



'''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       :						Union[str, Any]						=logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()


def 							__UpperCAmelCase				(				UpperCamelCase__				:List[Any]			)		->						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 							__UpperCAmelCase				(				UpperCamelCase__				:int   ,				UpperCamelCase__				:str   ,				UpperCamelCase__				:List[str]			)		->						Optional[int]:
				return max(metric_fn(UpperCamelCase_   ,				UpperCamelCase_			) for gt in ground_truths			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:List[Any]   ,				UpperCamelCase__				:List[Any]   ,				UpperCamelCase__				:Dict			)		->						Optional[int]:
				snake_case__      :   Dict      =    [line.strip() for line in open(UpperCamelCase_   ,				'''r'''			).readlines()]
				snake_case__      :   List[str]      =    []

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

				snake_case__      :   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_			)

				snake_case__      :   Optional[Any]      =    100.0 * em / total
				snake_case__      :   Optional[Any]      =    100.0 * fa / total

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


def 							__UpperCAmelCase				(				UpperCamelCase__				:Tuple   ,				UpperCamelCase__				:List[str]   ,				UpperCamelCase__				:Dict			)		->						Union[str, Any]:
				snake_case__      :   Dict      =    args.k
				snake_case__      :   Optional[int]      =    [line.strip() for line in open(UpperCamelCase_   ,				'''r'''			).readlines()]
				snake_case__      :   Union[str, Any]      =    [line.strip() for line in open(UpperCamelCase_   ,				'''r'''			).readlines()]

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

				snake_case__      :   int      =    100.0 * em / total
				logger.info(F'''Precision@{k}: {em: .2f}'''			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:Tuple   ,				UpperCamelCase__				:List[Any]   ,				UpperCamelCase__				:Any			)		->						List[str]:



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

				snake_case__      :   Optional[Any]      =    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    UpperCamelCase_   ,				return_tensors='''pt'''   ,				padding=UpperCamelCase_   ,				truncation=UpperCamelCase_   ,				)['''input_ids'''].to(args.device			)

				snake_case__      :   str      =    rag_model.rag.question_encoder(UpperCamelCase_			)
				snake_case__      :   Optional[int]      =    question_enc_outputs[0]

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


def 							__UpperCAmelCase				(				UpperCamelCase__				:Dict   ,				UpperCamelCase__				:List[str]   ,				UpperCamelCase__				:str			)		->						Optional[Any]:



				with torch.no_grad():
								snake_case__      :   Optional[Any]      =    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
								    UpperCamelCase_   ,				return_tensors='''pt'''   ,				padding=UpperCamelCase_   ,				truncation=UpperCamelCase_			)

								snake_case__      :   Dict      =    inputs_dict.input_ids.to(args.device			)
								snake_case__      :   Any      =    inputs_dict.attention_mask.to(args.device			)
								snake_case__      :   Optional[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]]   ,				)
								snake_case__      :   str      =    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 							__UpperCAmelCase				(				)		->						Optional[int]:
				snake_case__      :   Dict      =    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.'''   ,				)
				snake_case__      :   List[Any]      =    parser.parse_args()
				snake_case__      :   Union[str, Any]      =    torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''			)
				return args


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

				snake_case__      :   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_			)

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












from transformers import (
    DPRContextEncoderTokenizer,
    DPRContextEncoderTokenizerFast,
    DPRQuestionEncoderTokenizer,
    DPRQuestionEncoderTokenizerFast,
    DPRReaderOutput,
    DPRReaderTokenizer,
    DPRReaderTokenizerFast,
)
from transformers.testing_utils import require_tokenizers, slow
from transformers.tokenization_utils_base import BatchEncoding

from ..bert.test_tokenization_bert import BertTokenizationTest


@require_tokenizers
class      __SCREAMING_SNAKE_CASE       ( __lowercase):
						_SCREAMING_SNAKE_CASE						:							Tuple				=       DPRContextEncoderTokenizer
						_SCREAMING_SNAKE_CASE						:							Any				=       DPRContextEncoderTokenizerFast
						_SCREAMING_SNAKE_CASE						:							str				=       True




@require_tokenizers
class      __SCREAMING_SNAKE_CASE       ( __lowercase):
						_SCREAMING_SNAKE_CASE						:							Optional[Any]				=       DPRQuestionEncoderTokenizer
						_SCREAMING_SNAKE_CASE						:							List[Any]				=       DPRQuestionEncoderTokenizerFast
						_SCREAMING_SNAKE_CASE						:							Optional[Any]				=       True




@require_tokenizers
class      __SCREAMING_SNAKE_CASE       ( __lowercase):
						_SCREAMING_SNAKE_CASE						:							Any				=       DPRReaderTokenizer
						_SCREAMING_SNAKE_CASE						:							int				=       DPRReaderTokenizerFast
						_SCREAMING_SNAKE_CASE						:							List[str]				=       True






						@slow
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.tokenizer_class.from_pretrained('bert-base-uncased' )

												lowerCAmelCase__         =    tokenizer.encode('question sequence'  ,    add_special_tokens=_UpperCamelCase )
												lowerCAmelCase__         =    tokenizer.encode('title sequence'  ,    add_special_tokens=_UpperCamelCase )
												lowerCAmelCase__         =    tokenizer.encode('text sequence ' * 4  ,    add_special_tokens=_UpperCamelCase )
												lowerCAmelCase__         =    [[1_01] + text_a + [1_02] + text_a + [1_02] + text_a]
												lowerCAmelCase__         =    BatchEncoding({'input_ids': input_ids} )

												lowerCAmelCase__         =    [[0] * len(input_ids[0] )]
												lowerCAmelCase__         =    [[0] * len(input_ids[0] )]
												lowerCAmelCase__         =    [0]
												lowerCAmelCase__         =    DPRReaderOutput(_UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase )

												lowerCAmelCase__    ,							lowerCAmelCase__         =    8, 9
												lowerCAmelCase__         =    10
												lowerCAmelCase__         =    10
												lowerCAmelCase__         =    tokenizer.decode_best_spans(_UpperCamelCase  ,    _UpperCamelCase )
												self.assertEqual(predicted_spans[0].start_index  ,    _UpperCamelCase )
												self.assertEqual(predicted_spans[0].end_index  ,    _UpperCamelCase )
												self.assertEqual(predicted_spans[0].doc_id  ,    0 )







						@slow
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.tokenizer_class.from_pretrained('bert-base-uncased' )

												lowerCAmelCase__         =    tokenizer.encode('question sequence'  ,    add_special_tokens=_UpperCamelCase )
												lowerCAmelCase__         =    tokenizer.encode('title sequence'  ,    add_special_tokens=_UpperCamelCase )
												lowerCAmelCase__         =    tokenizer.encode('text sequence'  ,    add_special_tokens=_UpperCamelCase )
												lowerCAmelCase__         =    [1_01] + text_a + [1_02] + text_a + [1_02] + text_a
												lowerCAmelCase__         =    tokenizer(questions=['question sequence']  ,    titles=['title sequence']  ,    texts=['text sequence'] )
												self.assertIn('input_ids'  ,    _UpperCamelCase )
												self.assertIn('attention_mask'  ,    _UpperCamelCase )
												self.assertListEqual(encoded_input['input_ids'][0]  ,    _UpperCamelCase )





