
"""simple docstring"""







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        __lowerCAmelCase							(_UpperCamelCase						):
	__lowerCAmelCase     :		Dict		     = fname.split(os.path.sep						)[-1]
	return re.search(r'^(.*)_\d+\.jpg$'			,    _UpperCamelCase						).groups()[0]




class 					A__			(      _lowerCamelCase):

		def __init__( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=None			,						_SCREAMING_SNAKE_CASE=None					):
			__lowerCAmelCase     :		int		     = file_names
			__lowerCAmelCase     :		Union[str, Any]		     = image_transform
			__lowerCAmelCase     :		Union[str, Any]		     = label_to_id

		def __len__( self					):
			return len(self.file_names					)







		def __getitem__( self			,						_SCREAMING_SNAKE_CASE					):
			__lowerCAmelCase     :		Union[str, Any]		     = self.file_names[idx]
			__lowerCAmelCase     :		List[Any]		     = PIL.Image.open(_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		Dict		     = raw_image.convert('RGB'					)
			if self.image_transform is not None:
				__lowerCAmelCase     :		Union[str, Any]		     = self.image_transform(_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		Optional[Any]		     = extract_label(_SCREAMING_SNAKE_CASE					)
			if self.label_to_id is not None:
				__lowerCAmelCase     :		str		     = self.label_to_id[label]
			return {"image": image, "label": label}



def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase						):
	# Initialize accelerator
	__lowerCAmelCase     :		int		     = Accelerator(cpu=args.cpu			,    mixed_precision=args.mixed_precision						)

	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	__lowerCAmelCase     :		Optional[Any]		     = config['lr']
	__lowerCAmelCase     :		Union[str, Any]		     = int(config['num_epochs']						)
	__lowerCAmelCase     :		List[str]		     = int(config['seed']						)
	__lowerCAmelCase     :		str		     = int(config['batch_size']						)
	__lowerCAmelCase     :		List[str]		     = config['image_size']
	if not isinstance(_UpperCamelCase			,    (list, tuple)						):
		__lowerCAmelCase     :		List[Any]		     = (image_size, image_size)

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

	# Build the label correspondences
	__lowerCAmelCase     :		Dict		     = [extract_label(_UpperCamelCase						) for fname in file_names]
	__lowerCAmelCase     :		Optional[Any]		     = list(set(_UpperCamelCase						)						)
	id_to_label.sort()
	__lowerCAmelCase     :		Union[str, Any]		     = {lbl: i for i, lbl in enumerate(_UpperCamelCase						)}

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

	# Split our filenames between train and validation
	__lowerCAmelCase     :		str		     = np.random.permutation(len(_UpperCamelCase						)						)
	__lowerCAmelCase     :		Any		     = int(0.8 * len(_UpperCamelCase						)						)
	__lowerCAmelCase     :		Optional[Any]		     = random_perm[:cut]
	__lowerCAmelCase     :		List[str]		     = random_perm[cut:]

	# For training we use a simple RandomResizedCrop
	__lowerCAmelCase     :		List[str]		     = Compose([RandomResizedCrop(_UpperCamelCase			,    scale=(0.5, 1.0)						), ToTensor()]						)
	__lowerCAmelCase     :		int		     = PetsDataset(
	    [file_names[i] for i in train_split]			,    image_transform=_UpperCamelCase			,    label_to_id=_UpperCamelCase						)

	# For evaluation, we use a deterministic Resize
	__lowerCAmelCase     :		Any		     = Compose([Resize(_UpperCamelCase						), ToTensor()]						)
	__lowerCAmelCase     :		Tuple		     = PetsDataset([file_names[i] for i in eval_split]			,    image_transform=_UpperCamelCase			,    label_to_id=_UpperCamelCase						)

	# Instantiate dataloaders.
	__lowerCAmelCase     :		Tuple		     = DataLoader(_UpperCamelCase			,    shuffle=_UpperCamelCase			,    batch_size=_UpperCamelCase			,    num_workers=4						)
	__lowerCAmelCase     :		Union[str, Any]		     = DataLoader(_UpperCamelCase			,    shuffle=_UpperCamelCase			,    batch_size=_UpperCamelCase			,    num_workers=4						)

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

	# 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     :		Optional[int]		     = model.to(accelerator.device						)

	# Freezing the base model
	for param in model.parameters():
		__lowerCAmelCase     :		List[Any]		     = False
	for param in model.get_classifier().parameters():
		__lowerCAmelCase     :		Tuple		     = True

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

	# Instantiate optimizer
	__lowerCAmelCase     :		int		     = torch.optim.Adam(params=model.parameters()			,    lr=lr / 25						)

	# Instantiate learning rate scheduler
	__lowerCAmelCase     :		Any		     = OneCycleLR(optimizer=_UpperCamelCase			,    max_lr=_UpperCamelCase			,    epochs=_UpperCamelCase			,    steps_per_epoch=len(_UpperCamelCase						)						)

	# 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     :		Union[str, Any]		     = accelerator.prepare(
	    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase						)

