


"""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


a		:				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  __UpperCamelCase       (		a__		):






									def __init__(     self   ,      lowerCAmelCase__ = 101   )			->				List[Any]:
										a   :	Union[str, Any]			=       length






									def __len__(     self   )			->				List[str]:
										return self.length






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


		class  __UpperCamelCase       :






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


		class  __UpperCamelCase       (		nn.Module		):






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






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


class  __UpperCamelCase       (		a__		):






							@require_torch_neuroncore
							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	Any			=       f"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
								a   :	str			=       self.get_auto_remove_tmp_dir()
								a   :	Union[str, Any]			=       f"""--output_dir {output_dir}""".split()
								a   :	Optional[int]			=       ["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  __UpperCamelCase       (		a__		):






							@require_torch_multi_gpu
							def 					__a     (     self   )			->				List[str]:
								a   :	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()
								a   :	int			=       self.get_auto_remove_tmp_dir()
								a   :	int			=       f"""--output_dir {output_dir}""".split()
								a   :	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

		a		:				List[str]  			=    HfArgumentParser((TrainingArguments,))
		a		:				Tuple  			=    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]:
				a		:				List[Any]  			=    DummyDataset(dataset_length)






				def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					EvalPrediction      )				->Dict:






					'''simple docstring'''






					a   :	List[str]			=       list(range(len(_lowercase      )      )      )
					a   :	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}

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

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

				a		:				Optional[Any]  			=    2

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

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







				a		:				List[Any]  			=    None









"""simple docstring"""




import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
		import onnxruntime as ort


a		:				Any  			=    logging.get_logger(__name__)

a		:				Tuple  			=    {
    '''tensor(bool)''': np.bool_,
    '''tensor(int8)''': np.inta,
    '''tensor(uint8)''': np.uinta,
    '''tensor(int16)''': np.intaa,
    '''tensor(uint16)''': np.uintaa,
    '''tensor(int32)''': np.intaa,
    '''tensor(uint32)''': np.uintaa,
    '''tensor(int64)''': np.intaa,
    '''tensor(uint64)''': np.uintaa,
    '''tensor(float16)''': np.floataa,
    '''tensor(float)''': np.floataa,
    '''tensor(double)''': np.floataa,
}


class  __UpperCamelCase       :






							def __init__(     self   ,      lowerCAmelCase__=None   ,      **lowerCAmelCase__   )			->				str:
								logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future."   )
								a   :	Optional[int]			=       model
								a   :	int			=       kwargs.get("model_save_dir"   ,      lowerCAmelCase__   )
								a   :	Tuple			=       kwargs.get("latest_model_name"   ,      lowerCAmelCase__   )






							def __call__(     self   ,      **lowerCAmelCase__   )			->				Dict:
								a   :	List[str]			=       {k: np.array(lowerCAmelCase__   ) for k, v in kwargs.items()}
								return self.model.run(lowerCAmelCase__   ,      lowerCAmelCase__   )






							@staticmethod
							def 					__a     (     lowerCAmelCase__   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   )			->				Union[str, Any]:
								if provider is None:
									logger.info("No onnxruntime provider specified, using CPUExecutionProvider"   )
									a   :	List[str]			=       "CPUExecutionProvider"

								return ort.InferenceSession(lowerCAmelCase__   ,      providers=[provider]   ,      sess_options=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   )			->				int:
								a   :	List[str]			=       file_name if file_name is not None else ONNX_WEIGHTS_NAME

								a   :	Optional[int]			=       self.model_save_dir.joinpath(self.latest_model_name   )
								a   :	List[str]			=       Path(lowerCAmelCase__   ).joinpath(lowerCAmelCase__   )
								try:
									shutil.copyfile(lowerCAmelCase__   ,      lowerCAmelCase__   )
								except shutil.SameFileError:
									pass

								# copy external weights (for models >2GB)
								a   :	str			=       self.model_save_dir.joinpath(lowerCAmelCase__   )
								if src_path.exists():
									a   :	Any			=       Path(lowerCAmelCase__   ).joinpath(lowerCAmelCase__   )
									try:
										shutil.copyfile(lowerCAmelCase__   ,      lowerCAmelCase__   )
									except shutil.SameFileError:
										pass






							def 					__a     (     self   ,      lowerCAmelCase__   ,      **lowerCAmelCase__   ,      )			->				str:
								if os.path.isfile(lowerCAmelCase__   ):
									logger.error(f"""Provided path ({save_directory}) should be a directory, not a file"""   )
									return

								os.makedirs(lowerCAmelCase__   ,      exist_ok=lowerCAmelCase__   )

								# saving model weights/files
								self._save_pretrained(lowerCAmelCase__   ,      **lowerCAmelCase__   )






							@classmethod
							def 					__a     (     cls   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = False   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				Optional[int]:
								a   :	Tuple			=       file_name if file_name is not None else ONNX_WEIGHTS_NAME
								# load model from local directory
								if os.path.isdir(lowerCAmelCase__   ):
									a   :	Tuple			=       OnnxRuntimeModel.load_model(
									    os.path.join(lowerCAmelCase__   ,      lowerCAmelCase__   )   ,      provider=lowerCAmelCase__   ,      sess_options=lowerCAmelCase__   )
									a   :	Tuple			=       Path(lowerCAmelCase__   )
								# load model from hub
								else:
									# download model
									a   :	Optional[Any]			=       hf_hub_download(
									    repo_id=lowerCAmelCase__   ,      filename=lowerCAmelCase__   ,      use_auth_token=lowerCAmelCase__   ,      revision=lowerCAmelCase__   ,      cache_dir=lowerCAmelCase__   ,      force_download=lowerCAmelCase__   ,      )
									a   :	Optional[int]			=       Path(lowerCAmelCase__   ).parent
									a   :	List[Any]			=       Path(lowerCAmelCase__   ).name
									a   :	int			=       OnnxRuntimeModel.load_model(lowerCAmelCase__   ,      provider=lowerCAmelCase__   ,      sess_options=lowerCAmelCase__   )
								return cls(model=lowerCAmelCase__   ,      **lowerCAmelCase__   )






							@classmethod
							def 					__a     (     cls   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = True   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				List[str]:
								a   :	Any			=       None
								if len(str(lowerCAmelCase__   ).split("@"   )   ) == 2:
									a, a   :	Tuple			=       model_id.split("@"   )

								return cls._from_pretrained(
								    model_id=lowerCAmelCase__   ,      revision=lowerCAmelCase__   ,      cache_dir=lowerCAmelCase__   ,      force_download=lowerCAmelCase__   ,      use_auth_token=lowerCAmelCase__   ,      **lowerCAmelCase__   ,      )


