


"""simple docstring"""
import argparse

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
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


SCREAMING_SNAKE_CASE :    int									=				16
SCREAMING_SNAKE_CASE :    List[str]									=				32






def 		lowercase							(     _snake_case						:      Accelerator     ,   _snake_case						:      int = 16     )     ->Dict:
	"""simple docstring"""





	__snake_case	:						Optional[Any]						=				AutoTokenizer.from_pretrained('''bert-base-cased'''     )
	__snake_case	:						Any						=				load_dataset('''glue'''     ,   '''mrpc'''     )

	def tokenize_function(_snake_case						:      Union[str, Any]     ):
		# max_length=None => use the model max length (it's actually the default)
		__snake_case	:						int						=				tokenizer(examples['''sentence1''']     ,   examples['''sentence2''']     ,   truncation=SCREAMING_SNAKE_CASE__     ,   max_length=SCREAMING_SNAKE_CASE__     )
		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():
		__snake_case	:						Tuple						=				datasets.map(
		    SCREAMING_SNAKE_CASE__     ,   batched=SCREAMING_SNAKE_CASE__     ,   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
	__snake_case	:						List[Any]						=				tokenized_datasets.rename_column('''label'''     ,   '''labels'''     )

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

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

	# Instantiate dataloaders.
	__snake_case	:						str						=				DataLoader(
	    tokenized_datasets['''train''']     ,   shuffle=SCREAMING_SNAKE_CASE__     ,   collate_fn=SCREAMING_SNAKE_CASE__     ,   batch_size=SCREAMING_SNAKE_CASE__     ,   drop_last=SCREAMING_SNAKE_CASE__     )
	__snake_case	:						Tuple						=				DataLoader(
	    tokenized_datasets['''validation''']     ,   shuffle=SCREAMING_SNAKE_CASE__     ,   collate_fn=SCREAMING_SNAKE_CASE__     ,   batch_size=SCREAMING_SNAKE_CASE__     ,   drop_last=(accelerator.mixed_precision == '''fp8''')     ,   )

	return train_dataloader, eval_dataloader






def 		lowercase							(     _snake_case						:      Optional[Any]     ,   _snake_case						:      int     )     ->Union[str, Any]:
	"""simple docstring"""





	__snake_case	:						Optional[Any]						=				Accelerator(cpu=args.cpu     ,   mixed_precision=args.mixed_precision     )
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	__snake_case	:						Tuple						=				config['lr']
	__snake_case	:						List[str]						=				int(config['''num_epochs''']     )
	__snake_case	:						Any						=				int(config['''seed''']     )
	__snake_case	:						Optional[int]						=				int(config['''batch_size''']     )

	__snake_case	:						Any						=				evaluate.load('''glue'''     ,   '''mrpc'''     )

	# If the batch size is too big we use gradient accumulation
	__snake_case	:						str						=				1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		__snake_case	:						Optional[int]						=				batch_size // MAX_GPU_BATCH_SIZE
		__snake_case	:						Union[str, Any]						=				MAX_GPU_BATCH_SIZE

	set_seed(SCREAMING_SNAKE_CASE__     )
	__snake_case	:						List[Any]						=				get_dataloaders(SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__     )
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	__snake_case	:						List[str]						=				AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''     ,   return_dict=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).
	__snake_case	:						Tuple						=				model.to(accelerator.device     )
	# Instantiate optimizer
	__snake_case	:						Any						=				AdamW(params=model.parameters()     ,   lr=SCREAMING_SNAKE_CASE__     )

	# Instantiate scheduler
	__snake_case	:						int						=				get_linear_schedule_with_warmup(
	    optimizer=SCREAMING_SNAKE_CASE__     ,   num_warmup_steps=100     ,   num_training_steps=(len(SCREAMING_SNAKE_CASE__     ) * 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.

	__snake_case	:						Optional[Any]						=				accelerator.prepare(
	    SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__     )

	# Now we train the model
	for epoch in range(SCREAMING_SNAKE_CASE__     ):
		model.train()
		for step, batch in enumerate(SCREAMING_SNAKE_CASE__     ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device     )
			__snake_case	:						Dict						=				model(**SCREAMING_SNAKE_CASE__     )
			__snake_case	:						int						=				outputs.loss
			__snake_case	:						List[str]						=				loss / gradient_accumulation_steps
			accelerator.backward(SCREAMING_SNAKE_CASE__     )
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

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

		__snake_case	:						Dict						=				metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"""epoch {epoch}:"""     ,   SCREAMING_SNAKE_CASE__     )






def 		lowercase							(     )     ->Union[str, Any]:
	"""simple docstring"""





	__snake_case	:						Dict						=				argparse.ArgumentParser(description='''Simple example of training script.'''     )
	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.'''     )
	__snake_case	:						List[str]						=				parser.parse_args()
	__snake_case	:						Optional[Any]						=				{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
	training_function(SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
			main()









# using dfs for finding eulerian path traversal




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Any		,			SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      Optional[int]=None     ):
       '''simple docstring'''





       _lowerCamelCase       :		Optional[Any]		=(path or []) + [u]
       for v in graph[u]:
              if visited_edge[u][v] is False:
                     _lowerCamelCase	,     _lowerCamelCase       :		Dict		=True, True
                     _lowerCamelCase       :		Optional[Any]		=dfs(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       return path




def 	a_      (    SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      Optional[Any]     ):
       '''simple docstring'''





       _lowerCamelCase       :		Union[str, Any]		=0
       _lowerCamelCase       :		Union[str, Any]		=-1
       for i in range(SCREAMING_SNAKE_CASE__     ):
              if i not in graph.keys():
                     continue
              if len(graph[i]     ) % 2 == 1:
                     odd_degree_nodes += 1
                     _lowerCamelCase       :		Tuple		=i
       if odd_degree_nodes == 0:
              return 1, odd_node
       if odd_degree_nodes == 2:
              return 2, odd_node
       return 3, odd_node




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Union[str, Any]		,			SCREAMING_SNAKE_CASE__					:      Any     ):
       '''simple docstring'''





       _lowerCamelCase       :		Tuple		=[[False for _ in range(max_node + 1     )] for _ in range(max_node + 1     )]
       _lowerCamelCase	,     _lowerCamelCase       :		Optional[int]		=check_circuit_or_path(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       if check == 3:
              print('graph is not Eulerian'     )
              print('no path'     )
              return
       _lowerCamelCase       :		Any		=1
       if check == 2:
              _lowerCamelCase       :		Tuple		=odd_node
              print('graph has a Euler path'     )
       if check == 1:
              print('graph has a Euler cycle'     )
       _lowerCamelCase       :		int		=dfs(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       print(SCREAMING_SNAKE_CASE__     )




def 	a_      (    ):
       '''simple docstring'''





       _lowerCamelCase       :		Union[str, Any]		={1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
       _lowerCamelCase       :		str		={1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
       _lowerCamelCase       :		List[Any]		={1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
       _lowerCamelCase       :		Any		={1: [2, 3], 2: [1, 3], 3: [1, 2]}
       _lowerCamelCase       :		Dict		={
           1: [],
           2: []
           # all degree is zero
       }
       _lowerCamelCase       :		str		=10
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 main()


