






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 ):

					'''simple docstring'''







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





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							def __init__(  self:   int		,     __A:   Optional[int]		,     __A:   Tuple=None		,     __A:   List[str]=None   )    ->				Tuple:
												_A								= file_names
												_A								= image_transform
												_A								= label_to_id

							def __len__(  self:   Dict   )    ->				str:
												return len(self.file_names   )



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




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					if args.with_tracking:
										_A								= Accelerator(
										    cpu=args.cpu					,			mixed_precision=args.mixed_precision					,			log_with='''all'''					,			project_dir=args.project_dir )
					else:
										_A								= Accelerator(cpu=args.cpu					,			mixed_precision=args.mixed_precision )

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					_A								= config['''lr''']
					_A								= int(config['''num_epochs'''] )
					_A								= int(config['''seed'''] )
					_A								= int(config['''batch_size'''] )
					_A								= config['''image_size''']
					if not isinstance(_lowercase					,			(list, tuple) ):
										_A								= (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								= args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															_A								= int(args.checkpointing_steps )
										else:
															raise ValueError(
															    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.""" )
					else:
										_A								= None

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

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

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

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

					# Split our filenames between train and validation
					_A								= np.random.permutation(len(_lowercase ) )
					_A								= int(0.8 * len(_lowercase ) )
					_A								= random_perm[:cut]
					_A								= random_perm[cut:]

					# For training we use a simple RandomResizedCrop
					_A								= Compose([RandomResizedCrop(_lowercase					,			scale=(0.5, 1.0) ), ToTensor()] )
					_A								= PetsDataset(
					    [file_names[i] for i in train_split]					,			image_transform=_lowercase					,			label_to_id=_lowercase )

					# For evaluation, we use a deterministic Resize
					_A								= Compose([Resize(_lowercase ), ToTensor()] )
					_A								= PetsDataset([file_names[i] for i in eval_split]					,			image_transform=_lowercase					,			label_to_id=_lowercase )

					# Instantiate dataloaders.
					_A								= DataLoader(_lowercase					,			shuffle=_lowercase					,			batch_size=_lowercase					,			num_workers=4 )
					_A								= DataLoader(_lowercase					,			shuffle=_lowercase					,			batch_size=_lowercase					,			num_workers=4 )

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

					# 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								= model.to(accelerator.device )

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

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

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

					# Instantiate learning rate scheduler
					_A								= OneCycleLR(optimizer=_lowercase					,			max_lr=_lowercase					,			epochs=_lowercase					,			steps_per_epoch=len(_lowercase ) )

					# 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							,_A							,_A							,_A							,_A								= accelerator.prepare(
					    _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase )
					# We need to keep track of how many total steps we have iterated over
					_A								= 0
					# We also need to keep track of the starting epoch so files are named properly
					_A								= 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								= os.path.basename(args.resume_from_checkpoint )
										else:
															# Get the most recent checkpoint
															_A								= [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
															dirs.sort(key=os.path.getctime )
															_A								= dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										_A								= os.path.splitext(_lowercase )[0]

										if "epoch" in training_difference:
															_A								= int(training_difference.replace('''epoch_'''					,			'''''' ) ) + 1
															_A								= None
										else:
															_A								= int(training_difference.replace('''step_'''					,			'''''' ) )
															_A								= resume_step // len(_lowercase )
															resume_step -= starting_epoch * len(_lowercase )

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

										_A								= accurate.item() / num_elems
										# Use accelerator.print to print only on the main process.
										accelerator.print(f"""epoch {epoch}: {1_00 * eval_metric:.2f}""" )
										if args.with_tracking:
															accelerator.log(
															    {
															        '''accuracy''': 1_00 * eval_metric,
															        '''train_loss''': total_loss.item() / len(_lowercase ),
															        '''epoch''': epoch,
															    }					,			step=_lowercase					,			)
										if checkpointing_steps == "epoch":
															_A								= f"""epoch_{epoch}"""
															if args.output_dir is not None:
																				_A								= os.path.join(args.output_dir					,			_lowercase )
															accelerator.save_state(_lowercase )

					if args.with_tracking:
										accelerator.end_training()




def      __A					(     ):

					'''simple docstring'''







					_A								= argparse.ArgumentParser(description='''Simple example of training script.''' )
					parser.add_argument('''--data_dir'''					,			required=_lowercase					,			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=_lowercase					,			default=_lowercase					,			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=_lowercase					,			default=_lowercase					,			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=_lowercase					,			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=_lowercase					,			default=_lowercase					,			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=_lowercase					,			default='''logs'''					,			help='''Location on where to store experiment tracking logs` and relevent project information'''					,			)
					_A								= parser.parse_args()
					_A								= {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 2_24}
					training_function(_lowercase					,			_lowercase )


if __name__ == "__main__":
				main()











import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


__A      =				logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
				import torch_xla.core.xla_model as xm
				import torch_xla.debug.metrics as met





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							def __init__(  self:   int		,     *__A:   str		,     __A:   List[Any]=None		,     __A:   Union[str, Any]=None		,     __A:   List[Any]=None		,     **__A:   int   )    ->				List[Any]:
												super().__init__(*__A		,     **__A   )
												_A								= eval_examples
												_A								= post_process_function
												_A								= quant_trainer_args
												_A								= 1_28  # default number of calibration samples

							def       __A (  self:   Union[str, Any]		,     __A:   List[Any]=None   )    ->				Optional[Any]:
												if calib_dataset is None and self.calib_dataset is None:
																	raise ValueError('''Trainer: calibration requires an calib_dataset.'''   )
												_A								= calib_dataset if calib_dataset is not None else self.calib_dataset

