



'''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


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

transformers_logging.set_verbosity_info()




def lowerCAmelCase    (__A):



     """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 lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



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




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     _a           =    [line.strip() for line in open(__A    ,      '''r''').readlines()]
     _a           =    []

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

     _a           =    _a           =    _a           =    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)

     _a           =    1_00.0 * em / total
     _a           =    1_00.0 * fa / total

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




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     _a           =    args.k
     _a           =    [line.strip() for line in open(__A    ,      '''r''').readlines()]
     _a           =    [line.strip() for line in open(__A    ,      '''r''').readlines()]

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

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




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     def strip_title(__A):
          if title.startswith('''"'''):
               _a           =    title[1:]
          if title.endswith('''"'''):
               _a           =    title[:-1]
          return title

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

     _a           =    rag_model.rag.question_encoder(__A)
     _a           =    question_enc_outputs[0]

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




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     with torch.no_grad():
          _a           =    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
              __A    ,      return_tensors='''pt'''    ,      padding=__A    ,      truncation=__A)

          _a           =    inputs_dict.input_ids.to(args.device)
          _a           =    inputs_dict.attention_mask.to(args.device)
          _a           =    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]]    ,      )
          _a           =    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    ():



     """simple docstring"""



     _a           =    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.'''    ,      )
     _a           =    parser.parse_args()
     _a           =    torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''')
     return args




def lowerCAmelCase    (__A):



     """simple docstring"""



     _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'''    ,      __A)

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







import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase=True							,      _UpperCamelCase="pt"	):


			'''simple docstring'''







			__lowercase			       = {'''add_prefix_space''': True} if isinstance(_UpperCamelCase							,      _UpperCamelCase	) and not line.startswith(''' '''	) else {}
			__lowercase			       = padding_side
			return tokenizer(
			    [line]							,      max_length=_UpperCamelCase							,      padding='''max_length''' if pad_to_max_length else None							,      truncation=_UpperCamelCase							,      return_tensors=_UpperCamelCase							,      add_special_tokens=_UpperCamelCase							,      **_UpperCamelCase							,      )


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase=None							,      ):


			'''simple docstring'''







			__lowercase			       = input_ids.ne(_UpperCamelCase	).any(dim=0	)
			if attention_mask is None:
						return input_ids[:, keep_column_mask]
			else:
						return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_="train"			,							snake_case_=None			,							snake_case_=None			,							snake_case_=None			,							snake_case_=""			,							) ->      Tuple:







								'''simple docstring'''

								super().__init__()
								__lowercase			       = Path(snake_case_    ).joinpath(type_path + '''.source'''    )
								__lowercase			       = Path(snake_case_    ).joinpath(type_path + '''.target'''    )
								__lowercase			       = self.get_char_lens(self.src_file    )
								__lowercase			       = max_source_length
								__lowercase			       = max_target_length
								assert min(self.src_lens    ) > 0, F'found empty line in {self.src_file}'
								__lowercase			       = tokenizer
								__lowercase			       = prefix
								if n_obs is not None:
											__lowercase			       = self.src_lens[:n_obs]
								__lowercase			       = src_lang
								__lowercase			       = tgt_lang






					def __len__(					self    ) ->      List[Any]:







								'''simple docstring'''

								return len(self.src_lens    )






					def __getitem__(					self			,							snake_case_    ) ->      Dict[str, torch.Tensor]:







								'''simple docstring'''

								__lowercase			       = index + 1  # linecache starts at 1
								__lowercase			       = self.prefix + linecache.getline(str(self.src_file    )			,							snake_case_    ).rstrip('''\n'''    )
								__lowercase			       = linecache.getline(str(self.tgt_file    )			,							snake_case_    ).rstrip('''\n'''    )
								assert source_line, F'empty source line for index {index}'
								assert tgt_line, F'empty tgt line for index {index}'

								# Need to add eos token manually for T5
								if isinstance(self.tokenizer			,							snake_case_    ):
											source_line += self.tokenizer.eos_token
											tgt_line += self.tokenizer.eos_token

								# Pad source and target to the right
								__lowercase			       = (
								    self.tokenizer.question_encoder if isinstance(self.tokenizer			,							snake_case_    ) else self.tokenizer
								)
								__lowercase			       = self.tokenizer.generator if isinstance(self.tokenizer			,							snake_case_    ) else self.tokenizer

								__lowercase			       = encode_line(snake_case_			,							snake_case_			,							self.max_source_length			,							'''right'''    )
								__lowercase			       = encode_line(snake_case_			,							snake_case_			,							self.max_target_length			,							'''right'''    )

								__lowercase			       = source_inputs['''input_ids'''].squeeze()
								__lowercase			       = target_inputs['''input_ids'''].squeeze()
								__lowercase			       = source_inputs['''attention_mask'''].squeeze()
								return {
								    "input_ids": source_ids,
								    "attention_mask": src_mask,
								    "decoder_input_ids": target_ids,
								}






					@staticmethod
					def A   (					snake_case_    ) ->      Any:







