
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      (					__lowerCamelCase  :				Optional[int]					):
       snake_case :     List[str]           =   fname.split(os.path.sep					)[-1]
       return re.search(r"^(.*)_\d+\.jpg$"       ,      __lowerCamelCase					).groups()[0]






class 						UpperCAmelCase	(		A_				):



   def __init__(self	:      List[Any]					,		snake_case__	:      Any					,		snake_case__	:      str=None					,		snake_case__	:      Union[str, Any]=None				)	->			List[str]:


          '''simple docstring'''


          snake_case :     str           =   file_names
          snake_case :     List[str]           =   image_transform
          snake_case :     str           =   label_to_id



   def __len__(self	:      Tuple				)	->			str:


          '''simple docstring'''


          return len(self.file_names				)



   def __getitem__(self	:      List[str]					,		snake_case__	:      Tuple				)	->			List[str]:


          '''simple docstring'''


          snake_case :     int           =   self.file_names[idx]
          snake_case :     Optional[Any]           =   PIL.Image.open(snake_case__				)
          snake_case :     Any           =   raw_image.convert("RGB"				)
          if self.image_transform is not None:
                 snake_case :     List[str]           =   self.image_transform(snake_case__				)
          snake_case :     List[str]           =   extract_label(snake_case__				)
          if self.label_to_id is not None:
                 snake_case :     int           =   self.label_to_id[label]
          return {"image": image, "label": label}



def     UpperCamelCase      (					__lowerCamelCase  :				Union[str, Any]       ,      __lowerCamelCase  :				str					):
       # Initialize accelerator
       snake_case :     Union[str, Any]           =   Accelerator(cpu=args.cpu       ,      mixed_precision=args.mixed_precision					)

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

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

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

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

       # For evaluation, we use a deterministic Resize
       snake_case :     Optional[Any]           =   Compose([Resize(__lowerCamelCase					), ToTensor()]					)
       snake_case :     Union[str, Any]           =   PetsDataset([file_names[i] for i in eval_split]       ,      image_transform=__lowerCamelCase       ,      label_to_id=__lowerCamelCase					)

       # Instantiate dataloaders.
       snake_case :     Dict           =   DataLoader(__lowerCamelCase       ,      shuffle=__lowerCamelCase       ,      batch_size=__lowerCamelCase       ,      num_workers=4					)
       snake_case :     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)
       snake_case :     Optional[int]           =   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).
       snake_case :     int           =   model.to(accelerator.device					)

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

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

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

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

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

              snake_case :     int           =   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      (					):
       snake_case :     Dict           =   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."					)
       snake_case :     Any           =   parser.parse_args()
       snake_case :     str           =   {"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
       training_function(__lowerCamelCase       ,      __lowerCamelCase					)


if __name__ == "__main__":
       main()







from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest






@custom_tokenizers
class 						UpperCAmelCase	(		A_				):



   def        _SCREAMING_SNAKE_CASE     (self	:      List[str]					,		snake_case__	:      Dict					,		snake_case__	:      Tuple					,		snake_case__	:      Union[str, Any]					,		snake_case__	:      List[str]					,		snake_case__	:      List[Any]				)	->			Any:


          '''simple docstring'''


          return True



   def        _SCREAMING_SNAKE_CASE     (self	:      str				)	->			Dict:


          '''simple docstring'''


          snake_case :     Union[str, Any]           =   CpmTokenizer.from_pretrained("TsinghuaAI/CPM-Generate"				)
          snake_case :     str           =   "Hugging Face大法好，谁用谁知道。"
          snake_case :     Optional[Any]           =   "Hugging Face大法好,谁用谁知道。<unk>"
          snake_case :     int           =   "▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。".split()

          snake_case :     Union[str, Any]           =   tokenizer.tokenize(snake_case__				)
          self.assertListEqual(snake_case__					,		snake_case__				)

          snake_case :     Tuple           =   tokens + [tokenizer.unk_token]

          snake_case :     int           =   [1_37_89, 1_32_83, 14_21, 8, 10, 11_64, 1_36_08, 1_65_28, 63, 8, 9, 4_40, 1_08, 4_40, 1_21, 90, 8, 12, 0]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__				)					,		snake_case__				)

          snake_case :     Dict           =   tokenizer.decode(snake_case__				)
          self.assertEqual(snake_case__					,		snake_case__				)





