


"""simple docstring"""


from __future__ import annotations

from random import random
class     _UpperCamelCase    :
							'''simple docstring'''







							def __init__(	self       ,  __a = None      ):
										__lowerCAmelCase							       =     value
										__lowerCAmelCase							       =     random()
										__lowerCAmelCase							       =     None
										__lowerCAmelCase							       =     None

							def __repr__(	self      ):
										from pprint import pformat

										if self.left is None and self.right is None:
													return f"'{self.value}: {self.prior:.5}'"
										else:
													return pformat(
													    {f"{self.value}: {self.prior:.5}": (self.left, self.right)}       ,  indent=1      )



							def __str__(	self      ):
										__lowerCAmelCase							       =     str(self.value      ) + ''' '''
										__lowerCAmelCase							       =     str(self.left or ""      )
										__lowerCAmelCase							       =     str(self.right or ""      )
										return value + left + right


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			if root is None:  # None tree is split into 2 Nones
						return None, None
			elif root.value is None:
						return None, None
			else:
						if value < root.value:
									__lowerCAmelCase							       =     split(root.left       ,					snake_case__ )
									return left, root
						else:
									__lowerCAmelCase							       =     split(root.right       ,					snake_case__ )
									return root, right


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			if (not left) or (not right):  # If one node is None, return the other
						return left or right
			elif left.prior < right.prior:
						__lowerCAmelCase							       =     merge(left.right       ,					snake_case__ )
						return left
			else:
						__lowerCAmelCase							       =     merge(snake_case__       ,					right.left )
						return right


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     Node(snake_case__ )
			__lowerCAmelCase							       =     split(snake_case__       ,					snake_case__ )
			return merge(merge(snake_case__       ,					snake_case__ )       ,					snake_case__ )


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     split(snake_case__       ,					value - 1 )
			__lowerCAmelCase							       =     split(snake_case__       ,					snake_case__ )
			return merge(snake_case__       ,					snake_case__ )


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			if not root:  # None
						return
			else:
						inorder(root.left )
						print(root.value       ,					end="," )
						inorder(root.right )


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			for arg in args.split():
						if arg[0] == "+":
									__lowerCAmelCase							       =     insert(snake_case__       ,					int(arg[1:] ) )

						elif arg[0] == "-":
									__lowerCAmelCase							       =     erase(snake_case__       ,					int(arg[1:] ) )

						else:
									print("Unknown command" )

			return root


def _lowerCamelCase      (						):

			'''simple docstring'''




			__lowerCAmelCase							       =     None
			print(
			    "enter numbers to create a tree, + value to add value into treap, "
			    "- value to erase all nodes with value. \'q\' to quit. " )

			__lowerCAmelCase							       =     input()
			while args != "q":
						__lowerCAmelCase							       =     interact_treap(snake_case__       ,					snake_case__ )
						print(snake_case__ )
						__lowerCAmelCase							       =     input()

			print("good by!" )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()








"""simple docstring"""






import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


UpperCamelCase__				:Dict			   =	"""true"""





def 				A_       ( snake_case__       ,					snake_case__=82       ,					snake_case__=16		)					->  List[Any]:
			set_seed(42		)
			_UpperCamelCase					:Dict					     =	RegressionModel()
			_UpperCamelCase					:Dict					     =	deepcopy(snake_case__		)
			_UpperCamelCase					:List[Any]					     =	RegressionDataset(length=snake_case__		)
			_UpperCamelCase					:Optional[int]					     =	DataLoader(snake_case__       ,					batch_size=snake_case__		)
			model.to(accelerator.device		)
			_UpperCamelCase     ,      _UpperCamelCase					:List[str]					     =	accelerator.prepare(snake_case__       ,					snake_case__		)
			return model, ddp_model, dataloader





def 				A_       ( snake_case__       ,					snake_case__=False		)					->  Optional[int]:
			_UpperCamelCase					:Optional[Any]					     =	AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''		)
			_UpperCamelCase					:int					     =	load_dataset('''glue'''       ,					'''mrpc'''       ,					split='''validation'''		)

			def tokenize_function(snake_case__		):
						_UpperCamelCase					:Optional[int]					     =	tokenizer(examples['''sentence1''']       ,					examples['''sentence2''']       ,					truncation=snake_case__       ,					max_length=snake_case__		)
						return outputs

			with accelerator.main_process_first():
						_UpperCamelCase					:Optional[int]					     =	dataset.map(
						    snake_case__       ,					batched=snake_case__       ,					remove_columns=['''idx''', '''sentence1''', '''sentence2''']       ,					)

			_UpperCamelCase					:Tuple					     =	tokenized_datasets.rename_column('''label'''       ,					'''labels'''		)

			def collate_fn(snake_case__		):
						if use_longest:
									return tokenizer.pad(snake_case__       ,					padding='''longest'''       ,					return_tensors='''pt'''		)
						return tokenizer.pad(snake_case__       ,					padding='''max_length'''       ,					max_length=1_28       ,					return_tensors='''pt'''		)

			return DataLoader(snake_case__       ,					shuffle=snake_case__       ,					collate_fn=snake_case__       ,					batch_size=16		)





