






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


SCREAMING_SNAKE_CASE__      :			Optional[int]							=		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 						lowerCamelCase_				(      lowerCamelCase ):







									def __init__(						self       ,					__lowerCAmelCase = 1_0_1     ):
													"""simple docstring"""
													__magic_name__		:Tuple        =      length







									def __len__(						self     ):
													"""simple docstring"""
													return self.length







									def __getitem__(						self       ,					__lowerCAmelCase     ):
													"""simple docstring"""
													return i







					class 						lowerCamelCase_				:







									def __call__(						self       ,					__lowerCAmelCase     ):
													"""simple docstring"""
													return {"input_ids": torch.tensor(__lowerCAmelCase     ), "labels": torch.tensor(__lowerCAmelCase     )}







					class 						lowerCamelCase_				(      nn.Module ):







									def __init__(						self     ):
													"""simple docstring"""
													super().__init__()
													# Add some (unused) params otherwise DDP will complain.
													__magic_name__		:Optional[int]        =      nn.Linear(1_2_0       ,					8_0     )







									def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase=None     ):
													"""simple docstring"""
													if labels is not None:
																	return torch.tensor(0.0       ,					device=input_ids.device     ), input_ids
													else:
																	return input_ids
class 						lowerCamelCase_				(      lowerCamelCase ):







				@require_torch_neuroncore
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      F'''--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
								__magic_name__		:Union[str, Any]        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:Dict        =      F'''--output_dir {output_dir}'''.split()
								__magic_name__		:Any        =      ['''torchrun'''] + distributed_args + args
								execute_subprocess_async(__lowerCAmelCase       ,					env=self.get_env()     )
								# successful return here == success - any errors would have caused an error in the sub-call







class 						lowerCamelCase_				(      lowerCamelCase ):







				@require_torch_multi_gpu
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      F'''--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
								__magic_name__		:Optional[int]        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:str        =      F'''--output_dir {output_dir}'''.split()
								__magic_name__		:Tuple        =      ['''torchrun'''] + distributed_args + args
								execute_subprocess_async(__lowerCAmelCase       ,					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

					SCREAMING_SNAKE_CASE__      :			List[Any]							=		HfArgumentParser((TrainingArguments,))
					SCREAMING_SNAKE_CASE__      :			List[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 [1_01, 40, 7]:
										SCREAMING_SNAKE_CASE__      :			Any							=		DummyDataset(dataset_length)

										def      __lowercase					( snake_case ):







														"""simple docstring"""






														__magic_name__		:int        =      list(range(len(snake_case ) ) )
														__magic_name__		:Union[str, Any]        =      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}

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

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

										SCREAMING_SNAKE_CASE__      :			List[Any]							=		2

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

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

										SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		None














def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				global f  # a global dp table for knapsack
				if f[i][j] < 0:
								if j < wt[i - 1]:
												__magic_name__		:Dict        =      mf_knapsack(i - 1,  snake_case,  snake_case,  snake_case )
								else:
												__magic_name__		:Optional[Any]        =      max(
												    mf_knapsack(i - 1,  snake_case,  snake_case,  snake_case ),  mf_knapsack(i - 1,  snake_case,  snake_case,  j - wt[i - 1] ) + val[i - 1],  )
								__magic_name__		:List[Any]        =      val
				return f[i][j]

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:str        =      [[0] * (w + 1) for _ in range(n + 1 )]

				for i in range(1,  n + 1 ):
								for w_ in range(1,  w + 1 ):
												if wt[i - 1] <= w_:
																__magic_name__		:List[str]        =      max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]],  dp[i - 1][w_] )
												else:
																__magic_name__		:Optional[int]        =      dp[i - 1][w_]

				return dp[n][w_], dp

def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				if not (isinstance(snake_case,  (list, tuple) ) and isinstance(snake_case,  (list, tuple) )):
								raise ValueError(
								    '''Both the weights and values vectors must be either lists or tuples''' )

				__magic_name__		:Dict        =      len(snake_case )
				if num_items != len(snake_case ):
								__magic_name__		:str        =      (
								    '''The number of weights must be the same as the number of values.\n'''
								    f'''But got {num_items} weights and {len(snake_case )} values'''
								)
								raise ValueError(snake_case )
				for i in range(snake_case ):
								if not isinstance(wt[i],  snake_case ):
												__magic_name__		:str        =      (
												    '''All weights must be integers but got weight of '''
												    f'''type {type(wt[i] )} at index {i}'''
												)
												raise TypeError(snake_case )

				__magic_name__     ,				__magic_name__		:Tuple        =      knapsack(snake_case,  snake_case,  snake_case,  snake_case )
				__magic_name__		:set        =      set()
				_construct_solution(snake_case,  snake_case,  snake_case,  snake_case,  snake_case )

				return optimal_val, example_optional_set

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				if i > 0 and j > 0:
								if dp[i - 1][j] == dp[i][j]:
												_construct_solution(snake_case,  snake_case,  i - 1,  snake_case,  snake_case )
								else:
												optimal_set.add(snake_case )
												_construct_solution(snake_case,  snake_case,  i - 1,  j - wt[i - 1],  snake_case )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		[3, 2, 4, 4]
					SCREAMING_SNAKE_CASE__      :			Any							=		[4, 3, 2, 3]
					SCREAMING_SNAKE_CASE__      :			List[str]							=		4
					SCREAMING_SNAKE_CASE__      :			Optional[int]							=		6
					SCREAMING_SNAKE_CASE__      :			str							=		[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      :			List[str]							=		knapsack(w, wt, val, n)
					print(optimal_solution)
					print(mf_knapsack(n, wt, val, w))  # switched the n and w

					# testing the dynamic programming problem with example
					# the optimal subset for the above example are items 3 and 4
					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      :			List[str]							=		knapsack_with_example_solution(w, wt, val)
					assert optimal_solution == 8
					assert optimal_subset == {3, 4}
					print("""optimal_value = """, optimal_solution)
					print("""An optimal subset corresponding to the optimal value""", optimal_subset)






