

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


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

transformers_logging.set_verbosity_info()




def        _lowercase  (					a_					:       int					)					->						Optional[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        _lowercase  (					a_					:       Tuple       ,a_					:       Dict       ,a_					:       Dict					)					->						Optional[Any]:




					'''simple docstring'''







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




def        _lowercase  (					a_					:       Tuple       ,a_					:       str       ,a_					:       Union[str, Any]					)					->						List[str]:




					'''simple docstring'''







					__magic_name__				=     [line.strip() for line in open(UpperCamelCase__       ,'r'					).readlines()]
					__magic_name__				=     []

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

					__magic_name__				=     __magic_name__				=     __magic_name__				=     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__					)

					__magic_name__				=     100.0 * em / total
					__magic_name__				=     100.0 * fa / total

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




def        _lowercase  (					a_					:       Optional[int]       ,a_					:       List[str]       ,a_					:       int					)					->						List[Any]:




					'''simple docstring'''







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

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

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




def        _lowercase  (					a_					:       str       ,a_					:       str       ,a_					:       List[Any]					)					->						str:




					'''simple docstring'''







					def strip_title(a_					:       Any					):
										if title.startswith('\"'					):
															__magic_name__				=     title[1:]
										if title.endswith('\"'					):
															__magic_name__				=     title[:-1]
										return title

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

					__magic_name__				=     rag_model.rag.question_encoder(UpperCamelCase__					)
					__magic_name__				=     question_enc_outputs[0]

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




def        _lowercase  (					a_					:       int       ,a_					:       List[Any]       ,a_					:       Any					)					->						str:




					'''simple docstring'''







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

										__magic_name__				=     inputs_dict.input_ids.to(args.device					)
										__magic_name__				=     inputs_dict.attention_mask.to(args.device					)
										__magic_name__				=     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]]       ,)
										__magic_name__				=     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        _lowercase  (					)					->						Any:




					'''simple docstring'''







					__magic_name__				=     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=5_0       ,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.'       ,)
					__magic_name__				=     parser.parse_args()
					__magic_name__				=     torch.device('cuda' if torch.cuda.is_available() else 'cpu'					)
					return args




def        _lowercase  (					a_					:       Optional[Any]					)					->						Union[str, Any]:




					'''simple docstring'''







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

					__magic_name__				=     (
					    [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__					)

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










import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     str:







				'''simple docstring'''


				__lowerCAmelCase					=  min(re.search("""^ +"""						,			UpperCamelCase__   ).end() if t.startswith(""" """   ) else 0 for t in string.splitlines()   )
				return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(UpperCamelCase__   )]   )


__A						:		str          =						"\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

__A						:		int          =						"\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


__A						:		Any          =						"\\n# Begin of markdown\n\nSome cool dataset card\n"

class 		lowercase_       (	unittest.TestCase				):




					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									with tempfile.TemporaryDirectory() as tmp_dir:
													__lowerCAmelCase					=  Path(_lowercase) / """README.md"""
													with open(_lowercase,     """w+""") as readme_file:
																	readme_file.write(_lowercase)
													__lowerCAmelCase					=  DatasetMetadata.from_readme(_lowercase)
													self.assertDictEqual(_lowercase,     {"""language""": ["""zh""", """en"""], """task_ids""": ["""sentiment-classification"""]})

													with open(_lowercase,     """w+""") as readme_file:
																	readme_file.write(_lowercase)
													__lowerCAmelCase					=  DatasetMetadata.from_readme(_lowercase)
													self.assertDictEqual(_lowercase,     {})

													with open(_lowercase,     """w+""") as readme_file:
																	readme_file.write(_lowercase)
													__lowerCAmelCase					=  DatasetMetadata.from_readme(_lowercase)
													self.assertEqual(_lowercase,     {})







					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  _dedent(
									    """\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            """)
									assert DatasetMetadata.from_yaml_string(_lowercase)

									__lowerCAmelCase					=  _dedent(
									    """\
            annotations_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            task_ids:
            - open-domain-qa
            """)
									with self.assertRaises(_lowercase):
													DatasetMetadata.from_yaml_string(_lowercase)

									__lowerCAmelCase					=  _dedent(
									    """\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - text-classification
            task_ids:
            - multi-class-classification
            paperswithcode_id:
            - squad
            configs:
            - en
            train-eval-index:
            - config: en
              task: text-classification
              task_id: multi_class_classification
              splits:
                train_split: train
                eval_split: test
              col_mapping:
                text: text
                label: target
              metrics:
                - type: accuracy
                  name: Accuracy
            extra_gated_prompt: |
              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:
              [RESEARCHER_FULLNAME] (the \"Researcher\") has requested permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:
              1. Researcher shall use the Database only for non-commercial research and educational purposes.
            extra_gated_fields:
              Company: text
              Country: text
              I agree to use this model for non-commerical use ONLY: checkbox
            """)
									assert DatasetMetadata.from_yaml_string(_lowercase)





