



import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

_lowercase				:					int     					=16
_lowercase				:					Union[str, Any]     					=32




def    UpperCAmelCase					(  lowercase__						:					Accelerator				,					lowercase__						:					int = 16	):



			'''simple docstring'''





			a__					=  AutoTokenizer.from_pretrained("""bert-base-cased"""	)
			a__					=  load_dataset("""glue"""				,					"""mrpc"""	)

			def tokenize_function(lowercase__						:					int	):
						# max_length=None => use the model max length (it's actually the default)
						a__					=  tokenizer(examples["""sentence1"""]				,					examples["""sentence2"""]				,					truncation=lowercase__				,					max_length=lowercase__	)
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			# starting with the main process first:
			with accelerator.main_process_first():
						a__					=  datasets.map(
						    lowercase__				,					batched=lowercase__				,					remove_columns=["""idx""", """sentence1""", """sentence2"""]				,					)

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			a__					=  tokenized_datasets.rename_column("""label"""				,					"""labels"""	)

			def collate_fn(lowercase__						:					Optional[int]	):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						a__					=  128 if accelerator.distributed_type == DistributedType.TPU else None
						# When using mixed precision we want round multiples of 8/16
						if accelerator.mixed_precision == "fp8":
									a__					=  16
						elif accelerator.mixed_precision != "no":
									a__					=  8
						else:
									a__					=  None

						return tokenizer.pad(
						    lowercase__				,					padding="""longest"""				,					max_length=lowercase__				,					pad_to_multiple_of=lowercase__				,					return_tensors="""pt"""				,					)

			# Instantiate dataloaders.
			a__					=  DataLoader(
			    tokenized_datasets["""train"""]				,					shuffle=lowercase__				,					collate_fn=lowercase__				,					batch_size=lowercase__	)
			a__					=  DataLoader(
			    tokenized_datasets["""validation"""]				,					shuffle=lowercase__				,					collate_fn=lowercase__				,					batch_size=lowercase__	)

			return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
	from accelerate.test_utils.training import mocked_dataloaders

	_lowercase				:					int     					=mocked_dataloaders  # noqa: F811




def    UpperCAmelCase					(  lowercase__						:					str				,					lowercase__						:					str	):



			'''simple docstring'''





			if os.environ.get("""TESTING_MOCKED_DATALOADERS"""				,					lowercase__	) == "1":
						a__					=  2
			# Initialize accelerator
			a__					=  Accelerator(cpu=args.cpu				,					mixed_precision=args.mixed_precision	)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			a__					=  config["""lr"""]
			a__					=  int(config["""num_epochs"""]	)
			a__					=  int(config["""seed"""]	)
			a__					=  int(config["""batch_size"""]	)

			# New Code #
			# 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":
									a__					=  args.checkpointing_steps
						elif args.checkpointing_steps.isdigit():
									a__					=  int(args.checkpointing_steps	)
						else:
									raise ValueError(
									    f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'	)
			else:
						a__					=  None

			set_seed(lowercase__	)

			a__				,       a__					=  get_dataloaders(lowercase__				,					lowercase__	)
			a__					=  evaluate.load("""glue"""				,					"""mrpc"""	)

			# If the batch size is too big we use gradient accumulation
			a__					=  1
			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
						a__					=  batch_size // MAX_GPU_BATCH_SIZE
						a__					=  MAX_GPU_BATCH_SIZE

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			a__					=  AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""				,					return_dict=lowercase__	)

			# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
			# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
			# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
			a__					=  model.to(accelerator.device	)

			# Instantiate optimizer
			a__					=  AdamW(params=model.parameters()				,					lr=lowercase__	)

			# Instantiate scheduler
			a__					=  get_linear_schedule_with_warmup(
			    optimizer=lowercase__				,					num_warmup_steps=100				,					num_training_steps=(len(lowercase__	) * num_epochs) // gradient_accumulation_steps				,					)

			# 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.
			a__				,       a__				,       a__				,       a__				,       a__					=  accelerator.prepare(
			    lowercase__				,					lowercase__				,					lowercase__				,					lowercase__				,					lowercase__	)

			# New Code #
			# We need to keep track of how many total steps we have iterated over
			a__					=  0
			# We also need to keep track of the stating epoch so files are named properly
			a__					=  0

			# We need to load the checkpoint back in before training here with `load_state`
			# The total number of epochs is adjusted based on where the state is being loaded from,
			# as we assume continuation of the same training script
			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	)
									a__					=  os.path.basename(args.resume_from_checkpoint	)
						else:
									# Get the most recent checkpoint
									a__					=  [f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
									dirs.sort(key=os.path.getctime	)
									a__					=  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract `epoch_{i}` or `step_{i}`
						a__					=  os.path.splitext(lowercase__	)[0]

