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						)->  List[Any]:






      '''simple docstring'''





      UpperCAmelCase    : Dict      =fname.split(os.path.sep						)[-1]
      return re.search(R'''^(.*)_\d+\.jpg$'''		,					__lowerCAmelCase						).groups()[0]







class 			__snake_case   (     lowerCamelCase__   ):
 def __init__(  self		,   snake_case__		,   snake_case__=None		,   snake_case__=None						)	->							List[str]:
       '''simple docstring'''





       UpperCAmelCase    : List[Any]      =file_names
       UpperCAmelCase    : Optional[int]      =image_transform
       UpperCAmelCase    : Optional[Any]      =label_to_id
 def __len__(  self						)	->							Dict:
       '''simple docstring'''





       return len(self.file_names						)






 def __getitem__(  self		,   snake_case__						)	->							Optional[Any]:
       '''simple docstring'''





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





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase						)->  Union[str, Any]:






      '''simple docstring'''





      UpperCAmelCase    : 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    : Tuple      =config['''lr''']
      UpperCAmelCase    : Optional[int]      =int(config['''num_epochs''']						)
      UpperCAmelCase    : List[Any]      =int(config['''seed''']						)
      UpperCAmelCase    : Dict      =int(config['''batch_size''']						)
      UpperCAmelCase    : Optional[int]      =config['''image_size''']
      if not isinstance(__lowerCAmelCase		,					(list, tuple)						):
            UpperCAmelCase    : Optional[int]      =(image_size, image_size)

      # Grab all the image filenames
      UpperCAmelCase    : 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
      UpperCAmelCase    : str      =[extract_label(__lowerCAmelCase						) for fname in file_names]
      UpperCAmelCase    : Tuple      =list(set(__lowerCAmelCase						)						)
      id_to_label.sort()
      UpperCAmelCase    : 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
      UpperCAmelCase    : Optional[int]      =np.random.permutation(len(__lowerCAmelCase						)						)
      UpperCAmelCase    : Union[str, Any]      =int(0.8 * len(__lowerCAmelCase						)						)
      UpperCAmelCase    : Tuple      =random_perm[:cut]
      UpperCAmelCase    : Dict      =random_perm[cut:]

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

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

      # Instantiate dataloaders.
      UpperCAmelCase    : Any      =DataLoader(__lowerCAmelCase		,					shuffle=__lowerCAmelCase		,					batch_size=__lowerCAmelCase		,					num_workers=4						)
      UpperCAmelCase    : int      =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)
      UpperCAmelCase    : str      =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).
      UpperCAmelCase    : List[Any]      =model.to(accelerator.device						)

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

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

      # Instantiate optimizer
      UpperCAmelCase    : Optional[Any]      =torch.optim.Adam(params=model.parameters()		,					lr=lr / 25						)

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

            model.eval()
            UpperCAmelCase    : str      =0
            UpperCAmelCase    : Dict      =0
            for _, batch in enumerate(__lowerCAmelCase						):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  UpperCAmelCase    : Tuple      ={k: v.to(accelerator.device						) for k, v in batch.items()}
                  UpperCAmelCase    : Optional[int]      =(batch['''image'''] - mean) / std
                  with torch.no_grad():
                        UpperCAmelCase    : Tuple      =model(__lowerCAmelCase						)
                  UpperCAmelCase    : List[str]      =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    : Dict      =accurate.item() / num_elems
            # Use accelerator.print to print only on the main process.
            accelerator.print(f'''epoch {epoch}: {1_00 * eval_metric:.2f}'''						)





def   lowerCAmelCase_ (				)->  Union[str, Any]:






      '''simple docstring'''





      UpperCAmelCase    : List[Any]      =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.'''						)
      UpperCAmelCase    : str      =parser.parse_args()
      UpperCAmelCase    : Optional[int]      ={'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 2_24}
      training_function(__lowerCAmelCase		,					__lowerCAmelCase						)


if __name__ == "__main__":
     main()

import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode





def   lowerCAmelCase_ (				__lowerCAmelCase		,					**__lowerCAmelCase						)->  Union[str, Any]:






      '''simple docstring'''





      return RunBeamCommand(
          args.dataset		,					args.name		,					args.cache_dir		,					args.beam_pipeline_options		,					args.data_dir		,					args.all_configs		,					args.save_info or args.save_infos		,					args.ignore_verifications		,					args.force_redownload		,					**__lowerCAmelCase		,					)







class 			__snake_case   (     lowerCamelCase__   ):
 @staticmethod
 def 	UpperCAmelCase__       (  snake_case__						)	->							List[str]:
       '''simple docstring'''





