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


lowercase__    :       Any						=			logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()

def      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			Any:
							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      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__							)  ->			Any:
							return max(metric_fn(snake_case__     ,						snake_case__							) for gt in ground_truths							)

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__							)  ->			List[str]:
							lowerCAmelCase				=	[line.strip() for line in open(snake_case__     ,						'''r'''							).readlines()]
							lowerCAmelCase				=	[]

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

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

							lowerCAmelCase				=	1_00.0 * em / total
							lowerCAmelCase				=	1_00.0 * fa / total

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

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__							)  ->			str:
							lowerCAmelCase				=	args.k
							lowerCAmelCase				=	[line.strip() for line in open(snake_case__     ,						'''r'''							).readlines()]
							lowerCAmelCase				=	[line.strip() for line in open(snake_case__     ,						'''r'''							).readlines()]

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

							lowerCAmelCase				=	1_00.0 * em / total
							logger.info(f"Precision@{k}: {em: .2f}"							)

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__							)  ->			Optional[int]:
							def strip_title(snake_case__							):
														if title.startswith('''"'''							):
																					lowerCAmelCase				=	title[1:]
														if title.endswith('''"'''							):
																					lowerCAmelCase				=	title[:-1]
														return title

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

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

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

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__							)  ->			Dict:
							with torch.no_grad():
														lowerCAmelCase				=	rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
														    snake_case__     ,						return_tensors='''pt'''     ,						padding=snake_case__     ,						truncation=snake_case__							)

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

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

														return answers

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

def      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			Any:
							lowerCAmelCase				=	{}
							if args.model_type is None:
														lowerCAmelCase				=	infer_model_type(args.model_name_or_path							)
														assert args.model_type is not None
							if args.model_type.startswith('''rag'''							):
														lowerCAmelCase				=	RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
														lowerCAmelCase				=	args.n_docs
														if args.index_name is not None:
																					lowerCAmelCase				=	args.index_name
														if args.index_path is not None:
																					lowerCAmelCase				=	args.index_path
							else:
														lowerCAmelCase				=	BartForConditionalGeneration

							lowerCAmelCase				=	(
							    [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'''     ,						snake_case__							)

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

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

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

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


if __name__ == "__main__":
	lowercase__    :       Optional[int]						=			get_args()
	main(args)




import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator

def      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			Optional[Any]:
							lowerCAmelCase				=	fname.split(os.path.sep							)[-1]
							return re.search(R'''^(.*)_\d+\.jpg$'''     ,						snake_case__							).groups()[0]




class      lowercase_						(						UpperCamelCase_		):



	"""simple docstring"""





	def __init__(							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=None	,    __SCREAMING_SNAKE_CASE=None       )		->Optional[Any]:
								lowerCAmelCase				=	file_names
								lowerCAmelCase				=	image_transform
								lowerCAmelCase				=	label_to_id


	def __len__(							self       )		->Optional[int]:
								return len(self.file_names       )




	def __getitem__(							self	,    __SCREAMING_SNAKE_CASE       )		->int:
								lowerCAmelCase				=	self.file_names[idx]
								lowerCAmelCase				=	PIL.Image.open(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	raw_image.convert('''RGB'''       )
								if self.image_transform is not None:
															lowerCAmelCase				=	self.image_transform(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	extract_label(__SCREAMING_SNAKE_CASE       )
								if self.label_to_id is not None:
															lowerCAmelCase				=	self.label_to_id[label]
								return {"image": image, "label": label}

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			Any:
							# Initialize accelerator
							lowerCAmelCase				=	Accelerator(cpu=args.cpu     ,						mixed_precision=args.mixed_precision							)

							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							lowerCAmelCase				=	config['''lr''']
							lowerCAmelCase				=	int(config['''num_epochs''']							)
							lowerCAmelCase				=	int(config['''seed''']							)
							lowerCAmelCase				=	int(config['''batch_size''']							)
							lowerCAmelCase				=	config['''image_size''']
							if not isinstance(snake_case__     ,						(list, tuple)							):
														lowerCAmelCase				=	(image_size, image_size)

							# Grab all the image filenames
							lowerCAmelCase				=	[os.path.join(args.data_dir     ,						snake_case__							) for fname in os.listdir(args.data_dir							) if fname.endswith('''.jpg'''							)]

							# Build the label correspondences
							lowerCAmelCase				=	[extract_label(snake_case__							) for fname in file_names]
							lowerCAmelCase				=	list(set(snake_case__							)							)
							id_to_label.sort()
							lowerCAmelCase				=	{lbl: i for i, lbl in enumerate(snake_case__							)}

							# Set the seed before splitting the data.
							np.random.seed(snake_case__							)
							torch.manual_seed(snake_case__							)
							torch.cuda.manual_seed_all(snake_case__							)

							# Split our filenames between train and validation
							lowerCAmelCase				=	np.random.permutation(len(snake_case__							)							)
							lowerCAmelCase				=	int(0.8 * len(snake_case__							)							)
							lowerCAmelCase				=	random_perm[:cut]
							lowerCAmelCase				=	random_perm[cut:]

