





"""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


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

transformers_logging.set_verbosity_info()





def 	a_    (							_lowercase       ):
						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 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						return max(metric_fn(_lowercase	,						_lowercase       ) for gt in ground_truths       )





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : int         =   [line.strip() for line in open(_lowercase	,						'''r'''       ).readlines()]
						_UpperCamelCase      : List[Any]         =   []

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

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

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

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





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : Optional[Any]         =   args.k
						_UpperCamelCase      : Tuple         =   [line.strip() for line in open(_lowercase	,						'''r'''       ).readlines()]
						_UpperCamelCase      : str         =   [line.strip() for line in open(_lowercase	,						'''r'''       ).readlines()]

						_UpperCamelCase      : Dict         =   0
						for hypo, reference in zip(_lowercase	,						_lowercase       ):
												_UpperCamelCase      : List[Any]         =   set(hypo.split('''\t'''       )[:k]       )
												_UpperCamelCase      : Tuple         =   set(reference.split('''\t'''       )       )
												total += 1
												em += len(hypo_provenance & ref_provenance       ) / k

						_UpperCamelCase      : Union[str, Any]         =   100.0 * em / total
						logger.info(F"""Precision@{k}: {em: .2f}"""       )





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						def strip_title(_lowercase       ):
												if title.startswith('''"'''       ):
																		_UpperCamelCase      : Optional[Any]         =   title[1:]
												if title.endswith('''"'''       ):
																		_UpperCamelCase      : Optional[Any]         =   title[:-1]
												return title

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

						_UpperCamelCase      : Tuple         =   rag_model.rag.question_encoder(_lowercase       )
						_UpperCamelCase      : Union[str, Any]         =   question_enc_outputs[0]

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





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						with torch.no_grad():
												_UpperCamelCase      : Union[str, Any]         =   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
												    _lowercase	,						return_tensors='''pt'''	,						padding=_lowercase	,						truncation=_lowercase       )

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

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

												return answers





def 	a_    (							):
						_UpperCamelCase      : Dict         =   argparse.ArgumentParser()
						parser.add_argument(
						    '''--model_type'''	,						choices=['''rag_sequence''', '''rag_token''', '''bart''']	,						type=_lowercase	,						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=_lowercase	,						choices=['''exact''', '''compressed''', '''legacy''']	,						type=_lowercase	,						help='''RAG model retriever type'''	,						)
						parser.add_argument(
						    '''--index_path'''	,						default=_lowercase	,						type=_lowercase	,						help='''Path to the retrieval index'''	,						)
						parser.add_argument('''--n_docs'''	,						default=5	,						type=_lowercase	,						help='''Number of retrieved docs'''       )
						parser.add_argument(
						    '''--model_name_or_path'''	,						default=_lowercase	,						type=_lowercase	,						required=_lowercase	,						help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''	,						)
						parser.add_argument(
						    '''--eval_mode'''	,						choices=['''e2e''', '''retrieval''']	,						default='''e2e'''	,						type=_lowercase	,						help=(
						        '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
						        ''' precision@k.'''
						    )	,						)
						parser.add_argument('''--k'''	,						default=1	,						type=_lowercase	,						help='''k for the precision@k calculation'''       )
						parser.add_argument(
						    '''--evaluation_set'''	,						default=_lowercase	,						type=_lowercase	,						required=_lowercase	,						help='''Path to a file containing evaluation samples'''	,						)
						parser.add_argument(
						    '''--gold_data_path'''	,						default=_lowercase	,						type=_lowercase	,						required=_lowercase	,						help='''Path to a tab-separated file with gold samples'''	,						)
						parser.add_argument(
						    '''--gold_data_mode'''	,						default='''qa'''	,						type=_lowercase	,						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=_lowercase	,						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=_lowercase	,						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=_lowercase	,						help='''Number of beams to be used when generating answers'''	,						)
						parser.add_argument('''--min_length'''	,						default=1	,						type=_lowercase	,						help='''Min length of the generated answers'''       )
						parser.add_argument('''--max_length'''	,						default=50	,						type=_lowercase	,						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.'''	,						)
						_UpperCamelCase      : List[Any]         =   parser.parse_args()
						_UpperCamelCase      : Dict         =   torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''       )
						return args





def 	a_    (							_lowercase       ):
						_UpperCamelCase      : Dict         =   {}
						if args.model_type is None:
												_UpperCamelCase      : List[Any]         =   infer_model_type(args.model_name_or_path       )
												assert args.model_type is not None
						if args.model_type.startswith('''rag'''       ):
												_UpperCamelCase      : str         =   RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
												_UpperCamelCase      : Optional[int]         =   args.n_docs
												if args.index_name is not None:
																		_UpperCamelCase      : str         =   args.index_name
												if args.index_path is not None:
																		_UpperCamelCase      : int         =   args.index_path
						else:
												_UpperCamelCase      : List[Any]         =   BartForConditionalGeneration

