


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 	__a   (       UpperCAmelCase    ):
		_a			:						int    			=			['image_processor', 'tokenizer']
		_a			:						Union[str, Any]    			=			'AutoImageProcessor'
		_a			:						Optional[int]    			=			'AutoTokenizer'
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					**_SCREAMING_SNAKE_CASE		)				-> Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=    None
									if "feature_extractor" in kwargs:
																warnings.warn(
																    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																    ' instead.'    ,					_SCREAMING_SNAKE_CASE    ,					)
																_UpperCAmelCase					=    kwargs.pop('feature_extractor'		)

									_UpperCAmelCase					=    image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
																raise ValueError('You have to specify an image_processor.'		)
									if tokenizer is None:
																raise ValueError('You have to specify a tokenizer.'		)

									super().__init__(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    self.image_processor
		def __call__(    self    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					**_SCREAMING_SNAKE_CASE		)				-> Dict:




									"""simple docstring"""





									if text is None and images is None:
																raise ValueError('You have to specify either text or images. Both cannot be none.'		)

									if text is not None:
																_UpperCAmelCase					=    self.tokenizer(_SCREAMING_SNAKE_CASE    ,					return_tensors=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)

									if images is not None:
																_UpperCAmelCase					=    self.image_processor(_SCREAMING_SNAKE_CASE    ,					return_tensors=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)

									if text is not None and images is not None:
																_UpperCAmelCase					=    image_features.pixel_values
																return encoding
									elif text is not None:
																return encoding
									else:
																return BatchEncoding(data=dict(**_SCREAMING_SNAKE_CASE		)    ,					tensor_type=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self    ,					*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)				-> int:




									"""simple docstring"""





									return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self    ,					*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)				-> Tuple:




									"""simple docstring"""





									return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)
		@property
		def 			UpperCAmelCase__       (    self		)				-> Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=    self.tokenizer.model_input_names
									_UpperCAmelCase					=    self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)
		@property
		def 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





									warnings.warn(
									    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,					_SCREAMING_SNAKE_CASE    ,					)
									return self.image_processor_class






		@property
		def 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





									warnings.warn(
									    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,					_SCREAMING_SNAKE_CASE    ,					)
									return self.image_processor










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


lowerCAmelCase__						:List[Any]         =							logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()

def  lowerCAmelCase__				(    a__:	Tuple )						->	List[str]:


							'''simple docstring'''





							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__				(    a__:	Dict    ,						a__:	Optional[Any]    ,						a__:	Dict )						->	Optional[Any]:


							'''simple docstring'''





							return max(metric_fn(a__    ,						a__ ) for gt in ground_truths )

def  lowerCAmelCase__				(    a__:	Any    ,						a__:	Optional[Any]    ,						a__:	List[str] )						->	Tuple:


							'''simple docstring'''





							_UpperCAmelCase					=    [line.strip() for line in open(a__    ,						'r' ).readlines()]
							_UpperCAmelCase					=    []

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

							_UpperCAmelCase					=    _UpperCAmelCase					=    _UpperCAmelCase					=    0
							for prediction, ground_truths in zip(a__    ,						a__ ):
														total += 1
														em += metric_max_over_ground_truths(a__    ,						a__    ,						a__ )
														fa += metric_max_over_ground_truths(a__    ,						a__    ,						a__ )

							_UpperCAmelCase					=    100.0 * em / total
							_UpperCAmelCase					=    100.0 * fa / total

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

def  lowerCAmelCase__				(    a__:	Dict    ,						a__:	Optional[int]    ,						a__:	List[str] )						->	List[str]:


							'''simple docstring'''





							_UpperCAmelCase					=    args.k
							_UpperCAmelCase					=    [line.strip() for line in open(a__    ,						'r' ).readlines()]
							_UpperCAmelCase					=    [line.strip() for line in open(a__    ,						'r' ).readlines()]

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

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

def  lowerCAmelCase__				(    a__:	Dict    ,						a__:	str    ,						a__:	List[Any] )						->	Tuple:


							'''simple docstring'''





							def strip_title(a__:	Optional[int] ):
														if title.startswith('"' ):
																					_UpperCAmelCase					=    title[1:]
														if title.endswith('"' ):
																					_UpperCAmelCase					=    title[:-1]
														return title

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

							_UpperCAmelCase					=    rag_model.rag.question_encoder(a__ )
							_UpperCAmelCase					=    question_enc_outputs[0]

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

def  lowerCAmelCase__				(    a__:	List[str]    ,						a__:	Tuple    ,						a__:	Dict )						->	Dict:


							'''simple docstring'''





							with torch.no_grad():
														_UpperCAmelCase					=    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
														    a__    ,						return_tensors='pt'    ,						padding=a__    ,						truncation=a__ )

														_UpperCAmelCase					=    inputs_dict.input_ids.to(args.device )
														_UpperCAmelCase					=    inputs_dict.attention_mask.to(args.device )
														_UpperCAmelCase					=    rag_model.generate(  # rag_model overwrites generate
														    a__    ,						attention_mask=a__    ,						num_beams=args.num_beams    ,						min_length=args.min_length    ,						max_length=args.max_length    ,						early_stopping=a__    ,						num_return_sequences=1    ,						bad_words_ids=[[0, 0]]    ,						)
														_UpperCAmelCase					=    rag_model.retriever.generator_tokenizer.batch_decode(a__    ,						skip_special_tokens=a__ )

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

														return answers

def  lowerCAmelCase__				(    )						->	Tuple:


							'''simple docstring'''





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

def  lowerCAmelCase__				(    a__:	Any )						->	Any:


							'''simple docstring'''





							_UpperCAmelCase					=    {}
							if args.model_type is None:
														_UpperCAmelCase					=    infer_model_type(args.model_name_or_path )
														assert args.model_type is not None
							if args.model_type.startswith('rag' ):
														_UpperCAmelCase					=    RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
														_UpperCAmelCase					=    args.n_docs
														if args.index_name is not None:
																					_UpperCAmelCase					=    args.index_name
														if args.index_path is not None:
																					_UpperCAmelCase					=    args.index_path
							else:
														_UpperCAmelCase					=    BartForConditionalGeneration

							_UpperCAmelCase					=    (
							    [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'    ,						a__ )

							_UpperCAmelCase					=    get_scores if args.eval_mode == 'e2e' else get_precision_at_k
							_UpperCAmelCase					=    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(a__    ,						args.predictions_path    ,						args.gold_data_path )
																					continue

														logger.info('***** Running evaluation for {} *****'.format(a__ ) )
														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					=    RagRetriever.from_pretrained(a__    ,						**a__ )
																					_UpperCAmelCase					=    model_class.from_pretrained(a__    ,						retriever=a__    ,						**a__ )
																					model.retriever.init_retrieval()
														else:
																					_UpperCAmelCase					=    model_class.from_pretrained(a__    ,						**a__ )
														model.to(args.device )

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

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


if __name__ == "__main__":
		lowerCAmelCase__						:Tuple         =							get_args()
		main(args)
