




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    UpperCAmelCase							(  _snake_case				):
							lowerCAmelCase =		fname.split(os.path.sep				)[-1]
							return re.search(R'''^(.*)_\d+\.jpg$'''    ,			_snake_case				).groups()[0]


class 				__UpperCamelCase      (			__UpperCAmelCase	):





				'''simple docstring'''



				def __init__(			self							,			UpperCAmelCase_							,			UpperCAmelCase_=None							,			UpperCAmelCase_=None    ):
											lowerCAmelCase =		file_names
											lowerCAmelCase =		image_transform
											lowerCAmelCase =		label_to_id



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






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




def    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							# 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 / 25				)

							# 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}: {100 * eval_metric:.2f}"""				)



def    UpperCAmelCase							(  ):
							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''': 42, '''batch_size''': 64, '''image_size''': 224}
							training_function(_snake_case    ,			_snake_case				)


if __name__ == "__main__":
		main()









import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
		import torch


UpperCAmelCase_    =logging.get_logger(__name__)


class 				__UpperCamelCase      :





				'''simple docstring'''



				def __init__(			self							,			UpperCAmelCase_							,			UpperCAmelCase_=21							,			UpperCAmelCase_=[]							,			UpperCAmelCase_=None    ):
											lowerCAmelCase =		model
											lowerCAmelCase =		trace_batch_nums
											lowerCAmelCase =		abort_after_batch_num

											# keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
											lowerCAmelCase =		collections.deque([]							,			UpperCAmelCase_    )
											lowerCAmelCase =		[]
											lowerCAmelCase =		0
											lowerCAmelCase =		0
											lowerCAmelCase =		False
											lowerCAmelCase =		'''                 '''

											self.analyse_model()

											self.register_forward_hook()



				def    __snake_case		(			self							,			UpperCAmelCase_=None    ):
											if frame is not None:
																		self.expand_frame(UpperCAmelCase_    )
											self.frames.append('''\n'''.join(self.frame    )    )
											lowerCAmelCase =		[]  # start a new frame



				def    __snake_case		(			self							,			UpperCAmelCase_    ):
											self.frame.append(UpperCAmelCase_    )



				def    __snake_case		(			self    ):
											print('''\n'''.join(self.frames    )    )
											lowerCAmelCase =		[]



				def    __snake_case		(			self    ):
											lowerCAmelCase =		[]



				def    __snake_case		(			self    ):
											print(F"""\nDetected inf/nan during batch_number={self.batch_number}"""    )
											print(F"""Last {len(self.frames    )} forward frames:"""    )
											print(F"""{"abs min":8} {"abs max":8} metadata"""    )
											print('''\n'''.join(self.frames    )    )
											print('''\n\n'''    )
											lowerCAmelCase =		[]



				def    __snake_case		(			self    ):
											# extract the fully qualified module names, to be able to report at run time. e.g.:
											# encoder.block.2.layer.0.SelfAttention.o
											#
											# for shared weights only the first shared module name will be registered
											lowerCAmelCase =		{m: name for name, m in self.model.named_modules()}
											# self.longest_module_name = max(len(v) for v in self.module_names.values())



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											if torch.is_tensor(UpperCAmelCase_    ):
																		self.expand_frame(get_abs_min_max(UpperCAmelCase_							,			UpperCAmelCase_    )    )
																		if detect_overflow(UpperCAmelCase_							,			UpperCAmelCase_    ):
																									lowerCAmelCase =		True
											elif var is None:
																		self.expand_frame(F"""{"None":>17} {ctx}"""    )
											else:
																		self.expand_frame(F"""{"not a tensor":>17} {ctx}"""    )



				def    __snake_case		(			self    ):
											self.expand_frame(F"""\n\n{self.prefix} *** Starting batch number={self.batch_number} ***"""    )
											self.expand_frame(F"""{"abs min":8} {"abs max":8} metadata"""    )



				def    __snake_case		(			self    ):
											self.expand_frame(F"""{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"""    )



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											self.expand_frame(F"""{self.prefix} {self.module_names[module]} {module.__class__.__name__}"""    )

											# params
											for name, p in module.named_parameters(recurse=UpperCAmelCase_    ):
																		self.analyse_variable(UpperCAmelCase_							,			UpperCAmelCase_    )

