


import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCamelCase						=		16
lowerCamelCase						=		32






def 					lowerCamelCase_   (	_a     ,       _a = 16     ,       _a = "bert-base-cased"	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[str]   					= AutoTokenizer.from_pretrained(_a	)
		lowerCAmelCase__       :      List[str]   					= load_dataset('''glue'''     ,       '''mrpc'''	)

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

		# Apply the method we just defined to all the examples in all the splits of the dataset
		lowerCAmelCase__       :      Optional[int]   					= datasets.map(
		    _a     ,       batched=_a     ,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']     ,       load_from_cache_file=_a	)

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

		def collate_fn(_a	):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				if accelerator.distributed_type == DistributedType.TPU:
						return tokenizer.pad(_a     ,       padding='''max_length'''     ,       max_length=128     ,       return_tensors='''pt'''	)
				return tokenizer.pad(_a     ,       padding='''longest'''     ,       return_tensors='''pt'''	)

		# Instantiate dataloaders.
		lowerCAmelCase__       :      Optional[Any]   					= DataLoader(
		    tokenized_datasets['''train''']     ,       shuffle=_a     ,       collate_fn=_a     ,       batch_size=_a	)
		lowerCAmelCase__       :      List[Any]   					= DataLoader(
		    tokenized_datasets['''validation''']     ,       shuffle=_a     ,       collate_fn=_a     ,       batch_size=_a	)

		return train_dataloader, eval_dataloader






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Any   					= Accelerator()

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

		set_seed(_a	)
		lowerCAmelCase__					,     lowerCAmelCase__       :      Any   					= get_dataloaders(_a     ,       _a     ,       _a	)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		lowerCAmelCase__       :      Dict   					= AutoModelForSequenceClassification.from_pretrained(_a     ,       return_dict=_a	)

		# Instantiate optimizer
		lowerCAmelCase__       :      Optional[int]   					= (
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		lowerCAmelCase__       :      List[Any]   					= optimizer_cls(params=model.parameters()     ,       lr=_a	)

		if accelerator.state.deepspeed_plugin is not None:
				lowerCAmelCase__       :      int   					= accelerator.state.deepspeed_plugin.deepspeed_config[
				    '''gradient_accumulation_steps'''
				]
		else:
				lowerCAmelCase__       :      List[str]   					= 1
		lowerCAmelCase__       :      Union[str, Any]   					= (len(_a	) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				lowerCAmelCase__       :      Union[str, Any]   					= get_linear_schedule_with_warmup(
				    optimizer=_a     ,       num_warmup_steps=0     ,       num_training_steps=_a     ,       )
		else:
				lowerCAmelCase__       :      List[Any]   					= DummyScheduler(_a     ,       total_num_steps=_a     ,       warmup_num_steps=0	)

		# 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__       :      Optional[int]   					= accelerator.prepare(
		    _a     ,       _a     ,       _a     ,       _a     ,       _a	)

		# We need to keep track of how many total steps we have iterated over
		lowerCAmelCase__       :      Any   					= 0
		# We also need to keep track of the stating epoch so files are named properly
		lowerCAmelCase__       :      str   					= 0

		# Now we train the model
		lowerCAmelCase__       :      Tuple   					= evaluate.load('''glue'''     ,       '''mrpc'''	)
		lowerCAmelCase__       :      Any   					= 0
		lowerCAmelCase__       :      List[str]   					= {}
		for epoch in range(_a     ,       _a	):
				model.train()
				for step, batch in enumerate(_a	):
						lowerCAmelCase__       :      Dict   					= model(**_a	)
						lowerCAmelCase__       :      Tuple   					= outputs.loss
						lowerCAmelCase__       :      Optional[int]   					= loss / gradient_accumulation_steps
						accelerator.backward(_a	)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						overall_step += 1

				model.eval()
				lowerCAmelCase__       :      str   					= 0
				for step, batch in enumerate(_a	):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device	)
						with torch.no_grad():
								lowerCAmelCase__       :      str   					= model(**_a	)
						lowerCAmelCase__       :      List[str]   					= outputs.logits.argmax(dim=-1	)
						# It is slightly faster to call this once, than multiple times
						lowerCAmelCase__					,     lowerCAmelCase__       :      List[Any]   					= accelerator.gather(
						    (predictions, batch['''labels'''])	)  # If we are in a multiprocess environment, the last batch has duplicates
						if accelerator.use_distributed:
								if step == len(_a	) - 1:
										lowerCAmelCase__       :      Optional[Any]   					= predictions[: len(eval_dataloader.dataset	) - samples_seen]
										lowerCAmelCase__       :      List[str]   					= references[: len(eval_dataloader.dataset	) - samples_seen]
								else:
										samples_seen += references.shape[0]
						metric.add_batch(
						    predictions=_a     ,       references=_a     ,       )

