






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    :  List[Any]	):
  __UpperCAmelCase							:			int     =						fname.split(os.path.sep	)[-1]
  return re.search(R"""^(.*)_\d+\.jpg$"""						,	__lowerCamelCase	).groups()[0]
class 					a					(				A__     ):



      """simple docstring"""


      def __init__(   self      :    Any	,   __lowercase      :    List[str]	,   __lowercase      :    List[Any]=None	,   __lowercase      :    Optional[Any]=None      )							->   Union[str, Any]:
        __UpperCAmelCase							:			Dict     =						file_names
        __UpperCAmelCase							:			Any     =						image_transform
        __UpperCAmelCase							:			List[str]     =						label_to_id
      def __len__(   self      :    List[Any]      )							->   List[Any]:
        return len(self.file_names      )




      def __getitem__(   self      :    List[str]	,   __lowercase      :    int      )							->   Any:
        __UpperCAmelCase							:			str     =						self.file_names[idx]
        __UpperCAmelCase							:			str     =						PIL.Image.open(_lowerCAmelCase      )
        __UpperCAmelCase							:			List[Any]     =						raw_image.convert("""RGB"""      )
        if self.image_transform is not None:
          __UpperCAmelCase							:			Dict     =						self.image_transform(_lowerCAmelCase      )
        __UpperCAmelCase							:			str     =						extract_label(_lowerCAmelCase      )
        if self.label_to_id is not None:
          __UpperCAmelCase							:			Dict     =						self.label_to_id[label]
        return {"image": image, "label": label}






def        lowerCamelCase__   (					__lowerCamelCase    :  List[Any]						,	__lowerCamelCase    :  Union[str, Any]	):
  __UpperCAmelCase							:			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
  __UpperCAmelCase							:			Dict     =						config["""lr"""]
  __UpperCAmelCase							:			Tuple     =						int(config["""num_epochs"""]	)
  __UpperCAmelCase							:			List[Any]     =						int(config["""seed"""]	)
  __UpperCAmelCase							:			Tuple     =						int(config["""batch_size"""]	)
  __UpperCAmelCase							:			List[Any]     =						config["""image_size"""]
  if not isinstance(__lowerCamelCase						,	(list, tuple)	):
    __UpperCAmelCase							:			Any     =						(image_size, image_size)

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

  # Build the label correspondences
  __UpperCAmelCase							:			Dict     =						[extract_label(__lowerCamelCase	) for fname in file_names]
  __UpperCAmelCase							:			Tuple     =						list(set(__lowerCamelCase	)	)
  id_to_label.sort()
  __UpperCAmelCase							:			List[Any]     =						{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
  __UpperCAmelCase							:			List[Any]     =						np.random.permutation(len(__lowerCamelCase	)	)
  __UpperCAmelCase							:			List[str]     =						int(0.8 * len(__lowerCamelCase	)	)
  __UpperCAmelCase							:			Union[str, Any]     =						random_perm[:cut]
  __UpperCAmelCase							:			List[Any]     =						random_perm[cut:]

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

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

  # Instantiate dataloaders.
  __UpperCAmelCase							:			List[Any]     =						DataLoader(__lowerCamelCase						,	shuffle=__lowerCamelCase						,	batch_size=__lowerCamelCase						,	num_workers=4	)
  __UpperCAmelCase							:			Union[str, Any]     =						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)
  __UpperCAmelCase							:			Any     =						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).
  __UpperCAmelCase							:			Any     =						model.to(accelerator.device	)

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

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

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

  # Instantiate learning rate scheduler
  __UpperCAmelCase							:			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.
  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase							:			Union[str, Any]     =						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`.
      __UpperCAmelCase							:			Dict     =						{k: v.to(accelerator.device	) for k, v in batch.items()}
      __UpperCAmelCase							:			Union[str, Any]     =						(batch["""image"""] - mean) / std
      __UpperCAmelCase							:			int     =						model(__lowerCamelCase	)
      __UpperCAmelCase							:			Dict     =						torch.nn.functional.cross_entropy(__lowerCamelCase						,	batch["""label"""]	)
      accelerator.backward(__lowerCamelCase	)
      optimizer.step()
      lr_scheduler.step()
      optimizer.zero_grad()

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

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






def        lowerCamelCase__   (					):
  __UpperCAmelCase							:			Optional[int]     =						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."""	)
  __UpperCAmelCase							:			Optional[Any]     =						parser.parse_args()
  __UpperCAmelCase							:			Tuple     =						{"""lr""": 3E-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
  training_function(__lowerCamelCase						,	__lowerCamelCase	)


if __name__ == "__main__":
       main()










# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# 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.

from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


a	: Union[str, Any]      =				"Update an existing config file with the latest defaults while maintaining the old configuration."






def        lowerCamelCase__   (					__lowerCamelCase    :  str	):
  __UpperCAmelCase							:			Optional[Any]     =						args.config_file
  if config_file is None and Path(__lowerCamelCase	).exists():
    __UpperCAmelCase							:			int     =						default_config_file
  elif not Path(__lowerCamelCase	).exists():
    raise ValueError(f"""The passed config file located at {config_file} doesn't exist."""	)
  __UpperCAmelCase							:			Tuple     =						load_config_from_file(__lowerCamelCase	)

  if config_file.endswith(""".json"""	):
    config.to_json_file(__lowerCamelCase	)
  else:
    config.to_yaml_file(__lowerCamelCase	)
  return config_file






def        lowerCamelCase__   (					__lowerCamelCase    :  Dict						,	__lowerCamelCase    :  List[str]	):
  __UpperCAmelCase							:			List[str]     =						parser.add_parser("""update"""						,	parents=__lowerCamelCase						,	help=__lowerCamelCase						,	formatter_class=__lowerCamelCase	)
  parser.add_argument(
      """--config_file"""						,	default=__lowerCamelCase						,	help=(
          """The path to the config file to update. Will default to a file named default_config.yaml in the cache """
          """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
          """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
          """with 'huggingface'."""
      )						,	)

  parser.set_defaults(func=__lowerCamelCase	)
  return parser







def        lowerCamelCase__   (					__lowerCamelCase    :  Optional[int]	):
  __UpperCAmelCase							:			Optional[Any]     =						update_config(__lowerCamelCase	)
  print(f"""Sucessfully updated the configuration file at {config_file}."""	)