												_A								= self._remove_unused_columns(__A		,     description='''Calibration'''   )

												return DataLoader(
												    __A		,     batch_size=self.args.eval_batch_size		,     collate_fn=self.data_collator		,     drop_last=self.args.dataloader_drop_last		,     num_workers=self.args.dataloader_num_workers		,     pin_memory=self.args.dataloader_pin_memory		,     shuffle=__A		,     )

							def       __A (  self:   List[Any]		,     __A:   Any=None   )    ->				Optional[int]:
												_A								= self.train_dataset if calib_dataset is None else calib_dataset
												_A								= self.get_calib_dataloader(__A   )

												_A								= self.model
												quant_trainer.configure_model(__A		,     self.quant_trainer_args		,     calib=__A   )
												model.eval()
												quant_trainer.enable_calibration(__A   )

												logger.info('''***** Running calibration *****'''   )
												logger.info(f"""  Num examples = {self.calib_num}"""   )
												logger.info(f"""  Batch size = {calib_dataloader.batch_size}"""   )

												for step, inputs in enumerate(__A   ):
																	# Prediction step
																	_A							,_A							,_A								= self.prediction_step(__A		,     __A		,     prediction_loss_only=__A   )
																	if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
																						break

												quant_trainer.finish_calibration(__A		,     self.quant_trainer_args   )
												_A								= model

							def       __A (  self:   Any		,     __A:   Dict=None		,     __A:   Tuple=None		,     __A:   List[Any]=None		,     __A:   str = "eval"   )    ->				int:
												_A								= self.eval_dataset if eval_dataset is None else eval_dataset
												_A								= self.get_eval_dataloader(__A   )
												_A								= self.eval_examples if eval_examples is None else eval_examples

												# Temporarily disable metric computation, we will do it in the loop here.
												_A								= self.compute_metrics
												_A								= None
												_A								= self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
												try:
																	_A								= eval_loop(
																	    __A		,     description='''Evaluation'''		,     prediction_loss_only=True if compute_metrics is None else None		,     ignore_keys=__A		,     )
												finally:
																	_A								= compute_metrics

												if self.post_process_function is not None and self.compute_metrics is not None:
																	_A								= self.post_process_function(__A		,     __A		,     output.predictions   )
																	_A								= self.compute_metrics(__A   )

																	# Prefix all keys with metric_key_prefix + '_'
																	for key in list(metrics.keys()   ):
																						if not key.startswith(f"""{metric_key_prefix}_"""   ):
																											_A								= metrics.pop(__A   )

																	self.log(__A   )
												else:
																	_A								= {}

												if self.args.tpu_metrics_debug or self.args.debug:
																	# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
																	xm.master_print(met.metrics_report()   )

												_A								= self.callback_handler.on_evaluate(self.args		,     self.state		,     self.control		,     __A   )
												return metrics

							def       __A (  self:   Union[str, Any]		,     __A:   Optional[int]		,     __A:   int		,     __A:   List[Any]=None		,     __A:   str = "test"   )    ->				Union[str, Any]:
												_A								= self.get_test_dataloader(__A   )

												# Temporarily disable metric computation, we will do it in the loop here.
												_A								= self.compute_metrics
												_A								= None
												_A								= self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
												try:
																	_A								= eval_loop(
																	    __A		,     description='''Prediction'''		,     prediction_loss_only=True if compute_metrics is None else None		,     ignore_keys=__A		,     )
												finally:
																	_A								= compute_metrics

												if self.post_process_function is None or self.compute_metrics is None:
																	return output

												_A								= self.post_process_function(__A		,     __A		,     output.predictions		,     '''predict'''   )
												_A								= self.compute_metrics(__A   )

												# Prefix all keys with metric_key_prefix + '_'
												for key in list(metrics.keys()   ):
																	if not key.startswith(f"""{metric_key_prefix}_"""   ):
																						_A								= metrics.pop(__A   )

												return PredictionOutput(predictions=predictions.predictions		,     label_ids=predictions.label_ids		,     metrics=__A   )



							def       __A (  self:   Tuple		,     __A:   Optional[Any]="./"   )    ->				List[str]:
												_A								= self.eval_dataset
												_A								= self.get_eval_dataloader(__A   )

												_A								= next(iter(__A   )   )

												# saving device - to make it consistent
												_A								= torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''   )

												# convert to tuple
												_A								= tuple(v.to(__A   ) for k, v in batch.items()   )

												logger.info('''Converting model to be onnx compatible'''   )
												from pytorch_quantization.nn import TensorQuantizer

												_A								= True

												_A								= self.model.to(__A   )

												model.eval()
												model.float()

												_A								= model.module if hasattr(__A		,     '''module'''   ) else model
												quant_trainer.configure_model(__A		,     self.quant_trainer_args   )

												_A								= os.path.join(__A		,     '''model.onnx'''   )
												logger.info(f"""exporting model to {output_model_file}"""   )

												_A								= {0: '''batch_size''', 1: '''seq_len'''}

												torch.onnx.export(
												    __A		,     __A		,     __A		,     export_params=__A		,     opset_version=13		,     do_constant_folding=__A		,     input_names=['''input_ids''', '''attention_mask''', '''token_type_ids''']		,     output_names=['''output_start_logits''', '''output_end_logits''']		,     dynamic_axes={
												        '''input_ids''': axes,
												        '''attention_mask''': axes,
												        '''token_type_ids''': axes,
												        '''output_start_logits''': axes,
												        '''output_end_logits''': axes,
												    }		,     verbose=__A		,     )
												logger.info('''onnx export finished'''   )



