
"""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 				lowercase      (_lowerCAmelCase		):
						__lowerCAmelCase        =       fname.split(os.path.sep		)[-1]
						return re.search(r"""^(.*)_\d+\.jpg$"""				,  _lowerCAmelCase		).groups()[0]
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							def __init__(				self  ,							snake_case_  ,							snake_case_=None  ,							snake_case_=None							)   ->       Optional[Any]:
													__lowerCAmelCase        =       file_names
													__lowerCAmelCase        =       image_transform
													__lowerCAmelCase        =       label_to_id




							def __len__(				self							)   ->       Union[str, Any]:
													return len(self.file_names							)




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

def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase		):
						# 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(_lowerCAmelCase				,  (list, tuple)		):
												__lowerCAmelCase        =       (image_size, image_size)

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

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

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

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

						# Instantiate dataloaders.
						__lowerCAmelCase        =       DataLoader(_lowerCAmelCase				,  shuffle=_lowerCAmelCase				,  batch_size=_lowerCAmelCase				,  num_workers=4		)
						__lowerCAmelCase        =       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)
						__lowerCAmelCase        =       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).
						__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=_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.
						__lowerCAmelCase ,      __lowerCAmelCase ,      __lowerCAmelCase ,      __lowerCAmelCase ,      __lowerCAmelCase        =       accelerator.prepare(
						    _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		)

						# Now we train the model
						for epoch in range(_lowerCAmelCase		):
												model.train()
												for step, batch in enumerate(_lowerCAmelCase		):
																		# 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(_lowerCAmelCase		)
																		__lowerCAmelCase        =       torch.nn.functional.cross_entropy(_lowerCAmelCase				,  batch["""label"""]		)
																		accelerator.backward(_lowerCAmelCase		)
																		optimizer.step()
																		lr_scheduler.step()
																		optimizer.zero_grad()

												model.eval()
												__lowerCAmelCase        =       0
												__lowerCAmelCase        =       0
												for _, batch in enumerate(_lowerCAmelCase		):
																		# 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(_lowerCAmelCase		)
																		__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 				lowercase      ():
						__lowerCAmelCase        =       argparse.ArgumentParser(description="""Simple example of training script."""		)
						parser.add_argument("""--data_dir"""				,  required=_lowerCAmelCase				,  help="""The data folder on disk."""		)
						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(
						    """--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("""--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(_lowerCAmelCase				,  _lowerCAmelCase		)


if __name__ == "__main__":
			main()


"""simple docstring"""
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser

def 				lowercase      (_lowerCAmelCase=None				,  _lowerCAmelCase=None		):
						return field(default_factory=lambda: default				,  metadata=_lowerCAmelCase		)
@dataclass
class      lowerCAmelCase_						:



							'''simple docstring'''





							_snake_case		     =						field(
							    metadata={'''help''': '''The csv file to plot.'''}							,							)
							_snake_case		     =						field(
							    default=A__							,							metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}							,							)
							_snake_case		     =						field(
							    default=A__							,							metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}							,							)
							_snake_case		     =						field(
							    default=A__							,							metadata={'''help''': '''Disable logarithmic scale when plotting'''}							,							)
							_snake_case		     =						field(
							    default=A__							,							metadata={
							        '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
							    }							,							)
							_snake_case		     =						field(
							    default=A__							,							metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}							,							)
							_snake_case		     =						list_field(
							    default=A__							,							metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''}				)

def 				lowercase      (_lowerCAmelCase		):
						try:
												int(_lowerCAmelCase		)
												return True
						except ValueError:
												return False

def 				lowercase      (_lowerCAmelCase		):
						try:
												float(_lowerCAmelCase		)
												return True
						except ValueError:
												return False
class      lowerCAmelCase_						:



							'''simple docstring'''





							def __init__(				self  ,							snake_case_							)   ->       List[Any]:
													__lowerCAmelCase        =       args
													__lowerCAmelCase        =       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}							)

													with open(self.args.csv_file  ,							newline=""""""							) as csv_file:
																			__lowerCAmelCase        =       csv.DictReader(snake_case_							)
																			for row in reader:
																									__lowerCAmelCase        =       row["""model"""]
																									self.result_dict[model_name]["bsz"].append(int(row["""batch_size"""]							)							)
																									self.result_dict[model_name]["seq_len"].append(int(row["""sequence_length"""]							)							)
																									if can_convert_to_int(row["""result"""]							):
																															# value is not None
																															__lowerCAmelCase        =       int(row["""result"""]							)
																									elif can_convert_to_float(row["""result"""]							):
																															# value is not None
																															__lowerCAmelCase        =       float(row["""result"""]							)




							def  A__		(				self							)   ->       List[Any]:
													__lowerCAmelCase ,      __lowerCAmelCase        =       plt.subplots()
													__lowerCAmelCase        =       """Time usage""" if self.args.is_time else """Memory usage"""
													__lowerCAmelCase        =       title_str + """ for training""" if self.args.is_train else title_str + """ for inference"""

													if not self.args.no_log_scale:
																			# set logarithm scales
																			ax.set_xscale("""log"""							)
																			ax.set_yscale("""log"""							)

													for axis in [ax.xaxis, ax.yaxis]:
																			axis.set_major_formatter(ScalarFormatter()							)

													for model_name_idx, model_name in enumerate(self.result_dict.keys()							):
																			__lowerCAmelCase        =       sorted(set(self.result_dict[model_name]["""bsz"""]							)							)
																			__lowerCAmelCase        =       sorted(set(self.result_dict[model_name]["""seq_len"""]							)							)
																			__lowerCAmelCase        =       self.result_dict[model_name]["""result"""]

																			((__lowerCAmelCase) ,      (__lowerCAmelCase))        =       (
																			    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
																			)

																			__lowerCAmelCase        =       (
																			    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
																			)

																			for inner_loop_value in inner_loop_array:
																									if self.args.plot_along_batch:
																															__lowerCAmelCase        =       np.asarray(
																															    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]  ,							dtype=snake_case_  ,							)
																									else:
																															__lowerCAmelCase        =       np.asarray(
																															    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]  ,							dtype=np.floataa  ,							)

																									((__lowerCAmelCase) ,      (__lowerCAmelCase))        =       (
																									    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
																									)

																									__lowerCAmelCase        =       np.asarray(snake_case_  ,							snake_case_							)[: len(snake_case_							)]
																									plt.scatter(
																									    snake_case_  ,							snake_case_  ,							label=f"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""							)
																									plt.plot(snake_case_  ,							snake_case_  ,							"""--"""							)

																			title_str += f""" {label_model_name} vs."""

													__lowerCAmelCase        =       title_str[:-4]
													__lowerCAmelCase        =       """Time in s""" if self.args.is_time else """Memory in MB"""

													# plot
													plt.title(snake_case_							)
													plt.xlabel(snake_case_							)
													plt.ylabel(snake_case_							)
													plt.legend()

													if self.args.figure_png_file is not None:
																			plt.savefig(self.args.figure_png_file							)
													else:
																			plt.show()

def 				lowercase      ():
						__lowerCAmelCase        =       HfArgumentParser(_lowerCAmelCase		)
						__lowerCAmelCase        =       parser.parse_args_into_dataclasses()[0]
						__lowerCAmelCase        =       Plot(args=_lowerCAmelCase		)
						plot.plot()


if __name__ == "__main__":
			main()