											# inputs
											if isinstance(UpperCAmelCase_							,			UpperCAmelCase_    ):
																		for i, x in enumerate(UpperCAmelCase_    ):
																									self.analyse_variable(UpperCAmelCase_							,			F"""input[{i}]"""    )
											else:
																		self.analyse_variable(UpperCAmelCase_							,			'''input'''    )

											# outputs
											if isinstance(UpperCAmelCase_							,			UpperCAmelCase_    ):
																		for i, x in enumerate(UpperCAmelCase_    ):
																									# possibly a tuple of tuples
																									if isinstance(UpperCAmelCase_							,			UpperCAmelCase_    ):
																																for j, y in enumerate(UpperCAmelCase_    ):
																																							self.analyse_variable(UpperCAmelCase_							,			F"""output[{i}][{j}]"""    )
																									else:
																																self.analyse_variable(UpperCAmelCase_							,			F"""output[{i}]"""    )
											else:
																		self.analyse_variable(UpperCAmelCase_							,			'''output'''    )

											self.save_frame()



				def    __snake_case		(			self    ):
											self.model.apply(self._register_forward_hook    )



				def    __snake_case		(			self							,			UpperCAmelCase_    ):
											module.register_forward_hook(self.forward_hook    )






				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											# - input is a tuple of packed inputs (could be non-Tensors)
											# - output could be a Tensor or a tuple of Tensors and non-Tensors

											lowerCAmelCase =		False

											lowerCAmelCase =		True if self.batch_number in self.trace_batch_nums else False
											if trace_mode:
																		self.reset_saved_frames()

											if self.total_calls == 0:
																		self.batch_start_frame()
											self.total_calls += 1

											# count batch numbers - the very first forward hook of the batch will be called when the
											# batch completes - i.e. it gets called very last - we know this batch has finished
											if module == self.model:
																		self.batch_number += 1
																		lowerCAmelCase =		True

											self.create_frame(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )

											# if last_frame_of_batch:
											#     self.batch_end_frame()

											if trace_mode:
																		self.trace_frames()

											if last_frame_of_batch:
																		self.batch_start_frame()

											if self.detected_overflow and not trace_mode:
																		self.dump_saved_frames()

																		# now we can abort, as it's pointless to continue running
																		raise ValueError(
																		    '''DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. '''
																		    '''Please scroll up above this traceback to see the activation values prior to this event.'''    )

											# abort after certain batch if requested to do so
											if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
																		raise ValueError(
																		    F"""DebugUnderflowOverflow: aborting after {self.batch_number} batches due to"""
																		    F""" `abort_after_batch_num={self.abort_after_batch_num}` arg"""    )




def    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							lowerCAmelCase =		var.abs()
							return F"""{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"""



def    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							lowerCAmelCase =		False
							if torch.isnan(_snake_case				).any().item():
														lowerCAmelCase =		True
														print(F"""{ctx} has nans"""				)
							if torch.isinf(_snake_case				).any().item():
														lowerCAmelCase =		True
														print(F"""{ctx} has infs"""				)

							# if needed to monitor large elements can enable the following
							if 0:  # and detected:
														lowerCAmelCase =		var[torch.ge(var.abs()    ,			100				)]
														if naaa.numel() > 0:
																					print(F"""{ctx}:  n100={naaa.numel()}"""				)
														lowerCAmelCase =		var[torch.ge(var.abs()    ,			1000				)]
														if naaaa.numel() > 0:
																					print(F"""{ctx}: n1000={naaaa.numel()}"""				)
														lowerCAmelCase =		var[torch.ge(var.abs()    ,			10000				)]
														if naaaaa.numel() > 0:
																					print(F"""{ctx}: n10000={naaaaa.numel()}"""				)

							if 0:
														print(F"""min={var.min():9.2e} max={var.max():9.2e}"""				)

							if 0:
														print(F"""min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})"""				)

							return detected


class 				__UpperCamelCase      (			__UpperCAmelCase	):





				'''simple docstring'''



				__a				:			Tuple					    ="""underflow_overflow"""
				__a				:			Dict					    ="""tpu_metrics_debug"""



