
'''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(  a__    :  Union[str, Any]      ):







       '''simple docstring'''






       lowerCamelCase__				   =      fname.split(os.path.sep      )[-1]
       return re.search(R"^(.*)_\d+\.jpg$"      , a__      ).groups()[0]







class 		snake_case_       (						A__						):



   """simple docstring"""





   def __init__( self			,   UpperCamelCase			,   UpperCamelCase=None			,   UpperCamelCase=None):
          lowerCamelCase__				   =      file_names
          lowerCamelCase__				   =      image_transform
          lowerCamelCase__				   =      label_to_id







   def __len__( self):
          return len(self.file_names)







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







def   lowerCAmelCase(  a__    :  int      , a__    :  Union[str, Any]      ):







       '''simple docstring'''






       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(a__      , (list, tuple)      ):
              lowerCamelCase__				   =      (image_size, image_size)

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

       # Build the label correspondences
       lowerCamelCase__				   =      [extract_label(a__      ) for fname in file_names]
       lowerCamelCase__				   =      list(set(a__      )      )
       id_to_label.sort()
       lowerCamelCase__				   =      {lbl: i for i, lbl in enumerate(a__      )}

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

       # Split our filenames between train and validation
       lowerCamelCase__				   =      np.random.permutation(len(a__      )      )
       lowerCamelCase__				   =      int(0.8 * len(a__      )      )
       lowerCamelCase__				   =      random_perm[:cut]
       lowerCamelCase__				   =      random_perm[cut:]

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

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

       # Instantiate dataloaders.
       lowerCamelCase__				   =      DataLoader(a__      , shuffle=a__      , batch_size=a__      , num_workers=4      )
       lowerCamelCase__				   =      DataLoader(a__      , shuffle=a__      , batch_size=a__      , 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=a__      , num_classes=len(a__      )      )

       # 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=a__      , max_lr=a__      , epochs=a__      , steps_per_epoch=len(a__      )      )

       # 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(
           a__      , a__      , a__      , a__      , a__      )

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

              model.eval()
              lowerCamelCase__				   =      0
              lowerCamelCase__				   =      0
              for _, batch in enumerate(a__      ):
                     # 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(a__      )
                     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   lowerCAmelCase(  ):







       '''simple docstring'''






       lowerCamelCase__				   =      argparse.ArgumentParser(description="Simple example of training script."      )
       parser.add_argument("--data_dir"      , required=a__      , help="The data folder on disk."      )
       parser.add_argument(
           "--mixed_precision"      , type=a__      , default=a__      , 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=a__      , default=a__      , 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(a__      , a__      )


if __name__ == "__main__":
    main()



'''simple docstring'''







import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 		snake_case_       (						A__		,	unittest.TestCase						):



   """simple docstring"""





   __lowerCAmelCase	:							Optional[Any]								=HerbertTokenizer
   __lowerCAmelCase	:							Optional[Any]								=HerbertTokenizerFast
   __lowerCAmelCase	:							int								=True







   def     __UpperCAmelCase      ( self):
          super().setUp()

          # Use a simpler test file without japanese/chinese characters
          with open(f"""{get_tests_dir()}/fixtures/sample_text_no_unicode.txt"""			,   encoding="utf-8") as f_data:
                 lowerCamelCase__				   =      f_data.read().replace("\n\n"			,   "\n").strip()

          lowerCamelCase__				   =      [
              "<s>",
              "</s>",
              "l",
              "o",
              "w",
              "e",
              "r",
              "s",
              "t",
              "i",
              "d",
              "n",
              "w</w>",
              "r</w>",
              "t</w>",
              "lo",
              "low",
              "er</w>",
              "low</w>",
              "lowest</w>",
              "newer</w>",
              "wider</w>",
              ",</w>",
              "<unk>",
          ]
          lowerCamelCase__				   =      dict(zip(UpperCamelCase			,   range(len(UpperCamelCase))))
          lowerCamelCase__				   =      ["l o 123", "lo w 1456", "e r</w> 1789", ""]

          lowerCamelCase__				   =      os.path.join(self.tmpdirname			,   VOCAB_FILES_NAMES["vocab_file"])
          lowerCamelCase__				   =      os.path.join(self.tmpdirname			,   VOCAB_FILES_NAMES["merges_file"])
          with open(self.vocab_file			,   "w") as fp:
                 fp.write(json.dumps(UpperCamelCase))
          with open(self.merges_file			,   "w") as fp:
                 fp.write("\n".join(UpperCamelCase))







   def     __UpperCAmelCase      ( self			,   UpperCamelCase):
          lowerCamelCase__				   =      "lower newer"
          lowerCamelCase__				   =      "lower newer"
          return input_text, output_text







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      self.tokenizer_class(vocab_file=self.vocab_file			,   merges_file=self.merges_file)

          lowerCamelCase__				   =      "lower"
          lowerCamelCase__				   =      ["low", "er</w>"]
          lowerCamelCase__				   =      tokenizer.tokenize(UpperCamelCase)
          self.assertListEqual(UpperCamelCase			,   UpperCamelCase)

          lowerCamelCase__				   =      tokens + ["<unk>"]
          lowerCamelCase__				   =      [16, 17, 23]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase)			,   UpperCamelCase)







   def     __UpperCAmelCase      ( self):
          if not self.test_rust_tokenizer:
                 return

          lowerCamelCase__				   =      self.get_tokenizer()
          lowerCamelCase__				   =      self.get_rust_tokenizer()

          lowerCamelCase__				   =      "lower,newer"

          lowerCamelCase__				   =      tokenizer.tokenize(UpperCamelCase)
          lowerCamelCase__				   =      rust_tokenizer.tokenize(UpperCamelCase)
          self.assertListEqual(UpperCamelCase			,   UpperCamelCase)

          lowerCamelCase__				   =      tokenizer.encode(UpperCamelCase			,   add_special_tokens=UpperCamelCase)
          lowerCamelCase__				   =      rust_tokenizer.encode(UpperCamelCase			,   add_special_tokens=UpperCamelCase)
          self.assertListEqual(UpperCamelCase			,   UpperCamelCase)

          lowerCamelCase__				   =      self.get_rust_tokenizer()
          lowerCamelCase__				   =      tokenizer.encode(UpperCamelCase)
          lowerCamelCase__				   =      rust_tokenizer.encode(UpperCamelCase)
          self.assertListEqual(UpperCamelCase			,   UpperCamelCase)







   @slow
   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      self.tokenizer_class.from_pretrained("allegro/herbert-base-cased")

          lowerCamelCase__				   =      tokenizer.encode("konstruowanie sekwencji"			,   add_special_tokens=UpperCamelCase)
          lowerCamelCase__				   =      tokenizer.encode("konstruowanie wielu sekwencji"			,   add_special_tokens=UpperCamelCase)

          lowerCamelCase__				   =      tokenizer.build_inputs_with_special_tokens(UpperCamelCase)
          lowerCamelCase__				   =      tokenizer.build_inputs_with_special_tokens(UpperCamelCase			,   UpperCamelCase)

          assert encoded_sentence == [0] + text + [2]
          assert encoded_pair == [0] + text + [2] + text_a + [2]

