code: 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)
  
  
  
  
style_context: 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()
  
  
  
  