


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  a_	( __lowercase     :			Union[str, Any]				)				->						Optional[Any]:
			_snake_case						=    fname.split(os.path.sep				)[-1]
			return re.search(r'^(.*)_\d+\.jpg$' ,      snake_case__				).groups()[0]




class      SCREAMING_SNAKE_CASE__  (			UpperCamelCase_      ):





	'''simple docstring'''



	def __init__(						self			:					Tuple    ,						lowercase			:					Union[str, Any]    ,						lowercase			:					List[Any]=None    ,						lowercase			:					Optional[Any]=None			):


				'''simple docstring'''


				_snake_case						=    file_names
				_snake_case						=    image_transform
				_snake_case						=    label_to_id



	def __len__(						self			:					List[Any]			):


				'''simple docstring'''


				return len(self.file_names			)





	def __getitem__(						self			:					Union[str, Any]    ,						lowercase			:					Dict			):


				'''simple docstring'''


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






def  a_	( __lowercase     :			Union[str, Any] ,      __lowercase     :			List[str]				)				->						Any:
			# Initialize accelerator
			_snake_case						=    Accelerator(cpu=args.cpu ,      mixed_precision=args.mixed_precision				)

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

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

			# Build the label correspondences
			_snake_case						=    [extract_label(snake_case__				) for fname in file_names]
			_snake_case						=    list(set(snake_case__				)				)
			id_to_label.sort()
			_snake_case						=    {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
			_snake_case						=    np.random.permutation(len(snake_case__				)				)
			_snake_case						=    int(0.8 * len(snake_case__				)				)
			_snake_case						=    random_perm[:cut]
			_snake_case						=    random_perm[cut:]

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

			# Instantiate dataloaders.
			_snake_case						=    DataLoader(snake_case__ ,      shuffle=snake_case__ ,      batch_size=snake_case__ ,      num_workers=4				)
			_snake_case						=    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)
			_snake_case						=    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).
			_snake_case						=    model.to(accelerator.device				)

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

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

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

			# Instantiate learning rate scheduler
			_snake_case						=    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.
			_snake_case			,      _snake_case			,      _snake_case			,      _snake_case			,      _snake_case						=    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`.
									_snake_case						=    {k: v.to(accelerator.device				) for k, v in batch.items()}
									_snake_case						=    (batch['image'] - mean) / std
									_snake_case						=    model(snake_case__				)
									_snake_case						=    torch.nn.functional.cross_entropy(snake_case__ ,      batch['label']				)
									accelerator.backward(snake_case__				)
									optimizer.step()
									lr_scheduler.step()
									optimizer.zero_grad()

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

						_snake_case						=    accurate.item() / num_elems
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'''epoch {epoch}: {100 * eval_metric:.2f}'''				)






def  a_	( )				->						List[str]:
			_snake_case						=    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.'				)
			_snake_case						=    parser.parse_args()
			_snake_case						=    {'lr': 3E-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
			training_function(snake_case__ ,      snake_case__				)


if __name__ == "__main__":
			main()

import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def  a_	( __lowercase     :			Optional[Any] ,      __lowercase     :			Union[str, Any] ,      __lowercase     :			str				)				->						Any:
			# Construct configuration depending on size
			_snake_case						=    {'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			_snake_case			,      _snake_case			,      _snake_case						=    MODELS[model_size]  # set model hyperparameters
			_snake_case						=    ImageGPTConfig(n_embd=__lowercase ,      n_layer=__lowercase ,      n_head=__lowercase				)
			_snake_case						=    ImageGPTForCausalLM(__lowercase				)

			# Load weights from numpy
			load_tf_weights_in_imagegpt(__lowercase ,      __lowercase ,      __lowercase				)

			# Save pytorch-model
			_snake_case						=    pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			_snake_case						=    pytorch_dump_folder_path + '/' + CONFIG_NAME
			print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''				)
			torch.save(model.state_dict() ,      __lowercase				)
			print(f'''Save configuration file to {pytorch_config_dump_path}'''				)
			with open(__lowercase ,      'w' ,      encoding='utf-8'				) as f:
						f.write(config.to_json_string()				)


if __name__ == "__main__":
			_lowerCamelCase :       List[Any]     					=    argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--imagegpt_checkpoint_path''',
			    default=None,
			    type=str,
			    required=True,
			    help='''Path to the TensorFlow checkpoint path.''',
			)
			parser.add_argument(
			    '''--model_size''',
			    default=None,
			    type=str,
			    required=True,
			    help='''Size of the model (can be either \'small\', \'medium\' or \'large\').''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			_lowerCamelCase :       Optional[Any]     					=    parser.parse_args()
			convert_imagegpt_checkpoint_to_pytorch(
			    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
			)