





'''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 lowerCamelCase						(     __lowerCamelCase :    Tuple    )						->Tuple:
    _SCREAMING_SNAKE_CASE									=			fname.split(os.path.sep    )[-1]
    return re.search(R"""^(.*)_\d+\.jpg$"""					,       __lowerCamelCase    ).groups()[0]


class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		A=None ,		A=None       )		->   int:
        _SCREAMING_SNAKE_CASE									=			file_names
        _SCREAMING_SNAKE_CASE									=			image_transform
        _SCREAMING_SNAKE_CASE									=			label_to_id


    def __len__(		self       )		->   Optional[Any]:
        return len(self.file_names       )





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





def lowerCamelCase						(     __lowerCamelCase :    Any					,       __lowerCamelCase :    Tuple    )						->str:
    # Initialize accelerator
    if args.with_tracking:
        _SCREAMING_SNAKE_CASE									=			Accelerator(
            cpu=args.cpu					,       mixed_precision=args.mixed_precision					,       log_with="""all"""					,       project_dir=args.project_dir    )
    else:
        _SCREAMING_SNAKE_CASE									=			Accelerator(cpu=args.cpu					,       mixed_precision=args.mixed_precision    )

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

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

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

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

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

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

    # Instantiate dataloaders.
    _SCREAMING_SNAKE_CASE									=			DataLoader(__lowerCamelCase					,       shuffle=__lowerCamelCase					,       batch_size=__lowerCamelCase					,       num_workers=4    )
    _SCREAMING_SNAKE_CASE									=			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)
    _SCREAMING_SNAKE_CASE									=			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).
    _SCREAMING_SNAKE_CASE									=			model.to(accelerator.device    )

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

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

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

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

        if "epoch" in training_difference:
            _SCREAMING_SNAKE_CASE									=			int(training_difference.replace("""epoch_"""					,       """"""    )    ) + 1
            _SCREAMING_SNAKE_CASE									=			None
        else:
            _SCREAMING_SNAKE_CASE									=			int(training_difference.replace("""step_"""					,       """"""    )    )
            _SCREAMING_SNAKE_CASE									=			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:
            _SCREAMING_SNAKE_CASE									=			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
            _SCREAMING_SNAKE_CASE									=			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
            _SCREAMING_SNAKE_CASE									=			train_dataloader
        for batch in active_dataloader:
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            _SCREAMING_SNAKE_CASE									=			{k: v.to(accelerator.device    ) for k, v in batch.items()}
            _SCREAMING_SNAKE_CASE									=			(batch["""image"""] - mean) / std
            _SCREAMING_SNAKE_CASE									=			model(__lowerCamelCase    )
            _SCREAMING_SNAKE_CASE									=			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    ):
                _SCREAMING_SNAKE_CASE									=			F'step_{overall_step}'
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        _SCREAMING_SNAKE_CASE									=			os.path.join(args.output_dir					,       __lowerCamelCase    )
                    accelerator.save_state(__lowerCamelCase    )
        model.eval()
        _SCREAMING_SNAKE_CASE									=			0
        _SCREAMING_SNAKE_CASE									=			0
        for step, batch in enumerate(__lowerCamelCase    ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            _SCREAMING_SNAKE_CASE									=			{k: v.to(accelerator.device    ) for k, v in batch.items()}
            _SCREAMING_SNAKE_CASE									=			(batch["""image"""] - mean) / std
            with torch.no_grad():
                _SCREAMING_SNAKE_CASE									=			model(__lowerCamelCase    )
            _SCREAMING_SNAKE_CASE									=			outputs.argmax(dim=-1    )
            _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			accelerator.gather_for_metrics((predictions, batch["""label"""])    )
            _SCREAMING_SNAKE_CASE									=			predictions == references
            num_elems += accurate_preds.shape[0]
            accurate += accurate_preds.long().sum()

