



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_model_parallelism.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 1_6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 1_6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
    ])
class  __snake_case      (   unittest.TestCase):







							'''simple docstring'''






							def _a      (							self		):
											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=a_  ,  )
											assert hasattr(self  ,  """env"""		)


							def _a      (							self  ,  a_		):
											# configuration for running training on smdistributed Model Parallel
											a__				      =		{
											    """enabled""": True,
											    """processes_per_host""": 8,
											}
											a__				      =		{
											    """enabled""": True,
											    """parameters""": {
											        """microbatches""": 4,
											        """placement_strategy""": """spread""",
											        """pipeline""": """interleaved""",
											        """optimize""": """speed""",
											        """partitions""": 4,
											        """ddp""": True,
											    },
											}

											a__				      =		{"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

											a__				      =		"""trainer""" if self.script == """run_glue.py""" else """smtrainer"""
											# 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=F'''{self.env.base_job_name}-{instance_count}-smp-{name_extension}'''  ,  instance_count=a_  ,  instance_type=self.instance_type  ,  debugger_hook_config=a_  ,  hyperparameters={
											        **self.env.hyperparameters,
											        """model_name_or_path""": self.model_name_or_path,
											        """max_steps""": 500,
											    }  ,  metric_definitions=self.env.metric_definitions  ,  distribution=a_  ,  py_version="""py36"""  ,  )


							def _a      (							self  ,  a_		):
											TrainingJobAnalytics(a_		).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''		)







							@parameterized.expand([(1,)]		)
							def _a      (							self  ,  a_		):
											# create estimator
											a__				      =		self.create_estimator(a_		)

											# run training
											estimator.fit()

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

											# extract kpis
											a__				      =		list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]		)
											a__				      =		list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]		)
											# get train time from SageMaker job, this includes starting, preprocessing, stopping
											a__				      =		(
											    Session().describe_training_job(estimator.latest_training_job.name		).get("""TrainingTimeInSeconds"""  ,  999_999		)
											)

											# 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}  ,  a_		)






def 			A_      (	__a     :				Any		):

				"""simple docstring"""
				if not head:
								return True
				# split the list to two parts
				a__      ,			a__				      =		head.next, head
				while fast and fast.next:
								a__				      =		fast.next.next
								a__				      =		slow.next
				a__				      =		slow.next
				a__				      =		None  # Don't forget here! But forget still works!
				# reverse the second part
				a__				      =		None
				while second:
								a__				      =		second.next
								a__				      =		node
								a__				      =		second
								a__				      =		nxt
				# compare two parts
				# second part has the same or one less node
				while node:
								if node.val != head.val:
												return False
								a__				      =		node.next
								a__				      =		head.next
				return True

def 			A_      (	__a     :				int		):

				"""simple docstring"""
				if not head or not head.next:
								return True

				# 1. Get the midpoint (slow)
				a__				      =		a__				      =		a__				      =		head
				while fast and fast.next:
								a__      ,			a__				      =		fast.next.next, slow.next

				# 2. Push the second half into the stack
				a__				      =		[slow.val]
				while slow.next:
								a__				      =		slow.next
								stack.append(slow.val		)

				# 3. Comparison
				while stack:
								if stack.pop() != cur.val:
												return False
								a__				      =		cur.next

				return True




def 			A_      (	__a     :				Any		):

				"""simple docstring"""
				if not head or not head.next:
								return True
				a__				      =		{}
				a__				      =		0
				while head:
								if head.val in d:
												d[head.val].append(__a		)
								else:
												a__				      =		[pos]
								a__				      =		head.next
								pos += 1
				a__				      =		pos - 1
				a__				      =		0
				for v in d.values():
								if len(__a		) % 2 != 0:
												middle += 1
								else:
												a__				      =		0
												for i in range(0 ,		len(__a		)		):
																if v[i] + v[len(__a		) - 1 - step] != checksum:
																				return False
																step += 1
								if middle > 1:
												return False
				return True

