"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A	=					logging.get_logger(__name__)

__A	=					{
    '''naver-clova-ix/donut-base''': '''https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json''',
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}
class 			_snake_case  (       __UpperCAmelCase		):
						snake_case__      	=      'donut-swin'

						snake_case__      	=      {
						    'num_attention_heads': 'num_heads',
						    'num_hidden_layers': 'num_layers',
						}






						def __init__(							self   :      Union[str, Any]				,       UpperCAmelCase   :      Dict=224				,       UpperCAmelCase   :      Optional[int]=4				,       UpperCAmelCase   :      Optional[int]=3				,       UpperCAmelCase   :      Any=96				,       UpperCAmelCase   :      Union[str, Any]=[2, 2, 6, 2]				,       UpperCAmelCase   :      int=[3, 6, 12, 24]				,       UpperCAmelCase   :      int=7				,       UpperCAmelCase   :      int=4.0				,       UpperCAmelCase   :      Optional[int]=True				,       UpperCAmelCase   :      Optional[Any]=0.0				,       UpperCAmelCase   :      List[Any]=0.0				,       UpperCAmelCase   :      Tuple=0.1				,       UpperCAmelCase   :      Any="gelu"				,       UpperCAmelCase   :      Any=False				,       UpperCAmelCase   :      str=0.0_2				,       UpperCAmelCase   :      int=1E-5				,       **UpperCAmelCase   :      Optional[Any]				,       ):
													super().__init__(**lowerCAmelCase_     )

													__lowerCamelCase       :		List[str]		 =  image_size
													__lowerCamelCase       :		List[str]		 =  patch_size
													__lowerCamelCase       :		int		 =  num_channels
													__lowerCamelCase       :		str		 =  embed_dim
													__lowerCamelCase       :		List[Any]		 =  depths
													__lowerCamelCase       :		Optional[int]		 =  len(lowerCAmelCase_     )
													__lowerCamelCase       :		Tuple		 =  num_heads
													__lowerCamelCase       :		Union[str, Any]		 =  window_size
													__lowerCamelCase       :		str		 =  mlp_ratio
													__lowerCamelCase       :		Union[str, Any]		 =  qkv_bias
													__lowerCamelCase       :		str		 =  hidden_dropout_prob
													__lowerCamelCase       :		Dict		 =  attention_probs_dropout_prob
													__lowerCamelCase       :		Dict		 =  drop_path_rate
													__lowerCamelCase       :		str		 =  hidden_act
													__lowerCamelCase       :		List[str]		 =  use_absolute_embeddings
													__lowerCamelCase       :		List[Any]		 =  layer_norm_eps
													__lowerCamelCase       :		Any		 =  initializer_range
													# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
													# this indicates the channel dimension after the last stage of the model
													__lowerCamelCase       :		Tuple		 =  int(embed_dim * 2 ** (len(lowerCAmelCase_     ) - 1)     )







"""simple docstring"""
import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


lowerCAmelCase__    						=			16
lowerCAmelCase__    						=			32





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase = 16       ):
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    AutoTokenizer.from_pretrained('bert-base-cased'       )
				_SCREAMING_SNAKE_CASE   :		Any       		=    load_dataset('glue',						'mrpc'       )

				def tokenize_function(lowerCamelCase       ):
								# max_length=None => use the model max length (it's actually the default)
								_SCREAMING_SNAKE_CASE   :		int       		=    tokenizer(examples['sentence1'],						examples['sentence2'],						truncation=lowerCamelCase,						max_length=lowerCamelCase       )
								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():
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    datasets.map(
								    lowerCamelCase,						batched=lowerCamelCase,						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
				_SCREAMING_SNAKE_CASE   :		Dict       		=    tokenized_datasets.rename_column('label',						'labels'       )

				def collate_fn(lowerCamelCase       ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    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":
												_SCREAMING_SNAKE_CASE   :		List[str]       		=    16
								elif accelerator.mixed_precision != "no":
												_SCREAMING_SNAKE_CASE   :		Dict       		=    8
								else:
												_SCREAMING_SNAKE_CASE   :		str       		=    None

								return tokenizer.pad(
								    lowerCamelCase,						padding='longest',						max_length=lowerCamelCase,						pad_to_multiple_of=lowerCamelCase,						return_tensors='pt',						)

