

"""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    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Union[str, Any]:
		__lowerCAmelCase:	int   				=      fname.split(os.path.sep			)[-1]
		return re.search(R"^(.*)_\d+\.jpg$" ,			__SCREAMING_SNAKE_CASE			).groups()[0]





class    snake_case     (    __snake_case  ):

				def __init__(     self							:    Optional[int]					,  UpperCamelCase__							:    Dict					,  UpperCamelCase__							:    List[str]=None					,  UpperCamelCase__							:    Dict=None)->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      file_names
						__lowerCAmelCase:	List[Any]   				=      image_transform
						__lowerCAmelCase:	List[str]   				=      label_to_id

				def __len__(     self							:    Dict)->       Optional[int]:



						'''simple docstring'''


						return len(self.file_names)




				def __getitem__(     self							:    List[str]					,  UpperCamelCase__							:    Any)->       str:



						'''simple docstring'''


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






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     List[Any]:
		# Initialize accelerator
		if args.with_tracking:
				__lowerCAmelCase:	int   				=      Accelerator(
				    cpu=args.cpu ,			mixed_precision=args.mixed_precision ,			log_with="all" ,			project_dir=args.project_dir			)
		else:
				__lowerCAmelCase:	str   				=      Accelerator(cpu=args.cpu ,			mixed_precision=args.mixed_precision			)

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

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

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

		# Build the label correspondences
		__lowerCAmelCase:	List[str]   				=      [extract_label(__SCREAMING_SNAKE_CASE			) for fname in file_names]
		__lowerCAmelCase:	Tuple   				=      list(set(__SCREAMING_SNAKE_CASE			)			)
		id_to_label.sort()
		__lowerCAmelCase:	str   				=      {lbl: i for i, lbl in enumerate(__SCREAMING_SNAKE_CASE			)}

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

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

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

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

		# Instantiate dataloaders.
		__lowerCAmelCase:	Optional[int]   				=      DataLoader(__SCREAMING_SNAKE_CASE ,			shuffle=__SCREAMING_SNAKE_CASE ,			batch_size=__SCREAMING_SNAKE_CASE ,			num_workers=4			)
		__lowerCAmelCase:	Tuple   				=      DataLoader(__SCREAMING_SNAKE_CASE ,			shuffle=__SCREAMING_SNAKE_CASE ,			batch_size=__SCREAMING_SNAKE_CASE ,			num_workers=4			)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		__lowerCAmelCase:	int   				=      create_model("resnet50d" ,			pretrained=__SCREAMING_SNAKE_CASE ,			num_classes=len(__SCREAMING_SNAKE_CASE			)			)

		# 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:	str   				=      model.to(accelerator.device			)

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

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

		# Instantiate optimizer
		__lowerCAmelCase:	List[Any]   				=      torch.optim.Adam(params=model.parameters() ,			lr=lr / 2_5			)

		# Instantiate learning rate scheduler
		__lowerCAmelCase:	List[str]   				=      OneCycleLR(optimizer=__SCREAMING_SNAKE_CASE ,			max_lr=__SCREAMING_SNAKE_CASE ,			epochs=__SCREAMING_SNAKE_CASE ,			steps_per_epoch=len(__SCREAMING_SNAKE_CASE			)			)

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

				if "epoch" in training_difference:
						__lowerCAmelCase:	Tuple   				=      int(training_difference.replace("epoch_" ,			""			)			) + 1
						__lowerCAmelCase:	List[str]   				=      None
				else:
						__lowerCAmelCase:	Tuple   				=      int(training_difference.replace("step_" ,			""			)			)
						__lowerCAmelCase:	int   				=      resume_step // len(__SCREAMING_SNAKE_CASE			)
						resume_step -= starting_epoch * len(__SCREAMING_SNAKE_CASE			)

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

				__lowerCAmelCase:	Optional[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(__SCREAMING_SNAKE_CASE			),
						        "epoch": epoch,
						    } ,			step=__SCREAMING_SNAKE_CASE ,			)
				if checkpointing_steps == "epoch":
						__lowerCAmelCase:	Dict   				=      F"epoch_{epoch}"
						if args.output_dir is not None:
								__lowerCAmelCase:	List[Any]   				=      os.path.join(args.output_dir ,			__SCREAMING_SNAKE_CASE			)
						accelerator.save_state(__SCREAMING_SNAKE_CASE			)

		if args.with_tracking:
				accelerator.end_training()






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


if __name__ == "__main__":
			main()





"""simple docstring"""


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


__A						       =      "src/transformers"


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






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






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

		__lowerCAmelCase:	Dict   				=      0
		while line_index < len(__SCREAMING_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(__SCREAMING_SNAKE_CASE			):
				return None

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

		__lowerCAmelCase:	Tuple   				=      {"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.
				__lowerCAmelCase:	Optional[int]   				=      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:
						__lowerCAmelCase:	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

