
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        __UpperCamelCase					(    lowerCAmelCase__							:    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					(    lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    Dict  ,       lowerCAmelCase__							:    Dict  ):
 return max(metric_fn(lowerCAmelCase__  ,       lowerCAmelCase__  ) for gt in ground_truths  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    Dict  ,       lowerCAmelCase__							:    Union[str, Any]  ):
 __a			:  Any		    =  [line.strip() for line in open(lowerCAmelCase__  ,       '''r'''  ).readlines()]
 __a			:  Union[str, Any]		    =  []

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

 __a			:  Optional[int]		    =  0
 for prediction, ground_truths in zip(lowerCAmelCase__  ,       lowerCAmelCase__  ):
  total += 1
  em += metric_max_over_ground_truths(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
  fa += metric_max_over_ground_truths(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )

 __a			:  Dict		    =  1_00.0 * em / total
 __a			:  Optional[Any]		    =  1_00.0 * fa / total

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




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[Any]  ,       lowerCAmelCase__							:    Tuple  ,       lowerCAmelCase__							:    Dict  ):
 __a			:  Union[str, Any]		    =  args.k
 __a			:  str		    =  [line.strip() for line in open(lowerCAmelCase__  ,       '''r'''  ).readlines()]
 __a			:  Optional[int]		    =  [line.strip() for line in open(lowerCAmelCase__  ,       '''r'''  ).readlines()]

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

 __a			:  Tuple		    =  1_00.0 * em / total
 logger.info(f"Precision@{k}: {em: .2f}"  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    str  ):
 def strip_title(lowerCAmelCase__							:    str  ):
  if title.startswith('''"'''  ):
   __a			:  Optional[int]		    =  title[1:]
  if title.endswith('''"'''  ):
   __a			:  str		    =  title[:-1]
  return title

 __a			:  int		    =  rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
     lowerCAmelCase__  ,       return_tensors='''pt'''  ,       padding=lowerCAmelCase__  ,       truncation=lowerCAmelCase__  ,       )['''input_ids'''].to(args.device  )

 __a			:  List[Any]		    =  rag_model.rag.question_encoder(lowerCAmelCase__  )
 __a			:  Union[str, Any]		    =  question_enc_outputs[0]

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




def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    Optional[int]  ):
 with torch.no_grad():
  __a			:  List[str]		    =  rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
      lowerCAmelCase__  ,       return_tensors='''pt'''  ,       padding=lowerCAmelCase__  ,       truncation=lowerCAmelCase__  )

  __a			:  Optional[int]		    =  inputs_dict.input_ids.to(args.device  )
  __a			:  int		    =  inputs_dict.attention_mask.to(args.device  )
  __a			:  Any		    =  rag_model.generate(  # rag_model overwrites generate
      lowerCAmelCase__  ,       attention_mask=lowerCAmelCase__  ,       num_beams=args.num_beams  ,       min_length=args.min_length  ,       max_length=args.max_length  ,       early_stopping=lowerCAmelCase__  ,       num_return_sequences=1  ,       bad_words_ids=[[0, 0]]  ,       )
  __a			:  str		    =  rag_model.retriever.generator_tokenizer.batch_decode(lowerCAmelCase__  ,       skip_special_tokens=lowerCAmelCase__  )

  if args.print_predictions:
   for q, a in zip(lowerCAmelCase__  ,       lowerCAmelCase__  ):
    logger.info('''Q: {} - A: {}'''.format(lowerCAmelCase__  ,       lowerCAmelCase__  )  )

