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
#
########################################################################


_lowercase:	List[str]       			=		1_6
_lowercase:	Optional[int]       			=		3_2






def _lowerCamelCase  (		snake_case     , snake_case = 16  ):
							_lowerCAmelCase							=							AutoTokenizer.from_pretrained('bert-base-cased'  )
							_lowerCAmelCase							=							load_dataset('glue'     , 'mrpc'  )

							def tokenize_function(snake_case  ):
														# max_length=None => use the model max length (it's actually the default)
														_lowerCAmelCase							=							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():
														_lowerCAmelCase							=							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
							_lowerCAmelCase							=							tokenized_datasets.rename_column('label'     , 'labels'  )

							def collate_fn(snake_case  ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														_lowerCAmelCase							=							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":
																					_lowerCAmelCase							=							16
														elif accelerator.mixed_precision != "no":
																					_lowerCAmelCase							=							8
														else:
																					_lowerCAmelCase							=							None

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

							# Instantiate dataloaders.
							_lowerCAmelCase							=							DataLoader(
							    tokenized_datasets['train']     , shuffle=lowerCamelCase__     , collate_fn=lowerCamelCase__     , batch_size=lowerCamelCase__  )
							_lowerCAmelCase							=							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

				_lowercase:	int       			=		mocked_dataloaders  # noqa: F811






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

							_lowerCAmelCase							=							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(snake_case  ):
														# 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)
														_lowerCAmelCase							=							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).
														_lowerCAmelCase							=							model.to(accelerator.device  )

														# Instantiate optimizer
														_lowerCAmelCase							=							AdamW(params=model.parameters()     , lr=lowerCamelCase__  )
														_lowerCAmelCase			,		_lowerCAmelCase							=							get_dataloaders(lowerCamelCase__     , lowerCamelCase__  )

														# Instantiate scheduler
														_lowerCAmelCase							=							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.
														_lowerCAmelCase			,		_lowerCAmelCase			,		_lowerCAmelCase			,		_lowerCAmelCase			,		_lowerCAmelCase							=							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  )
																												_lowerCAmelCase							=							model(**lowerCamelCase__  )
																												_lowerCAmelCase							=							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():
																																			_lowerCAmelCase							=							model(**lowerCamelCase__  )
																												_lowerCAmelCase							=							outputs.logits.argmax(dim=-1  )
																												_lowerCAmelCase			,		_lowerCAmelCase							=							accelerator.gather_for_metrics((predictions, batch['labels'])  )
																												metric.add_batch(
																												    predictions=lowerCamelCase__     , references=lowerCamelCase__     , )

																					_lowerCAmelCase							=							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 _lowerCamelCase  (		):
							_lowerCAmelCase							=							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.'  )
							_lowerCAmelCase							=							parser.parse_args()
							_lowerCAmelCase							=							{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
							training_function(lowerCamelCase__     , lowerCamelCase__  )


if __name__ == "__main__":
				main()








class        _SCREAMING_SNAKE_CASE	:



	def __init__(       self		,    lowercase		,    lowercase=None		,    lowercase=None					)							->			List[Any]:
					lowerCamelCase_         =     data
					lowerCamelCase_         =     previous
					lowerCamelCase_         =     next_node



	def __str__(       self					)							->			str:
					return f'{self.data}'



	def        SCREAMING_SNAKE_CASE_(       self					)							->			int:
					return self.data



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Union[str, Any]:
					return self.next



	def        SCREAMING_SNAKE_CASE_(       self					)							->			int:
					return self.previous




class        _SCREAMING_SNAKE_CASE	:



	def __init__(       self		,    lowercase					)							->			Optional[int]:
					lowerCamelCase_         =     head



	def __iter__(       self					)							->			int:
					return self



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[Any]:
					if not self.current:
									raise StopIteration
					else:
									lowerCamelCase_         =     self.current.get_data()
									lowerCamelCase_         =     self.current.get_next()
									return value




class        _SCREAMING_SNAKE_CASE	:



	def __init__(       self					)							->			Union[str, Any]:
					lowerCamelCase_         =     None  # First node in list
					lowerCamelCase_         =     None  # Last node in list



	def __str__(       self					)							->			Optional[Any]:
					lowerCamelCase_         =     self.head
					lowerCamelCase_         =     []
					while current is not None:
									nodes.append(current.get_data()					)
									lowerCamelCase_         =     current.get_next()
					return " ".join(str(lowercase					) for node in nodes					)



	def __contains__(       self		,    lowercase					)							->			Optional[int]:
					lowerCamelCase_         =     self.head
					while current:
									if current.get_data() == value:
													return True
									lowerCamelCase_         =     current.get_next()
					return False



	def __iter__(       self					)							->			List[str]:
					return LinkedListIterator(self.head					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					if self.head:
									return self.head.get_data()
					return None



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Any:
					if self.tail:
									return self.tail.get_data()
					return None



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase					)							->			None:
					if self.head is None:
									lowerCamelCase_         =     node
									lowerCamelCase_         =     node
					else:
									self.insert_before_node(self.head		,    lowercase					)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase					)							->			None:
					if self.head is None:
									self.set_head(lowercase					)
					else:
									self.insert_after_node(self.tail		,    lowercase					)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase					)							->			None:
					lowerCamelCase_         =     Node(lowercase					)
					if self.head is None:
									self.set_head(lowercase					)
					else:
									self.set_tail(lowercase					)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase					)							->			None:
					lowerCamelCase_         =     node
					lowerCamelCase_         =     node.previous

					if node.get_previous() is None:
									lowerCamelCase_         =     node_to_insert
					else:
									lowerCamelCase_         =     node_to_insert

					lowerCamelCase_         =     node_to_insert



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase					)							->			None:
					lowerCamelCase_         =     node
					lowerCamelCase_         =     node.next

					if node.get_next() is None:
									lowerCamelCase_         =     node_to_insert
					else:
									lowerCamelCase_         =     node_to_insert

					lowerCamelCase_         =     node_to_insert



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase					)							->			None:
					lowerCamelCase_         =     1
					lowerCamelCase_         =     Node(lowercase					)
					lowerCamelCase_         =     self.head
					while node:
									if current_position == position:
													self.insert_before_node(lowercase		,    lowercase					)
													return
									current_position += 1
									lowerCamelCase_         =     node.next
					self.insert_after_node(self.tail		,    lowercase					)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase					)							->			Node:
					lowerCamelCase_         =     self.head
					while node:
									if node.get_data() == item:
													return node
									lowerCamelCase_         =     node.get_next()
					raise Exception("Node not found"					)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase					)							->			Union[str, Any]:
					if (node := self.get_node(lowercase					)) is not None:
									if node == self.head:
													lowerCamelCase_         =     self.head.get_next()

									if node == self.tail:
													lowerCamelCase_         =     self.tail.get_previous()

									self.remove_node_pointers(lowercase					)



	@staticmethod
	def        SCREAMING_SNAKE_CASE_(       lowercase					)							->			None:
					if node.get_next():
									lowerCamelCase_         =     node.previous

					if node.get_previous():
									lowerCamelCase_         =     node.next

					lowerCamelCase_         =     None
					lowerCamelCase_         =     None



	def        SCREAMING_SNAKE_CASE_(       self					)							->			int:
					return self.head is None





def      lowerCamelCase_					(							):
				pass


if __name__ == "__main__":
			import doctest

			doctest.testmod()


