '''   ).title()   )[0]
          print(F"""{md_prefix(__magic_name__   )} [{filename}]({url})"""   )


if __name__ == "__main__":
 print_directory_md('.')