							# For training we use a simple RandomResizedCrop
							lowerCAmelCase				=	Compose([RandomResizedCrop(snake_case__     ,						scale=(0.5, 1.0)							), ToTensor()]							)
							lowerCAmelCase				=	PetsDataset(
							    [file_names[i] for i in train_split]     ,						image_transform=snake_case__     ,						label_to_id=snake_case__							)

							# For evaluation, we use a deterministic Resize
							lowerCAmelCase				=	Compose([Resize(snake_case__							), ToTensor()]							)
							lowerCAmelCase				=	PetsDataset([file_names[i] for i in eval_split]     ,						image_transform=snake_case__     ,						label_to_id=snake_case__							)

							# Instantiate dataloaders.
							lowerCAmelCase				=	DataLoader(snake_case__     ,						shuffle=snake_case__     ,						batch_size=snake_case__     ,						num_workers=4							)
							lowerCAmelCase				=	DataLoader(snake_case__     ,						shuffle=snake_case__     ,						batch_size=snake_case__     ,						num_workers=4							)

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							lowerCAmelCase				=	create_model('''resnet50d'''     ,						pretrained=snake_case__     ,						num_classes=len(snake_case__							)							)

							# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
							# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
							# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
							lowerCAmelCase				=	model.to(accelerator.device							)

							# Freezing the base model
							for param in model.parameters():
														lowerCAmelCase				=	False
							for param in model.get_classifier().parameters():
														lowerCAmelCase				=	True

							# We normalize the batches of images to be a bit faster.
							lowerCAmelCase				=	torch.tensor(model.default_cfg['''mean''']							)[None, :, None, None].to(accelerator.device							)
							lowerCAmelCase				=	torch.tensor(model.default_cfg['''std''']							)[None, :, None, None].to(accelerator.device							)

							# Instantiate optimizer
							lowerCAmelCase				=	torch.optim.Adam(params=model.parameters()     ,						lr=lr / 2_5							)

							# Instantiate learning rate scheduler
							lowerCAmelCase				=	OneCycleLR(optimizer=snake_case__     ,						max_lr=snake_case__     ,						epochs=snake_case__     ,						steps_per_epoch=len(snake_case__							)							)

							# Prepare everything
							# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
							# prepare method.
							lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase				=	accelerator.prepare(
							    snake_case__     ,						snake_case__     ,						snake_case__     ,						snake_case__     ,						snake_case__							)

							# Now we train the model
							for epoch in range(snake_case__							):
														model.train()
														for step, batch in enumerate(snake_case__							):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					lowerCAmelCase				=	{k: v.to(accelerator.device							) for k, v in batch.items()}
																					lowerCAmelCase				=	(batch['''image'''] - mean) / std
																					lowerCAmelCase				=	model(snake_case__							)
																					lowerCAmelCase				=	torch.nn.functional.cross_entropy(snake_case__     ,						batch['''label''']							)
																					accelerator.backward(snake_case__							)
																					optimizer.step()
																					lr_scheduler.step()
																					optimizer.zero_grad()

														model.eval()
														lowerCAmelCase				=	0
														lowerCAmelCase				=	0
														for _, batch in enumerate(snake_case__							):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					lowerCAmelCase				=	{k: v.to(accelerator.device							) for k, v in batch.items()}
																					lowerCAmelCase				=	(batch['''image'''] - mean) / std
																					with torch.no_grad():
																												lowerCAmelCase				=	model(snake_case__							)
																					lowerCAmelCase				=	outputs.argmax(dim=-1							)
																					lowerCAmelCase	,   lowerCAmelCase				=	accelerator.gather_for_metrics((predictions, batch['''label'''])							)
																					lowerCAmelCase				=	predictions == references
																					num_elems += accurate_preds.shape[0]
																					accurate += accurate_preds.long().sum()

														lowerCAmelCase				=	accurate.item() / num_elems
														# Use accelerator.print to print only on the main process.
														accelerator.print(f"epoch {epoch}: {1_0_0 * eval_metric:.2f}"							)

def      SCREAMING_SNAKE_CASE_  (		)  ->			List[str]:
							lowerCAmelCase				=	argparse.ArgumentParser(description='''Simple example of training script.'''							)
							parser.add_argument('''--data_dir'''     ,						required=snake_case__     ,						help='''The data folder on disk.'''							)
							parser.add_argument(
							    '''--mixed_precision'''     ,						type=snake_case__     ,						default=snake_case__     ,						choices=['''no''', '''fp16''', '''bf16''', '''fp8''']     ,						help='''Whether to use mixed precision. Choose'''
							    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
							    '''and an Nvidia Ampere GPU.'''     ,						)
							parser.add_argument(
							    '''--checkpointing_steps'''     ,						type=snake_case__     ,						default=snake_case__     ,						help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''     ,						)
							parser.add_argument('''--cpu'''     ,						action='''store_true'''     ,						help='''If passed, will train on the CPU.'''							)
							lowerCAmelCase				=	parser.parse_args()
							lowerCAmelCase				=	{'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 6_4, '''image_size''': 2_2_4}
							training_function(snake_case__     ,						snake_case__							)


if __name__ == "__main__":
	main()