								'''simple docstring'''

								return [len(snake_case_    ) for x in Path(snake_case_    ).open().readlines()]






					def A   (					self			,							snake_case_    ) ->      Dict[str, torch.Tensor]:







								'''simple docstring'''

								__lowercase			       = torch.stack([x['''input_ids'''] for x in batch]    )
								__lowercase			       = torch.stack([x['''attention_mask'''] for x in batch]    )
								__lowercase			       = torch.stack([x['''decoder_input_ids'''] for x in batch]    )
								__lowercase			       = (
								    self.tokenizer.generator.pad_token_id
								    if isinstance(self.tokenizer			,							snake_case_    )
								    else self.tokenizer.pad_token_id
								)
								__lowercase			       = (
								    self.tokenizer.question_encoder.pad_token_id
								    if isinstance(self.tokenizer			,							snake_case_    )
								    else self.tokenizer.pad_token_id
								)
								__lowercase			       = trim_batch(snake_case_			,							snake_case_    )
								__lowercase       ,			__lowercase			       = trim_batch(snake_case_			,							snake_case_			,							attention_mask=snake_case_    )
								__lowercase			       = {
								    '''input_ids''': source_ids,
								    '''attention_mask''': source_mask,
								    '''decoder_input_ids''': y,
								}
								return batch




a			:			int												=					getLogger(__name__)


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			return list(itertools.chain.from_iterable(_UpperCamelCase	)	)


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = get_git_info()
			save_json(_UpperCamelCase							,      os.path.join(_UpperCamelCase							,      '''git_log.json'''	)	)


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase=4							,      **_UpperCamelCase	):


			'''simple docstring'''







			with open(_UpperCamelCase							,      '''w'''	) as f:
						json.dump(_UpperCamelCase							,      _UpperCamelCase							,      indent=_UpperCamelCase							,      **_UpperCamelCase	)


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			with open(_UpperCamelCase	) as f:
						return json.load(_UpperCamelCase	)


def 		lowercase_	(  ):


			'''simple docstring'''







			__lowercase			       = git.Repo(search_parent_directories=_UpperCamelCase	)
			__lowercase			       = {
			    '''repo_id''': str(_UpperCamelCase	),
			    '''repo_sha''': str(repo.head.object.hexsha	),
			    '''repo_branch''': str(repo.active_branch	),
			    '''hostname''': str(socket.gethostname()	),
			}
			return repo_infos


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			return list(map(_UpperCamelCase							,      _UpperCamelCase	)	)


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			with open(_UpperCamelCase							,      '''wb'''	) as f:
						return pickle.dump(_UpperCamelCase							,      _UpperCamelCase	)


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			def remove_articles(_UpperCamelCase	):
						return re.sub(r'''\b(a|an|the)\b'''							,      ''' '''							,      _UpperCamelCase	)

			def white_space_fix(_UpperCamelCase	):
						return " ".join(text.split()	)

			def remove_punc(_UpperCamelCase	):
						__lowercase			       = set(string.punctuation	)
						return "".join(ch for ch in text if ch not in exclude	)

			def lower(_UpperCamelCase	):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(_UpperCamelCase	)	)	)	)


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = normalize_answer(_UpperCamelCase	).split()
			__lowercase			       = normalize_answer(_UpperCamelCase	).split()
			__lowercase			       = Counter(_UpperCamelCase	) & Counter(_UpperCamelCase	)
			__lowercase			       = sum(common.values()	)
			if num_same == 0:
						return 0
			__lowercase			       = 1.0 * num_same / len(_UpperCamelCase	)
			__lowercase			       = 1.0 * num_same / len(_UpperCamelCase	)
			__lowercase			       = (2 * precision * recall) / (precision + recall)
			return fa


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			return normalize_answer(_UpperCamelCase	) == normalize_answer(_UpperCamelCase	)


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			assert len(_UpperCamelCase	) == len(_UpperCamelCase	)
			__lowercase			       = 0
			for hypo, pred in zip(_UpperCamelCase							,      _UpperCamelCase	):
						em += exact_match_score(_UpperCamelCase							,      _UpperCamelCase	)
			if len(_UpperCamelCase	) > 0:
						em /= len(_UpperCamelCase	)
			return {"em": em}


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			return model_prefix.startswith('''rag'''	)





def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = {p: p for p in extra_params}
			# T5 models don't have `dropout` param, they have `dropout_rate` instead
			__lowercase			       = '''dropout_rate'''
			for p in extra_params:
						if getattr(_UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	):
									if not hasattr(_UpperCamelCase							,      _UpperCamelCase	) and not hasattr(_UpperCamelCase							,      equivalent_param[p]	):
												logger.info('''config doesn\'t have a `{}` attribute'''.format(_UpperCamelCase	)	)
												delattr(_UpperCamelCase							,      _UpperCamelCase	)
												continue
									__lowercase			       = p if hasattr(_UpperCamelCase							,      _UpperCamelCase	) else equivalent_param[p]
									setattr(_UpperCamelCase							,      _UpperCamelCase							,      getattr(_UpperCamelCase							,      _UpperCamelCase	)	)
									delattr(_UpperCamelCase							,      _UpperCamelCase	)
			return hparams, config


