



"""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': 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 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       Optional[int]:

				"""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=_SCREAMING_SNAKE_CASE       ,			)
				assert hasattr(self       ,			"env")

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		Optional[Any])				->       str:

				"""simple docstring"""

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

				__lowerCAmelCase      :  Tuple												=       {"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

				__lowerCAmelCase      :  Union[str, Any]												=       "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=_SCREAMING_SNAKE_CASE       ,			instance_type=self.instance_type       ,			debugger_hook_config=_SCREAMING_SNAKE_CASE       ,			hyperparameters={
				        **self.env.hyperparameters,
				        "model_name_or_path": self.model_name_or_path,
				        "max_steps": 500,
				    }       ,			metric_definitions=self.env.metric_definitions       ,			distribution=_SCREAMING_SNAKE_CASE       ,			py_version="py36"       ,			)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple       ,			_SCREAMING_SNAKE_CASE:		Dict)				->       List[Any]:

				"""simple docstring"""

				TrainingJobAnalytics(_SCREAMING_SNAKE_CASE).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""")







	@parameterized.expand([(1,)])
	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		int)				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       self.create_estimator(_SCREAMING_SNAKE_CASE)

				# run training
				estimator.fit()

				# result dataframe
				__lowerCAmelCase      :  Optional[Any]												=       TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

				# extract kpis
				__lowerCAmelCase      :  Tuple												=       list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"])
				__lowerCAmelCase      :  Union[str, Any]												=       list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"])
				# get train time from SageMaker job, this includes starting, preprocessing, stopping
				__lowerCAmelCase      :  Dict												=       (
				    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}       ,			_SCREAMING_SNAKE_CASE)




"""simple docstring"""






from __future__ import annotations

import time

import numpy as np

__snake_case							:   Optional[Any]      =    [8, 5, 9, 7]
__snake_case							:   List[Any]      =    [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
__snake_case							:   Optional[int]      =    [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]



class 			A__   :




	'''simple docstring'''





	def __init__(     self:		Any       ,			_SCREAMING_SNAKE_CASE:		list[int]       ,			_SCREAMING_SNAKE_CASE:		list[list[int]]       ,			_SCREAMING_SNAKE_CASE:		list[list[int]]       ,			)				->       None:

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       claim_vector
				__lowerCAmelCase      :  Tuple												=       allocated_resources_table
				__lowerCAmelCase      :  Tuple												=       maximum_claim_table

	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       list[int]:

				"""simple docstring"""

				return [
				    sum(p_item[i] for p_item in self.__allocated_resources_table)
				    for i in range(len(self.__allocated_resources_table[0]))
				]

	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       list[int]:

				"""simple docstring"""

				return np.array(self.__claim_vector) - np.array(
				    self.__processes_resource_summation())

	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       list[list[int]]:

				"""simple docstring"""

				return [
				    list(np.array(self.__maximum_claim_table[i]) - np.array(_SCREAMING_SNAKE_CASE))
				    for i, allocated_resource in enumerate(self.__allocated_resources_table)
				]

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       dict[int, list[int]]:

				"""simple docstring"""

				return {self.__need().index(_SCREAMING_SNAKE_CASE): i for i in self.__need()}

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any]       ,			**_SCREAMING_SNAKE_CASE:		List[Any])				->       None:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       self.__need()
				__lowerCAmelCase      :  int												=       self.__allocated_resources_table
				__lowerCAmelCase      :  Dict												=       self.__available_resources()
				__lowerCAmelCase      :  str												=       self.__need_index_manager()
				for kw, val in kwargs.items():
							if kw and val is True:
										self.__pretty_data()
				print("_" * 50 + "\n")
				while need_list:
							__lowerCAmelCase      :  int												=       False
							for each_need in need_list:
										__lowerCAmelCase      :  Dict												=       True
										for index, need in enumerate(_SCREAMING_SNAKE_CASE):
													if need > available_resources[index]:
																__lowerCAmelCase      :  Dict												=       False
																break
										if execution:
													__lowerCAmelCase      :  Any												=       True
													# get the original index of the process from ind_ctrl db
													for original_need_index, need_clone in need_index_manager.items():
																if each_need == need_clone:
																			__lowerCAmelCase      :  Union[str, Any]												=       original_need_index
													print(F"""Process {process_number + 1} is executing.""")
													# remove the process run from stack
													need_list.remove(_SCREAMING_SNAKE_CASE)
													# update available/freed resources stack
													__lowerCAmelCase      :  Dict												=       np.array(_SCREAMING_SNAKE_CASE) + np.array(
													    alloc_resources_table[process_number])
													print(
													    "Updated available resource stack for processes: "
													    + " ".join([str(_SCREAMING_SNAKE_CASE) for x in available_resources]))
													break
							if safe:
										print("The process is in a safe state.\n")
							else:
										print("System in unsafe state. Aborting...\n")
										break







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

				"""simple docstring"""

				print(" " * 9 + "Allocated Resource Table")
				for item in self.__allocated_resources_table:
							print(
							    F"""P{self.__allocated_resources_table.index(_SCREAMING_SNAKE_CASE) + 1}"""
							    + " ".join(F"""{it:>8}""" for it in item)
							    + "\n")
				print(" " * 9 + "System Resource Table")
				for item in self.__maximum_claim_table:
							print(
							    F"""P{self.__maximum_claim_table.index(_SCREAMING_SNAKE_CASE) + 1}"""
							    + " ".join(F"""{it:>8}""" for it in item)
							    + "\n")
				print(
				    "Current Usage by Active Processes: "
				    + " ".join(str(_SCREAMING_SNAKE_CASE) for x in self.__claim_vector))
				print(
				    "Initial Available Resources:       "
				    + " ".join(str(_SCREAMING_SNAKE_CASE) for x in self.__available_resources()))
				time.sleep(1)


if __name__ == "__main__":
	import doctest

	doctest.testmod()