def 				A_       ( snake_case__       ,					snake_case__		)					->  Tuple:
			_UpperCamelCase					:Union[str, Any]					     =	Accelerator(dispatch_batches=snake_case__       ,					split_batches=snake_case__		)
			_UpperCamelCase					:Any					     =	get_dataloader(snake_case__       ,					not dispatch_batches		)
			_UpperCamelCase					:Optional[int]					     =	AutoModelForSequenceClassification.from_pretrained(
			    '''hf-internal-testing/mrpc-bert-base-cased'''       ,					return_dict=snake_case__		)
			_UpperCamelCase     ,      _UpperCamelCase					:int					     =	accelerator.prepare(snake_case__       ,					snake_case__		)
			return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  List[Any]:
			_UpperCamelCase					:Any					     =	[]
			for batch in dataloader:
						_UpperCamelCase     ,      _UpperCamelCase					:List[Any]					     =	batch.values()
						with torch.no_grad():
									_UpperCamelCase					:List[Any]					     =	model(snake_case__		)
									_UpperCamelCase     ,      _UpperCamelCase					:Optional[int]					     =	accelerator.gather_for_metrics((logit, target)		)
									logits_and_targets.append((logit, target)		)
			_UpperCamelCase     ,      _UpperCamelCase					:Optional[Any]					     =	[], []
			for logit, targ in logits_and_targets:
						logits.append(snake_case__		)
						targs.append(snake_case__		)
			_UpperCamelCase     ,      _UpperCamelCase					:List[str]					     =	torch.cat(snake_case__		), torch.cat(snake_case__		)
			return logits, targs





def 				A_       ( snake_case__       ,					snake_case__=82       ,					snake_case__=False       ,					snake_case__=False       ,					snake_case__=16		)					->  Dict:
			_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase					:List[str]					     =	get_basic_setup(snake_case__       ,					snake_case__       ,					snake_case__		)
			_UpperCamelCase     ,      _UpperCamelCase					:Tuple					     =	generate_predictions(snake_case__       ,					snake_case__       ,					snake_case__		)
			assert (
			    len(snake_case__		) == num_samples
			), f"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(snake_case__		)}"





def 				A_       ( snake_case__ = False       ,					snake_case__ = False		)					->  Optional[Any]:
			_UpperCamelCase					:Any					     =	evaluate.load('''glue'''       ,					'''mrpc'''		)
			_UpperCamelCase     ,      _UpperCamelCase					:Optional[int]					     =	get_mrpc_setup(snake_case__       ,					snake_case__		)
			# First do baseline
			_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase					:Tuple					     =	setup['''no''']
			model.to(snake_case__		)
			model.eval()
			for batch in dataloader:
						batch.to(snake_case__		)
						with torch.inference_mode():
									_UpperCamelCase					:str					     =	model(**snake_case__		)
						_UpperCamelCase					:Dict					     =	outputs.logits.argmax(dim=-1		)
						metric.add_batch(predictions=snake_case__       ,					references=batch['''labels''']		)
			_UpperCamelCase					:int					     =	metric.compute()

			# Then do distributed
			_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase					:Optional[int]					     =	setup['''ddp''']
			model.eval()
			for batch in dataloader:
						with torch.inference_mode():
									_UpperCamelCase					:Dict					     =	model(**snake_case__		)
						_UpperCamelCase					:str					     =	outputs.logits.argmax(dim=-1		)
						_UpperCamelCase					:Union[str, Any]					     =	batch['''labels''']
						_UpperCamelCase     ,      _UpperCamelCase					:Tuple					     =	accelerator.gather_for_metrics((preds, references)		)
						metric.add_batch(predictions=snake_case__       ,					references=snake_case__		)
			_UpperCamelCase					:Optional[Any]					     =	metric.compute()

			for key in "accuracy f1".split():
						assert math.isclose(
						    baseline[key]       ,					distributed[key]		), f"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"





def 				A_       ( )					->  Optional[Any]:
			_UpperCamelCase					:int					     =	Accelerator(split_batches=snake_case__       ,					dispatch_batches=snake_case__		)
			if accelerator.is_local_main_process:
						datasets.utils.logging.set_verbosity_warning()
						transformers.utils.logging.set_verbosity_warning()
			else:
						datasets.utils.logging.set_verbosity_error()
						transformers.utils.logging.set_verbosity_error()
			# These are a bit slower so they should only be ran on the GPU or TPU
			if torch.cuda.is_available() or is_tpu_available():
						if accelerator.is_local_main_process:
									print('''**Testing gather_for_metrics**'''		)
						for split_batches in [True, False]:
									for dispatch_batches in [True, False]:
												if accelerator.is_local_main_process:
															print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"		)
												test_mrpc(snake_case__       ,					snake_case__		)
												accelerator.state._reset_state()
			if accelerator.is_local_main_process:
						print('''**Test torch metrics**'''		)
			for split_batches in [True, False]:
						for dispatch_batches in [True, False]:
									_UpperCamelCase					:Tuple					     =	Accelerator(split_batches=snake_case__       ,					dispatch_batches=snake_case__		)
									if accelerator.is_local_main_process:
												print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"		)
									test_torch_metrics(snake_case__       ,					99		)
									accelerator.state._reset_state()
			if accelerator.is_local_main_process:
						print('''**Test last batch is not dropped when perfectly divisible**'''		)
			_UpperCamelCase					:Any					     =	Accelerator()
			test_torch_metrics(snake_case__       ,					5_12		)
			accelerator.state._reset_state()





def 				A_       ( snake_case__		)					->  Dict:
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
					main()

