'''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 _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							int  					=       fname.split(os.path.sep				)[-1]
     return re.search(r'^(.*)_\d+\.jpg$'	,							SCREAMING_SNAKE_CASE_				).groups()[0]


class    UpperCAmelCase__	(							_snake_case      ):






   """simple docstring"""
   def __init__(self							,					_a							,					_a=None							,					_a=None					)     ->  Dict:
        lowercase_       :							Union[str, Any]  					=       file_names
        lowercase_       :							Tuple  					=       image_transform
        lowercase_       :							Any  					=       label_to_id
   def __len__(self					)     ->  Optional[int]:
        return len(self.file_names					)





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



def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     # Initialize accelerator
     lowercase_       :							List[Any]  					=       Accelerator(cpu=args.cpu	,							mixed_precision=args.mixed_precision				)

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     lowercase_       :							List[str]  					=       config['lr']
     lowercase_       :							Union[str, Any]  					=       int(config['num_epochs']				)
     lowercase_       :							int  					=       int(config['seed']				)
     lowercase_       :							int  					=       int(config['batch_size']				)
     lowercase_       :							Union[str, Any]  					=       config['image_size']
     if not isinstance(SCREAMING_SNAKE_CASE_	,							(list, tuple)				):
          lowercase_       :							List[Any]  					=       (image_size, image_size)

     # Grab all the image filenames
     lowercase_       :							Optional[int]  					=       [os.path.join(args.data_dir	,							SCREAMING_SNAKE_CASE_				) for fname in os.listdir(args.data_dir				) if fname.endswith('.jpg'				)]

     # Build the label correspondences
     lowercase_       :							Tuple  					=       [extract_label(SCREAMING_SNAKE_CASE_				) for fname in file_names]
     lowercase_       :							str  					=       list(set(SCREAMING_SNAKE_CASE_				)				)
     id_to_label.sort()
     lowercase_       :							Dict  					=       {lbl: i for i, lbl in enumerate(SCREAMING_SNAKE_CASE_				)}

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

     # Split our filenames between train and validation
     lowercase_       :							List[Any]  					=       np.random.permutation(len(SCREAMING_SNAKE_CASE_				)				)
     lowercase_       :							int  					=       int(0.8 * len(SCREAMING_SNAKE_CASE_				)				)
     lowercase_       :							Optional[Any]  					=       random_perm[:cut]
     lowercase_       :							List[str]  					=       random_perm[cut:]

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

     # For evaluation, we use a deterministic Resize
     lowercase_       :							List[Any]  					=       Compose([Resize(SCREAMING_SNAKE_CASE_				), ToTensor()]				)
     lowercase_       :							str  					=       PetsDataset([file_names[i] for i in eval_split]	,							image_transform=SCREAMING_SNAKE_CASE_	,							label_to_id=SCREAMING_SNAKE_CASE_				)

     # Instantiate dataloaders.
     lowercase_       :							List[str]  					=       DataLoader(SCREAMING_SNAKE_CASE_	,							shuffle=SCREAMING_SNAKE_CASE_	,							batch_size=SCREAMING_SNAKE_CASE_	,							num_workers=4				)
     lowercase_       :							List[Any]  					=       DataLoader(SCREAMING_SNAKE_CASE_	,							shuffle=SCREAMING_SNAKE_CASE_	,							batch_size=SCREAMING_SNAKE_CASE_	,							num_workers=4				)

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     lowercase_       :							Union[str, Any]  					=       create_model('resnet50d'	,							pretrained=SCREAMING_SNAKE_CASE_	,							num_classes=len(SCREAMING_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).
     lowercase_       :							int  					=       model.to(accelerator.device				)

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

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

     # Instantiate optimizer
     lowercase_       :							List[str]  					=       torch.optim.Adam(params=model.parameters()	,							lr=lr / 25				)

     # Instantiate learning rate scheduler
     lowercase_       :							str  					=       OneCycleLR(optimizer=SCREAMING_SNAKE_CASE_	,							max_lr=SCREAMING_SNAKE_CASE_	,							epochs=SCREAMING_SNAKE_CASE_	,							steps_per_epoch=len(SCREAMING_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.
     lowercase_					,lowercase_					,lowercase_					,lowercase_					,lowercase_       :							Optional[Any]  					=       accelerator.prepare(
         SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				)

     # Now we train the model
     for epoch in range(SCREAMING_SNAKE_CASE_				):
          model.train()
          for step, batch in enumerate(SCREAMING_SNAKE_CASE_				):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               lowercase_       :							int  					=       {k: v.to(accelerator.device				) for k, v in batch.items()}
               lowercase_       :							Tuple  					=       (batch['image'] - mean) / std
               lowercase_       :							Optional[int]  					=       model(SCREAMING_SNAKE_CASE_				)
               lowercase_       :							Union[str, Any]  					=       torch.nn.functional.cross_entropy(SCREAMING_SNAKE_CASE_	,							batch['label']				)
               accelerator.backward(SCREAMING_SNAKE_CASE_				)
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()

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

          lowercase_       :							str  					=       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 (							):
     lowercase_       :							Union[str, Any]  					=       argparse.ArgumentParser(description='Simple example of training script.'				)
     parser.add_argument('--data_dir'	,							required=SCREAMING_SNAKE_CASE_	,							help='The data folder on disk.'				)
     parser.add_argument(
         '--mixed_precision'	,							type=SCREAMING_SNAKE_CASE_	,							default=SCREAMING_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=SCREAMING_SNAKE_CASE_	,							default=SCREAMING_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.'				)
     lowercase_       :							Optional[Any]  					=       parser.parse_args()
     lowercase_       :							str  					=       {'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
     training_function(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				)


if __name__ == "__main__":
 main()





'''simple docstring'''




import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_A										= logging.get_logger(__name__)

_A										= ['UtteranceLevel']

@torch.no_grad()
def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							List[str]  					=       torch.load(SCREAMING_SNAKE_CASE_	,							map_location='cpu'				)
     if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
          raise NotImplementedError(f'''The supported s3prl models are {SUPPORTED_MODELS}'''				)

     lowercase_       :							Dict  					=       checkpoint['Downstream']

     lowercase_       :							List[Any]  					=       HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE_				)
     lowercase_       :							List[Any]  					=       HubertForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_	,							config=SCREAMING_SNAKE_CASE_				)
     lowercase_       :							List[str]  					=       WavaVecaFeatureExtractor.from_pretrained(
         SCREAMING_SNAKE_CASE_	,							return_attention_mask=SCREAMING_SNAKE_CASE_	,							do_normalize=SCREAMING_SNAKE_CASE_				)

     if hf_congfig.use_weighted_layer_sum:
          lowercase_       :							List[str]  					=       checkpoint['Featurizer']['weights']

     lowercase_       :							Tuple  					=       downstream_dict['projector.weight']
     lowercase_       :							List[Any]  					=       downstream_dict['projector.bias']
     lowercase_       :							List[Any]  					=       downstream_dict['model.post_net.linear.weight']
     lowercase_       :							str  					=       downstream_dict['model.post_net.linear.bias']

     hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_				)
     hf_model.save_pretrained(SCREAMING_SNAKE_CASE_				)


if __name__ == "__main__":
 _A										= argparse.ArgumentParser()
 parser.add_argument(
     '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
 )
 parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
 parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
 parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
 _A										= parser.parse_args()
 convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




