



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


UpperCAmelCase__     :   Union[str, 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     __lowerCAmelCase					(    A		):



										def __init__(		self     :						int				,	A     :						int = 1_01)     ->					Optional[int]:



													"""simple docstring"""






													_UpperCAmelCase	 =     length



										def __len__(		self     :						Union[str, Any])     ->					List[str]:



													"""simple docstring"""






													return self.length



										def __getitem__(		self     :						Tuple				,	A     :						Union[str, Any])     ->					int:



													"""simple docstring"""






													return i







							class     __lowerCAmelCase					:



										def __call__(		self     :						Optional[Any]				,	A     :						str)     ->					Union[str, Any]:



													"""simple docstring"""






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







							class     __lowerCAmelCase					(    nn.Module		):



										def __init__(		self     :						int)     ->					Tuple:



													"""simple docstring"""






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



										def 					_lowerCamelCase (		self     :						int				,	A     :						str				,	A     :						Tuple=None)     ->					List[str]:



													"""simple docstring"""






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







class     __lowerCAmelCase					(    A		):



			@require_torch_neuroncore
			def 					_lowerCamelCase (		self     :						List[Any])     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     F"--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
						_UpperCAmelCase	 =     self.get_auto_remove_tmp_dir()
						_UpperCAmelCase	 =     F"--output_dir {output_dir}".split()
						_UpperCAmelCase	 =     ['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     __lowerCAmelCase					(    A		):



			@require_torch_multi_gpu
			def 					_lowerCamelCase (		self     :						Union[str, Any])     ->					Any:



						"""simple docstring"""






						_UpperCAmelCase	 =     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()
						_UpperCAmelCase	 =     self.get_auto_remove_tmp_dir()
						_UpperCAmelCase	 =     F"--output_dir {output_dir}".split()
						_UpperCAmelCase	 =     ['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

							UpperCAmelCase__     :   str     = HfArgumentParser((TrainingArguments,))
							UpperCAmelCase__     :   Dict     = 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]:
														UpperCAmelCase__     :   Any     = DummyDataset(dataset_length)





														def 							A			(  _UpperCAmelCase       :     EvalPrediction					)       ->				Dict:







																	'''simple docstring'''
																	_UpperCAmelCase	 =     list(range(len(_UpperCAmelCase					)					)					)
																	_UpperCAmelCase	 =     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}

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

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

														UpperCAmelCase__     :   Union[str, Any]     = 2

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

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

														UpperCAmelCase__     :   List[str]     = None





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__     = {
    "configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
    "tokenization_ctrl": ["CTRLTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__     = [
							    "CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "CTRLForSequenceClassification",
							    "CTRLLMHeadModel",
							    "CTRLModel",
							    "CTRLPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__     = [
							    "TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFCTRLForSequenceClassification",
							    "TFCTRLLMHeadModel",
							    "TFCTRLModel",
							    "TFCTRLPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
							from .tokenization_ctrl import CTRLTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_ctrl import (
														    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CTRLForSequenceClassification,
														    CTRLLMHeadModel,
														    CTRLModel,
														    CTRLPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_ctrl import (
														    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFCTRLForSequenceClassification,
														    TFCTRLLMHeadModel,
														    TFCTRLModel,
														    TFCTRLPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase__     = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
