





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		(  _lowerCAmelCase     ):
			'''simple docstring'''


			A_     :				Optional[Any]			      =	fname.split(os.path.sep     )[-1]
			return re.search(r"""^(.*)_\d+\.jpg$"""  ,_lowerCAmelCase     ).groups()[0]

class    _UpperCAmelCase							(		_lowerCamelCase     ):
						def __init__(		self      ,       a__      ,       a__=None      ,       a__=None       ):
									A_     :				str			      =	file_names
									A_     :				List[str]			      =	image_transform
									A_     :				List[Any]			      =	label_to_id
						def __len__(		self       ):
									return len(self.file_names       )






						def __getitem__(		self      ,       a__       ):
									A_     :				Tuple			      =	self.file_names[idx]
									A_     :				Tuple			      =	PIL.Image.open(a__       )
									A_     :				str			      =	raw_image.convert("""RGB"""       )
									if self.image_transform is not None:
												A_     :				List[Any]			      =	self.image_transform(a__       )
									A_     :				Tuple			      =	extract_label(a__       )
									if self.label_to_id is not None:
												A_     :				List[Any]			      =	self.label_to_id[label]
									return {"image": image, "label": label}






def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
			'''simple docstring'''


			if args.with_tracking:
						A_     :				Union[str, Any]			      =	Accelerator(
						    cpu=args.cpu  ,mixed_precision=args.mixed_precision  ,log_with="""all"""  ,project_dir=args.project_dir     )
			else:
						A_     :				Optional[int]			      =	Accelerator(cpu=args.cpu  ,mixed_precision=args.mixed_precision     )

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

			# Parse out whether we are saving every epoch or after a certain number of batches
			if hasattr(args.checkpointing_steps  ,"""isdigit"""     ):
						if args.checkpointing_steps == "epoch":
									A_     :				Any			      =	args.checkpointing_steps
						elif args.checkpointing_steps.isdigit():
									A_     :				str			      =	int(args.checkpointing_steps     )
						else:
									raise ValueError(
									    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""     )
			else:
						A_     :				Any			      =	None

			# We need to initialize the trackers we use, and also store our configuration
			if args.with_tracking:
						A_     :				str			      =	os.path.split(_lowerCAmelCase     )[-1].split("""."""     )[0]
						accelerator.init_trackers(_lowerCAmelCase  ,_lowerCAmelCase     )

			# Grab all the image filenames
			A_     :				List[str]			      =	[os.path.join(args.data_dir  ,_lowerCAmelCase     ) for fname in os.listdir(args.data_dir     ) if fname.endswith(""".jpg"""     )]

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

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

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

			# For training we use a simple RandomResizedCrop
			A_     :				Optional[int]			      =	Compose([RandomResizedCrop(_lowerCAmelCase  ,scale=(0.5, 1.0)     ), ToTensor()]     )
			A_     :				Any			      =	PetsDataset(
			    [file_names[i] for i in train_split]  ,image_transform=_lowerCAmelCase  ,label_to_id=_lowerCAmelCase     )

			# For evaluation, we use a deterministic Resize
			A_     :				Tuple			      =	Compose([Resize(_lowerCAmelCase     ), ToTensor()]     )
			A_     :				Optional[int]			      =	PetsDataset([file_names[i] for i in eval_split]  ,image_transform=_lowerCAmelCase  ,label_to_id=_lowerCAmelCase     )

			# Instantiate dataloaders.
			A_     :				List[Any]			      =	DataLoader(_lowerCAmelCase  ,shuffle=_lowerCAmelCase  ,batch_size=_lowerCAmelCase  ,num_workers=4     )
			A_     :				Dict			      =	DataLoader(_lowerCAmelCase  ,shuffle=_lowerCAmelCase  ,batch_size=_lowerCAmelCase  ,num_workers=4     )

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			A_     :				List[str]			      =	create_model("""resnet50d"""  ,pretrained=_lowerCAmelCase  ,num_classes=len(_lowerCAmelCase     )     )

			# 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).
			A_     :				Union[str, Any]			      =	model.to(accelerator.device     )

