
from transformers import PretrainedConfig





class      UpperCamelCase_			(			__a		):







						'''simple docstring'''



						UpperCamelCase			:     List[str]							      =				'''custom'''





						def __init__( self       :Tuple		,						lowerCAmelCase__       :int=1		,						**lowerCAmelCase__       :List[Any]    )     ->int:
										lowercase						    =			attribute
										super().__init__(**lowerCAmelCase__    )





class      UpperCamelCase_			(			__a		):







						'''simple docstring'''



						UpperCamelCase			:     Union[str, Any]							      =				'''custom'''





						def __init__( self       :Union[str, Any]		,						lowerCAmelCase__       :Dict=1		,						**lowerCAmelCase__       :Dict    )     ->List[str]:
										lowercase						    =			attribute


# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


_snake_case			:       Optional[Any]      =						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      UpperCamelCase_			(			__a		):







												'''simple docstring'''



												def __init__( self       :Optional[int]		,						lowerCAmelCase__       :int = 101    )     ->Any:
																lowercase						    =			length





												def __len__( self       :Tuple    )     ->Any:
																return self.length





												def __getitem__( self       :Any		,						lowerCAmelCase__       :Tuple    )     ->int:
																return i





						class      UpperCamelCase_			:







												'''simple docstring'''



												def __call__( self       :Optional[Any]		,						lowerCAmelCase__       :List[Any]    )     ->int:
																return {"input_ids": torch.tensor(lowerCAmelCase__    ), "labels": torch.tensor(lowerCAmelCase__    )}





						class      UpperCamelCase_			(			nn.Module		):







												'''simple docstring'''



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





												def 					SCREAMING_SNAKE_CASE( self       :int		,						lowerCAmelCase__       :Optional[int]		,						lowerCAmelCase__       :Union[str, Any]=None    )     ->List[Any]:
																if labels is not None:
																				return torch.tensor(0.0		,						device=input_ids.device    ), input_ids
																else:
																				return input_ids

def    __snake_case       (  ):







				'''simple docstring'''


				lowercase						    =			HfArgumentParser((TrainingArguments,)	)
				sys.argv += ["--output_dir", "./examples"]
				lowercase						    =			parser.parse_args_into_dataclasses()[0]

				logger.warning(
				    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, '''
				    F'''tpu_num_cores: {training_args.tpu_num_cores}'''		,						)

				# 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 [1001, 256, 15]:
								lowercase						    =			DummyDataset(__magic_name__	)

								def compute_metrics(__magic_name__	) -> Dict:
												lowercase						    =			list(range(len(__magic_name__	)	)	)
												lowercase						    =			p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
												return {"success": success}

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

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

								lowercase						    =			2

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

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

								lowercase						    =			None

				logger.info("🔥 All distributed tests successful"	)

def    __snake_case       (  __magic_name__	):







				'''simple docstring'''


				main()


if __name__ == "__main__":
						main()
