


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""": 16_00, """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""": 16_00, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
    ]     )
class 					snake_case__							(unittest.TestCase     ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(   self       :  Optional[Any]							)      ->  Optional[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=UpperCamelCase__      ,   )
							assert hasattr(self      ,   "env"							)





				def 							__UpperCAmelCase							(   self       :  List[str]      ,   __lowerCamelCase       :  Union[str, Any]							)      ->  int:
							# 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=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": 5_00,
							    }      ,   metric_definitions=self.env.metric_definitions      ,   distribution=UpperCamelCase__      ,   py_version="py36"      ,   )





				def 							__UpperCAmelCase							(   self       :  str      ,   __lowerCamelCase       :  Union[str, Any]							)      ->  Optional[int]:
							TrainingJobAnalytics(UpperCamelCase__							).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""							)





				@parameterized.expand([(1,)]							)
				def 							__UpperCAmelCase							(   self       :  Optional[Any]      ,   __lowerCamelCase       :  Dict							)      ->  str:
							# create estimator
							a					=    self.create_estimator(UpperCamelCase__							)

							# 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"      ,   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}      ,   UpperCamelCase__							)












from __future__ import annotations

import time

import numpy as np

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




class 					snake_case__							:
				"""simple docstring"""



				def __init__(   self       :  Any      ,   __lowerCamelCase       :  list[int]      ,   __lowerCamelCase       :  list[list[int]]      ,   __lowerCamelCase       :  list[list[int]]      ,   )      ->  None:
							a					=    claim_vector
							a					=    allocated_resources_table
							a					=    maximum_claim_table





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  list[int]:
							return [
							    sum(p_item[i] for p_item in self.__allocated_resources_table							)
							    for i in range(len(self.__allocated_resources_table[0]							)							)
							]





				def 							__UpperCAmelCase							(   self       :  str							)      ->  list[int]:
							return np.array(self.__claim_vector							) - np.array(
							    self.__processes_resource_summation()							)





				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  list[list[int]]:
							return [
							    list(np.array(self.__maximum_claim_table[i]							) - np.array(__lowerCamelCase							)							)
							    for i, allocated_resource in enumerate(self.__allocated_resources_table							)
							]





				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  dict[int, list[int]]:
							return {self.__need().index(__lowerCamelCase							): i for i in self.__need()}





				def 							__UpperCAmelCase							(   self       :  Optional[Any]      ,   **__lowerCamelCase       :  Any							)      ->  None:
							a					=    self.__need()
							a					=    self.__allocated_resources_table
							a					=    self.__available_resources()
							a					=    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:
										a					=    False
										for each_need in need_list:
													a					=    True
													for index, need in enumerate(__lowerCamelCase							):
																if need > available_resources[index]:
																			a					=    False
																			break
													if execution:
																a					=    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:
																						a					=    original_need_index
																print(f"""Process {process_number + 1} is executing."""							)
																# remove the process run from stack
																need_list.remove(__lowerCamelCase							)
																# update available/freed resources stack
																a					=    np.array(__lowerCamelCase							) + np.array(
																    alloc_resources_table[process_number]							)
																print(
																    "Updated available resource stack for processes: "
																    + " ".join([str(__lowerCamelCase							) 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 							__UpperCAmelCase							(   self       :  Any							)      ->  str:
							print(" " * 9 + "Allocated Resource Table"							)
							for item in self.__allocated_resources_table:
										print(
										    f"""P{self.__allocated_resources_table.index(__lowerCamelCase							) + 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(__lowerCamelCase							) + 1}"""
										    + " ".join(f"""{it:>8}""" for it in item							)
										    + "\n"							)
							print(
							    "Current Usage by Active Processes: "
							    + " ".join(str(__lowerCamelCase							) for x in self.__claim_vector							)							)
							print(
							    "Initial Available Resources:       "
							    + " ".join(str(__lowerCamelCase							) for x in self.__available_resources()							)							)
							time.sleep(1							)


if __name__ == "__main__":
		import doctest

		doctest.testmod()






