
'''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 snake_case_     (					lowercase__		):
  UpperCAmelCase__      :					Any				       =							fname.split(os.path.sep		)[-1]
  return re.search(R"^(.*)_\d+\.jpg$"   ,       lowercase__		).groups()[0]






class 							UpperCAmelCase_							(    A				):

 '''simple docstring'''







 def __init__(					self		:  Union[str, Any]			,				snake_case__		:  Optional[int]			,				snake_case__		:  int=None			,				snake_case__		:  Optional[int]=None ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							file_names
   UpperCAmelCase__      :					List[str]				       =							image_transform
   UpperCAmelCase__      :					int				       =							label_to_id







 def __len__(					self		:  Tuple ):



   '''simple docstring'''




   return len(self.file_names )







 def __getitem__(					self		:  str			,				snake_case__		:  Dict ):



   '''simple docstring'''




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







def snake_case_     (					lowercase__   ,       lowercase__		):
  # Initialize accelerator
  if args.with_tracking:
    UpperCAmelCase__      :					str				       =							Accelerator(
        cpu=args.cpu   ,       mixed_precision=args.mixed_precision   ,       log_with="all"   ,       project_dir=args.project_dir		)
  else:
    UpperCAmelCase__      :					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
  UpperCAmelCase__      :					Any				       =							config["lr"]
  UpperCAmelCase__      :					Any				       =							int(config["num_epochs"]		)
  UpperCAmelCase__      :					List[str]				       =							int(config["seed"]		)
  UpperCAmelCase__      :					List[str]				       =							int(config["batch_size"]		)
  UpperCAmelCase__      :					Dict				       =							config["image_size"]
  if not isinstance(lowercase__   ,       (list, tuple)		):
    UpperCAmelCase__      :					Union[str, Any]				       =							(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":
      UpperCAmelCase__      :					int				       =							args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      UpperCAmelCase__      :					Optional[Any]				       =							int(args.checkpointing_steps		)
    else:
      raise ValueError(
          F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""		)
  else:
    UpperCAmelCase__      :					Dict				       =							None

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

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

  # Build the label correspondences
  UpperCAmelCase__      :					Any				       =							[extract_label(lowercase__		) for fname in file_names]
  UpperCAmelCase__      :					Optional[int]				       =							list(set(lowercase__		)		)
  id_to_label.sort()
  UpperCAmelCase__      :					int				       =							{lbl: i for i, lbl in enumerate(lowercase__		)}

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

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

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

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

  # Instantiate dataloaders.
  UpperCAmelCase__      :					Union[str, Any]				       =							DataLoader(lowercase__   ,       shuffle=lowercase__   ,       batch_size=lowercase__   ,       num_workers=4		)
  UpperCAmelCase__      :					Tuple				       =							DataLoader(lowercase__   ,       shuffle=lowercase__   ,       batch_size=lowercase__   ,       num_workers=4		)

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  UpperCAmelCase__      :					Optional[Any]				       =							create_model("resnet50d"   ,       pretrained=lowercase__   ,       num_classes=len(lowercase__		)		)

  # 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__      :					List[Any]				       =							model.to(accelerator.device		)

  # Freezing the base model
  for param in model.parameters():
    UpperCAmelCase__      :					List[str]				       =							False
  for param in model.get_classifier().parameters():
    UpperCAmelCase__      :					Optional[int]				       =							True

  # We normalize the batches of images to be a bit faster.
  UpperCAmelCase__      :					Tuple				       =							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__      :					List[Any]				       =							torch.optim.Adam(params=model.parameters()   ,       lr=lr / 2_5		)

  # Instantiate learning rate scheduler
  UpperCAmelCase__      :					str				       =							OneCycleLR(optimizer=lowercase__   ,       max_lr=lowercase__   ,       epochs=lowercase__   ,       steps_per_epoch=len(lowercase__		)		)

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

    if "epoch" in training_difference:
      UpperCAmelCase__      :					Tuple				       =							int(training_difference.replace("epoch_"   ,       ""		)		) + 1
      UpperCAmelCase__      :					Tuple				       =							None
    else:
      UpperCAmelCase__      :					Tuple				       =							int(training_difference.replace("step_"   ,       ""		)		)
      UpperCAmelCase__      :					Any				       =							resume_step // len(lowercase__		)
      resume_step -= starting_epoch * len(lowercase__		)

