



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      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



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





class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     def __init__(self					,      __a					,      __a=None					,      __a=None     ):






           '''simple docstring'''



           lowerCamelCase             =		file_names
           lowerCamelCase             =		image_transform
           lowerCamelCase             =		label_to_id







     def __len__(self     ):






           '''simple docstring'''



           return len(self.file_names     )







     def __getitem__(self					,      __a     ):






           '''simple docstring'''



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



def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      if args.with_tracking:
            lowerCamelCase             =		Accelerator(
                cpu=args.cpu , mixed_precision=args.mixed_precision , log_with="all" , project_dir=args.project_dir			)
      else:
            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(UpperCAmelCase__ , (list, tuple)			):
            lowerCamelCase             =		(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":
                  lowerCamelCase             =		args.checkpointing_steps
            elif args.checkpointing_steps.isdigit():
                  lowerCamelCase             =		int(args.checkpointing_steps			)
            else:
                  raise ValueError(
                      F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""			)
      else:
            lowerCamelCase             =		None

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

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

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

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

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

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

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

      # Instantiate dataloaders.
      lowerCamelCase             =		DataLoader(UpperCAmelCase__ , shuffle=UpperCAmelCase__ , batch_size=UpperCAmelCase__ , num_workers=4			)
      lowerCamelCase             =		DataLoader(UpperCAmelCase__ , shuffle=UpperCAmelCase__ , batch_size=UpperCAmelCase__ , 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=UpperCAmelCase__ , num_classes=len(UpperCAmelCase__			)			)

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

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

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

    # Now we train the model
      for epoch in range(UpperCAmelCase__ , UpperCAmelCase__			):
            model.train()
            if args.with_tracking:
                  lowerCamelCase             =		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
                  lowerCamelCase             =		accelerator.skip_first_batches(UpperCAmelCase__ , UpperCAmelCase__			)
                  overall_step += resume_step
            else:
                  # After the first iteration though, we need to go back to the original dataloader
                  lowerCamelCase             =		train_dataloader
            for batch in active_dataloader:
                  # 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(UpperCAmelCase__			)
                  lowerCamelCase             =		torch.nn.functional.cross_entropy(UpperCAmelCase__ , batch["label"]			)
                  # We keep track of the loss at each epoch
                  if args.with_tracking:
                        total_loss += loss.detach().float()
                  accelerator.backward(UpperCAmelCase__			)
                  optimizer.step()
                  lr_scheduler.step()
                  optimizer.zero_grad()
                  overall_step += 1
                  if isinstance(UpperCAmelCase__ , UpperCAmelCase__			):
                        lowerCamelCase             =		F"""step_{overall_step}"""
                        if overall_step % checkpointing_steps == 0:
                              if args.output_dir is not None:
                                    lowerCamelCase             =		os.path.join(args.output_dir , UpperCAmelCase__			)
                              accelerator.save_state(UpperCAmelCase__			)
            model.eval()
            lowerCamelCase             =		0
            lowerCamelCase             =		0
            for step, batch in enumerate(UpperCAmelCase__			):
                  # 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(UpperCAmelCase__			)
                  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}"""			)
            if args.with_tracking:
                  accelerator.log(
                      {
                          "accuracy": 100 * eval_metric,
                          "train_loss": total_loss.item() / len(UpperCAmelCase__			),
                          "epoch": epoch,
                      } , step=UpperCAmelCase__ , )
            if checkpointing_steps == "epoch":
                  lowerCamelCase             =		F"""epoch_{epoch}"""
                  if args.output_dir is not None:
                        lowerCamelCase             =		os.path.join(args.output_dir , UpperCAmelCase__			)
                  accelerator.save_state(UpperCAmelCase__			)

      if args.with_tracking:
            accelerator.end_training()

def      __lowercase(						):


