

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
# and perform gradient accumulation
#
# 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
#
########################################################################


A											=     16
A											=     32





def a(lowercase__			, lowercase__ = 16	):





	'''simple docstring'''
	snake_case_      		= AutoTokenizer.from_pretrained('bert-base-cased'	)
	snake_case_      		= load_dataset('glue'			, 'mrpc'	)

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

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

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

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

		A											=     mocked_dataloaders  # noqa: F811





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	# For testing only
	if os.environ.get('TESTING_MOCKED_DATALOADERS'			, lowercase__	) == "1":
		snake_case_      		= 2
	# New Code #
	snake_case_      		= int(args.gradient_accumulation_steps	)
	# Initialize accelerator
	snake_case_      		= Accelerator(
	    cpu=args.cpu			, mixed_precision=args.mixed_precision			, gradient_accumulation_steps=lowercase__	)
	if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
		raise NotImplementedError(
		    'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'	)
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	snake_case_      		= config['lr']
	snake_case_      		= int(config['num_epochs']	)
	snake_case_      		= int(config['seed']	)
	snake_case_      		= int(config['batch_size']	)

	snake_case_      		= evaluate.load('glue'			, 'mrpc'	)

	set_seed(lowercase__	)
	snake_case_		,       snake_case_      		= get_dataloaders(lowercase__			, lowercase__	)
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	snake_case_      		= 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).
	snake_case_      		= model.to(accelerator.device	)

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

	# Instantiate scheduler
	snake_case_      		= get_linear_schedule_with_warmup(
	    optimizer=lowercase__			, num_warmup_steps=100			, num_training_steps=(len(lowercase__	) * 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.
	snake_case_		,       snake_case_		,       snake_case_		,       snake_case_		,       snake_case_      		= accelerator.prepare(
	    lowercase__			, lowercase__			, lowercase__			, lowercase__			, lowercase__	)

	# Now we train the model
	for epoch in range(lowercase__	):
		model.train()
		for step, batch in enumerate(lowercase__	):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device	)
			# New code #
			# We use the new `accumulate` context manager to perform gradient accumulation
			# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
			with accelerator.accumulate(lowercase__	):
				snake_case_      		= model(**lowercase__	)
				snake_case_      		= output.loss
				accelerator.backward(lowercase__	)
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

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

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





def a():





	'''simple docstring'''
	snake_case_      		= 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.'			, )
	# New Code #
	parser.add_argument(
	    '--gradient_accumulation_steps'			, type=lowercase__			, default=1			, help='The number of minibatches to be ran before gradients are accumulated.'			, )
	parser.add_argument('--cpu'			, action='store_true'			, help='If passed, will train on the CPU.'	)
	snake_case_      		= parser.parse_args()
	snake_case_      		= {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
	training_function(lowercase__			, lowercase__	)


if __name__ == "__main__":
		main()



class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= name
				snake_case_      		= val


			def __str__(    self							):

				"""simple docstring"""


				return f"""{self.__class__.__name__}({self.name}, {self.val})"""







			def __lt__(    self ,				__UpperCamelCase							):

				"""simple docstring"""


				return self.val < other.val







class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= {}
				snake_case_      		= {}
				snake_case_      		= self.build_heap(__UpperCamelCase							)


			def __getitem__(    self ,				__UpperCamelCase							):

				"""simple docstring"""


				return self.get_value(__UpperCamelCase							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				return (idx - 1) // 2


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				return idx * 2 + 1


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				return idx * 2 + 2


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				return self.heap_dict[key]


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= len(__UpperCamelCase							) - 1
				snake_case_      		= self.get_parent_idx(__UpperCamelCase							)

				for idx, i in enumerate(__UpperCamelCase							):
					snake_case_      		= idx
					snake_case_      		= i.val

				for i in range(__UpperCamelCase ,				-1 ,				-1							):
					self.sift_down(__UpperCamelCase ,				__UpperCamelCase							)
				return array


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				while True:
					snake_case_      		= self.get_left_child_idx(__UpperCamelCase							)  # noqa: E741
					snake_case_      		= self.get_right_child_idx(__UpperCamelCase							)

					snake_case_      		= idx
					if l < len(__UpperCamelCase							) and array[l] < array[idx]:
						snake_case_      		= l
					if r < len(__UpperCamelCase							) and array[r] < array[smallest]:
						snake_case_      		= r

					if smallest != idx:
						snake_case_		,       snake_case_      		= array[smallest], array[idx]
						(
						    (
						    snake_case_
						)		,       (
						    snake_case_
						)		,       
						)      		= (
						    self.idx_of_element[array[smallest]],
						    self.idx_of_element[array[idx]],
						)
						snake_case_      		= smallest
					else:
						break


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.get_parent_idx(__UpperCamelCase							)
				while p >= 0 and self.heap[p] > self.heap[idx]:
					snake_case_		,       snake_case_      		= self.heap[idx], self.heap[p]
					snake_case_		,       snake_case_      		= (
					    self.idx_of_element[self.heap[idx]],
					    self.idx_of_element[self.heap[p]],
					)
					snake_case_      		= p
					snake_case_      		= self.get_parent_idx(__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return self.heap[0]


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.heap[-1], self.heap[0]
				snake_case_		,       snake_case_      		= (
				    self.idx_of_element[self.heap[-1]],
				    self.idx_of_element[self.heap[0]],
				)

				snake_case_      		= self.heap.pop()
				del self.idx_of_element[x]
				self.sift_down(0 ,				self.heap							)
				return x


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				self.heap.append(__UpperCamelCase							)
				snake_case_      		= len(self.heap							) - 1
				snake_case_      		= node.val
				self.sift_up(len(self.heap							) - 1							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return len(self.heap							) == 0







			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				assert (
				    self.heap[self.idx_of_element[node]].val > new_value
				), "newValue must be less that current value"
				snake_case_      		= new_value
				snake_case_      		= new_value
				self.sift_up(self.idx_of_element[node]							)







A											=     Node('R', -1)
A											=     Node('B', 6)
A											=     Node('A', 3)
A											=     Node('X', 1)
A											=     Node('E', 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
A											=     MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print('Min Heap - before decrease key')
for i in my_min_heap.heap:
		print(i)

print('Min Heap - After decrease key of node [B -> -17]')
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
		print(i)

if __name__ == "__main__":
		import doctest

		doctest.testmod()