    # Now we train the model
  for epoch in range(lowercase__   ,       lowercase__		):
    model.train()
    if args.with_tracking:
      UpperCAmelCase__      :					Dict				       =							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
      UpperCAmelCase__      :					Optional[int]				       =							accelerator.skip_first_batches(lowercase__   ,       lowercase__		)
      overall_step += resume_step
    else:
      # After the first iteration though, we need to go back to the original dataloader
      UpperCAmelCase__      :					List[str]				       =							train_dataloader
    for batch in active_dataloader:
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      UpperCAmelCase__      :					str				       =							{k: v.to(accelerator.device		) for k, v in batch.items()}
      UpperCAmelCase__      :					str				       =							(batch["image"] - mean) / std
      UpperCAmelCase__      :					Optional[Any]				       =							model(lowercase__		)
      UpperCAmelCase__      :					Dict				       =							torch.nn.functional.cross_entropy(lowercase__   ,       batch["label"]		)
      # We keep track of the loss at each epoch
      if args.with_tracking:
        total_loss += loss.detach().float()
      accelerator.backward(lowercase__		)
      optimizer.step()
      lr_scheduler.step()
      optimizer.zero_grad()
      overall_step += 1
      if isinstance(lowercase__   ,       lowercase__		):
        UpperCAmelCase__      :					Union[str, Any]				       =							F"""step_{overall_step}"""
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            UpperCAmelCase__      :					Optional[int]				       =							os.path.join(args.output_dir   ,       lowercase__		)
          accelerator.save_state(lowercase__		)
    model.eval()
    UpperCAmelCase__      :					Optional[int]				       =							0
    UpperCAmelCase__      :					Union[str, Any]				       =							0
    for step, batch in enumerate(lowercase__		):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      UpperCAmelCase__      :					int				       =							{k: v.to(accelerator.device		) for k, v in batch.items()}
      UpperCAmelCase__      :					str				       =							(batch["image"] - mean) / std
      with torch.no_grad():
        UpperCAmelCase__      :					Tuple				       =							model(lowercase__		)
      UpperCAmelCase__      :					Dict				       =							outputs.argmax(dim=-1		)
      UpperCAmelCase__       ,			UpperCAmelCase__      :					str				       =							accelerator.gather_for_metrics((predictions, batch["label"])		)
      UpperCAmelCase__      :					Tuple				       =							predictions == references
      num_elems += accurate_preds.shape[0]
      accurate += accurate_preds.long().sum()

    UpperCAmelCase__      :					int				       =							accurate.item() / num_elems
    # Use accelerator.print to print only on the main process.
    accelerator.print(F"""epoch {epoch}: {1_0_0 * eval_metric:.2f}"""		)
    if args.with_tracking:
      accelerator.log(
          {
              "accuracy": 1_0_0 * eval_metric,
              "train_loss": total_loss.item() / len(lowercase__		),
              "epoch": epoch,
          }   ,       step=lowercase__   ,       )
    if checkpointing_steps == "epoch":
      UpperCAmelCase__      :					Optional[int]				       =							F"""epoch_{epoch}"""
      if args.output_dir is not None:
        UpperCAmelCase__      :					Optional[Any]				       =							os.path.join(args.output_dir   ,       lowercase__		)
      accelerator.save_state(lowercase__		)

  if args.with_tracking:
    accelerator.end_training()







def snake_case_     (					):
  UpperCAmelCase__      :					Optional[int]				       =							argparse.ArgumentParser(description="Simple example of training script."		)
  parser.add_argument("--data_dir"   ,       required=lowercase__   ,       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=lowercase__   ,       default=lowercase__   ,       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=lowercase__   ,       default=lowercase__   ,       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=lowercase__   ,       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=lowercase__   ,       default=lowercase__   ,       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=lowercase__   ,       default="logs"   ,       help="Location on where to store experiment tracking logs` and relevent project information"   ,       )
  UpperCAmelCase__      :					Union[str, Any]				       =							parser.parse_args()
  UpperCAmelCase__      :					Optional[int]				       =							{"lr": 3E-2, "num_epochs": 3, "seed": 4_2, "batch_size": 6_4, "image_size": 2_2_4}
  training_function(lowercase__   ,       lowercase__		)


if __name__ == "__main__":
      main()





'''simple docstring'''





import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


SCREAMING_SNAKE_CASE     =       get_tests_dir("""fixtures""")
SCREAMING_SNAKE_CASE     =       get_tests_dir("""fixtures/dummy_feature_extractor_config.json""")
SCREAMING_SNAKE_CASE     =       get_tests_dir("""fixtures/dummy-config.json""")






class 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




   UpperCAmelCase__      :					Any				       =							0







 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[int]				       =							AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h" )
   self.assertIsInstance(snake_case__			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[int]				       =							AutoFeatureExtractor.from_pretrained(snake_case__ )
   self.assertIsInstance(snake_case__			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   with tempfile.TemporaryDirectory() as tmpdirname:
     UpperCAmelCase__      :					Optional[Any]				       =							WavaVecaConfig()

     # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
     UpperCAmelCase__      :					Any				       =							AutoFeatureExtractor.from_pretrained(snake_case__ ).to_dict()

     config_dict.pop("feature_extractor_type" )
     UpperCAmelCase__      :					Union[str, Any]				       =							WavaVecaFeatureExtractor(**snake_case__ )

     # save in new folder
     model_config.save_pretrained(snake_case__ )
     config.save_pretrained(snake_case__ )

     UpperCAmelCase__      :					Tuple				       =							AutoFeatureExtractor.from_pretrained(snake_case__ )

     # make sure private variable is not incorrectly saved
     UpperCAmelCase__      :					List[Any]				       =							json.loads(config.to_json_string() )
     self.assertTrue("_processor_class" not in dict_as_saved )

   self.assertIsInstance(snake_case__			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