						_UpperCamelCase      : 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'''	,						_lowercase       )

						_UpperCamelCase      : Optional[int]         =   get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
						_UpperCamelCase      : Optional[int]         =   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(_lowercase	,						args.predictions_path	,						args.gold_data_path       )
																		continue

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

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

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


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










"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor




@require_vision
class 			_a						(				unittest.TestCase    ):






				def     snake_case     (			self		:   Tuple							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : int         =   tempfile.mkdtemp()

										_UpperCamelCase      : List[str]         =   [
										    '''[UNK]''',
										    '''[CLS]''',
										    '''[SEP]''',
										    '''[PAD]''',
										    '''[MASK]''',
										    '''的''',
										    '''价''',
										    '''格''',
										    '''是''',
										    '''15''',
										    '''便''',
										    '''alex''',
										    '''##andra''',
										    '''，''',
										    '''。''',
										    '''-''',
										    '''t''',
										    '''shirt''',
										]
										_UpperCamelCase      : Optional[Any]         =   os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES['''vocab_file''']							)
										with open(self.vocab_file,			'''w''',			encoding='''utf-8'''							) as vocab_writer:
																vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]							)							)

										_UpperCamelCase      : Dict         =   {
										    '''do_resize''': True,
										    '''size''': {'''height''': 2_2_4, '''width''': 2_2_4},
										    '''do_center_crop''': True,
										    '''crop_size''': {'''height''': 1_8, '''width''': 1_8},
										    '''do_normalize''': True,
										    '''image_mean''': [0.48_145_466, 0.4_578_275, 0.40_821_073],
										    '''image_std''': [0.26_862_954, 0.26_130_258, 0.27_577_711],
										    '''do_convert_rgb''': True,
										}
										_UpperCamelCase      : Optional[Any]         =   os.path.join(self.tmpdirname,			lowerCAmelCase__							)
										with open(self.image_processor_file,			'''w''',			encoding='''utf-8'''							) as fp:
																json.dump(lowerCAmelCase__,			lowerCAmelCase__							)






				def     snake_case     (			self		:   str,			**lowerCAmelCase__		:   List[Any]							)     ->				Optional[int]:




										'''simple docstring'''



										return BertTokenizer.from_pretrained(self.tmpdirname,			**lowerCAmelCase__							)






				def     snake_case     (			self		:   Union[str, Any],			**lowerCAmelCase__		:   Tuple							)     ->				str:




										'''simple docstring'''



										return BertTokenizerFast.from_pretrained(self.tmpdirname,			**lowerCAmelCase__							)






				def     snake_case     (			self		:   Any,			**lowerCAmelCase__		:   Optional[int]							)     ->				Optional[Any]:




										'''simple docstring'''



										return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname,			**lowerCAmelCase__							)






				def     snake_case     (			self		:   str							)     ->				Optional[int]:




										'''simple docstring'''



										shutil.rmtree(self.tmpdirname							)






				def     snake_case     (			self		:   Any							)     ->				int:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   [np.random.randint(2_5_5,			size=(3, 3_0, 4_0_0),			dtype=np.uinta							)]

										_UpperCamelCase      : List[Any]         =   [Image.fromarray(np.moveaxis(lowerCAmelCase__,			0,			-1							)							) for x in image_inputs]

										return image_inputs






				def     snake_case     (			self		:   str							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   self.get_tokenizer()
										_UpperCamelCase      : int         =   self.get_rust_tokenizer()
										_UpperCamelCase      : int         =   self.get_image_processor()

										_UpperCamelCase      : Tuple         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)
										processor_slow.save_pretrained(self.tmpdirname							)
										_UpperCamelCase      : List[Any]         =   ChineseCLIPProcessor.from_pretrained(self.tmpdirname,			use_fast=lowerCAmelCase__							)

										_UpperCamelCase      : List[Any]         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)
										processor_fast.save_pretrained(self.tmpdirname							)
										_UpperCamelCase      : List[Any]         =   ChineseCLIPProcessor.from_pretrained(self.tmpdirname							)

										self.assertEqual(processor_slow.tokenizer.get_vocab(),			tokenizer_slow.get_vocab()							)
										self.assertEqual(processor_fast.tokenizer.get_vocab(),			tokenizer_fast.get_vocab()							)
										self.assertEqual(tokenizer_slow.get_vocab(),			tokenizer_fast.get_vocab()							)
										self.assertIsInstance(processor_slow.tokenizer,			lowerCAmelCase__							)
										self.assertIsInstance(processor_fast.tokenizer,			lowerCAmelCase__							)

										self.assertEqual(processor_slow.image_processor.to_json_string(),			image_processor.to_json_string()							)
										self.assertEqual(processor_fast.image_processor.to_json_string(),			image_processor.to_json_string()							)
										self.assertIsInstance(processor_slow.image_processor,			lowerCAmelCase__							)
										self.assertIsInstance(processor_fast.image_processor,			lowerCAmelCase__							)






				def     snake_case     (			self		:   int							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   ChineseCLIPProcessor(tokenizer=self.get_tokenizer(),			image_processor=self.get_image_processor()							)
										processor.save_pretrained(self.tmpdirname							)