  return answers




def        __UpperCamelCase					(    ):
 __a			:  List[str]		    =  argparse.ArgumentParser()
 parser.add_argument(
     '''--model_type'''  ,       choices=['''rag_sequence''', '''rag_token''', '''bart''']  ,       type=lowerCAmelCase__  ,       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=lowerCAmelCase__  ,       choices=['''exact''', '''compressed''', '''legacy''']  ,       type=lowerCAmelCase__  ,       help='''RAG model retriever type'''  ,       )
 parser.add_argument(
     '''--index_path'''  ,       default=lowerCAmelCase__  ,       type=lowerCAmelCase__  ,       help='''Path to the retrieval index'''  ,       )
 parser.add_argument('''--n_docs'''  ,       default=5  ,       type=lowerCAmelCase__  ,       help='''Number of retrieved docs'''  )
 parser.add_argument(
     '''--model_name_or_path'''  ,       default=lowerCAmelCase__  ,       type=lowerCAmelCase__  ,       required=lowerCAmelCase__  ,       help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''  ,       )
 parser.add_argument(
     '''--eval_mode'''  ,       choices=['''e2e''', '''retrieval''']  ,       default='''e2e'''  ,       type=lowerCAmelCase__  ,       help=(
         '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
         ''' precision@k.'''
     )  ,       )
 parser.add_argument('''--k'''  ,       default=1  ,       type=lowerCAmelCase__  ,       help='''k for the precision@k calculation'''  )
 parser.add_argument(
     '''--evaluation_set'''  ,       default=lowerCAmelCase__  ,       type=lowerCAmelCase__  ,       required=lowerCAmelCase__  ,       help='''Path to a file containing evaluation samples'''  ,       )
 parser.add_argument(
     '''--gold_data_path'''  ,       default=lowerCAmelCase__  ,       type=lowerCAmelCase__  ,       required=lowerCAmelCase__  ,       help='''Path to a tab-separated file with gold samples'''  ,       )
 parser.add_argument(
     '''--gold_data_mode'''  ,       default='''qa'''  ,       type=lowerCAmelCase__  ,       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=lowerCAmelCase__  ,       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=lowerCAmelCase__  ,       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=lowerCAmelCase__  ,       help='''Number of beams to be used when generating answers'''  ,       )
 parser.add_argument('''--min_length'''  ,       default=1  ,       type=lowerCAmelCase__  ,       help='''Min length of the generated answers'''  )
 parser.add_argument('''--max_length'''  ,       default=5_0  ,       type=lowerCAmelCase__  ,       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			:  Optional[Any]		    =  parser.parse_args()
 __a			:  Optional[Any]		    =  torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''  )
 return args




def        __UpperCamelCase					(    lowerCAmelCase__							:    Tuple  ):
 __a			:  Dict		    =  {}
 if args.model_type is None:
  __a			:  Any		    =  infer_model_type(args.model_name_or_path  )
  assert args.model_type is not None
 if args.model_type.startswith('''rag'''  ):
  __a			:  Any		    =  RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
  __a			:  Dict		    =  args.n_docs
  if args.index_name is not None:
   __a			:  str		    =  args.index_name
  if args.index_path is not None:
   __a			:  int		    =  args.index_path
 else:
  __a			:  int		    =  BartForConditionalGeneration

 __a			:  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'''  ,       lowerCAmelCase__  )

 __a			:  Any		    =  get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
 __a			:  List[str]		    =  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(lowerCAmelCase__  ,       args.predictions_path  ,       args.gold_data_path  )
   continue

  logger.info('''***** Running evaluation for {} *****'''.format(lowerCAmelCase__  )  )
  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			:  Dict		    =  RagRetriever.from_pretrained(lowerCAmelCase__  ,       **lowerCAmelCase__  )
   __a			:  Union[str, Any]		    =  model_class.from_pretrained(lowerCAmelCase__  ,       retriever=lowerCAmelCase__  ,       **lowerCAmelCase__  )
   model.retriever.init_retrieval()
  else:
   __a			:  Optional[Any]		    =  model_class.from_pretrained(lowerCAmelCase__  ,       **lowerCAmelCase__  )
  model.to(args.device  )