			# Freezing the base model
			for param in model.parameters():
						A_     :				Union[str, Any]			      =	False
			for param in model.get_classifier().parameters():
						A_     :				Any			      =	True

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

			# Instantiate optimizer
			A_     :				List[str]			      =	torch.optim.Adam(params=model.parameters()  ,lr=lr / 2_5     )

			# Instantiate learning rate scheduler
			A_     :				Optional[Any]			      =	OneCycleLR(optimizer=_lowerCAmelCase  ,max_lr=_lowerCAmelCase  ,epochs=_lowerCAmelCase  ,steps_per_epoch=len(_lowerCAmelCase     )     )

			# 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.
			A_     :				Optional[Any]			      =	accelerator.prepare(
			    _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     )
			# We need to keep track of how many total steps we have iterated over
			A_     :				str			      =	0
			# We also need to keep track of the starting epoch so files are named properly
			A_     :				Dict			      =	0

			# Potentially load in the weights and states from a previous save
			if args.resume_from_checkpoint:
						if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
									accelerator.print(f"""Resumed from checkpoint: {args.resume_from_checkpoint}"""     )
									accelerator.load_state(args.resume_from_checkpoint     )
									A_     :				Any			      =	os.path.basename(args.resume_from_checkpoint     )
						else:
									# Get the most recent checkpoint
									A_     :				Tuple			      =	[f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
									dirs.sort(key=os.path.getctime     )
									A_     :				Any			      =	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract `epoch_{i}` or `step_{i}`
						A_     :				str			      =	os.path.splitext(_lowerCAmelCase     )[0]

						if "epoch" in training_difference:
									A_     :				Tuple			      =	int(training_difference.replace("""epoch_"""  ,""""""     )     ) + 1
									A_     :				Optional[Any]			      =	None
						else:
									A_     :				Any			      =	int(training_difference.replace("""step_"""  ,""""""     )     )
									A_     :				Union[str, Any]			      =	resume_step // len(_lowerCAmelCase     )
									resume_step -= starting_epoch * len(_lowerCAmelCase     )

    # Now we train the model
			for epoch in range(_lowerCAmelCase  ,_lowerCAmelCase     ):
						model.train()
						if args.with_tracking:
									A_     :				Dict			      =	0
						if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
									# We need to skip steps until we reach the resumed step
									A_     :				Dict			      =	accelerator.skip_first_batches(_lowerCAmelCase  ,_lowerCAmelCase     )
									overall_step += resume_step
						else:
									# After the first iteration though, we need to go back to the original dataloader
									A_     :				Optional[int]			      =	train_dataloader
						for batch in active_dataloader:
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									A_     :				Optional[int]			      =	{k: v.to(accelerator.device     ) for k, v in batch.items()}
									A_     :				str			      =	(batch["""image"""] - mean) / std
									A_     :				List[Any]			      =	model(_lowerCAmelCase     )
									A_     :				int			      =	torch.nn.functional.cross_entropy(_lowerCAmelCase  ,batch["""label"""]     )
									# We keep track of the loss at each epoch
									if args.with_tracking:
												total_loss += loss.detach().float()
									accelerator.backward(_lowerCAmelCase     )
									optimizer.step()
									lr_scheduler.step()
									optimizer.zero_grad()
									overall_step += 1
									if isinstance(_lowerCAmelCase  ,_lowerCAmelCase     ):
												A_     :				Dict			      =	f"""step_{overall_step}"""
												if overall_step % checkpointing_steps == 0:
															if args.output_dir is not None:
																		A_     :				Optional[int]			      =	os.path.join(args.output_dir  ,_lowerCAmelCase     )
															accelerator.save_state(_lowerCAmelCase     )
						model.eval()
						A_     :				Tuple			      =	0
						A_     :				List[str]			      =	0
						for step, batch in enumerate(_lowerCAmelCase     ):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									A_     :				Union[str, Any]			      =	{k: v.to(accelerator.device     ) for k, v in batch.items()}
									A_     :				List[Any]			      =	(batch["""image"""] - mean) / std
									with torch.no_grad():
												A_     :				Any			      =	model(_lowerCAmelCase     )
									A_     :				List[Any]			      =	outputs.argmax(dim=-1     )
									A_     :				Optional[int]			      =	accelerator.gather_for_metrics((predictions, batch["""label"""])     )
									A_     :				Optional[int]			      =	predictions == references
									num_elems += accurate_preds.shape[0]
									accurate += accurate_preds.long().sum()