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

    # Now we train the model
			for epoch in range(lowercase__				,					lowercase__	):
						model.train()
						# New Code #
						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
									a__					=  accelerator.skip_first_batches(lowercase__				,					lowercase__	)
									overall_step += resume_step
						else:
									# After the first iteration though, we need to go back to the original dataloader
									a__					=  train_dataloader
						for step, batch in enumerate(lowercase__	):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device	)
									a__					=  model(**lowercase__	)
									a__					=  outputs.loss
									a__					=  loss / gradient_accumulation_steps
									accelerator.backward(lowercase__	)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()
									# New Code #
									overall_step += 1

									# New Code #
									# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
									# These are saved to folders named `step_{overall_step}`
									# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
									# If mixed precision was used, will also save a "scalar.bin" file
									if isinstance(lowercase__				,					lowercase__	):
												a__					=  f'step_{overall_step}'
												if overall_step % checkpointing_steps == 0:
															if args.output_dir is not None:
																		a__					=  os.path.join(args.output_dir				,					lowercase__	)
															accelerator.save_state(lowercase__	)

						model.eval()
						for step, batch in enumerate(lowercase__	):
									# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
									batch.to(accelerator.device	)
									with torch.no_grad():
												a__					=  model(**lowercase__	)
									a__					=  outputs.logits.argmax(dim=-1	)
									a__				,       a__					=  accelerator.gather_for_metrics((predictions, batch["""labels"""])	)
									metric.add_batch(
									    predictions=lowercase__				,					references=lowercase__				,					)

						a__					=  metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'epoch {epoch}:'				,					lowercase__	)

						# New Code #
						# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
						# These are saved to folders named `epoch_{epoch}`
						# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
						# If mixed precision was used, will also save a "scalar.bin" file
						if checkpointing_steps == "epoch":
									a__					=  f'epoch_{epoch}'
									if args.output_dir is not None:
												a__					=  os.path.join(args.output_dir				,					lowercase__	)
									accelerator.save_state(lowercase__	)




def    UpperCAmelCase					(  ):



			'''simple docstring'''





			a__					=  argparse.ArgumentParser(description="""Simple example of training script."""	)
			parser.add_argument(
			    """--mixed_precision"""				,					type=lowercase__				,					default=lowercase__				,					choices=["""no""", """fp16""", """bf16""", """fp8"""]				,					help="""Whether to use mixed precision. Choose"""
			    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
			    """and an Nvidia Ampere GPU."""				,					)
			parser.add_argument("""--cpu"""				,					action="""store_true"""				,					help="""If passed, will train on the CPU."""	)
			parser.add_argument(
			    """--checkpointing_steps"""				,					type=lowercase__				,					default=lowercase__				,					help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""				,					)
			parser.add_argument(
			    """--output_dir"""				,					type=lowercase__				,					default="""."""				,					help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""				,					)
			parser.add_argument(
			    """--resume_from_checkpoint"""				,					type=lowercase__				,					default=lowercase__				,					help="""If the training should continue from a checkpoint folder."""				,					)
			a__					=  parser.parse_args()
			a__					=  {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
			training_function(lowercase__				,					lowercase__	)


if __name__ == "__main__":
	main()






from ..utils import DummyObject, requires_backends
class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     List[str]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Dict									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Union[str, Any]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Any									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Dict									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Dict									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     str									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Any									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Any									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Dict									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     List[str]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Union[str, Any]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     str									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     List[str]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     List[str]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Union[str, Any]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Union[str, Any]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Optional[int]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Union[str, Any]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     List[Any]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Dict									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     List[str]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Union[str, Any]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     List[Any]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     int									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     str									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Dict									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     str									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Optional[Any]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     List[Any]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Optional[int]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Optional[int]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Tuple									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Dict									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     int									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Dict									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Any									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Optional[int]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     List[str]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Any									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Optional[Any]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Optional[int]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     List[Any]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Dict									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Optional[Any]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Any									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Optional[int]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     int									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     List[str]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Union[str, Any]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Tuple									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Union[str, Any]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     str									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     List[str]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Any									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     List[Any]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     int									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Optional[Any]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Optional[int]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Union[str, Any]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     List[str]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Tuple									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Tuple									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Tuple									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     List[Any]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     List[str]									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Tuple									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     str									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     int									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Any									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     int									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     str									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Any									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )


class lowerCAmelCase_       (   metaclass=A_			):







	'''simple docstring'''




	A_       :     Dict									=		['vision']

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



				'''simple docstring'''


				requires_backends(self   ,       ["""vision"""]       )

