


import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available






def 					__lowerCAmelCase		(  *__lowerCamelCase	:    Any	)  ->						str:
					if not isinstance(__lowerCamelCase				,   __lowerCamelCase	):
										__lowerCAmelCase	   =list(__lowerCamelCase	)
					for i in range(len(__lowerCamelCase	)	):
										__lowerCAmelCase	   =None
					gc.collect()
					if is_xpu_available():
										torch.xpu.empty_cache()
					elif is_npu_available():
										torch.npu.empty_cache()
					else:
										torch.cuda.empty_cache()
					return objects






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Exception	)  ->						bool:
					__lowerCAmelCase	   =[
					    """CUDA out of memory.""",  # CUDA OOM
					    """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
					    """DefaultCPUAllocator: can't allocate memory""",  # CPU OOM
					]
					if isinstance(__lowerCamelCase				,   __lowerCamelCase	) and len(exception.args	) == 1:
										return any(err in exception.args[0] for err in _statements	)
					return False






def 					__lowerCAmelCase		(  __lowerCamelCase	:    callable = None				,   __lowerCamelCase	:    int = 128	)  ->						List[Any]:
					if function is None:
										return functools.partial(__lowerCamelCase				,   starting_batch_size=__lowerCamelCase	)

					__lowerCAmelCase	   =starting_batch_size

					def decorator(*__lowerCamelCase	:    Any				,   **__lowerCamelCase	:    Tuple	):
										nonlocal batch_size
										gc.collect()
										if is_xpu_available():
															torch.xpu.empty_cache()
										elif is_npu_available():
															torch.npu.empty_cache()
										else:
															torch.cuda.empty_cache()
										__lowerCAmelCase	   =list(inspect.signature(__lowerCamelCase	).parameters.keys()	)
										# Guard against user error
										if len(__lowerCamelCase	) < (len(__lowerCamelCase	) + 1):
															__lowerCAmelCase	   =""", """.join([f"""{arg}={value}""" for arg, value in zip(params[1:]				,   args[1:]	)]	)
															raise TypeError(
															    f"""Batch size was passed into `{function.__name__}` as the first argument when called."""
															    f"""Remove this as the decorator already does so: `{function.__name__}({arg_str})`"""	)
										while True:
															if batch_size == 0:
																				raise RuntimeError("""No executable batch size found, reached zero."""	)
															try:
																				return function(__lowerCamelCase				,   *__lowerCamelCase				,   **__lowerCamelCase	)
															except Exception as e:
																				if should_reduce_batch_size(__lowerCamelCase	):
																									gc.collect()
																									if is_xpu_available():
																														torch.xpu.empty_cache()
																									elif is_npu_available():
																														torch.npu.empty_cache()
																									else:
																														torch.cuda.empty_cache()
																									batch_size //= 2
																				else:
																									raise

					return decorator









import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
				from sagemaker import Session, TrainingJobAnalytics
				from sagemaker.huggingface import HuggingFace






@pytest.mark.skipif(
    literal_eval(os.getenv("TEST_SAGEMAKER"	,    "False"				)				) is not True	,    reason="Skipping test because should only be run when releasing minor transformers version"	,    )
@pytest.mark.usefixtures("sm_env"				)
@parameterized_class(
    [
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 650, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "pytorch",
            "script": "run_ddp.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "tensorflow",
            "script": "run_tf_dist.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.6, "eval_loss": 0.7},
        },
    ]				)
class 						__a (     unittest.TestCase				):



	def 		UpperCamelCase							(						self							:    Dict)->							Any:
						if self.framework == "pytorch":
											subprocess.run(
											    F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()				,	encoding="""utf-8"""				,	check=snake_case_				,	)
						assert hasattr(self				,	"""env""")



	def 		UpperCamelCase							(						self							:    Dict				,	snake_case_							:    str)->							Dict:
						__lowerCAmelCase	   =F"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
						# distributed data settings
						__lowerCAmelCase	   ={"""smdistributed""": {"""dataparallel""": {"""enabled""": True}}} if self.script != """run_ddp.py""" else None

						# creates estimator
						return HuggingFace(
						    entry_point=self.script				,	source_dir=self.env.test_path				,	role=self.env.role				,	image_uri=self.env.image_uri				,	base_job_name=snake_case_				,	instance_count=snake_case_				,	instance_type=self.instance_type				,	debugger_hook_config=snake_case_				,	hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path}				,	metric_definitions=self.env.metric_definitions				,	distribution=snake_case_				,	py_version="""py36"""				,	)



	def 		UpperCamelCase							(						self							:    int				,	snake_case_							:    List[Any])->							int:
						TrainingJobAnalytics(snake_case_).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""")



	@parameterized.expand([(2,)])
	def 		UpperCamelCase							(						self							:    Any				,	snake_case_							:    Dict)->							Optional[Any]:
						# create estimator
						__lowerCAmelCase	   =self.create_estimator(snake_case_)

						# run training
						estimator.fit()

						# result dataframe
						__lowerCAmelCase	   =TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

						# extract kpis
						__lowerCAmelCase	   =list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""])
						__lowerCAmelCase	   =list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""])
						# get train time from SageMaker job, this includes starting, preprocessing, stopping
						__lowerCAmelCase	   =(
						    Session().describe_training_job(estimator.latest_training_job.name).get("""TrainingTimeInSeconds"""				,	99_99_99)
						)

						# assert kpis
						assert train_runtime <= self.results["train_runtime"]
						assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy)
						assert all(t <= self.results["""eval_loss"""] for t in eval_loss)

						# dump tests result into json file to share in PR
						with open(F"""{estimator.latest_training_job.name}.json"""				,	"""w""") as outfile:
											json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss}				,	snake_case_)