      """simple docstring"""



      lowerCamelCase             =		argparse.ArgumentParser(description="Simple example of training script."			)
      parser.add_argument("--data_dir" , required=UpperCAmelCase__ , 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=UpperCAmelCase__ , default=UpperCAmelCase__ , 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=UpperCAmelCase__ , default=UpperCAmelCase__ , 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=UpperCAmelCase__ , 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=UpperCAmelCase__ , default=UpperCAmelCase__ , 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=UpperCAmelCase__ , default="logs" , help="Location on where to store experiment tracking logs` and relevent project information" , )
      lowerCamelCase             =		parser.parse_args()
      lowerCamelCase             =		{"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
      training_function(UpperCAmelCase__ , UpperCAmelCase__			)


if __name__ == "__main__":
     main()




import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints

import yaml


a_    :  Union[str, Any]						  =		NewType('DataClass', Any)
a_    :  int						  =		NewType('DataClassType', Any)

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      if isinstance(UpperCAmelCase__ , UpperCAmelCase__			):
            return v
      if v.lower() in ("yes", "true", "t", "y", "1"):
            return True
      elif v.lower() in ("no", "false", "f", "n", "0"):
            return False
      else:
            raise ArgumentTypeError(
                F"""Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive)."""			)

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		{str(UpperCAmelCase__			): choice for choice in choices}
      return lambda UpperCAmelCase__			: str_to_choice.get(UpperCAmelCase__ , UpperCAmelCase__			)

def      __lowercase(						*,
    UpperCAmelCase__ = None , UpperCAmelCase__ = None , UpperCAmelCase__ = dataclasses.MISSING , UpperCAmelCase__ = dataclasses.MISSING , UpperCAmelCase__ = None , **UpperCAmelCase__ , ):


      """simple docstring"""



      if metadata is None:
            # Important, don't use as default param in function signature because dict is mutable and shared across function calls
            lowerCamelCase             =		{}
      if aliases is not None:
            lowerCamelCase             =		aliases
      if help is not None:
            lowerCamelCase             =		help

      return dataclasses.field(metadata=UpperCAmelCase__ , default=UpperCAmelCase__ , default_factory=UpperCAmelCase__ , **UpperCAmelCase__			)






class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     _A      =      42







     def __init__(self					,      __a					,      **__a     ):






           '''simple docstring'''



           if "formatter_class" not in kwargs:
                 lowerCamelCase             =		ArgumentDefaultsHelpFormatter
           super().__init__(**__a     )
           if dataclasses.is_dataclass(__a     ):
                 lowerCamelCase             =		[dataclass_types]
           lowerCamelCase             =		list(__a     )
           for dtype in self.dataclass_types:
                 self._add_dataclass_arguments(__a     )







     @staticmethod
     def     _a   (__a					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		F"""--{field.name}"""
           lowerCamelCase             =		field.metadata.copy()
           # field.metadata is not used at all by Data Classes,
           # it is provided as a third-party extension mechanism.
           if isinstance(field.type					,      __a     ):
                 raise RuntimeError(
                     "Unresolved type detected, which should have been done with the help of "
                     "`typing.get_type_hints` method by default"     )

           lowerCamelCase             =		kwargs.pop("aliases"					,      []     )
           if isinstance(__a					,      __a     ):
                 lowerCamelCase             =		[aliases]

           lowerCamelCase             =		getattr(field.type					,      "__origin__"					,      field.type     )
           if origin_type is Union or (hasattr(__a					,      "UnionType"     ) and isinstance(__a					,      types.UnionType     )):
                 if str not in field.type.__args__ and (
                     len(field.type.__args__     ) != 2 or type(__a     ) not in field.type.__args__
                 ):
                       raise ValueError(
                           "Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because"
                           " the argument parser only supports one type per argument."
                           F""" Problem encountered in field '{field.name}'."""     )
                 if type(__a     ) not in field.type.__args__:
                       # filter `str` in Union
                       lowerCamelCase             =		field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
                       lowerCamelCase             =		getattr(field.type					,      "__origin__"					,      field.type     )
                 elif bool not in field.type.__args__:
                       # filter `NoneType` in Union (except for `Union[bool, NoneType]`)
                       lowerCamelCase             =		(
                           field.type.__args__[0] if isinstance(__a					,      field.type.__args__[1]     ) else field.type.__args__[1]
                       )
                       lowerCamelCase             =		getattr(field.type					,      "__origin__"					,      field.type     )