       UpperCAmelCase    : List[str]      =parser.add_parser('''run_beam'''		,   help='''Run a Beam dataset processing pipeline'''						)
       run_beam_parser.add_argument('''dataset'''		,   type=snake_case__		,   help='''Name of the dataset to download'''						)
       run_beam_parser.add_argument('''--name'''		,   type=snake_case__		,   default=snake_case__		,   help='''Dataset config name'''						)
       run_beam_parser.add_argument(
           '''--cache_dir'''		,   type=snake_case__		,   default=snake_case__		,   help='''Cache directory where the datasets are stored'''		,   )
       run_beam_parser.add_argument(
           '''--beam_pipeline_options'''		,   type=snake_case__		,   default=''''''		,   help='''Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`'''		,   )
       run_beam_parser.add_argument(
           '''--data_dir'''		,   type=snake_case__		,   default=snake_case__		,   help='''Can be used to specify a manual directory to get the files from'''		,   )
       run_beam_parser.add_argument('''--all_configs'''		,   action='''store_true'''		,   help='''Test all dataset configurations'''						)
       run_beam_parser.add_argument('''--save_info'''		,   action='''store_true'''		,   help='''Save the dataset infos file'''						)
       run_beam_parser.add_argument(
           '''--ignore_verifications'''		,   action='''store_true'''		,   help='''Run the test without checksums and splits checks'''						)
       run_beam_parser.add_argument('''--force_redownload'''		,   action='''store_true'''		,   help='''Force dataset redownload'''						)
       # aliases
       run_beam_parser.add_argument('''--save_infos'''		,   action='''store_true'''		,   help='''alias for save_info'''						)
       run_beam_parser.set_defaults(func=snake_case__						)
 def __init__(  self		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   **snake_case__		,   )	->							Any:
       '''simple docstring'''





       UpperCAmelCase    : str      =dataset
       UpperCAmelCase    : Optional[Any]      =name
       UpperCAmelCase    : str      =cache_dir
       UpperCAmelCase    : Union[str, Any]      =beam_pipeline_options
       UpperCAmelCase    : List[str]      =data_dir
       UpperCAmelCase    : int      =all_configs
       UpperCAmelCase    : List[Any]      =save_infos
       UpperCAmelCase    : Dict      =ignore_verifications
       UpperCAmelCase    : Optional[int]      =force_redownload
       UpperCAmelCase    : Optional[int]      =config_kwargs






 def 	UpperCAmelCase__       (  self						)	->							str:
       '''simple docstring'''





       import apache_beam as beam

       if self._name is not None and self._all_configs:
             print('''Both parameters `name` and `all_configs` can\'t be used at once.'''						)
             exit(1						)
       UpperCAmelCase  ,						UpperCAmelCase    : str      =self._dataset, self._name
       UpperCAmelCase    : Any      =dataset_module_factory(snake_case__						)
       UpperCAmelCase    : str      =import_main_class(dataset_module.module_path						)
       UpperCAmelCase    : List[DatasetBuilder]      =[]
       if self._beam_pipeline_options:
             UpperCAmelCase    : Optional[Any]      =beam.options.pipeline_options.PipelineOptions(
                 flags=[f'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(''','''						) if opt]						)
       else:
             UpperCAmelCase    : int      =None
       if self._all_configs and len(builder_cls.BUILDER_CONFIGS						) > 0:
             for builder_config in builder_cls.BUILDER_CONFIGS:
                   builders.append(
                       builder_cls(
                           config_name=builder_config.name		,   data_dir=self._data_dir		,   hash=dataset_module.hash		,   beam_options=snake_case__		,   cache_dir=self._cache_dir		,   base_path=dataset_module.builder_kwargs.get('''base_path'''						)		,   )						)
       else:
             builders.append(
                 builder_cls(
                     config_name=snake_case__		,   data_dir=self._data_dir		,   beam_options=snake_case__		,   cache_dir=self._cache_dir		,   base_path=dataset_module.builder_kwargs.get('''base_path'''						)		,   **self._config_kwargs		,   )						)

       for builder in builders:
             builder.download_and_prepare(
                 download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
                 if not self._force_redownload
                 else DownloadMode.FORCE_REDOWNLOAD		,   download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH						)		,   verification_mode=VerificationMode.NO_CHECKS
                 if self._ignore_verifications
                 else VerificationMode.ALL_CHECKS		,   try_from_hf_gcs=snake_case__		,   )
             if self._save_infos:
                   builder._save_infos()

       print('''Apache beam run successful.'''						)

       # If save_infos=True, the dataset infos file is created next to the loaded module file.
       # Let's move it to the original directory of the dataset script, to allow the user to
       # upload them on S3 at the same time afterwards.
       if self._save_infos:
             UpperCAmelCase    : Dict      =os.path.join(builder_cls.get_imported_module_dir()		,   config.DATASETDICT_INFOS_FILENAME						)

             UpperCAmelCase    : List[str]      =Path(snake_case__						).name + '''.py'''

             UpperCAmelCase    : Tuple      =os.path.join(snake_case__		,   snake_case__						)
             if os.path.isfile(snake_case__						):
                   UpperCAmelCase    : Dict      =os.path.dirname(snake_case__						)
             elif os.path.isfile(snake_case__						):
                   UpperCAmelCase    : Union[str, Any]      =path
             else:  # in case of a remote dataset
                   print(f'''Dataset Infos file saved at {dataset_infos_path}'''						)
                   exit(1						)

             # Move datasetinfo back to the user
             UpperCAmelCase    : Tuple      =os.path.join(snake_case__		,   config.DATASETDICT_INFOS_FILENAME						)
             copyfile(snake_case__		,   snake_case__						)
             print(f'''Dataset Infos file saved at {user_dataset_infos_path}'''						)
