






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	(  lowerCamelCase       ):




	'''simple docstring'''







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


class 							__UpperCamelCase     (  _lowerCAmelCase ):







	def __init__(	self   :				int							,					_lowerCAmelCase   :				Union[str, Any]							,					_lowerCAmelCase   :				Tuple=None							,					_lowerCAmelCase   :				str=None      )		->			Any:
		"""simple docstring"""







		__lowercase   							=	file_names
		__lowercase   							=	image_transform
		__lowercase   							=	label_to_id







	def __len__(	self   :				Tuple      )		->			Optional[int]:
		"""simple docstring"""







		return len(self.file_names      )







	def __getitem__(	self   :				List[Any]							,					_lowerCAmelCase   :				Tuple      )		->			List[str]:
		"""simple docstring"""







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





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase       ):




	'''simple docstring'''







	if args.with_tracking:
		__lowercase   							=	Accelerator(
		    cpu=args.cpu					,	mixed_precision=args.mixed_precision					,	log_with="""all"""					,	project_dir=args.project_dir       )
	else:
		__lowercase   							=	Accelerator(cpu=args.cpu					,	mixed_precision=args.mixed_precision       )

	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	__lowercase   							=	config["""lr"""]
	__lowercase   							=	int(config["""num_epochs"""]       )
	__lowercase   							=	int(config["""seed"""]       )
	__lowercase   							=	int(config["""batch_size"""]       )
	__lowercase   							=	config["""image_size"""]
	if not isinstance(lowerCamelCase					,	(list, tuple)       ):
		__lowercase   							=	(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":
			__lowercase   							=	args.checkpointing_steps
		elif args.checkpointing_steps.isdigit():
			__lowercase   							=	int(args.checkpointing_steps       )
		else:
			raise ValueError(
			    F'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'       )
	else:
		__lowercase   							=	None

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

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

	# Build the label correspondences
	__lowercase   							=	[extract_label(lowerCamelCase       ) for fname in file_names]
	__lowercase   							=	list(set(lowerCamelCase       )       )
	id_to_label.sort()
	__lowercase   							=	{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
	__lowercase   							=	np.random.permutation(len(lowerCamelCase       )       )
	__lowercase   							=	int(0.8 * len(lowerCamelCase       )       )
	__lowercase   							=	random_perm[:cut]
	__lowercase   							=	random_perm[cut:]

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

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

	# Instantiate dataloaders.
	__lowercase   							=	DataLoader(lowerCamelCase					,	shuffle=lowerCamelCase					,	batch_size=lowerCamelCase					,	num_workers=4       )
	__lowercase   							=	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)
	__lowercase   							=	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).
	__lowercase   							=	model.to(accelerator.device       )

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

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

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

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

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

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

		__lowercase   							=	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(lowerCamelCase       ),
			        """epoch""": epoch,
			    }					,	step=lowerCamelCase					,	)
		if checkpointing_steps == "epoch":
			__lowercase   							=	F'epoch_{epoch}'
			if args.output_dir is not None:
				__lowercase   							=	os.path.join(args.output_dir					,	lowerCamelCase       )
			accelerator.save_state(lowerCamelCase       )

	if args.with_tracking:
		accelerator.end_training()





def 					snake_case	(  ):




	'''simple docstring'''







	__lowercase   							=	argparse.ArgumentParser(description="""Simple example of training script."""       )
	parser.add_argument("""--data_dir"""					,	required=lowerCamelCase					,	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=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("""--cpu"""					,	action="""store_true"""					,	help="""If passed, will train on the CPU."""       )
	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(
	    """--output_dir"""					,	type=lowerCamelCase					,	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=lowerCamelCase					,	default=lowerCamelCase					,	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=lowerCamelCase					,	default="""logs"""					,	help="""Location on where to store experiment tracking logs` and relevent project information"""					,	)
	__lowercase   							=	parser.parse_args()
	__lowercase   							=	{"""lr""": 3e-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
	training_function(lowerCamelCase					,	lowerCamelCase       )


if __name__ == "__main__":
						main()





import collections
import importlib.util
import os
import re
from pathlib import Path


SCREAMING_SNAKE_CASE__					:     List[Any]       =     'src/transformers'