        # A variable to store kwargs for a boolean field, if needed
        # so that we can init a `no_*` complement argument (see below)
           lowerCamelCase             =		{}
           if origin_type is Literal or (isinstance(field.type					,      __a     ) and issubclass(field.type					,      __a     )):
                 if origin_type is Literal:
                       lowerCamelCase             =		field.type.__args__
                 else:
                       lowerCamelCase             =		[x.value for x in field.type]

                 lowerCamelCase             =		make_choice_type_function(kwargs["choices"]     )

                 if field.default is not dataclasses.MISSING:
                       lowerCamelCase             =		field.default
                 else:
                       lowerCamelCase             =		True
           elif field.type is bool or field.type == Optional[bool]:
                 # Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
                 # We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
                 lowerCamelCase             =		copy(__a     )

                 # Hack because type=bool in argparse does not behave as we want.
                 lowerCamelCase             =		string_to_bool
                 if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
                       # Default value is False if we have no default when of type bool.
                       lowerCamelCase             =		False if field.default is dataclasses.MISSING else field.default
                       # This is the value that will get picked if we don't include --field_name in any way
                       lowerCamelCase             =		default
                       # This tells argparse we accept 0 or 1 value after --field_name
                       lowerCamelCase             =		"?"
                       # This is the value that will get picked if we do --field_name (without value)
                       lowerCamelCase             =		True
           elif isclass(__a     ) and issubclass(__a					,      __a     ):
                 lowerCamelCase             =		field.type.__args__[0]
                 lowerCamelCase             =		"+"
                 if field.default_factory is not dataclasses.MISSING:
                       lowerCamelCase             =		field.default_factory()
                 elif field.default is dataclasses.MISSING:
                       lowerCamelCase             =		True
           else:
                 lowerCamelCase             =		field.type
                 if field.default is not dataclasses.MISSING:
                       lowerCamelCase             =		field.default
                 elif field.default_factory is not dataclasses.MISSING:
                       lowerCamelCase             =		field.default_factory()
                 else:
                       lowerCamelCase             =		True
           parser.add_argument(__a					,      *__a					,      **__a     )

           # Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
           # Order is important for arguments with the same destination!
           # We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
           # here and we do not need those changes/additional keys.
           if field.default is True and (field.type is bool or field.type == Optional[bool]):
                 lowerCamelCase             =		False
                 parser.add_argument(F"""--no_{field.name}"""					,      action="store_false"					,      dest=field.name					,      **__a     )







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           if hasattr(__a					,      "_argument_group_name"     ):
                 lowerCamelCase             =		self.add_argument_group(dtype._argument_group_name     )
           else:
                 lowerCamelCase             =		self

           try:
                 lowerCamelCase             =		get_type_hints(__a     )
           except NameError:
                 raise RuntimeError(
                     F"""Type resolution failed for {dtype}. Try declaring the class in global scope or """
                     "removing line of `from __future__ import annotations` which opts in Postponed "
                     "Evaluation of Annotations (PEP 563)"     )
           except TypeError as ex:
                 # Remove this block when we drop Python 3.9 support
                 if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(__a     ):
                       lowerCamelCase             =		".".join(map(__a					,      sys.version_info[:3]     )     )
                       raise RuntimeError(
                           F"""Type resolution failed for {dtype} on Python {python_version}. Try removing """
                           "line of `from __future__ import annotations` which opts in union types as "
                           "`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To "
                           "support Python versions that lower than 3.10, you need to use "
                           "`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of "
                           "`X | None`."     ) from ex
                 raise

           for field in dataclasses.fields(__a     ):
                 if not field.init:
                       continue
                 lowerCamelCase             =		type_hints[field.name]
                 self._parse_dataclass_field(__a					,      __a     )







     def     _a   (self					,      __a=None					,      __a=False					,      __a=True					,      __a=None					,      __a=None					,      ):






