






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					:		Optional[int]	  =						logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()
def      a_ (      _A  )			->			List[Any]:







				"""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      a_ (      _A      ,   _A      ,   _A  )			->			Optional[Any]:







				"""simple docstring"""
				return max(metric_fn(_A      ,   _A  ) for gt in ground_truths  )
def      a_ (      _A      ,   _A      ,   _A  )			->			Union[str, Any]:







				"""simple docstring"""
				snake_case__									=     [line.strip() for line in open(_A      ,   'r'  ).readlines()]
				snake_case__									=     []

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

				snake_case__									=     snake_case__									=     snake_case__									=     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  )

				snake_case__									=     100.0 * em / total
				snake_case__									=     100.0 * fa / total

				logger.info(f'''F1: {fa:.2f}'''  )
				logger.info(f'''EM: {em:.2f}'''  )
def      a_ (      _A      ,   _A      ,   _A  )			->			Optional[int]:







				"""simple docstring"""
				snake_case__									=     args.k
				snake_case__									=     [line.strip() for line in open(_A      ,   'r'  ).readlines()]
				snake_case__									=     [line.strip() for line in open(_A      ,   'r'  ).readlines()]

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

				snake_case__									=     100.0 * em / total
				logger.info(f'''Precision@{k}: {em: .2f}'''  )
def      a_ (      _A      ,   _A      ,   _A  )			->			List[Any]:







				"""simple docstring"""
				def strip_title(_A  ):
								if title.startswith('"'  ):
												snake_case__									=     title[1:]
								if title.endswith('"'  ):
												snake_case__									=     title[:-1]
								return title

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

				snake_case__									=     rag_model.rag.question_encoder(_A  )
				snake_case__									=     question_enc_outputs[0]

				snake_case__									=     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'      ,   )
				snake_case__									=     rag_model.retriever.index.get_doc_dicts(result.doc_ids  )
				snake_case__									=     []
				for docs in all_docs:
								snake_case__									=     [strip_title(_A  ) for title in docs['title']]
								provenance_strings.append('\t'.join(_A  )  )
				return provenance_strings
def      a_ (      _A      ,   _A      ,   _A  )			->			Dict:







				"""simple docstring"""
				with torch.no_grad():
								snake_case__									=     rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
								    _A      ,   return_tensors='pt'      ,   padding=_A      ,   truncation=_A  )

								snake_case__									=     inputs_dict.input_ids.to(args.device  )
								snake_case__									=     inputs_dict.attention_mask.to(args.device  )
								snake_case__									=     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]]      ,   )
								snake_case__									=     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      a_ (      )			->			List[Any]:







				"""simple docstring"""
				snake_case__									=     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=50      ,   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.'      ,   )
				snake_case__									=     parser.parse_args()
				snake_case__									=     torch.device('cuda' if torch.cuda.is_available() else 'cpu'  )
				return args
def      a_ (      _A  )			->			Optional[Any]:







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

				snake_case__									=     (
				    [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  )

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












import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


__UpperCamelCase					:		Optional[Any]	  =						logging.getLogger()


@unittest.skip("Temporarily disable the doc tests."	)
@require_torch
@require_tf
@slow
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def lowerCAmelCase_ (     self:      Dict       ,	UpperCamelCase:      Path       ,	UpperCamelCase:      Union[str, None] = None       ,	UpperCamelCase:      Union[List[str], None] = None       ,	UpperCamelCase:      Union[str, List[str], None] = None       ,	UpperCamelCase:      bool = True       ,	)		->		int:
											snake_case__									=     [file for file in os.listdir(UpperCamelCase						) if os.path.isfile(os.path.join(UpperCamelCase       ,	UpperCamelCase						)						)]

											if identifier is not None:
															snake_case__									=     [file for file in files if identifier in file]

											if n_identifier is not None:
															if isinstance(UpperCamelCase       ,	UpperCamelCase						):
																			for n_ in n_identifier:
																							snake_case__									=     [file for file in files if n_ not in file]
															else:
																			snake_case__									=     [file for file in files if n_identifier not in file]

											snake_case__									=     ignore_files or []
											ignore_files.append('__init__.py'						)
											snake_case__									=     [file for file in files if file not in ignore_files]

											for file in files:
															# Open all files
															print('Testing'       ,	UpperCamelCase						)

															if only_modules:
																			snake_case__									=     file.split('.'						)[0]
																			try:
																							snake_case__									=     getattr(UpperCamelCase       ,	UpperCamelCase						)
																							snake_case__									=     doctest.DocTestSuite(UpperCamelCase						)
																							snake_case__									=     unittest.TextTestRunner().run(UpperCamelCase						)
																							self.assertIs(len(result.failures						)       ,	0						)
																			except AttributeError:
																							logger.info(F'''{module_identifier} is not a module.'''						)
															else:
																			snake_case__									=     doctest.testfile(str('..' / directory / file						)       ,	optionflags=doctest.ELLIPSIS						)
																			self.assertIs(result.failed       ,	0						)
							def lowerCAmelCase_ (     self:      Tuple						)		->		int:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     'modeling'
											snake_case__									=     [
											    'modeling_ctrl.py',
											    'modeling_tf_ctrl.py',
											]
											self.analyze_directory(UpperCamelCase       ,	identifier=UpperCamelCase       ,	ignore_files=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		str:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     'tokenization'
											self.analyze_directory(UpperCamelCase       ,	identifier=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		int:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     'configuration'
											self.analyze_directory(UpperCamelCase       ,	identifier=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Dict						)		->		List[Any]:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     ['configuration', 'modeling', 'tokenization']
											self.analyze_directory(UpperCamelCase       ,	n_identifier=UpperCamelCase						)


							def lowerCAmelCase_ (     self:      Tuple						)		->		Union[str, Any]:
											snake_case__									=     Path('docs/source'						)
											snake_case__									=     ['favicon.ico']
											self.analyze_directory(UpperCamelCase       ,	ignore_files=UpperCamelCase       ,	only_modules=UpperCamelCase						)