# Matches is_xxx_available()
SCREAMING_SNAKE_CASE__					:     List[Any]       =     re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
SCREAMING_SNAKE_CASE__					:     str       =     re.compile(R'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
SCREAMING_SNAKE_CASE__					:     Tuple       =     re.compile(R'\s+"\S*":\s+\[([^\]]*)\]')
# Catches a line if not is_foo_available
SCREAMING_SNAKE_CASE__					:     Dict       =     re.compile(R'^\s*if\s+not\s+is\_[a-z_]*\_available\(\)')
# Catches a line _import_struct["bla"].append("foo")
SCREAMING_SNAKE_CASE__					:     Union[str, Any]       =     re.compile(R'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     re.compile(R'^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]')
# Catches a line with an object between quotes and a comma:     "MyModel",
SCREAMING_SNAKE_CASE__					:     Optional[int]       =     re.compile('^\s+"([^"]+)",')
# Catches a line with objects between brackets only:    ["foo", "bar"],
SCREAMING_SNAKE_CASE__					:     Union[str, Any]       =     re.compile('^\s+\[([^\]]+)\]')
# Catches a line with from foo import bar, bla, boo
SCREAMING_SNAKE_CASE__					:     List[Any]       =     re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
# Catches a line with try:
SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     re.compile(R'^\s*try:')
# Catches a line with else:
SCREAMING_SNAKE_CASE__					:     Union[str, Any]       =     re.compile(R'^\s*else:')




def    a__    (		snake_case__					:							Union[str, Any]     ):
  if _re_test_backend.search(snake_case__     ) is None:
    return None
  _UpperCAmelCase		:							str							=     [b[0] for b in _re_backend.findall(snake_case__     )]
  backends.sort()
  return "_and_".join(snake_case__     )




def    a__    (		snake_case__					:							Optional[int]     ):
  with open(snake_case__						,   """r"""						,   encoding="""utf-8"""						,   newline="""\n"""     ) as f:
    _UpperCAmelCase		:							Optional[Any]							=     f.readlines()

  _UpperCAmelCase		:							int							=     0
  while line_index < len(snake_case__     ) and not lines[line_index].startswith("""_import_structure = {"""     ):
    line_index += 1

  # If this is a traditional init, just return.
  if line_index >= len(snake_case__     ):
    return None

  # First grab the objects without a specific backend in _import_structure
  _UpperCAmelCase		:							int							=     []
  while not lines[line_index].startswith("""if TYPE_CHECKING"""     ) and find_backend(lines[line_index]     ) is None:
    _UpperCAmelCase		:							int							=     lines[line_index]
    # If we have everything on a single line, let's deal with it.
    if _re_one_line_import_struct.search(snake_case__     ):
      _UpperCAmelCase		:							Optional[Any]							=     _re_one_line_import_struct.search(snake_case__     ).groups()[0]
      _UpperCAmelCase		:							str							=     re.findall("""\[([^\]]+)\]"""						,   snake_case__     )
      for imp in imports:
        objects.extend([obj[1:-1] for obj in imp.split(""", """     )]     )
      line_index += 1
      continue
    _UpperCAmelCase		:							int							=     _re_import_struct_key_value.search(snake_case__     )
    if single_line_import_search is not None:
      _UpperCAmelCase		:							Dict							=     [obj[1:-1] for obj in single_line_import_search.groups()[0].split(""", """     ) if len(snake_case__     ) > 0]
      objects.extend(snake_case__     )
    elif line.startswith(""" """ * 8 + """\""""     ):
      objects.append(line[9:-3]     )
    line_index += 1