						A_     :				Tuple			      =	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}"""     )
						if args.with_tracking:
									accelerator.log(
									    {
									        """accuracy""": 1_0_0 * eval_metric,
									        """train_loss""": total_loss.item() / len(_lowerCAmelCase     ),
									        """epoch""": epoch,
									    }  ,step=_lowerCAmelCase  ,)
						if checkpointing_steps == "epoch":
									A_     :				Optional[int]			      =	f"""epoch_{epoch}"""
									if args.output_dir is not None:
												A_     :				List[Any]			      =	os.path.join(args.output_dir  ,_lowerCAmelCase     )
									accelerator.save_state(_lowerCAmelCase     )

			if args.with_tracking:
						accelerator.end_training()



def     _lowerCAmelCase		(  ):
			'''simple docstring'''


			A_     :				Union[str, Any]			      =	argparse.ArgumentParser(description="""Simple example of training script."""     )
			parser.add_argument("""--data_dir"""  ,required=_lowerCAmelCase  ,help="""The data folder on disk."""     )
			parser.add_argument("""--fp16"""  ,action="""store_true"""  ,help="""If passed, will use FP16 training."""     )
			parser.add_argument(
			    """--mixed_precision"""  ,type=_lowerCAmelCase  ,default=_lowerCAmelCase  ,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("""--cpu"""  ,action="""store_true"""  ,help="""If passed, will train on the CPU."""     )
			parser.add_argument(
			    """--checkpointing_steps"""  ,type=_lowerCAmelCase  ,default=_lowerCAmelCase  ,help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""  ,)
			parser.add_argument(
			    """--output_dir"""  ,type=_lowerCAmelCase  ,default="""."""  ,help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""  ,)
			parser.add_argument(
			    """--resume_from_checkpoint"""  ,type=_lowerCAmelCase  ,default=_lowerCAmelCase  ,help="""If the training should continue from a checkpoint folder."""  ,)
			parser.add_argument(
			    """--with_tracking"""  ,action="""store_true"""  ,help="""Whether to load in all available experiment trackers from the environment and use them for logging."""  ,)
			parser.add_argument(
			    """--project_dir"""  ,type=_lowerCAmelCase  ,default="""logs"""  ,help="""Location on where to store experiment tracking logs` and relevent project information"""  ,)
			A_     :				Any			      =	parser.parse_args()
			A_     :				Dict			      =	{"""lr""": 3e-2, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 6_4, """image_size""": 2_2_4}
			training_function(_lowerCAmelCase  ,_lowerCAmelCase     )


if __name__ == "__main__":
						main()













# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


_lowerCAmelCase 				=     """3"""

print("""Python version:""", sys.version)
print("""transformers version:""", transformers.__version__)

try:
      import torch

      print("""Torch version:""", torch.__version__)
      print("""Cuda available:""", torch.cuda.is_available())
      print("""Cuda version:""", torch.version.cuda)
      print("""CuDNN version:""", torch.backends.cudnn.version())
      print("""Number of GPUs available:""", torch.cuda.device_count())
      print("""NCCL version:""", torch.cuda.nccl.version())
except ImportError:
      print("""Torch version:""", None)

try:
      import deepspeed

      print("""DeepSpeed version:""", deepspeed.__version__)
except ImportError:
      print("""DeepSpeed version:""", None)

try:
      import tensorflow as tf

      print("""TensorFlow version:""", tf.__version__)
      print("""TF GPUs available:""", bool(tf.config.list_physical_devices("""GPU""")))
      print("""Number of TF GPUs available:""", len(tf.config.list_physical_devices("""GPU""")))
except ImportError:
      print("""TensorFlow version:""", None)