           '''simple docstring'''



           if args_file_flag or args_filename or (look_for_args_file and len(sys.argv     )):
                 lowerCamelCase             =		[]

                 if args_filename:
                       args_files.append(Path(__a     )     )
                 elif look_for_args_file and len(sys.argv     ):
                       args_files.append(Path(sys.argv[0]     ).with_suffix(".args"     )     )

                 # args files specified via command line flag should overwrite default args files so we add them last
                 if args_file_flag:
                       # Create special parser just to extract the args_file_flag values
                       lowerCamelCase             =		ArgumentParser()
                       args_file_parser.add_argument(__a					,      type=__a					,      action="append"     )

                       # Use only remaining args for further parsing (remove the args_file_flag)
                       lowerCamelCase      ,				lowerCamelCase             =		args_file_parser.parse_known_args(args=__a     )
                       lowerCamelCase             =		vars(__a     ).get(args_file_flag.lstrip("-"     )					,      __a     )

                       if cmd_args_file_paths:
                             args_files.extend([Path(__a     ) for p in cmd_args_file_paths]     )

                 lowerCamelCase             =		[]
                 for args_file in args_files:
                       if args_file.exists():
                             file_args += args_file.read_text().split()

            # in case of duplicate arguments the last one has precedence
            # args specified via the command line should overwrite args from files, so we add them last
                 lowerCamelCase             =		file_args + args if args is not None else file_args + sys.argv[1:]
           lowerCamelCase      ,				lowerCamelCase             =		self.parse_known_args(args=__a     )
           lowerCamelCase             =		[]
           for dtype in self.dataclass_types:
                 lowerCamelCase             =		{f.name for f in dataclasses.fields(__a     ) if f.init}
                 lowerCamelCase             =		{k: v for k, v in vars(__a     ).items() if k in keys}
                 for k in keys:
                       delattr(__a					,      __a     )
                 lowerCamelCase             =		dtype(**__a     )
                 outputs.append(__a     )
           if len(namespace.__dict__     ) > 0:
                 # additional namespace.
                 outputs.append(__a     )
           if return_remaining_strings:
                 return (*outputs, remaining_args)
           else:
                 if remaining_args:
                       raise ValueError(F"""Some specified arguments are not used by the HfArgumentParser: {remaining_args}"""     )

                 return (*outputs,)







     def     _a   (self					,      __a					,      __a = False     ):






           '''simple docstring'''



           lowerCamelCase             =		set(args.keys()     )
           lowerCamelCase             =		[]
           for dtype in self.dataclass_types:
                 lowerCamelCase             =		{f.name for f in dataclasses.fields(__a     ) if f.init}
                 lowerCamelCase             =		{k: v for k, v in args.items() if k in keys}
                 unused_keys.difference_update(inputs.keys()     )
                 lowerCamelCase             =		dtype(**__a     )
                 outputs.append(__a     )
           if not allow_extra_keys and unused_keys:
                 raise ValueError(F"""Some keys are not used by the HfArgumentParser: {sorted(__a     )}"""     )
           return tuple(__a     )







     def     _a   (self					,      __a					,      __a = False     ):






           '''simple docstring'''



           with open(Path(__a     )					,      encoding="utf-8"     ) as open_json_file:
                 lowerCamelCase             =		json.loads(open_json_file.read()     )
           lowerCamelCase             =		self.parse_dict(__a					,      allow_extra_keys=__a     )
           return tuple(__a     )







     def     _a   (self					,      __a					,      __a = False     ):






           '''simple docstring'''



           lowerCamelCase             =		self.parse_dict(yaml.safe_load(Path(__a     ).read_text()     )					,      allow_extra_keys=__a     )
           return tuple(__a     )