  _UpperCAmelCase		:							str							=     {"""none""": objects}
  # Let's continue with backend-specific objects in _import_structure
  while not lines[line_index].startswith("""if TYPE_CHECKING"""     ):
    # If the line is an if not is_backend_available, we grab all objects associated.
    _UpperCAmelCase		:							Tuple							=     find_backend(lines[line_index]     )
    # Check if the backend declaration is inside a try block:
    if _re_try.search(lines[line_index - 1]     ) is None:
      _UpperCAmelCase		:							List[Any]							=     None

    if backend is not None:
      line_index += 1

      # Scroll until we hit the else block of try-except-else
      while _re_else.search(lines[line_index]     ) is None:
        line_index += 1

      line_index += 1

      _UpperCAmelCase		:							List[Any]							=     []
      # Until we unindent, add backend objects to the list
      while len(lines[line_index]     ) <= 1 or lines[line_index].startswith(""" """ * 4     ):
        _UpperCAmelCase		:							Optional[int]							=     lines[line_index]
        if _re_import_struct_add_one.search(snake_case__     ) is not None:
          objects.append(_re_import_struct_add_one.search(snake_case__     ).groups()[0]     )
        elif _re_import_struct_add_many.search(snake_case__     ) is not None:
          _UpperCAmelCase		:							str							=     _re_import_struct_add_many.search(snake_case__     ).groups()[0].split(""", """     )
          _UpperCAmelCase		:							Optional[Any]							=     [obj[1:-1] for obj in imports if len(snake_case__     ) > 0]
          objects.extend(snake_case__     )
        elif _re_between_brackets.search(snake_case__     ) is not None:
          _UpperCAmelCase		:							str							=     _re_between_brackets.search(snake_case__     ).groups()[0].split(""", """     )
          _UpperCAmelCase		:							List[str]							=     [obj[1:-1] for obj in imports if len(snake_case__     ) > 0]
          objects.extend(snake_case__     )
        elif _re_quote_object.search(snake_case__     ) is not None:
          objects.append(_re_quote_object.search(snake_case__     ).groups()[0]     )
        elif line.startswith(""" """ * 8 + """\""""     ):
          objects.append(line[9:-3]     )
        elif line.startswith(""" """ * 12 + """\""""     ):
          objects.append(line[13:-3]     )
        line_index += 1

      _UpperCAmelCase		:							str							=     objects
    else:
      line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
  _UpperCAmelCase		:							Optional[Any]							=     []
  while (
      line_index < len(snake_case__     )
      and find_backend(lines[line_index]     ) is None
      and not lines[line_index].startswith("""else"""     )
  ):
    _UpperCAmelCase		:							Union[str, Any]							=     lines[line_index]
    _UpperCAmelCase		:							str							=     _re_import.search(snake_case__     )
    if single_line_import_search is not None:
      objects.extend(single_line_import_search.groups()[0].split(""", """     )     )
    elif line.startswith(""" """ * 8     ):
      objects.append(line[8:-2]     )
    line_index += 1

  _UpperCAmelCase		:							int							=     {"""none""": objects}
  # Let's continue with backend-specific objects
  while line_index < len(snake_case__     ):
    # If the line is an if is_backend_available, we grab all objects associated.
    _UpperCAmelCase		:							str							=     find_backend(lines[line_index]     )
    # Check if the backend declaration is inside a try block:
    if _re_try.search(lines[line_index - 1]     ) is None:
      _UpperCAmelCase		:							Optional[int]							=     None

    if backend is not None:
      line_index += 1

      # Scroll until we hit the else block of try-except-else
      while _re_else.search(lines[line_index]     ) is None:
        line_index += 1

      line_index += 1

      _UpperCAmelCase		:							Dict							=     []
      # Until we unindent, add backend objects to the list
      while len(lines[line_index]     ) <= 1 or lines[line_index].startswith(""" """ * 8     ):
        _UpperCAmelCase		:							Union[str, Any]							=     lines[line_index]
        _UpperCAmelCase		:							Any							=     _re_import.search(snake_case__     )
        if single_line_import_search is not None:
          objects.extend(single_line_import_search.groups()[0].split(""", """     )     )
        elif line.startswith(""" """ * 12     ):
          objects.append(line[12:-2]     )
        line_index += 1

      _UpperCAmelCase		:							str							=     objects
    else:
      line_index += 1

  return import_dict_objects, type_hint_objects




def    a__    (		snake_case__					:							Any						,   snake_case__					:							Optional[int]     ):

  def find_duplicates(snake_case__					:							Dict     ):
    return [k for k, v in collections.Counter(snake_case__     ).items() if v > 1]

  if list(import_dict_objects.keys()     ) != list(type_hint_objects.keys()     ):
    return ["Both sides of the init do not have the same backends!"]

