# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


_snake_case			=						logging.get_logger(__name__)


if is_torch_available():
	import torch
	from torch import nn
	from torch.utils.data import Dataset

	from transformers import Trainer




	class 					_snake_case						(						_lowercase				):
						def __init__(  self:				Optional[int]	,  __lowerCamelCase:				int = 1_01     )    ->				str:
													__UpperCAmelCase						:			Any         =   length
						def __len__(  self:				Tuple     )    ->				Union[str, Any]:
													return self.length





						def __getitem__(  self:				List[str]	,  __lowerCamelCase:				Dict     )    ->				int:
													return i




	class 					_snake_case						:
						def __call__(  self:				Any	,  __lowerCamelCase:				Union[str, Any]     )    ->				Optional[Any]:
													return {"input_ids": torch.tensor(__lowerCamelCase     ), "labels": torch.tensor(__lowerCamelCase     )}




	class 					_snake_case						(						nn.Module				):
						def __init__(  self:				Any     )    ->				Any:
													super().__init__()
													# Add some (unused) params otherwise DDP will complain.
													__UpperCAmelCase						:			List[str]         =   nn.Linear(1_20	,  80     )





						def    _lowerCamelCase (  self:				str	,  __lowerCamelCase:				Tuple	,  __lowerCamelCase:				Any=None     )    ->				Any:
													if labels is not None:
																				return torch.tensor(0.0	,  device=input_ids.device     ), input_ids
													else:
																				return input_ids




def   _UpperCamelCase   (			)						->   Union[str, Any]:
							__UpperCAmelCase						:			List[str]         =   HfArgumentParser((TrainingArguments,)							)
							sys.argv += ["--output_dir", "./examples"]
							__UpperCAmelCase						:			int         =   parser.parse_args_into_dataclasses()[0]

							logger.warning(
							    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, '''
							    f'''tpu_num_cores: {training_args.tpu_num_cores}''',    )

							# Essentially, what we want to verify in the distributed case is
							# that we get all samples back, in the right order.
							# (this is crucial for prediction for instance)
							for dataset_length in [1001, 256, 15]:
														__UpperCAmelCase						:			List[str]         =   DummyDataset(snake_case__							)

														def compute_metrics(snake_case__							) -> Dict:
																					__UpperCAmelCase						:			str         =   list(range(len(snake_case__							)							)							)
																					__UpperCAmelCase						:			str         =   p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
																					return {"success": success}

														__UpperCAmelCase						:			List[str]         =   Trainer(
														    model=DummyModel(),    args=snake_case__,    data_collator=DummyDataCollator(),    eval_dataset=snake_case__,    compute_metrics=snake_case__,    )
														__UpperCAmelCase						:			List[Any]         =   trainer.evaluate()
														logger.info(snake_case__							)
														if metrics["eval_success"] is not True:
																					logger.error(snake_case__							)
																					exit(1							)

														__UpperCAmelCase						:			str         =   trainer.predict(snake_case__							)
														logger.info(p.metrics							)
														if p.metrics["test_success"] is not True:
																					logger.error(p.metrics							)
																					exit(1							)

														__UpperCAmelCase						:			Union[str, Any]         =   2

														__UpperCAmelCase						:			List[Any]         =   trainer.evaluate()
														logger.info(snake_case__							)
														if metrics["eval_success"] is not True:
																					logger.error(snake_case__							)
																					exit(1							)

														__UpperCAmelCase						:			Any         =   trainer.predict(snake_case__							)
														logger.info(p.metrics							)
														if p.metrics["test_success"] is not True:
																					logger.error(p.metrics							)
																					exit(1							)

														__UpperCAmelCase						:			str         =   None

							logger.info("🔥 All distributed tests successful"							)




def   _UpperCamelCase   (			snake_case__							)						->   Union[str, Any]:
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
	main()





from __future__ import annotations




class 					_snake_case						:
					def __init__(  self:				List[Any]	,  __lowerCamelCase:				int = 0     )    ->				None:
												__UpperCAmelCase						:			Dict         =   value
												__UpperCAmelCase						:			Node | None         =   None
												__UpperCAmelCase						:			Node | None         =   None




def   _UpperCamelCase   (			snake_case__,    snake_case__							)						->   Node | None:
							if treea is None:
														return treea
							if treea is None:
														return treea

							__UpperCAmelCase						:			Tuple         =   treea.value + treea.value
							__UpperCAmelCase						:			Optional[Any]         =   merge_two_binary_trees(treea.left,    treea.left							)
							__UpperCAmelCase						:			List[str]         =   merge_two_binary_trees(treea.right,    treea.right							)
							return treea




def   _UpperCamelCase   (			snake_case__							)						->   None:
							if root:
														print(root.value							)
														print_preorder(root.left							)
														print_preorder(root.right							)


if __name__ == "__main__":
	_snake_case			=						Node(1)
	_snake_case			=						Node(2)
	_snake_case			=						Node(3)
	_snake_case			=						Node(4)

	_snake_case			=						Node(2)
	_snake_case			=						Node(4)
	_snake_case			=						Node(6)
	_snake_case			=						Node(9)
	_snake_case			=						Node(5)

	print('''Tree1 is: ''')
	print_preorder(treea)
	print('''Tree2 is: ''')
	print_preorder(treea)
	_snake_case			=						merge_two_binary_trees(treea, treea)
	print('''Merged Tree is: ''')
	print_preorder(merged_tree)




