


"""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        lowercase							(		lowerCAmelCase__ :     Optional[int]			) ->      Optional[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        lowercase							(		lowerCAmelCase__ :     Optional[Any]    ,					lowerCAmelCase__ :     Tuple    ,					lowerCAmelCase__ :     str			) ->      List[str]:
		return max(metric_fn(lowerCAmelCase__    ,					lowerCAmelCase__			) for gt in ground_truths			)

def        lowercase							(		lowerCAmelCase__ :     Tuple    ,					lowerCAmelCase__ :     Optional[Any]    ,					lowerCAmelCase__ :     Dict			) ->      int:
		__a           =			[line.strip() for line in open(lowerCAmelCase__    ,					'''r'''			).readlines()]
		__a           =			[]

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

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

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

def        lowercase							(		lowerCAmelCase__ :     Dict    ,					lowerCAmelCase__ :     Tuple    ,					lowerCAmelCase__ :     Any			) ->      Any:
		__a           =			args.k
		__a           =			[line.strip() for line in open(lowerCAmelCase__    ,					'''r'''			).readlines()]
		__a           =			[line.strip() for line in open(lowerCAmelCase__    ,					'''r'''			).readlines()]

		__a           =			__a           =			0
		for hypo, reference in zip(lowerCAmelCase__    ,					lowerCAmelCase__			):
				__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        lowercase							(		lowerCAmelCase__ :     List[str]    ,					lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     str			) ->      Dict:
		def strip_title(lowerCAmelCase__ :     Tuple			):
				if title.startswith('''"'''			):
						__a           =			title[1:]
				if title.endswith('''"'''			):
						__a           =			title[:-1]
				return title

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

		__a           =			rag_model.rag.question_encoder(lowerCAmelCase__			)
		__a           =			question_enc_outputs[0]

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

def        lowercase							(		lowerCAmelCase__ :     Union[str, Any]    ,					lowerCAmelCase__ :     Dict    ,					lowerCAmelCase__ :     Any			) ->      Any:
		with torch.no_grad():
				__a           =			rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    lowerCAmelCase__    ,					return_tensors='''pt'''    ,					padding=lowerCAmelCase__    ,					truncation=lowerCAmelCase__			)

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

def        lowercase							(		lowerCAmelCase__ :     Union[str, Any]			) ->      str:
		__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'''    ,					lowerCAmelCase__			)

		__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(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           =			RagRetriever.from_pretrained(lowerCAmelCase__    ,					**lowerCAmelCase__			)
						__a           =			model_class.from_pretrained(lowerCAmelCase__    ,					retriever=lowerCAmelCase__    ,					**lowerCAmelCase__			)
						model.retriever.init_retrieval()
				else:
						__a           =			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           =			[]
						for line in tqdm(lowerCAmelCase__			):
								questions.append(line.strip()			)
								if len(lowerCAmelCase__			) == args.eval_batch_size:
										__a           =			evaluate_batch_fn(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)
										preds_file.write('''\n'''.join(lowerCAmelCase__			) + '''\n'''			)
										preds_file.flush()
										__a           =			[]
						if len(lowerCAmelCase__			) > 0:
								__a           =			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 os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase           =		'''▁'''

lowerCAmelCase           =		{'''vocab_file''': '''spiece.model'''}

lowerCAmelCase           =		{
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''}
}

lowerCAmelCase           =		{
    '''google/pegasus-xsum''': 5_1_2,
}


lowerCAmelCase           =		logging.get_logger(__name__)





class       A    (   A_ ):
							UpperCamelCase_   :							Union[str, Any]       =VOCAB_FILES_NAMES

							UpperCamelCase_   :							List[Any]       =VOCAB_FILES_NAMES
							UpperCamelCase_   :							int       =PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase_   :							Tuple       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase_   :							int       =['''input_ids''', '''attention_mask''']


							def __init__(self      ,				lowerCAmelCase      ,				lowerCAmelCase="<pad>"      ,				lowerCAmelCase="</s>"      ,				lowerCAmelCase="<unk>"      ,				lowerCAmelCase="<mask_2>"      ,				lowerCAmelCase="<mask_1>"      ,				lowerCAmelCase=None      ,				lowerCAmelCase=1_0_3      ,				lowerCAmelCase = None      ,				**lowerCAmelCase      ,				):
									__lowercase=			offset
									if additional_special_tokens is not None:
											if not isinstance(lowerCAmelCase      ,				lowerCAmelCase					):
													raise TypeError(
													    f'additional_special_tokens should be of type {type(lowerCAmelCase					)}, but is'
													    f' {type(lowerCAmelCase					)}'					)

											__lowercase=			(
											    ([mask_token_sent] + additional_special_tokens)
											    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
											    else additional_special_tokens
											)
											# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
											additional_special_tokens_extended += [
											    f'<unk_{i}>' for i in range(len(lowerCAmelCase					)      ,				self.offset - 1					)
											]

											if len(set(lowerCAmelCase					)					) != len(lowerCAmelCase					):
													raise ValueError(
													    'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
													    f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'					)
											__lowercase=			additional_special_tokens_extended
									else:
											__lowercase=			[mask_token_sent] if mask_token_sent is not None else []
											additional_special_tokens += [f'<unk_{i}>' for i in range(2      ,				self.offset					)]

