




"""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


_lowerCamelCase  :			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        lowercase      (    __UpperCAmelCase):







						def __init__(							self   :					List[Any]      ,     _lowerCamelCase   :					int = 1_01       ):



							"""simple docstring"""

							A_  :				Any	   =		length







						def __len__(							self   :					str       ):



							"""simple docstring"""

							return self.length







						def __getitem__(							self   :					Any      ,     _lowerCamelCase   :					List[Any]       ):



							"""simple docstring"""

							return i

		class        lowercase      :







						def __call__(							self   :					int      ,     _lowerCamelCase   :					Union[str, Any]       ):



							"""simple docstring"""

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

		class        lowercase      (    nn.Module):







						def __init__(							self   :					Tuple       ):



							"""simple docstring"""

							super().__init__()
							# Add some (unused) params otherwise DDP will complain.
							A_  :				Tuple	   =		nn.Linear(1_20      ,     80       )







						def 					a_  (							self   :					Optional[Any]      ,     _lowerCamelCase   :					Optional[Any]      ,     _lowerCamelCase   :					Any=None       ):



							"""simple docstring"""

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

class        lowercase      (    __UpperCAmelCase):







				@require_torch_neuroncore
				def 					a_  (							self   :					Union[str, Any]       ):



					"""simple docstring"""

					A_  :				int	   =		F"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
					A_  :				Optional[int]	   =		self.get_auto_remove_tmp_dir()
					A_  :				List[str]	   =		F"""--output_dir {output_dir}""".split()
					A_  :				Dict	   =		['''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        lowercase      (    __UpperCAmelCase):







				@require_torch_multi_gpu
				def 					a_  (							self   :					Optional[Any]       ):



					"""simple docstring"""

					A_  :				Optional[int]	   =		F"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
					A_  :				Union[str, Any]	   =		self.get_auto_remove_tmp_dir()
					A_  :				List[str]	   =		F"""--output_dir {output_dir}""".split()
					A_  :				List[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


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  :			Optional[int]        =       HfArgumentParser((TrainingArguments,))
		_lowerCamelCase  :			List[str]        =       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 		lowercase_      (		_UpperCAmelCase	):




					"""simple docstring"""






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

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

				_lowerCamelCase  :			Any        =       2

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

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











"""simple docstring"""

# Function to print upper half of diamond (pyramid)

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	for i in range(0  ,      _UpperCAmelCase	):
		for _ in range(0  ,      n - i - 1	):  # printing spaces
			print(''' '''  ,      end=''''''	)
		for _ in range(0  ,      i + 1	):  # printing stars
			print('''* '''  ,      end=''''''	)
		print()

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	for i in range(_UpperCAmelCase  ,      0  ,      -1	):
		for _ in range(_UpperCAmelCase  ,      0  ,      -1	):  # printing stars
			print('''* '''  ,      end=''''''	)
		print()
		for _ in range(n - i + 1  ,      0  ,      -1	):  # printing spaces
			print(''' '''  ,      end=''''''	)

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	if n <= 0:
		print('''       ...       ....        nothing printing :('''	)
		return
	floyd(_UpperCAmelCase	)  # upper half
	reverse_floyd(_UpperCAmelCase	)  # lower half


if __name__ == "__main__":
		print(r'| /\ | |- |  |-  |--| |\  /| |-')
		print(r'|/  \| |- |_ |_  |__| | \/ | |_')
		_lowerCamelCase  :			Tuple        =       1
		while K:
				_lowerCamelCase  :			Optional[int]        =       int(input('enter the number and , and see the magic : '))
				print()
				pretty_print(user_number)
				_lowerCamelCase  :			Optional[Any]        =       int(input('press 0 to exit... and 1 to continue...'))

		print('Good Bye...')





