




'''simple docstring'''

from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


a  :				List[str]									=  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 						a				(   _lowerCamelCase     ):


											def __init__(		self    :					List[Any]       ,				lowercase_    :					int = 101					):
																snake_case_									=				length


											def __len__(		self    :					Optional[Any]					):
																return self.length


											def __getitem__(		self    :					Any       ,				lowercase_    :					Any					):
																return i







							class 						a				:


											def __call__(		self    :					Dict       ,				lowercase_    :					str					):
																return {"input_ids": torch.tensor(lowercase_					), "labels": torch.tensor(lowercase_					)}







							class 						a				(   nn.Module     ):


											def __init__(		self    :					Any					):
																super().__init__()
																# Add some (unused) params otherwise DDP will complain.
																snake_case_									=				nn.Linear(120       ,				80					)


											def   A_ (		self    :					Tuple       ,				lowercase_    :					List[str]       ,				lowercase_    :					List[Any]=None					):
																if labels is not None:
																					return torch.tensor(0.0       ,				device=input_ids.device					), input_ids
																else:
																					return input_ids






class 						a				(   _lowerCamelCase     ):


				@require_torch_neuroncore
				def   A_ (		self    :					List[Any]					):
									snake_case_									=				F"--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
									snake_case_									=				self.get_auto_remove_tmp_dir()
									snake_case_									=				F"--output_dir {output_dir}".split()
									snake_case_									=				['''torchrun'''] + distributed_args + args
									execute_subprocess_async(lowercase_       ,				env=self.get_env()					)
									# successful return here == success - any errors would have caused an error in the sub-call







class 						a				(   _lowerCamelCase     ):


				@require_torch_multi_gpu
				def   A_ (		self    :					List[str]					):
									snake_case_									=				F"--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
									snake_case_									=				self.get_auto_remove_tmp_dir()
									snake_case_									=				F"--output_dir {output_dir}".split()
									snake_case_									=				['''torchrun'''] + distributed_args + args
									execute_subprocess_async(lowercase_       ,				env=self.get_env()					)
									# successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
							# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
							#
							# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

							a  :				List[Any]									=  HfArgumentParser((TrainingArguments,))
							a  :				Any									=  parser.parse_args_into_dataclasses()[0]

							logger.warning(
							    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '''
							    f'''distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'''
							)

							# 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 [101, 40, 7]:
														a  :				List[Any]									=  DummyDataset(dataset_length)


														def 		__magic_name__    (	__UpperCAmelCase	)		->					Dict:

																			'''simple docstring'''

																			snake_case_									=				list(range(len(__UpperCAmelCase	)	)	)
																			snake_case_									=				p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
																			if not success and training_args.local_rank == 0:
																								logger.warning(
																								    '''Predictions and/or labels do not match expected results:\n  - predictions: '''
																								    F"{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}"	)
																			return {"success": success}

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

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

														a  :				Any									=  2

														a  :				int									=  trainer.evaluate()
														logger.info(metrics)
														if metrics["eval_success"] is not True:
																					logger.error(metrics)
																					exit(1)

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

														a  :				Tuple									=  None







'''simple docstring'''

from __future__ import annotations

import math


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					int:

					'''simple docstring'''

					if depth < 0:
										raise ValueError('''Depth cannot be less than 0'''	)

					if len(__UpperCAmelCase	) == 0:
										raise ValueError('''Scores cannot be empty'''	)

					if depth == height:
										return scores[node_index]

					if is_max:
										return max(
										    minimax(depth + 1,      node_index * 2,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	),      minimax(depth + 1,      node_index * 2 + 1,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	),      )

					return min(
					    minimax(depth + 1,      node_index * 2,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	),      minimax(depth + 1,      node_index * 2 + 1,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	),      )


def 		__magic_name__    (	)		->					None:

					'''simple docstring'''

					snake_case_									=				[90, 23, 6, 33, 21, 65, 123, 3_4423]
					snake_case_									=				math.log(len(__UpperCAmelCase	),      2	)
					print('''Optimal value : ''',      end=''''''	)
					print(minimax(0,      0,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()