   UpperCAmelCase__      :					Any				       =							AutoFeatureExtractor.from_pretrained(snake_case__ )
   self.assertIsInstance(snake_case__			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   with self.assertRaisesRegex(
       snake_case__			,				"bert-base is not a local folder and is not a valid model identifier" ):
     UpperCAmelCase__      :					Optional[int]				       =							AutoFeatureExtractor.from_pretrained("bert-base" )







 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   with self.assertRaisesRegex(
       snake_case__			,				R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ):
     UpperCAmelCase__      :					List[str]				       =							AutoFeatureExtractor.from_pretrained(snake_case__			,				revision="aaaaaa" )







 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




   with self.assertRaisesRegex(
       snake_case__			,				"hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."			,				):
     UpperCAmelCase__      :					Optional[Any]				       =							AutoFeatureExtractor.from_pretrained("hf-internal-testing/config-no-model" )







 def 							UpperCamelCase  (					self		:  Optional[Any] ):



   '''simple docstring'''




   with self.assertRaises(snake_case__ ):
     UpperCAmelCase__      :					List[str]				       =							AutoFeatureExtractor.from_pretrained(
         "hf-internal-testing/test_dynamic_feature_extractor" )
   # If remote code is disabled, we can't load this config.
   with self.assertRaises(snake_case__ ):
     UpperCAmelCase__      :					Any				       =							AutoFeatureExtractor.from_pretrained(
         "hf-internal-testing/test_dynamic_feature_extractor"			,				trust_remote_code=snake_case__ )

   UpperCAmelCase__      :					Optional[Any]				       =							AutoFeatureExtractor.from_pretrained(
       "hf-internal-testing/test_dynamic_feature_extractor"			,				trust_remote_code=snake_case__ )
   self.assertEqual(feature_extractor.__class__.__name__			,				"NewFeatureExtractor" )

   # Test feature extractor can be reloaded.
   with tempfile.TemporaryDirectory() as tmp_dir:
     feature_extractor.save_pretrained(snake_case__ )
     UpperCAmelCase__      :					Any				       =							AutoFeatureExtractor.from_pretrained(snake_case__			,				trust_remote_code=snake_case__ )
   self.assertEqual(reloaded_feature_extractor.__class__.__name__			,				"NewFeatureExtractor" )







 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   try:
     AutoConfig.register("custom"			,				snake_case__ )
     AutoFeatureExtractor.register(snake_case__			,				snake_case__ )
     # Trying to register something existing in the Transformers library will raise an error
     with self.assertRaises(snake_case__ ):
       AutoFeatureExtractor.register(snake_case__			,				snake_case__ )

     # Now that the config is registered, it can be used as any other config with the auto-API
     UpperCAmelCase__      :					str				       =							CustomFeatureExtractor.from_pretrained(snake_case__ )
     with tempfile.TemporaryDirectory() as tmp_dir:
       feature_extractor.save_pretrained(snake_case__ )
       UpperCAmelCase__      :					Optional[int]				       =							AutoFeatureExtractor.from_pretrained(snake_case__ )
       self.assertIsInstance(snake_case__			,				snake_case__ )

   finally:
     if "custom" in CONFIG_MAPPING._extra_content:
       del CONFIG_MAPPING._extra_content["custom"]
     if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
       del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]







 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   class 							UpperCAmelCase_							(    A				):

    '''simple docstring'''
    lowercase_					:    Optional[Any]				=							True

   try:
     AutoConfig.register("custom"			,				snake_case__ )
     AutoFeatureExtractor.register(snake_case__			,				snake_case__ )
     # If remote code is not set, the default is to use local
     UpperCAmelCase__      :					Any				       =							AutoFeatureExtractor.from_pretrained(
         "hf-internal-testing/test_dynamic_feature_extractor" )
     self.assertEqual(feature_extractor.__class__.__name__			,				"NewFeatureExtractor" )
     self.assertTrue(feature_extractor.is_local )

     # If remote code is disabled, we load the local one.
     UpperCAmelCase__      :					Optional[int]				       =							AutoFeatureExtractor.from_pretrained(
         "hf-internal-testing/test_dynamic_feature_extractor"			,				trust_remote_code=snake_case__ )
     self.assertEqual(feature_extractor.__class__.__name__			,				"NewFeatureExtractor" )
     self.assertTrue(feature_extractor.is_local )

     # If remote is enabled, we load from the Hub
     UpperCAmelCase__      :					int				       =							AutoFeatureExtractor.from_pretrained(
         "hf-internal-testing/test_dynamic_feature_extractor"			,				trust_remote_code=snake_case__ )
     self.assertEqual(feature_extractor.__class__.__name__			,				"NewFeatureExtractor" )
     self.assertTrue(not hasattr(snake_case__			,				"is_local" ) )

   finally:
     if "custom" in CONFIG_MAPPING._extra_content:
       del CONFIG_MAPPING._extra_content["custom"]
     if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
       del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]