  with open(args.evaluation_set  ,       '''r'''  ) as eval_file, open(args.predictions_path  ,       '''w'''  ) as preds_file:
   __a			:  Optional[Any]		    =  []
   for line in tqdm(lowerCAmelCase__  ):
    questions.append(line.strip()  )
    if len(lowerCAmelCase__  ) == args.eval_batch_size:
     __a			:  Dict		    =  evaluate_batch_fn(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
     preds_file.write('''\n'''.join(lowerCAmelCase__  ) + '''\n'''  )
     preds_file.flush()
     __a			:  str		    =  []
   if len(lowerCAmelCase__  ) > 0:
    __a			:  List[Any]		    =  evaluate_batch_fn(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
    preds_file.write('''\n'''.join(lowerCAmelCase__  )  )
    preds_file.flush()

   score_fn(lowerCAmelCase__  ,       args.predictions_path  ,       args.gold_data_path  )


if __name__ == "__main__":
      lowercase__          =get_args()
      main(args)


import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase




def        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    Any  ):
 if isinstance(lowerCAmelCase__  ,       lowerCAmelCase__  ):
  __a			:  Union[str, Any]		    =  np.full((len(lowerCAmelCase__  ), sequence_length, 2)  ,       lowerCAmelCase__  )
 else:
  __a			:  str		    =  np.full((len(lowerCAmelCase__  ), sequence_length)  ,       lowerCAmelCase__  )

 for i, tensor in enumerate(lowerCAmelCase__  ):
  if padding_side == "right":
   if isinstance(lowerCAmelCase__  ,       lowerCAmelCase__  ):
    __a			:  Any		    =  tensor[:sequence_length]
   else:
    __a			:  List[Any]		    =  tensor[:sequence_length]
  else:
   if isinstance(lowerCAmelCase__  ,       lowerCAmelCase__  ):
    __a			:  Dict		    =  tensor[:sequence_length]
   else:
    __a			:  int		    =  tensor[:sequence_length]

 return out_tensor.tolist()




def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[Any]  ):
 __a			:  str		    =  ord(lowerCAmelCase__  )
 if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
  return True
 __a			:  List[str]		    =  unicodedata.category(lowerCAmelCase__  )
 if cat.startswith('''P'''  ):
  return True
 return False



@dataclass
class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							PreTrainedTokenizerBase
   _SCREAMING_SNAKE_CASE				:							Union[bool, str, PaddingStrategy]     	=				True
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				None
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				None
   _SCREAMING_SNAKE_CASE				:							int     	=				-100
   _SCREAMING_SNAKE_CASE				:							str     	=				"pt"
   def 				lowerCAmelCase      (self			: str ,    snake_case_			: Tuple       ):
    import torch

    __a			:  Union[str, Any]		    =  '''label''' if '''label''' in features[0].keys() else '''labels'''
    __a			:  Tuple		    =  [feature[label_name] for feature in features] if label_name in features[0].keys() else None
    __a			:  Union[str, Any]		    =  self.tokenizer.pad(
        snake_case_ ,    padding=self.padding ,    max_length=self.max_length ,    pad_to_multiple_of=self.pad_to_multiple_of ,    return_tensors='''pt''' if labels is None else None ,    )

    if labels is None:
     return batch

    __a			:  List[str]		    =  torch.tensor(batch['''entity_ids''']       ).shape[1]
    __a			:  Tuple		    =  self.tokenizer.padding_side
    if padding_side == "right":
     __a			:  Union[str, Any]		    =  [
         list(snake_case_       ) + [self.label_pad_token_id] * (sequence_length - len(snake_case_       )) for label in labels
     ]
    else:
     __a			:  Dict		    =  [
         [self.label_pad_token_id] * (sequence_length - len(snake_case_       )) + list(snake_case_       ) for label in labels
     ]

    __a			:  Dict		    =  [feature['''ner_tags'''] for feature in features]
    __a			:  Optional[Any]		    =  padding_tensor(snake_case_ ,    -1 ,    snake_case_ ,    snake_case_       )
    __a			:  Union[str, Any]		    =  [feature['''original_entity_spans'''] for feature in features]
    __a			:  Optional[int]		    =  padding_tensor(snake_case_ ,    (-1, -1) ,    snake_case_ ,    snake_case_       )
    __a			:  List[str]		    =  {k: torch.tensor(snake_case_ ,    dtype=torch.intaa       ) for k, v in batch.items()}

    return batch
