


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

transformers_logging.set_verbosity_info()



def      lowerCAmelCase	(						lowerCAmelCase_	)->	int:
				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_	)->	str:
				return max(metric_fn(lowerCAmelCase_	,				lowerCAmelCase_	) for gt in ground_truths	)



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	int:
				lowerCAmelCase_	: Dict      			=			[line.strip() for line in open(lowerCAmelCase_	,				'''r'''	).readlines()]
				lowerCAmelCase_	: Optional[int]      			=			[]

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

				lowerCAmelCase_	: Any      			=			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_	)

				lowerCAmelCase_	: Tuple      			=			100.0 * em / total
				lowerCAmelCase_	: Dict      			=			100.0 * fa / total

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



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	str:
				lowerCAmelCase_	: List[str]      			=			args.k
				lowerCAmelCase_	: int      			=			[line.strip() for line in open(lowerCAmelCase_	,				'''r'''	).readlines()]
				lowerCAmelCase_	: List[Any]      			=			[line.strip() for line in open(lowerCAmelCase_	,				'''r'''	).readlines()]

				lowerCAmelCase_	: Tuple      			=			0
				for hypo, reference in zip(lowerCAmelCase_	,				lowerCAmelCase_	):
								lowerCAmelCase_	: Optional[int]      			=			set(hypo.split('''\t'''	)[:k]	)
								lowerCAmelCase_	: Union[str, Any]      			=			set(reference.split('''\t'''	)	)
								total += 1
								em += len(hypo_provenance & ref_provenance	) / k

				lowerCAmelCase_	: Any      			=			100.0 * em / total
				logger.info(f"""Precision@{k}: {em: .2f}"""	)



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	Optional[int]:
				def strip_title(lowerCAmelCase_	):
								if title.startswith('''"'''	):
												lowerCAmelCase_	: Union[str, Any]      			=			title[1:]
								if title.endswith('''"'''	):
												lowerCAmelCase_	: Any      			=			title[:-1]
								return title

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

				lowerCAmelCase_	: Any      			=			rag_model.rag.question_encoder(lowerCAmelCase_	)
				lowerCAmelCase_	: Tuple      			=			question_enc_outputs[0]

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



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	Optional[Any]:
				with torch.no_grad():
								lowerCAmelCase_	: Union[str, Any]      			=			rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
								    lowerCAmelCase_	,				return_tensors='''pt'''	,				padding=lowerCAmelCase_	,				truncation=lowerCAmelCase_	)

								lowerCAmelCase_	: Optional[Any]      			=			inputs_dict.input_ids.to(args.device	)
								lowerCAmelCase_	: str      			=			inputs_dict.attention_mask.to(args.device	)
								lowerCAmelCase_	: List[str]      			=			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]]	,				)
								lowerCAmelCase_	: Optional[Any]      			=			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	(						)->	Any:
				lowerCAmelCase_	: List[str]      			=			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.'''	,				)
				lowerCAmelCase_	: List[Any]      			=			parser.parse_args()
				lowerCAmelCase_	: Tuple      			=			torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''	)
				return args



def      lowerCAmelCase	(						lowerCAmelCase_	)->	Tuple:
				lowerCAmelCase_	: List[str]      			=			{}
				if args.model_type is None:
								lowerCAmelCase_	: List[str]      			=			infer_model_type(args.model_name_or_path	)
								assert args.model_type is not None
				if args.model_type.startswith('''rag'''	):
								lowerCAmelCase_	: Dict      			=			RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
								lowerCAmelCase_	: List[Any]      			=			args.n_docs
								if args.index_name is not None:
												lowerCAmelCase_	: Union[str, Any]      			=			args.index_name
								if args.index_path is not None:
												lowerCAmelCase_	: Tuple      			=			args.index_path
				else:
								lowerCAmelCase_	: Tuple      			=			BartForConditionalGeneration

				lowerCAmelCase_	: List[str]      			=			(
				    [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_	)

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



import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
						_UpperCAmelCase     :					Optional[int]       =argparse.ArgumentParser()

						# Hyperparameters sent by the client are passed as command-line arguments to the script.
						parser.add_argument("""--epochs""", type=int, default=1)
						parser.add_argument("""--per_device_train_batch_size""", type=int, default=16)
						parser.add_argument("""--per_device_eval_batch_size""", type=int, default=8)
						parser.add_argument("""--model_name_or_path""", type=str)
						parser.add_argument("""--learning_rate""", type=str, default=5E-5)
						parser.add_argument("""--do_train""", type=bool, default=True)
						parser.add_argument("""--do_eval""", type=bool, default=True)
						parser.add_argument("""--output_dir""", type=str)

						_UpperCAmelCase       ,   _UpperCAmelCase     :					List[str]       =parser.parse_known_args()

						# overwrite batch size until we have tf_glue.py
						_UpperCAmelCase     :					Optional[Any]       =16
						_UpperCAmelCase     :					List[str]       =16

						# Set up logging
						_UpperCAmelCase     :					Optional[Any]       =logging.getLogger(__name__)

						logging.basicConfig(
						    level=logging.getLevelName("""INFO"""),
						    handlers=[logging.StreamHandler(sys.stdout)],
						    format="""%(asctime)s - %(name)s - %(levelname)s - %(message)s""",
						)

						# Load model and tokenizer
						_UpperCAmelCase     :					List[str]       =TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
						_UpperCAmelCase     :					Any       =AutoTokenizer.from_pretrained(args.model_name_or_path)

						# Load dataset
						_UpperCAmelCase       ,   _UpperCAmelCase     :					Union[str, Any]       =load_dataset("""imdb""", split=["""train""", """test"""])
						_UpperCAmelCase     :					int       =train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
						_UpperCAmelCase     :					str       =test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

						# Preprocess train dataset
						_UpperCAmelCase     :					str       =train_dataset.map(
						    lambda e: tokenizer(e["""text"""], truncation=True, padding="""max_length"""), batched=True
						)
						train_dataset.set_format(type="""tensorflow""", columns=["""input_ids""", """attention_mask""", """label"""])

						_UpperCAmelCase     :					List[Any]       ={
						    x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
						    for x in ["""input_ids""", """attention_mask"""]
						}
						_UpperCAmelCase     :					Optional[Any]       =tf.data.Dataset.from_tensor_slices((train_features, train_dataset["""label"""])).batch(
						    args.per_device_train_batch_size
						)

						# Preprocess test dataset
						_UpperCAmelCase     :					List[Any]       =test_dataset.map(
						    lambda e: tokenizer(e["""text"""], truncation=True, padding="""max_length"""), batched=True
						)
						test_dataset.set_format(type="""tensorflow""", columns=["""input_ids""", """attention_mask""", """label"""])

						_UpperCAmelCase     :					Dict       ={
						    x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
						    for x in ["""input_ids""", """attention_mask"""]
						}
						_UpperCAmelCase     :					Any       =tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])).batch(
						    args.per_device_eval_batch_size
						)

						# fine optimizer and loss
						_UpperCAmelCase     :					List[Any]       =tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
						_UpperCAmelCase     :					str       =tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
						_UpperCAmelCase     :					Optional[Any]       =[tf.keras.metrics.SparseCategoricalAccuracy()]
						model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

						_UpperCAmelCase     :					Tuple       =time.time()
						_UpperCAmelCase     :					List[str]       =model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
						_UpperCAmelCase     :					Optional[int]       =time.time() - start_train_time

						logger.info("""*** Train ***""")
						logger.info(f"""train_runtime = {end_train_time}""")
						for key, value in train_results.history.items():
												logger.info(f"""  {key} = {value}""")