


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


_UpperCAmelCase     :					int       =logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()



def      lowerCAmelCase	(						lowerCAmelCase_	)->	int:
				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	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	str:
				return max(metric_fn(lowerCAmelCase_	,				lowerCAmelCase_	) for gt in ground_truths	)



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	int:
				lowerCAmelCase_	: Dict      			=			[line.strip() for line in open(lowerCAmelCase_	,				'''r'''	).readlines()]
				lowerCAmelCase_	: Optional[int]      			=			[]

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

				lowerCAmelCase_	: Any      			=			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_	)

				lowerCAmelCase_	: Tuple      			=			100.0 * em / total
				lowerCAmelCase_	: Dict      			=			100.0 * fa / total

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



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	str:
				lowerCAmelCase_	: List[str]      			=			args.k
				lowerCAmelCase_	: int      			=			[line.strip() for line in open(lowerCAmelCase_	,				'''r'''	).readlines()]
				lowerCAmelCase_	: List[Any]      			=			[line.strip() for line in open(lowerCAmelCase_	,				'''r'''	).readlines()]

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

				lowerCAmelCase_	: Any      			=			100.0 * em / total
				logger.info(f"""Precision@{k}: {em: .2f}"""	)



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	Optional[int]:
				def strip_title(lowerCAmelCase_	):
								if title.startswith('''"'''	):
												lowerCAmelCase_	: Union[str, Any]      			=			title[1:]
								if title.endswith('''"'''	):
												lowerCAmelCase_	: Any      			=			title[:-1]
								return title

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

				lowerCAmelCase_	: Any      			=			rag_model.rag.question_encoder(lowerCAmelCase_	)
				lowerCAmelCase_	: Tuple      			=			question_enc_outputs[0]

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



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	Optional[Any]:
				with torch.no_grad():
								lowerCAmelCase_	: Union[str, Any]      			=			rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
								    lowerCAmelCase_	,				return_tensors='''pt'''	,				padding=lowerCAmelCase_	,				truncation=lowerCAmelCase_	)

								lowerCAmelCase_	: Optional[Any]      			=			inputs_dict.input_ids.to(args.device	)
								lowerCAmelCase_	: str      			=			inputs_dict.attention_mask.to(args.device	)
								lowerCAmelCase_	: List[str]      			=			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]]	,				)
								lowerCAmelCase_	: Optional[Any]      			=			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      lowerCAmelCase	(						)->	Any:
				lowerCAmelCase_	: 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=50	,				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.'''	,				)
				lowerCAmelCase_	: List[Any]      			=			parser.parse_args()
				lowerCAmelCase_	: Tuple      			=			torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''	)
				return args



def      lowerCAmelCase	(						lowerCAmelCase_	)->	Tuple:
				lowerCAmelCase_	: List[str]      			=			{}
				if args.model_type is None:
								lowerCAmelCase_	: List[str]      			=			infer_model_type(args.model_name_or_path	)
								assert args.model_type is not None
				if args.model_type.startswith('''rag'''	):
								lowerCAmelCase_	: Dict      			=			RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
								lowerCAmelCase_	: List[Any]      			=			args.n_docs
								if args.index_name is not None:
												lowerCAmelCase_	: Union[str, Any]      			=			args.index_name
								if args.index_path is not None:
												lowerCAmelCase_	: Tuple      			=			args.index_path
				else:
								lowerCAmelCase_	: Tuple      			=			BartForConditionalGeneration

				lowerCAmelCase_	: 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'''	,				lowerCAmelCase_	)

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



from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase     :					List[str]       =logging.get_logger(__name__)







class 			snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__ :					List[str]   =							["""input_ids""", """attention_mask"""]




						def __init__(			self				,  __lowercase="[PAD]"				,  __lowercase="[BOS]"				,  __lowercase="[EOS]"				,  __lowercase="[MASK]"				,  __lowercase="[CLS]"				,  __lowercase="[SEP]"				,  __lowercase=2_0_4_8				,  **__lowercase				,  )   ->      None:
										lowerCAmelCase_	: Optional[int]      			=			AddedToken(__lowercase				,  lstrip=__lowercase				,  rstrip=__lowercase							) if isinstance(__lowercase				,  __lowercase							) else pad_token
										lowerCAmelCase_	: str      			=			AddedToken(__lowercase				,  lstrip=__lowercase				,  rstrip=__lowercase							) if isinstance(__lowercase				,  __lowercase							) else bos_token
										lowerCAmelCase_	: Dict      			=			AddedToken(__lowercase				,  lstrip=__lowercase				,  rstrip=__lowercase							) if isinstance(__lowercase				,  __lowercase							) else eos_token
										lowerCAmelCase_	: List[str]      			=			AddedToken(__lowercase				,  lstrip=__lowercase				,  rstrip=__lowercase							) if isinstance(__lowercase				,  __lowercase							) else mask_token
										lowerCAmelCase_	: Dict      			=			AddedToken(__lowercase				,  lstrip=__lowercase				,  rstrip=__lowercase							) if isinstance(__lowercase				,  __lowercase							) else cls_token
										lowerCAmelCase_	: Dict      			=			AddedToken(__lowercase				,  lstrip=__lowercase				,  rstrip=__lowercase							) if isinstance(__lowercase				,  __lowercase							) else sep_token