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

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

		__lowerCAmelCase     :		List[str]		     = accurate.item() / num_elems
		# Use accelerator.print to print only on the main process.
		accelerator.print(F"epoch {epoch}: {100 * eval_metric:.2f}"						)
def        __lowerCAmelCase							():
	__lowerCAmelCase     :		Any		     = argparse.ArgumentParser(description='Simple example of training script.'						)
	parser.add_argument('--data_dir'			,    required=_UpperCamelCase			,    help='The data folder on disk.'						)
	parser.add_argument(
	    '--mixed_precision'			,    type=_UpperCamelCase			,    default=_UpperCamelCase			,    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=_UpperCamelCase			,    default=_UpperCamelCase			,    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     :		int		     = parser.parse_args()
	__lowerCAmelCase     :		List[Any]		     = {'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
	training_function(_UpperCamelCase			,    _UpperCamelCase						)


if __name__ == "__main__":
							main()

"""simple docstring"""







from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import Dataset

from .deepspeed import is_deepspeed_zeroa_enabled
from .generation.configuration_utils import GenerationConfig
from .trainer import Trainer
from .utils import logging


if TYPE_CHECKING:
							from .data.data_collator import DataCollator
							from .modeling_utils import PreTrainedModel
							from .tokenization_utils_base import PreTrainedTokenizerBase
							from .trainer_callback import TrainerCallback
							from .trainer_utils import EvalPrediction, PredictionOutput
							from .training_args import TrainingArguments


lowerCamelCase__     =						logging.get_logger(__name__)




class 					A__			(      _lowerCamelCase):

		def __init__( self			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = (None, None)			,						_SCREAMING_SNAKE_CASE = None			,						):
			super().__init__(
			    model=_SCREAMING_SNAKE_CASE			,						args=_SCREAMING_SNAKE_CASE			,						data_collator=_SCREAMING_SNAKE_CASE			,						train_dataset=_SCREAMING_SNAKE_CASE			,						eval_dataset=_SCREAMING_SNAKE_CASE			,						tokenizer=_SCREAMING_SNAKE_CASE			,						model_init=_SCREAMING_SNAKE_CASE			,						compute_metrics=_SCREAMING_SNAKE_CASE			,						callbacks=_SCREAMING_SNAKE_CASE			,						optimizers=_SCREAMING_SNAKE_CASE			,						preprocess_logits_for_metrics=_SCREAMING_SNAKE_CASE			,						)

			# Override self.model.generation_config if a GenerationConfig is specified in args.
			# Priority: args.generation_config > model.generation_config > default GenerationConfig.
			if self.args.generation_config is not None:
				__lowerCAmelCase     :		Tuple		     = self.load_generation_config(self.args.generation_config					)
				__lowerCAmelCase     :		int		     = gen_config

		@staticmethod
		def 				__lowerCamelCase				( _SCREAMING_SNAKE_CASE					):

			# GenerationConfig provided, nothing to do
			if isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
				return deepcopy(_SCREAMING_SNAKE_CASE					)

			# str or Path
			__lowerCAmelCase     :		Dict		     = Path(_SCREAMING_SNAKE_CASE					) if isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					) else gen_config_arg
			__lowerCAmelCase     :		Any		     = None

			# Figuring if it is path pointing to a file, pointing to a directory or else a model id or URL
			# This step is required in order to determine config_file_name
			if pretrained_model_name.is_file():
				__lowerCAmelCase     :		List[Any]		     = pretrained_model_name.name
				__lowerCAmelCase     :		Tuple		     = pretrained_model_name.parent
			# dir path
			elif pretrained_model_name.is_dir():
				pass
			# model id or URL
			else:
				__lowerCAmelCase     :		List[Any]		     = gen_config_arg

			__lowerCAmelCase     :		Optional[int]		     = GenerationConfig.from_pretrained(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					)
			return gen_config

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = "eval"			,						**_SCREAMING_SNAKE_CASE			,						):
			__lowerCAmelCase     :		Any		     = gen_kwargs.copy()
			if gen_kwargs.get('max_length'					) is None and gen_kwargs.get('max_new_tokens'					) is None:
				__lowerCAmelCase     :		Optional[Any]		     = self.args.generation_max_length
			__lowerCAmelCase     :		Tuple		     = (
			    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'					) is not None else self.args.generation_num_beams
			)
			__lowerCAmelCase     :		Optional[int]		     = gen_kwargs

			return super().evaluate(_SCREAMING_SNAKE_CASE			,						ignore_keys=_SCREAMING_SNAKE_CASE			,						metric_key_prefix=_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = "test"			,						**_SCREAMING_SNAKE_CASE			,						):
			__lowerCAmelCase     :		Dict		     = gen_kwargs.copy()
			if gen_kwargs.get('max_length'					) is None and gen_kwargs.get('max_new_tokens'					) is None:
				__lowerCAmelCase     :		Optional[Any]		     = self.args.generation_max_length
			__lowerCAmelCase     :		int		     = (
			    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'					) is not None else self.args.generation_num_beams
			)
			__lowerCAmelCase     :		Tuple		     = gen_kwargs

			return super().predict(_SCREAMING_SNAKE_CASE			,						ignore_keys=_SCREAMING_SNAKE_CASE			,						metric_key_prefix=_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None			,						**_SCREAMING_SNAKE_CASE			,						):

			if not self.args.predict_with_generate or prediction_loss_only:
				return super().prediction_step(
				    _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						prediction_loss_only=_SCREAMING_SNAKE_CASE			,						ignore_keys=_SCREAMING_SNAKE_CASE					)