										_UpperCamelCase      : Dict         =   self.get_tokenizer(cls_token='''(CLS)''',			sep_token='''(SEP)'''							)
										_UpperCamelCase      : List[str]         =   self.get_image_processor(do_normalize=lowerCAmelCase__							)

										_UpperCamelCase      : Optional[Any]         =   ChineseCLIPProcessor.from_pretrained(
										    self.tmpdirname,			cls_token='''(CLS)''',			sep_token='''(SEP)''',			do_normalize=lowerCAmelCase__							)

										self.assertEqual(processor.tokenizer.get_vocab(),			tokenizer_add_kwargs.get_vocab()							)
										self.assertIsInstance(processor.tokenizer,			lowerCAmelCase__							)

										self.assertEqual(processor.image_processor.to_json_string(),			image_processor_add_kwargs.to_json_string()							)
										self.assertIsInstance(processor.image_processor,			lowerCAmelCase__							)






				def     snake_case     (			self		:   Optional[int]							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   self.get_image_processor()
										_UpperCamelCase      : str         =   self.get_tokenizer()

										_UpperCamelCase      : Optional[Any]         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : List[str]         =   self.prepare_image_inputs()

										_UpperCamelCase      : Any         =   image_processor(lowerCAmelCase__,			return_tensors='''np'''							)
										_UpperCamelCase      : Any         =   processor(images=lowerCAmelCase__,			return_tensors='''np'''							)

										for key in input_feat_extract.keys():
																self.assertAlmostEqual(input_feat_extract[key].sum(),			input_processor[key].sum(),			delta=1e-2							)






				def     snake_case     (			self		:   Optional[Any]							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Tuple         =   self.get_image_processor()
										_UpperCamelCase      : Optional[Any]         =   self.get_tokenizer()

										_UpperCamelCase      : Any         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : Tuple         =   '''Alexandra，T-shirt的价格是15便士。'''

										_UpperCamelCase      : List[str]         =   processor(text=lowerCAmelCase__							)

										_UpperCamelCase      : Any         =   tokenizer(lowerCAmelCase__							)

										for key in encoded_tok.keys():
																self.assertListEqual(encoded_tok[key],			encoded_processor[key]							)






				def     snake_case     (			self		:   Dict							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : Tuple         =   self.get_image_processor()
										_UpperCamelCase      : Optional[Any]         =   self.get_tokenizer()

										_UpperCamelCase      : Dict         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : Any         =   '''Alexandra，T-shirt的价格是15便士。'''
										_UpperCamelCase      : Union[str, Any]         =   self.prepare_image_inputs()

										_UpperCamelCase      : str         =   processor(text=lowerCAmelCase__,			images=lowerCAmelCase__							)

										self.assertListEqual(list(inputs.keys()							),			['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']							)

										# test if it raises when no input is passed
										with pytest.raises(lowerCAmelCase__							):
																processor()






				def     snake_case     (			self		:   Optional[Any]							)     ->				Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase      : int         =   self.get_image_processor()
										_UpperCamelCase      : int         =   self.get_tokenizer()

										_UpperCamelCase      : Optional[Any]         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : List[Any]         =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										_UpperCamelCase      : List[Any]         =   processor.batch_decode(lowerCAmelCase__							)
										_UpperCamelCase      : Dict         =   tokenizer.batch_decode(lowerCAmelCase__							)

										self.assertListEqual(lowerCAmelCase__,			lowerCAmelCase__							)






				def     snake_case     (			self		:   Union[str, Any]							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   self.get_image_processor()
										_UpperCamelCase      : Optional[int]         =   self.get_tokenizer()

										_UpperCamelCase      : Optional[Any]         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : Any         =   '''Alexandra，T-shirt的价格是15便士。'''
										_UpperCamelCase      : int         =   self.prepare_image_inputs()

										_UpperCamelCase      : Dict         =   processor(text=lowerCAmelCase__,			images=lowerCAmelCase__							)

										self.assertListEqual(list(inputs.keys()							),			processor.model_input_names							)