				# Instantiate dataloaders.
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    DataLoader(
				    tokenized_datasets['train'],						shuffle=lowerCamelCase,						collate_fn=lowerCamelCase,						batch_size=lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Dict       		=    DataLoader(
				    tokenized_datasets['validation'],						shuffle=lowerCamelCase,						collate_fn=lowerCamelCase,						batch_size=lowerCamelCase       )

				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

			lowerCAmelCase__    						=			mocked_dataloaders  # noqa: F811





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				# For testing only
				if os.environ.get('TESTING_MOCKED_DATALOADERS',						lowerCamelCase       ) == "1":
								_SCREAMING_SNAKE_CASE   :		int       		=    2
				# Initialize accelerator
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    Accelerator(cpu=args.cpu,						mixed_precision=args.mixed_precision       )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				_SCREAMING_SNAKE_CASE   :		Any       		=    config['lr']
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    int(config['num_epochs']       )
				_SCREAMING_SNAKE_CASE   :		Dict       		=    int(config['seed']       )
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    int(config['batch_size']       )

				_SCREAMING_SNAKE_CASE   :		Tuple       		=    evaluate.load('glue',						'mrpc'       )

				# New Code #
				# We now can define an inner training loop function. It should take a batch size as the only parameter,
				# and build the dataloaders in there.
				# It also gets our decorator
				@find_executable_batch_size(starting_batch_size=lowerCamelCase       )
				def inner_training_loop(lowerCamelCase       ):
								# And now just move everything below under this function
								# We need to bring in the Accelerator object from earlier
								nonlocal accelerator
								# And reset all of its attributes that could hold onto any memory:
								accelerator.free_memory()

								# Then we can declare the model, optimizer, and everything else:
								set_seed(lowerCamelCase       )

								# Instantiate the model (we build the model here so that the seed also control new weights initialization)
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    AutoModelForSequenceClassification.from_pretrained('bert-base-cased',						return_dict=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).
								_SCREAMING_SNAKE_CASE   :		Any       		=    model.to(accelerator.device       )

								# Instantiate optimizer
								_SCREAMING_SNAKE_CASE   :		str       		=    AdamW(params=model.parameters(),						lr=lowerCamelCase       )
								_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    get_dataloaders(lowerCamelCase,						lowerCamelCase       )

								# Instantiate scheduler
								_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    get_linear_schedule_with_warmup(
								    optimizer=lowerCamelCase,						num_warmup_steps=100,						num_training_steps=(len(lowerCamelCase       ) * num_epochs),						)

								# 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.
								_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		List[Any]       		=    accelerator.prepare(
								    lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase       )

								# Now we train the model
								for epoch in range(lowerCamelCase       ):
												model.train()
												for step, batch in enumerate(lowerCamelCase       ):
																# We could avoid this line since we set the accelerator with `device_placement=True`.
																batch.to(accelerator.device       )
																_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    model(**lowerCamelCase       )
																_SCREAMING_SNAKE_CASE   :		List[str]       		=    outputs.loss
																accelerator.backward(lowerCamelCase       )
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

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

												_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"""epoch {epoch}:""",						lowerCamelCase       )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
				inner_training_loop()





def 				lowercase__		(					):
				_SCREAMING_SNAKE_CASE   :		str       		=    argparse.ArgumentParser(description='Simple example of training script.'       )
				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.'       )
				_SCREAMING_SNAKE_CASE   :		Any       		=    parser.parse_args()
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
				training_function(lowerCamelCase,						lowerCamelCase       )


if __name__ == "__main__":
			main()