			__lowerCAmelCase     :		str		     = 'labels' in inputs
			__lowerCAmelCase     :		List[str]		     = self._prepare_inputs(_SCREAMING_SNAKE_CASE					)

			# XXX: adapt synced_gpus for fairscale as well
			# Priority (handled in generate):
			# gen_kwargs > model.generation_config > default GenerationConfig()

			if len(_SCREAMING_SNAKE_CASE					) == 0 and hasattr(self			,						'_gen_kwargs'					):
				__lowerCAmelCase     :		Dict		     = self._gen_kwargs.copy()

			if gen_kwargs.get('max_length'					) is None and gen_kwargs.get('max_new_tokens'					) is None:
				__lowerCAmelCase     :		Tuple		     = self.model.config.max_length
			__lowerCAmelCase     :		Optional[Any]		     = (
			    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'					) is not None else self.model.config.num_beams
			)
			__lowerCAmelCase     :		Union[str, Any]		     = True if is_deepspeed_zeroa_enabled() else False
			__lowerCAmelCase     :		int		     = (
			    gen_kwargs['synced_gpus'] if gen_kwargs.get('synced_gpus'					) is not None else default_synced_gpus
			)

			# If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
			# (otherwise, it would continue generating from the padded `decoder_input_ids`)
			if (
			    "labels" in inputs
			    and "decoder_input_ids" in inputs
			    and inputs["labels"].shape == inputs["decoder_input_ids"].shape
			):
				__lowerCAmelCase     :		Optional[int]		     = {k: v for k, v in inputs.items() if k != 'decoder_input_ids'}
			__lowerCAmelCase     :		Union[str, Any]		     = self.model.generate(**_SCREAMING_SNAKE_CASE			,						**_SCREAMING_SNAKE_CASE					)

			# Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
			# TODO: remove this hack when the legacy code that initializes generation_config from a model config is
			# removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183
			if self.model.generation_config._from_model_config:
				__lowerCAmelCase     :		str		     = False

			# Retrieves GenerationConfig from model.generation_config
			__lowerCAmelCase     :		List[Any]		     = self.model.generation_config
			# in case the batch is shorter than max length, the output should be padded
			if generated_tokens.shape[-1] < gen_config.max_length:
				__lowerCAmelCase     :		Any		     = self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE			,						gen_config.max_length					)
			elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
				__lowerCAmelCase     :		Optional[Any]		     = self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE			,						gen_config.max_new_tokens + 1					)

			with torch.no_grad():
				if has_labels:
					with self.compute_loss_context_manager():
						__lowerCAmelCase     :		List[Any]		     = model(**_SCREAMING_SNAKE_CASE					)
					if self.label_smoother is not None:
						__lowerCAmelCase     :		Optional[int]		     = self.label_smoother(_SCREAMING_SNAKE_CASE			,						inputs['labels']					).mean().detach()
					else:
						__lowerCAmelCase     :		Optional[int]		     = (outputs['loss'] if isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					) else outputs[0]).mean().detach()
				else:
					__lowerCAmelCase     :		Optional[int]		     = None

			if self.args.prediction_loss_only:
				return loss, None, None

			if has_labels:
				__lowerCAmelCase     :		Tuple		     = inputs['labels']
				if labels.shape[-1] < gen_config.max_length:
					__lowerCAmelCase     :		List[str]		     = self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE			,						gen_config.max_length					)
				elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
					__lowerCAmelCase     :		int		     = self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE			,						gen_config.max_new_tokens + 1					)
			else:
				__lowerCAmelCase     :		Optional[int]		     = None

			return loss, generated_tokens, labels







		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
			if self.tokenizer is not None and hasattr(self.tokenizer			,						'pad_token_id'					):
				# If PAD token is not defined at least EOS token has to be defined
				__lowerCAmelCase     :		Any		     = (
				    self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
				)
			else:
				if self.model.config.pad_token_id is not None:
					__lowerCAmelCase     :		List[str]		     = self.model.config.pad_token_id
				else:
					raise ValueError('Pad_token_id must be set in the configuration of the model, in order to pad tensors'					)

			__lowerCAmelCase     :		Dict		     = pad_token_id * torch.ones(
			    (tensor.shape[0], max_length)			,						dtype=tensor.dtype			,						device=tensor.device					)
			__lowerCAmelCase     :		Tuple		     = tensor
			return padded_tensor