






"""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


SCREAMING_SNAKE_CASE						:						Tuple					   =       logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()



def  __lowerCamelCase					(					lowerCAmelCase__				):
				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					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				return max(metric_fn(lowerCAmelCase__       ,lowerCAmelCase__				) for gt in ground_truths				)



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       [line.strip() for line in open(lowerCAmelCase__       ,'r'				).readlines()]
				A__							 =       []

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

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

				A__							 =       1_0_0.0 * em / total
				A__							 =       1_0_0.0 * fa / total

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



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       args.k
				A__							 =       [line.strip() for line in open(lowerCAmelCase__       ,'r'				).readlines()]
				A__							 =       [line.strip() for line in open(lowerCAmelCase__       ,'r'				).readlines()]

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

				A__							 =       1_0_0.0 * em / total
				logger.info(f'''Precision@{k}: {em: .2f}'''				)



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				def strip_title(lowerCAmelCase__				):
								if title.startswith('"'				):
												A__							 =       title[1:]
								if title.endswith('"'				):
												A__							 =       title[:-1]
								return title

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

				A__							 =       rag_model.rag.question_encoder(lowerCAmelCase__				)
				A__							 =       question_enc_outputs[0]

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



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				with torch.no_grad():
								A__							 =       rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
								    lowerCAmelCase__       ,return_tensors='pt'       ,padding=lowerCAmelCase__       ,truncation=lowerCAmelCase__				)

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

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

								return answers



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



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

				A__							 =       (
				    [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'       ,lowerCAmelCase__				)

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

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

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

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


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










"""simple docstring"""






from math import ceil



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       list(range(0       ,lowerCAmelCase__				)				)

				A__							 =       [item for sublist in list(device_map.values()				) for item in sublist]

				# Duplicate check
				A__							 =       []
				for i in device_map_blocks:
								if device_map_blocks.count(lowerCAmelCase__				) > 1 and i not in duplicate_blocks:
												duplicate_blocks.append(lowerCAmelCase__				)
    # Missing blocks
				A__							 =       [i for i in blocks if i not in device_map_blocks]
				A__							 =       [i for i in device_map_blocks if i not in blocks]

				if len(lowerCAmelCase__				) != 0:
								raise ValueError(
								    'Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'
								    ' These attention blocks were specified more than once: ' + str(lowerCAmelCase__				)				)
				if len(lowerCAmelCase__				) != 0:
								raise ValueError(
								    'There are attention blocks for this model that are not specified in the device_map. Add these attention '
								    'blocks to a device on the device_map: ' + str(lowerCAmelCase__				)				)
				if len(lowerCAmelCase__				) != 0:
								raise ValueError(
								    'The device_map contains more attention blocks than this model has. Remove these from the device_map:'
								    + str(lowerCAmelCase__				)				)







def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       list(range(lowerCAmelCase__				)				)
				A__							 =       int(ceil(n_layers / len(lowerCAmelCase__				)				)				)
				A__							 =       [layers[i : i + n_blocks] for i in range(0       ,lowerCAmelCase__       ,lowerCAmelCase__				)]

				return dict(zip(lowerCAmelCase__       ,lowerCAmelCase__				)				)