  _UpperCAmelCase		:							int							=     []
  for key in import_dict_objects.keys():
    _UpperCAmelCase		:							Any							=     find_duplicates(import_dict_objects[key]     )
    if duplicate_imports:
      errors.append(f'''Duplicate _import_structure definitions for: {duplicate_imports}'''     )
    _UpperCAmelCase		:							Optional[int]							=     find_duplicates(type_hint_objects[key]     )
    if duplicate_type_hints:
      errors.append(f'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}'''     )

    if sorted(set(import_dict_objects[key]     )     ) != sorted(set(type_hint_objects[key]     )     ):
      _UpperCAmelCase		:							Optional[int]							=     """base imports""" if key == """none""" else f'''{key} backend'''
      errors.append(f'''Differences for {name}:'''     )
      for a in type_hint_objects[key]:
        if a not in import_dict_objects[key]:
          errors.append(f'''  {a} in TYPE_HINT but not in _import_structure.'''     )
      for a in import_dict_objects[key]:
        if a not in type_hint_objects[key]:
          errors.append(f'''  {a} in _import_structure but not in TYPE_HINT.'''     )
  return errors




def    a__    (		):
  _UpperCAmelCase		:							Any							=     []
  for root, _, files in os.walk(snake_case__     ):
    if "__init__.py" in files:
      _UpperCAmelCase		:							Optional[Any]							=     os.path.join(snake_case__						,   """__init__.py"""     )
      _UpperCAmelCase		:							List[str]							=     parse_init(snake_case__     )
      if objects is not None:
        _UpperCAmelCase		:							int							=     analyze_results(*snake_case__     )
        if len(snake_case__     ) > 0:
          _UpperCAmelCase		:							Union[str, Any]							=     f'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
          failures.append("""\n""".join(snake_case__     )     )
  if len(snake_case__     ) > 0:
    raise ValueError("""\n\n""".join(snake_case__     )     )




def    a__    (		):
  _UpperCAmelCase		:							Tuple							=     []
  for path, directories, files in os.walk(snake_case__     ):
    for folder in directories:
      # Ignore private modules
      if folder.startswith("""_"""     ):
        directories.remove(snake_case__     )
        continue
      # Ignore leftovers from branches (empty folders apart from pycache)
      if len(list((Path(snake_case__     ) / folder).glob("""*.py"""     )     )     ) == 0:
        continue
      _UpperCAmelCase		:							Dict							=     str((Path(snake_case__     ) / folder).relative_to(snake_case__     )     )
      _UpperCAmelCase		:							Union[str, Any]							=     short_path.replace(os.path.sep						,   """."""     )
      submodules.append(snake_case__     )
    for fname in files:
      if fname == "__init__.py":
        continue
      _UpperCAmelCase		:							Optional[Any]							=     str((Path(snake_case__     ) / fname).relative_to(snake_case__     )     )
      _UpperCAmelCase		:							Optional[Any]							=     short_path.replace(""".py"""						,   """"""     ).replace(os.path.sep						,   """."""     )
      if len(submodule.split("""."""     )     ) == 1:
        submodules.append(snake_case__     )
  return submodules


SCREAMING_SNAKE_CASE__					:     List[Any]       =     [
    'convert_pytorch_checkpoint_to_tf2',
    'modeling_flax_pytorch_utils',
]




def    a__    (		):
  # This is to make sure the transformers module imported is the one in the repo.
  _UpperCAmelCase		:							int							=     importlib.util.spec_from_file_location(
      """transformers"""						,   os.path.join(snake_case__						,   """__init__.py"""     )						,   submodule_search_locations=[PATH_TO_TRANSFORMERS]						,   )
  _UpperCAmelCase		:							Optional[int]							=     spec.loader.load_module()

  _UpperCAmelCase		:							int							=     [
      module
      for module in get_transformers_submodules()
      if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
  ]
  if len(snake_case__     ) > 0:
    _UpperCAmelCase		:							Dict							=     """\n""".join(f'''- {module}''' for module in module_not_registered     )
    raise ValueError(
        """The following submodules are not properly registered in the main init of Transformers:\n"""
        f'''{list_of_modules}\n'''
        """Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value."""     )


if __name__ == "__main__":
 check_all_inits()
 check_submodules()