									__lowercase=			{} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    eos_token=lowerCAmelCase      ,				unk_token=lowerCAmelCase      ,				mask_token=lowerCAmelCase      ,				pad_token=lowerCAmelCase      ,				mask_token_sent=lowerCAmelCase      ,				offset=lowerCAmelCase      ,				additional_special_tokens=lowerCAmelCase      ,				sp_model_kwargs=self.sp_model_kwargs      ,				**lowerCAmelCase      ,				)
									__lowercase=			mask_token_sent
									__lowercase=			vocab_file
									__lowercase=			spm.SentencePieceProcessor(**self.sp_model_kwargs					)
									self.sp_model.Load(lowerCAmelCase					)

									# add special tokens to encoder dict
									__lowercase=			{
									    0: self.pad_token,
									    1: self.eos_token,
									}

									if self.mask_token_sent is not None:
											self.encoder.update(
											    {
											        2: self.mask_token_sent,
											        3: self.mask_token,
											    }					)

									if self.offset > 0:
											# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
											# mask_token_sent is already added to list -> so start at 1
											self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1      ,				self.offset - 1					)}					)

									__lowercase=			{v: k for k, v in self.encoder.items()}


							@property
							def 			_A       (self					):
									return len(self.sp_model					) + self.offset


							def 			_A       (self					):
									__lowercase=			{self.convert_ids_to_tokens(lowerCAmelCase					): i for i in range(self.vocab_size					)}
									vocab.update(self.added_tokens_encoder					)
									return vocab


							def __getstate__(self					):
									__lowercase=			self.__dict__.copy()
									__lowercase=			None
									return state


							def __setstate__(self      ,				lowerCAmelCase					):
									__lowercase=			d

									# for backward compatibility
									if not hasattr(self      ,				'sp_model_kwargs'					):
											__lowercase=			{}

									__lowercase=			spm.SentencePieceProcessor(**self.sp_model_kwargs					)
									self.sp_model.Load(self.vocab_file					)


							def 			_A       (self      ,				lowerCAmelCase					):
									return self.sp_model.encode(lowerCAmelCase      ,				out_type=lowerCAmelCase					)


							def 			_A       (self      ,				lowerCAmelCase					):
									if token in self.decoder:
											return self.decoder[token]
									elif token in self.added_tokens_decoder:
											return self.added_tokens_decoder[token]
									__lowercase=			self.sp_model.piece_to_id(lowerCAmelCase					)
									return sp_id + self.offset


							def 			_A       (self      ,				lowerCAmelCase					):
									if index in self.encoder:
											return self.encoder[index]
									elif index in self.added_tokens_encoder:
											return self.added_tokens_encoder[index]
									else:
											__lowercase=			self.sp_model.IdToPiece(index - self.offset					)
									return token


							def 			_A       (self      ,				lowerCAmelCase					):
									__lowercase=			[]
									__lowercase=			''
									for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
													out_string += self.sp_model.decode(lowerCAmelCase					) + token
													__lowercase=			[]
											else:
													current_sub_tokens.append(lowerCAmelCase					)
									out_string += self.sp_model.decode(lowerCAmelCase					)
									return out_string.strip()


							def 			_A       (self      ,				lowerCAmelCase=False					):
									return 1


							def 			_A       (self      ,				lowerCAmelCase					):
									__lowercase=			set(self.all_special_ids					)  # call it once instead of inside list comp
									all_special_ids.remove(self.unk_token_id					)  # <unk> is only sometimes special

									return [1 if x in all_special_ids else 0 for x in seq]


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = None      ,				lowerCAmelCase = False					):
									if already_has_special_tokens:
											return self._special_token_mask(lowerCAmelCase					)
									elif token_ids_a is None:
											return self._special_token_mask(lowerCAmelCase					) + [1]
									else:
											return self._special_token_mask(token_ids_a + token_ids_a					) + [1]


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase=None					):
									if token_ids_a is None:
											return token_ids_a + [self.eos_token_id]
									# We don't expect to process pairs, but leave the pair logic for API consistency
									return token_ids_a + token_ids_a + [self.eos_token_id]







							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = None					):
									if not os.path.isdir(lowerCAmelCase					):
											logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
											return
									__lowercase=			os.path.join(
									    lowerCAmelCase      ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

									if os.path.abspath(self.vocab_file					) != os.path.abspath(lowerCAmelCase					) and os.path.isfile(self.vocab_file					):
											copyfile(self.vocab_file      ,				lowerCAmelCase					)
									elif not os.path.isfile(self.vocab_file					):
											with open(lowerCAmelCase      ,				'wb'					) as fi:
													__lowercase=			self.sp_model.serialized_model_proto()
													fi.write(lowerCAmelCase					)

									return (out_vocab_file,)