        _SCREAMING_SNAKE_CASE									=			accurate.item() / num_elems
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'epoch {epoch}: {100 * eval_metric:.2f}'    )
        if args.with_tracking:
            accelerator.log(
                {
                    """accuracy""": 100 * eval_metric,
                    """train_loss""": total_loss.item() / len(__lowerCamelCase    ),
                    """epoch""": epoch,
                }					,       step=__lowerCamelCase					,       )
        if checkpointing_steps == "epoch":
            _SCREAMING_SNAKE_CASE									=			F'epoch_{epoch}'
            if args.output_dir is not None:
                _SCREAMING_SNAKE_CASE									=			os.path.join(args.output_dir					,       __lowerCamelCase    )
            accelerator.save_state(__lowerCamelCase    )

    if args.with_tracking:
        accelerator.end_training()





def lowerCamelCase						(     )						->int:
    _SCREAMING_SNAKE_CASE									=			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"""					,       )
    _SCREAMING_SNAKE_CASE									=			parser.parse_args()
    _SCREAMING_SNAKE_CASE									=			{"""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 argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_					= logging.get_logger()


@dataclass
class 					a_     :

    '''simple docstring'''
    UpperCamelCase     = 42
    UpperCamelCase     = field(default_factory=snake_case_       )
    UpperCamelCase     = field(default_factory=snake_case_       )


    def 			snake_case_(		self ,		A ,		A ,		A       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			len(list(m.modules()       )       ) == 1 or isinstance(A ,		nn.Convad       ) or isinstance(A ,		nn.BatchNormad       )
        if has_not_submodules:
            self.traced.append(A       )


    def __call__(		self ,		A       )		->   str:
        for m in self.module.modules():
            self.handles.append(m.register_forward_hook(self._forward_hook       )       )
        self.module(A       )
        [x.remove() for x in self.handles]
        return self





    @property
    def 			snake_case_(		self       )		->   str:
        # check the len of the state_dict keys to see if we have learnable params
        return list(filter(lambda A       : len(list(x.state_dict().keys()       )       ) > 0 ,		self.traced       )       )




@dataclass
class 					a_     :

    '''simple docstring'''
    UpperCamelCase     = 42
    UpperCamelCase     = 42
    UpperCamelCase     = 0
    UpperCamelCase     = field(default_factory=snake_case_       )
    UpperCamelCase     = field(default_factory=snake_case_       )


    def __call__(		self ,		A       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			Tracker(self.dest       )(A       ).parametrized
        _SCREAMING_SNAKE_CASE									=			Tracker(self.src       )(A       ).parametrized

        _SCREAMING_SNAKE_CASE									=			list(filter(lambda A       : type(A       ) not in self.src_skip ,		A       )       )
        _SCREAMING_SNAKE_CASE									=			list(filter(lambda A       : type(A       ) not in self.dest_skip ,		A       )       )

        if len(A       ) != len(A       ):
            raise Exception(
                f'Numbers of operations are different. Source module has {len(A       )} operations while'
                f' destination module has {len(A       )}.'       )

        for dest_m, src_m in zip(A ,		A       ):
            dest_m.load_state_dict(src_m.state_dict()       )
            if self.verbose == 1:
                print(f'Transfered from={src_m} to={dest_m}'       )





def lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    ResNetConfig					,       __lowerCamelCase :    Path					,       __lowerCamelCase :    bool = True    )						->int:
    print(F'Converting {name}...'    )
    with torch.no_grad():
        _SCREAMING_SNAKE_CASE									=			timm.create_model(__lowerCamelCase					,       pretrained=__lowerCamelCase    ).eval()
        _SCREAMING_SNAKE_CASE									=			ResNetForImageClassification(__lowerCamelCase    ).eval()
        _SCREAMING_SNAKE_CASE									=			ModuleTransfer(src=__lowerCamelCase					,       dest=__lowerCamelCase    )
        _SCREAMING_SNAKE_CASE									=			torch.randn((1, 3, 224, 224)    )
        module_transfer(__lowerCamelCase    )

    assert torch.allclose(from_model(__lowerCamelCase    )					,       our_model(__lowerCamelCase    ).logits    ), "The model logits don't match the original one."

