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


lowerCAmelCase			:							Tuple     =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	( __a		):





										'''simple docstring'''






										def __init__(					self     :				Dict    ,  _UpperCamelCase     :				int = 101)				->List[str]:


														"""simple docstring"""


														_lowerCamelCase     :		Optional[Any] 						=						length



										def __len__(					self     :				Dict)				->Tuple:


														"""simple docstring"""


														return self.length




										def __getitem__(					self     :				Optional[Any]    ,  _UpperCamelCase     :				Union[str, Any])				->Union[str, Any]:


														"""simple docstring"""


														return i





				class 	__snake_case	:





										'''simple docstring'''






										def __call__(					self     :				Tuple    ,  _UpperCamelCase     :				Optional[int])				->Dict:


														"""simple docstring"""


														return {"input_ids": torch.tensor(a_), "labels": torch.tensor(a_)}





				class 	__snake_case	( nn.Module		):





										'''simple docstring'''






										def __init__(					self     :				Union[str, Any])				->Optional[int]:


														"""simple docstring"""


														super().__init__()
														# Add some (unused) params otherwise DDP will complain.
														_lowerCamelCase     :		Optional[int] 						=						nn.Linear(120    ,  80)




										def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				Optional[int]=None)				->str:


														"""simple docstring"""


														if labels is not None:
																		return torch.tensor(0.0    ,  device=input_ids.device), input_ids
														else:
																		return input_ids




class 	__snake_case	( __a		):





						'''simple docstring'''






						@require_torch_neuroncore
						def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->List[str]:


										"""simple docstring"""


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





class 	__snake_case	( __a		):





						'''simple docstring'''






						@require_torch_multi_gpu
						def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->int:


										"""simple docstring"""


										_lowerCamelCase     :		Optional[int] 						=						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()
										_lowerCamelCase     :		Tuple 						=						self.get_auto_remove_tmp_dir()
										_lowerCamelCase     :		Tuple 						=						F"""--output_dir {output_dir}""".split()
										_lowerCamelCase     :		Optional[int] 						=						["""torchrun"""] + distributed_args + args
										execute_subprocess_async(a_    ,  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

				lowerCAmelCase			:							List[Any]     =HfArgumentParser((TrainingArguments,))
				lowerCAmelCase			:							int     =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]:
								lowerCAmelCase			:							Tuple     =DummyDataset(dataset_length)







								def 						A__  (					__A	):







												'''simple docstring'''
												_lowerCamelCase     :		Tuple 						=						list(range(len(snake_case__	)	)	)
												_lowerCamelCase     :		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}

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

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

								lowerCAmelCase			:							List[Any]     =2

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

								lowerCAmelCase			:							str     =trainer.predict(dataset)
								logger.info(p.metrics)
								if p.metrics["test_success"] is not True:
												logger.error(p.metrics)
												exit(1)
								lowerCAmelCase			:							List[Any]     =None





from __future__ import annotations

lowerCAmelCase			:							int     =[]







def 						A__  (					__A   , __A   , __A	):







    '''simple docstring'''
    for i in range(len(__A	)	):
        if board[row][i] == 1:
            return False
    for i in range(len(__A	)	):
        if board[i][column] == 1:
            return False
    for i, j in zip(range(__A   , -1   , -1	)   , range(__A   , -1   , -1	)	):
        if board[i][j] == 1:
            return False
    for i, j in zip(range(__A   , -1   , -1	)   , range(__A   , len(__A	)	)	):
        if board[i][j] == 1:
            return False
    return True







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    if row >= len(__A	):
        solution.append(__A	)
        printboard(__A	)
        print()
        return True
    for i in range(len(__A	)	):
        if is_safe(__A   , __A   , __A	):
            _lowerCamelCase     :		int 						=						1
            solve(__A   , row + 1	)
            _lowerCamelCase     :		List[str] 						=						0
    return False







def 						A__  (					__A	):







    '''simple docstring'''
    for i in range(len(__A	)	):
        for j in range(len(__A	)	):
            if board[i][j] == 1:
                print("""Q"""   , end=""" """	)
            else:
                print("""."""   , end=""" """	)
        print()


# n=int(input("The no. of queens"))
lowerCAmelCase			:							int     =8
lowerCAmelCase			:							Union[str, Any]     =[[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print("The total no. of solutions are :", len(solution))