						__lowerCAmelCase:	Optional[int]   				=      []
						# Until we unindent, add backend objects to the list
						while len(lines[line_index]			) <= 1 or lines[line_index].startswith(" " * 4			):
								__lowerCAmelCase:	Optional[Any]   				=      lines[line_index]
								if _re_import_struct_add_one.search(__SCREAMING_SNAKE_CASE			) is not None:
										objects.append(_re_import_struct_add_one.search(__SCREAMING_SNAKE_CASE			).groups()[0]			)
								elif _re_import_struct_add_many.search(__SCREAMING_SNAKE_CASE			) is not None:
										__lowerCAmelCase:	Union[str, Any]   				=      _re_import_struct_add_many.search(__SCREAMING_SNAKE_CASE			).groups()[0].split(", "			)
										__lowerCAmelCase:	int   				=      [obj[1:-1] for obj in imports if len(__SCREAMING_SNAKE_CASE			) > 0]
										objects.extend(__SCREAMING_SNAKE_CASE			)
								elif _re_between_brackets.search(__SCREAMING_SNAKE_CASE			) is not None:
										__lowerCAmelCase:	Tuple   				=      _re_between_brackets.search(__SCREAMING_SNAKE_CASE			).groups()[0].split(", "			)
										__lowerCAmelCase:	Tuple   				=      [obj[1:-1] for obj in imports if len(__SCREAMING_SNAKE_CASE			) > 0]
										objects.extend(__SCREAMING_SNAKE_CASE			)
								elif _re_quote_object.search(__SCREAMING_SNAKE_CASE			) is not None:
										objects.append(_re_quote_object.search(__SCREAMING_SNAKE_CASE			).groups()[0]			)
								elif line.startswith(" " * 8 + "\""			):
										objects.append(line[9:-3]			)
								elif line.startswith(" " * 1_2 + "\""			):
										objects.append(line[1_3:-3]			)
								line_index += 1

						__lowerCAmelCase:	Union[str, Any]   				=      objects
				else:
						line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
		__lowerCAmelCase:	str   				=      []
		while (
		    line_index < len(__SCREAMING_SNAKE_CASE			)
		    and find_backend(lines[line_index]			) is None
		    and not lines[line_index].startswith("else"			)
		):
				__lowerCAmelCase:	List[Any]   				=      lines[line_index]
				__lowerCAmelCase:	Tuple   				=      _re_import.search(__SCREAMING_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

		__lowerCAmelCase:	Any   				=      {"none": objects}
		# Let's continue with backend-specific objects
		while line_index < len(__SCREAMING_SNAKE_CASE			):
				# If the line is an if is_backend_available, we grab all objects associated.
				__lowerCAmelCase:	Optional[Any]   				=      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:
						__lowerCAmelCase:	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

						__lowerCAmelCase:	List[Any]   				=      []
						# Until we unindent, add backend objects to the list
						while len(lines[line_index]			) <= 1 or lines[line_index].startswith(" " * 8			):
								__lowerCAmelCase:	Optional[int]   				=      lines[line_index]
								__lowerCAmelCase:	Any   				=      _re_import.search(__SCREAMING_SNAKE_CASE			)
								if single_line_import_search is not None:
										objects.extend(single_line_import_search.groups()[0].split(", "			)			)
								elif line.startswith(" " * 1_2			):
										objects.append(line[1_2:-2]			)
								line_index += 1

						__lowerCAmelCase:	Union[str, Any]   				=      objects
				else:
						line_index += 1

		return import_dict_objects, type_hint_objects






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Tuple:

		def find_duplicates(__SCREAMING_SNAKE_CASE			):
				return [k for k, v in collections.Counter(__SCREAMING_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!"]

		__lowerCAmelCase:	Optional[int]   				=      []
		for key in import_dict_objects.keys():
				__lowerCAmelCase:	Union[str, Any]   				=      find_duplicates(import_dict_objects[key]			)
				if duplicate_imports:
						errors.append(F"Duplicate _import_structure definitions for: {duplicate_imports}"			)
				__lowerCAmelCase:	Optional[Any]   				=      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]			)			):
						__lowerCAmelCase:	Union[str, Any]   				=      "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__   (   )     ->     Tuple:
		__lowerCAmelCase:	Optional[Any]   				=      []
		for root, _, files in os.walk(__SCREAMING_SNAKE_CASE			):
				if "__init__.py" in files:
						__lowerCAmelCase:	List[str]   				=      os.path.join(__SCREAMING_SNAKE_CASE ,			"__init__.py"			)
						__lowerCAmelCase:	int   				=      parse_init(__SCREAMING_SNAKE_CASE			)
						if objects is not None:
								__lowerCAmelCase:	Optional[Any]   				=      analyze_results(*__SCREAMING_SNAKE_CASE			)
								if len(__SCREAMING_SNAKE_CASE			) > 0:
										__lowerCAmelCase:	Union[str, Any]   				=      F"Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"
										failures.append("\n".join(__SCREAMING_SNAKE_CASE			)			)
		if len(__SCREAMING_SNAKE_CASE			) > 0:
				raise ValueError("\n\n".join(__SCREAMING_SNAKE_CASE			)			)






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


__A						       =      [
    "convert_pytorch_checkpoint_to_tf2",
    "modeling_flax_pytorch_utils",
]






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

		__lowerCAmelCase:	str   				=      [
		    module
		    for module in get_transformers_submodules()
		    if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
		]
		if len(__SCREAMING_SNAKE_CASE			) > 0:
				__lowerCAmelCase:	Optional[int]   				=      "\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()


