
'''simple docstring'''
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""": 1600, """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""": 1600, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
    ]					)
class 	snake_case__       (      unittest.TestCase					):







		"""simple docstring"""

		def       lowerCAmelCase				(      self				:			int      )						->			List[Any]:






				"""simple docstring"""







				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=UpperCamelCase__  ,	)
				assert hasattr(self  ,	'''env'''      )



		def       lowerCAmelCase				(      self				:			str  ,	UpperCamelCase__				:			Any      )						->			str:






				"""simple docstring"""







				snake_case  :       Any     			=						{
				    '''enabled''': True,
				    '''processes_per_host''': 8,
				}
				snake_case  :       Any     			=						{
				    '''enabled''': True,
				    '''parameters''': {
				        '''microbatches''': 4,
				        '''placement_strategy''': '''spread''',
				        '''pipeline''': '''interleaved''',
				        '''optimize''': '''speed''',
				        '''partitions''': 4,
				        '''ddp''': True,
				    },
				}

				snake_case  :       int     			=						{'''smdistributed''': {'''modelparallel''': smp_options}, '''mpi''': mpi_options}

				snake_case  :       Tuple     			=						'''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=UpperCamelCase__  ,	instance_type=self.instance_type  ,	debugger_hook_config=UpperCamelCase__  ,	hyperparameters={
				        **self.env.hyperparameters,
				        '''model_name_or_path''': self.model_name_or_path,
				        '''max_steps''': 500,
				    }  ,	metric_definitions=self.env.metric_definitions  ,	distribution=UpperCamelCase__  ,	py_version='''py36'''  ,	)



		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			List[str]      )						->			Dict:






				"""simple docstring"""







				TrainingJobAnalytics(UpperCamelCase__      ).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv'      )



		@parameterized.expand([(1,)]      )
		def       lowerCAmelCase				(      self				:			Any  ,	UpperCamelCase__				:			Tuple      )						->			str:






				"""simple docstring"""







				snake_case  :       int     			=						self.create_estimator(UpperCamelCase__      )

				# run training
				estimator.fit()

				# result dataframe
				snake_case  :       Dict     			=						TrainingJobAnalytics(estimator.latest_training_job.name      ).dataframe()

				# extract kpis
				snake_case  :       Optional[int]     			=						list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']      )
				snake_case  :       List[str]     			=						list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']      )
				# get train time from SageMaker job, this includes starting, preprocessing, stopping
				snake_case  :       Union[str, Any]     			=						(
				    Session().describe_training_job(estimator.latest_training_job.name      ).get('''TrainingTimeInSeconds'''  ,	99_9999      )
				)

				# 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}  ,	UpperCamelCase__      )




'''simple docstring'''
from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

lowercase__						       =      TypeVar("T")






class 	snake_case__       (      Generic[T]					):







		"""simple docstring"""

		def __init__(      self				:			str  ,	UpperCamelCase__				:			list[T]  ,	UpperCamelCase__				:			Callable[[T, T], T]      )						->			None:






				"""simple docstring"""







				snake_case  :       Any | T     			=						None

				snake_case  :       int     			=						len(UpperCamelCase__      )
				snake_case  :       list[T]     			=						[any_type for _ in range(self.N      )] + arr
				snake_case  :       Optional[int]     			=						fnc
				self.build()



		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			None:






				"""simple docstring"""







				for p in range(self.N - 1  ,	0  ,	-1      ):
						snake_case  :       List[Any]     			=						self.fn(self.st[p * 2]  ,	self.st[p * 2 + 1]      )



		def       lowerCAmelCase				(      self				:			List[Any]  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			T      )						->			None:






				"""simple docstring"""







				p += self.N
				snake_case  :       int     			=						v
				while p > 1:
						snake_case  :       List[Any]     			=						p // 2
						snake_case  :       List[Any]     			=						self.fn(self.st[p * 2]  ,	self.st[p * 2 + 1]      )



		def       lowerCAmelCase				(      self				:			Union[str, Any]  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			int      )						->			T | None:  # noqa: E741






				"""simple docstring"""







				snake_case			,snake_case  :       Union[str, Any]     			=						l + self.N, r + self.N

				snake_case  :       T | None     			=						None
				while l <= r:
						if l % 2 == 1:
								snake_case  :       str     			=						self.st[l] if res is None else self.fn(UpperCamelCase__  ,	self.st[l]      )
						if r % 2 == 0:
								snake_case  :       Tuple     			=						self.st[r] if res is None else self.fn(UpperCamelCase__  ,	self.st[r]      )
						snake_case			,snake_case  :       Dict     			=						(l + 1) // 2, (r - 1) // 2
				return res


if __name__ == "__main__":
						from functools import reduce

						lowercase__						       =      [1, 1_0, -2, 9, -3, 8, 4, -7, 5, 6, 1_1, -1_2]

						lowercase__						       =      {
						    0: 7,
						    1: 2,
						    2: 6,
						    3: -1_4,
						    4: 5,
						    5: 4,
						    6: 7,
						    7: -1_0,
						    8: 9,
						    9: 1_0,
						    1_0: 1_2,
						    1_1: 1,
						}

						lowercase__						       =      SegmentTree(test_array, min)
						lowercase__						       =      SegmentTree(test_array, max)
						lowercase__						       =      SegmentTree(test_array, lambda a, b: a + b)




						def    _UpperCamelCase						(       )  ->							None:


								'''simple docstring'''


								for i in range(len(SCREAMING_SNAKE_CASE__			)			):
										for j in range(SCREAMING_SNAKE_CASE__ ,       len(SCREAMING_SNAKE_CASE__			)			):
												snake_case  :       int     			=						reduce(SCREAMING_SNAKE_CASE__ ,       test_array[i : j + 1]			)
												snake_case  :       List[str]     			=						reduce(SCREAMING_SNAKE_CASE__ ,       test_array[i : j + 1]			)
												snake_case  :       Tuple     			=						reduce(lambda SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			: a + b ,       test_array[i : j + 1]			)
												assert min_range == min_segment_tree.query(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
												assert max_range == max_segment_tree.query(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
												assert sum_range == sum_segment_tree.query(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)

						test_all_segments()

						for index, value in test_updates.items():
												lowercase__						       =      value
												min_segment_tree.update(index, value)
												max_segment_tree.update(index, value)
												sum_segment_tree.update(index, value)
												test_all_segments()


