


'''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


__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    lowercase	(							A__					):

				"""simple docstring"""

				def __init__(		self    ,   UpperCamelCase_ = 101							):

									'''simple docstring'''


									UpperCamelCase__   :int												= length




				def __len__(		self							):

									'''simple docstring'''


									return self.length




				def __getitem__(		self    ,   UpperCamelCase_							):

									'''simple docstring'''


									return i



		class    lowercase	:

				"""simple docstring"""

				def __call__(		self    ,   UpperCamelCase_							):

									'''simple docstring'''


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



		class    lowercase	(							nn.Module					):

				"""simple docstring"""

				def __init__(		self							):

									'''simple docstring'''


									super().__init__()
									# Add some (unused) params otherwise DDP will complain.
									UpperCamelCase__   :List[Any]												= nn.Linear(120    ,   80							)




				def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_=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	(							A__					):

		"""simple docstring"""

		@require_torch_neuroncore
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[Any]												= F'''--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
							UpperCamelCase__   :Optional[Any]												= self.get_auto_remove_tmp_dir()
							UpperCamelCase__   :Tuple												= F'''--output_dir {output_dir}'''.split()
							UpperCamelCase__   :Tuple												= ['''torchrun'''] + distributed_args + args
							execute_subprocess_async(UpperCamelCase_    ,   env=self.get_env()							)
							# successful return here == success - any errors would have caused an error in the sub-call



class    lowercase	(							A__					):

		"""simple docstring"""

		@require_torch_multi_gpu
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[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()
							UpperCamelCase__   :int												= self.get_auto_remove_tmp_dir()
							UpperCamelCase__   :List[Any]												= F'''--output_dir {output_dir}'''.split()
							UpperCamelCase__   :str												= ['''torchrun'''] + distributed_args + args
							execute_subprocess_async(UpperCamelCase_    ,   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

		__snake_case							      =     HfArgumentParser((TrainingArguments,))
		__snake_case							      =     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]:
				__snake_case							      =     DummyDataset(dataset_length)


				def 			a      (			__a   )  ->	Dict:



									'''simple docstring'''






									UpperCamelCase__   :List[Any]												= list(range(len(__a   )   )   )
									UpperCamelCase__   :Optional[int]												= 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}

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

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

				__snake_case							      =     2

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

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







				__snake_case							      =     None



'''simple docstring'''
def 			a      (			__a   )  ->	"list[int]":



					'''simple docstring'''






					if upper_limit < 0:
										raise ValueError('''Limit for the Catalan sequence must be ≥ 0'''   )

					UpperCamelCase__   :Optional[Any]												= [0] * (upper_limit + 1)

					# Base case: C(0) = C(1) = 1
					UpperCamelCase__   :int												= 1
					if upper_limit > 0:
										UpperCamelCase__   :int												= 1

					# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
					for i in range(2 ,					upper_limit + 1   ):
										for j in range(__a   ):
															catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

					return catalan_list


if __name__ == "__main__":
		print('''\n********* Catalan Numbers Using Dynamic Programming ************\n''')
		print('''\n*** Enter -1 at any time to quit ***''')
		print('''\nEnter the upper limit (≥ 0) for the Catalan number sequence: ''', end='''''')
		try:
				while True:
						__snake_case							      =     int(input().strip())
						if N < 0:
								print('''\n********* Goodbye!! ************''')
								break
						else:
								print(F"""The Catalan numbers from 0 through {N} are:""")
								print(catalan_numbers(N))
								print('''Try another upper limit for the sequence: ''', end='''''')
		except (NameError, ValueError):
				print('''\n********* Invalid input, goodbye! ************\n''')

		import doctest

		doctest.testmod()