
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_600, "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_600, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]     )
class        lowercase      (	unittest.TestCase     ):



		def __UpperCamelCase				(				self )						->      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=A__       ,   )
				assert hasattr(self       ,   'env' )



		def __UpperCamelCase				(				self       ,   A_ )						->      Optional[int]:
				"""simple docstring"""
				# configuration for running training on smdistributed Model Parallel
				UpperCamelCase						=  {
				    """enabled""": True,
				    """processes_per_host""": 8,
				}
				UpperCamelCase						=  {
				    """enabled""": True,
				    """parameters""": {
				        """microbatches""": 4,
				        """placement_strategy""": """spread""",
				        """pipeline""": """interleaved""",
				        """optimize""": """speed""",
				        """partitions""": 4,
				        """ddp""": True,
				    },
				}

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

				UpperCamelCase						=  """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 __UpperCamelCase				(				self       ,   A_ )						->      List[str]:
				"""simple docstring"""
				TrainingJobAnalytics(A__ ).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv''' )



		@parameterized.expand([(1,)] )
		def __UpperCamelCase				(				self       ,   A_ )						->      Optional[Any]:
				"""simple docstring"""
				# create estimator
				UpperCamelCase						=  self.create_estimator(A__ )

				# run training
				estimator.fit()

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

				# extract kpis
				UpperCamelCase						=  list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value'] )
				UpperCamelCase						=  list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value'] )
				# get train time from SageMaker job, this includes starting, preprocessing, stopping
				UpperCamelCase						=  (
				    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__ )


from collections.abc import Callable


def    A (      lowercase					, lowercase					, lowercase				)     ->						float:
		'''simple docstring'''

		UpperCamelCase						=  a
		UpperCamelCase						=  b
		if function(lowercase				) == 0:  # one of the a or b is a root for the function
				return a
		elif function(lowercase				) == 0:
				return b
		elif (
		    function(lowercase				) * function(lowercase				) > 0
		):  # if none of these are root and they are both positive or negative,
				# then this algorithm can't find the root
				raise ValueError('could not find root in given interval.'				)
		else:
				UpperCamelCase						=  start + (end - start) / 2.0
				while abs(start - mid				) > 10**-7:  # until precisely equals to 10^-7
						if function(lowercase				) == 0:
								return mid
						elif function(lowercase				) * function(lowercase				) < 0:
								UpperCamelCase						=  mid
						else:
								UpperCamelCase						=  mid
						UpperCamelCase						=  start + (end - start) / 2.0
				return mid


def    A (      lowercase				)     ->						float:
		'''simple docstring'''

		return x**3 - 2 * x - 5


if __name__ == "__main__":
					print(bisection(f, 1, 1_000))

					import doctest

					doctest.testmod()