    _SCREAMING_SNAKE_CASE									=			F'resnet{"-".join(name.split("resnet"    )    )}'
    print(__lowerCamelCase    )

    if push_to_hub:
        our_model.push_to_hub(
            repo_path_or_name=save_directory / checkpoint_name					,       commit_message="""Add model"""					,       use_temp_dir=__lowerCamelCase					,       )

        # we can use the convnext one
        _SCREAMING_SNAKE_CASE									=			AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""    )
        image_processor.push_to_hub(
            repo_path_or_name=save_directory / checkpoint_name					,       commit_message="""Add image processor"""					,       use_temp_dir=__lowerCamelCase					,       )

        print(F'Pushed {checkpoint_name}'    )





def lowerCamelCase						(     __lowerCamelCase :    Path					,       __lowerCamelCase :    str = None					,       __lowerCamelCase :    bool = True    )						->Any:
    _SCREAMING_SNAKE_CASE									=			"""imagenet-1k-id2label.json"""
    _SCREAMING_SNAKE_CASE									=			1000
    _SCREAMING_SNAKE_CASE									=			(1, num_labels)

    _SCREAMING_SNAKE_CASE									=			"""huggingface/label-files"""
    _SCREAMING_SNAKE_CASE									=			num_labels
    _SCREAMING_SNAKE_CASE									=			json.load(open(hf_hub_download(__lowerCamelCase					,       __lowerCamelCase					,       repo_type="""dataset"""    )					,       """r"""    )    )
    _SCREAMING_SNAKE_CASE									=			{int(__lowerCamelCase    ): v for k, v in idalabel.items()}

    _SCREAMING_SNAKE_CASE									=			idalabel
    _SCREAMING_SNAKE_CASE									=			{v: k for k, v in idalabel.items()}

    _SCREAMING_SNAKE_CASE									=			partial(__lowerCamelCase					,       num_labels=__lowerCamelCase					,       idalabel=__lowerCamelCase					,       labelaid=__lowerCamelCase    )

    _SCREAMING_SNAKE_CASE									=			{
        """resnet18""": ImageNetPreTrainedConfig(
            depths=[2, 2, 2, 2]					,       hidden_sizes=[64, 128, 256, 512]					,       layer_type="""basic"""    ),
        """resnet26""": ImageNetPreTrainedConfig(
            depths=[2, 2, 2, 2]					,       hidden_sizes=[256, 512, 1024, 2048]					,       layer_type="""bottleneck"""    ),
        """resnet34""": ImageNetPreTrainedConfig(
            depths=[3, 4, 6, 3]					,       hidden_sizes=[64, 128, 256, 512]					,       layer_type="""basic"""    ),
        """resnet50""": ImageNetPreTrainedConfig(
            depths=[3, 4, 6, 3]					,       hidden_sizes=[256, 512, 1024, 2048]					,       layer_type="""bottleneck"""    ),
        """resnet101""": ImageNetPreTrainedConfig(
            depths=[3, 4, 23, 3]					,       hidden_sizes=[256, 512, 1024, 2048]					,       layer_type="""bottleneck"""    ),
        """resnet152""": ImageNetPreTrainedConfig(
            depths=[3, 8, 36, 3]					,       hidden_sizes=[256, 512, 1024, 2048]					,       layer_type="""bottleneck"""    ),
    }

    if model_name:
        convert_weight_and_push(__lowerCamelCase					,       names_to_config[model_name]					,       __lowerCamelCase					,       __lowerCamelCase    )
    else:
        for model_name, config in names_to_config.items():
            convert_weight_and_push(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )
    return config, expected_shape


if __name__ == "__main__":
      lowercase_					= argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model_name""",
          default=None,
          type=str,
          help=(
              """The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
              """ currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."""
          ),
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""",
          default=None,
          type=Path,
          required=True,
          help="""Path to the output PyTorch model directory.""",
      )
      parser.add_argument(
          """--push_to_hub""",
          default=True,
          type=bool,
          required=False,
          help="""If True, push model and image processor to the hub.""",
      )

      lowercase_					= parser.parse_args()
      lowercase_					= args.pytorch_dump_folder_path
      pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
      convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)