				lowerCAmelCase__       :      Tuple   					= metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'epoch {epoch}:'     ,       _a	)
				lowerCAmelCase__       :      Any   					= eval_metric['''accuracy''']

				if best_performance < eval_metric["accuracy"]:
						lowerCAmelCase__       :      Optional[Any]   					= eval_metric['''accuracy''']

		if args.performance_lower_bound is not None:
				assert (
				    args.performance_lower_bound <= best_performance
				), f'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'

		accelerator.wait_for_everyone()
		if accelerator.is_main_process:
				with open(os.path.join(args.output_dir     ,       '''all_results.json'''	)     ,       '''w'''	) as f:
						json.dump(_a     ,       _a	)






def 					lowerCamelCase_   (	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[Any]   					= argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''	)
		parser.add_argument(
		    '''--model_name_or_path'''     ,       type=_a     ,       default='''bert-base-cased'''     ,       help='''Path to pretrained model or model identifier from huggingface.co/models.'''     ,       required=_a     ,       )
		parser.add_argument(
		    '''--output_dir'''     ,       type=_a     ,       default='''.'''     ,       help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''     ,       )
		parser.add_argument(
		    '''--performance_lower_bound'''     ,       type=_a     ,       default=_a     ,       help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'''     ,       )
		parser.add_argument(
		    '''--num_epochs'''     ,       type=_a     ,       default=3     ,       help='''Number of train epochs.'''     ,       )
		lowerCAmelCase__       :      Any   					= parser.parse_args()
		lowerCAmelCase__       :      Dict   					= {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
		training_function(_a     ,       _a	)


if __name__ == "__main__":
		main()








from collections.abc import Iterable
from typing import Any





class  _a							:




	def __init__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		int | None = None		)->	Tuple:
			lowerCAmelCase__       :      Union[str, Any]   					= value
			lowerCAmelCase__       :      Node | None   					= None  # Added in order to delete a node easier
			lowerCAmelCase__       :      Node | None   					= None
			lowerCAmelCase__       :      Node | None   					= None




	def __repr__( self						:		List[Any]		)->	str:
			from pprint import pformat

			if self.left is None and self.right is None:
					return str(self.value		)
			return pformat({F'{self.value}': (self.left, self.right)}		, indent=1		)





class  _a							:




	def __init__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Node | None = None		)->	int:
			lowerCAmelCase__       :      Dict   					= root




	def __str__( self						:		Tuple		)->	str:
			return str(self.root		)




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Node		, _SCREAMING_SNAKE_CASE						:		Node | None		)->	None:
			if new_children is not None:  # reset its kids
					lowerCAmelCase__       :      Union[str, Any]   					= node.parent
			if node.parent is not None:  # reset its parent
					if self.is_right(_SCREAMING_SNAKE_CASE		):  # If it is the right children
							lowerCAmelCase__       :      List[str]   					= new_children
					else:
							lowerCAmelCase__       :      Any   					= new_children
			else:
					lowerCAmelCase__       :      Optional[Any]   					= new_children




	def 							UpperCAmelCase__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		Node		)->	bool:
			if node.parent and node.parent.right:
					return node == node.parent.right
			return False




	def 							UpperCAmelCase__( self						:		int		)->	bool:
			return self.root is None




	def 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		)->	None:
			lowerCAmelCase__       :      Any   					= Node(_SCREAMING_SNAKE_CASE		)  # create a new Node
			if self.empty():  # if Tree is empty
					lowerCAmelCase__       :      Optional[Any]   					= new_node  # set its root
			else:  # Tree is not empty
					lowerCAmelCase__       :      str   					= self.root  # from root
					if parent_node is None:
							return
					while True:  # While we don't get to a leaf
							if value < parent_node.value:  # We go left
									if parent_node.left is None:
											lowerCAmelCase__       :      Dict   					= new_node  # We insert the new node in a leaf
											break
									else:
											lowerCAmelCase__       :      List[Any]   					= parent_node.left
							else:
									if parent_node.right is None:
											lowerCAmelCase__       :      Tuple   					= new_node
											break
									else:
											lowerCAmelCase__       :      Union[str, Any]   					= parent_node.right
					lowerCAmelCase__       :      Dict   					= parent_node