										super().__init__(
										    pad_token=__lowercase				,  bos_token=__lowercase				,  eos_token=__lowercase				,  mask_token=__lowercase				,  cls_token=__lowercase				,  sep_token=__lowercase				,  model_max_length=__lowercase				,  **__lowercase				,  )

										lowerCAmelCase_	: Dict      			=			2**8  # utf is 8 bits

										# define special tokens dict
										lowerCAmelCase_	: Dict[str, int]      			=			{
										    self.pad_token: 0,
										    self.bos_token: 1,
										    self.eos_token: 2,
										    self.mask_token: 3,
										    self.cls_token: 4,
										    self.sep_token: 5,
										}
										lowerCAmelCase_	: List[Any]      			=			len(self.special_tokens_encoder							)
										lowerCAmelCase_	: Dict[int, str]      			=			{v: k for k, v in self.special_tokens_encoder.items()}




						def   lowercase_	(			self							)   ->      Dict[str, int]:
										lowerCAmelCase_	: int      			=			self.special_tokens_encoder.copy()
										vocab.update(self.added_tokens_encoder							)
										for i in range(self._utf_vocab_size							):
														lowerCAmelCase_	: int      			=			chr(__lowercase							)
														lowerCAmelCase_	: Optional[Any]      			=			i + len(self.special_tokens_encoder							)
										return vocab




						@property
						def   lowercase_	(			self							)   ->      str:
										return self._utf_vocab_size + self._num_special_tokens




						def   lowercase_	(			self				,  __lowercase				,  __lowercase = None				,  __lowercase = False							)   ->      List[int]:
										if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=__lowercase				,  token_ids_a=__lowercase				,  already_has_special_tokens=__lowercase							)

										# normal case: some special tokens
										if token_ids_a is None:
														return [1] + [0] * len(__lowercase							) + [1]
										return [1] + ([0] * len(__lowercase							)) + [1] + ([0] * len(__lowercase							)) + [1]




						def   lowercase_	(			self				,  __lowercase				,  __lowercase = None							)   ->      List[int]:
										if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										else:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]




						def   lowercase_	(			self				,  __lowercase							)   ->      List[str]:
										lowerCAmelCase_	: Optional[int]      			=			[chr(__lowercase							) for i in text.encode('''utf-8'''							)]
										return tokens




						def   lowercase_	(			self				,  __lowercase							)   ->      Tuple:
										if token in self.special_tokens_encoder:
														lowerCAmelCase_	: Optional[int]      			=			self.special_tokens_encoder[token]
										elif token in self.added_tokens_encoder:
														lowerCAmelCase_	: Any      			=			self.added_tokens_encoder[token]
										elif len(__lowercase							) != 1:
														lowerCAmelCase_	: Any      			=			self.unk_token_id
										else:
														lowerCAmelCase_	: List[Any]      			=			ord(__lowercase							) + self._num_special_tokens
										return token_id




						def   lowercase_	(			self				,  __lowercase							)   ->      Optional[int]:
										if index in self.special_tokens_decoder:
														lowerCAmelCase_	: List[Any]      			=			self.special_tokens_decoder[index]
										elif index in self.added_tokens_decoder:
														lowerCAmelCase_	: List[str]      			=			self.added_tokens_decoder[index]
										else:
														lowerCAmelCase_	: str      			=			chr(index - self._num_special_tokens							)
										return token




						def   lowercase_	(			self				,  __lowercase							)   ->      Optional[int]:
										lowerCAmelCase_	: Tuple      			=			B''''''
										for token in tokens:
														if token in self.special_tokens_decoder:
																		lowerCAmelCase_	: int      			=			self.special_tokens_decoder[token].encode('''utf-8'''							)
														elif token in self.added_tokens_decoder:
																		lowerCAmelCase_	: Tuple      			=			self.special_tokens_decoder[token].encode('''utf-8'''							)
														elif token in self.special_tokens_encoder:
																		lowerCAmelCase_	: Any      			=			token.encode('''utf-8'''							)
														elif token in self.added_tokens_encoder:
																		lowerCAmelCase_	: Tuple      			=			token.encode('''utf-8'''							)
														else:
																		lowerCAmelCase_	: str      			=			bytes([ord(__lowercase							)]							)
														bstring += tok_string
										lowerCAmelCase_	: str      			=			bstring.decode('''utf-8'''				,  errors='''replace'''							)
										return string




						def   lowercase_	(			self				,  __lowercase				,  __lowercase = None							)   ->      Tuple[str]:
										return ()