	def 							UpperCAmelCase__( self						:		str		, *_SCREAMING_SNAKE_CASE						:		List[Any]		)->	None:
			for value in values:
					self.__insert(_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		)->	Node | None:
			if self.empty():
					raise IndexError('''Warning: Tree is empty! please use another.'''		)
			else:
					lowerCAmelCase__       :      Union[str, Any]   					= self.root
					# use lazy evaluation here to avoid NoneType Attribute error
					while node is not None and node.value is not value:
							lowerCAmelCase__       :      List[str]   					= node.left if value < node.value else node.right
					return node




	def 							UpperCAmelCase__( self						:		Dict		, _SCREAMING_SNAKE_CASE						:		Node | None = None		)->	Node | None:
			if node is None:
					if self.root is None:
							return None
					lowerCAmelCase__       :      List[Any]   					= self.root

			if not self.empty():
					while node.right is not None:
							lowerCAmelCase__       :      str   					= node.right
			return node




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Node | None = None		)->	Node | None:
			if node is None:
					lowerCAmelCase__       :      Any   					= self.root
			if self.root is None:
					return None
			if not self.empty():
					lowerCAmelCase__       :      Dict   					= self.root
					while node.left is not None:
							lowerCAmelCase__       :      Tuple   					= node.left
			return node




	def 							UpperCAmelCase__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		int		)->	None:
			lowerCAmelCase__       :      Optional[Any]   					= self.search(_SCREAMING_SNAKE_CASE		)  # Look for the node with that label
			if node is not None:
					if node.left is None and node.right is None:  # If it has no children
							self.__reassign_nodes(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)
					elif node.left is None:  # Has only right children
							self.__reassign_nodes(_SCREAMING_SNAKE_CASE		, node.right		)
					elif node.right is None:  # Has only left children
							self.__reassign_nodes(_SCREAMING_SNAKE_CASE		, node.left		)
					else:
							lowerCAmelCase__       :      Tuple   					= self.get_max(
							    node.left		)  # Gets the max value of the left branch
							self.remove(tmp_node.value		)  # type: ignore
							lowerCAmelCase__       :      Optional[Any]   					= (
							    tmp_node.value  # type: ignore
							)  # Assigns the value to the node to delete and keep tree structure




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Node | None		)->	Iterable:
			if node is not None:
					yield node  # Preorder Traversal
					yield from self.preorder_traverse(node.left		)
					yield from self.preorder_traverse(node.right		)




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Tuple=None		)->	Any:
			if traversal_function is None:
					return self.preorder_traverse(self.root		)
			else:
					return traversal_function(self.root		)




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		list		, _SCREAMING_SNAKE_CASE						:		Node | None		)->	None:
			if node:
					self.inorder(_SCREAMING_SNAKE_CASE		, node.left		)
					arr.append(node.value		)
					self.inorder(_SCREAMING_SNAKE_CASE		, node.right		)




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		int		, _SCREAMING_SNAKE_CASE						:		Node		)->	int:
			lowerCAmelCase__       :      list[int]   					= []
			self.inorder(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)  # append all values to list using inorder traversal
			return arr[k - 1]






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[str]   					= []
		if curr_node is not None:
				lowerCAmelCase__       :      Optional[int]   					= postorder(curr_node.left	) + postorder(curr_node.right	) + [curr_node]
		return node_list






def 					lowerCamelCase_   (	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[Any]   					= (8, 3, 6, 1, 10, 14, 13, 4, 7)
		lowerCAmelCase__       :      str   					= BinarySearchTree()
		for i in testlist:
				t.insert(_a	)

		# Prints all the elements of the list in order traversal
		print(_a	)

		if t.search(6	) is not None:
				print('''The value 6 exists'''	)
		else:
				print('''The value 6 doesn\'t exist'''	)

		if t.search(-1	) is not None:
				print('''The value -1 exists'''	)
		else:
				print('''The value -1 doesn\'t exist'''	)

		if not t.empty():
				print('''Max Value: '''     ,       t.get_max().value	)  # type: ignore
				print('''Min Value: '''     ,       t.get_min().value	)  # type: ignore

		for i in testlist:
				t.remove(_a	)
				print(_a	)


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)




