






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 							__a							(  unittest.TestCase						):





		'''simple docstring'''



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


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						# configuration for running training on smdistributed Model Parallel
						SCREAMING_SNAKE_CASE_   :	List[str]													=   {
						    'enabled': True,
						    'processes_per_host': 8,
						}
						SCREAMING_SNAKE_CASE_   :	Tuple													=   {
						    'enabled': True,
						    'parameters': {
						        'microbatches': 4,
						        'placement_strategy': 'spread',
						        'pipeline': 'interleaved',
						        'optimize': 'speed',
						        'partitions': 4,
						        'ddp': True,
						    },
						}

						SCREAMING_SNAKE_CASE_   :	Any													=   {'smdistributed': {'modelparallel': smp_options}, 'mpi': mpi_options}

						SCREAMING_SNAKE_CASE_   :	int													=   '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   __snake_case			(						self ,    UpperCamelCase__       ):
						TrainingJobAnalytics(UpperCamelCase__       ).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''       )






		@parameterized.expand([(1,)]       )
		def   __snake_case			(						self ,    UpperCamelCase__       ):
						# create estimator
						SCREAMING_SNAKE_CASE_   :	int													=   self.create_estimator(UpperCamelCase__       )

						# run training
						estimator.fit()

						# result dataframe
						SCREAMING_SNAKE_CASE_   :	Any													=   TrainingJobAnalytics(estimator.latest_training_job.name       ).dataframe()

						# extract kpis
						SCREAMING_SNAKE_CASE_   :	List[str]													=   list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']       )
						SCREAMING_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
						SCREAMING_SNAKE_CASE_   :	List[str]													=   (
						    Session().describe_training_job(estimator.latest_training_job.name       ).get('TrainingTimeInSeconds' ,    999999       )
						)

						# 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__       )







import colorsys

from PIL import Image  # type: ignore



def 					_lowerCamelCase(			lowerCAmelCase__				:		float    ,    lowerCAmelCase__				:		float    ,    lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Any													=   x
				SCREAMING_SNAKE_CASE_   :	Tuple													=   y
				for step in range(lowerCAmelCase__      ):  # noqa: B007
								SCREAMING_SNAKE_CASE_   :	str													=   a * a - b * b + x
								SCREAMING_SNAKE_CASE_   :	Any													=   2 * a * b + y
								SCREAMING_SNAKE_CASE_   :	int													=   a_new

								# divergence happens for all complex number with an absolute value
								# greater than 4
								if a * a + b * b > 4:
												break
				return step / (max_step - 1)



def 					_lowerCamelCase(			lowerCAmelCase__				:		float      ):





				'''simple docstring'''







				if distance == 1:
								return (0, 0, 0)
				else:
								return (255, 255, 255)



def 					_lowerCamelCase(			lowerCAmelCase__				:		float      ):





				'''simple docstring'''







				if distance == 1:
								return (0, 0, 0)
				else:
								return tuple(round(i * 255      ) for i in colorsys.hsv_to_rgb(lowerCAmelCase__    ,    1    ,    1      )      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		int = 800    ,    lowerCAmelCase__				:		int = 600    ,    lowerCAmelCase__				:		float = -0.6    ,    lowerCAmelCase__				:		float = 0    ,    lowerCAmelCase__				:		float = 3.2    ,    lowerCAmelCase__				:		int = 50    ,    lowerCAmelCase__				:		bool = True    ,    ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   Image.new('RGB'    ,    (image_width, image_height)      )
				SCREAMING_SNAKE_CASE_   :	List[str]													=   img.load()

				# loop through the image-coordinates
				for image_x in range(lowerCAmelCase__      ):
								for image_y in range(lowerCAmelCase__      ):
												# determine the figure-coordinates based on the image-coordinates
												SCREAMING_SNAKE_CASE_   :	List[str]													=   figure_width / image_width * image_height
												SCREAMING_SNAKE_CASE_   :	List[Any]													=   figure_center_x + (image_x / image_width - 0.5) * figure_width
												SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   figure_center_y + (image_y / image_height - 0.5) * figure_height

												SCREAMING_SNAKE_CASE_   :	str													=   get_distance(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )

												# color the corresponding pixel based on the selected coloring-function
												if use_distance_color_coding:
																SCREAMING_SNAKE_CASE_   :	int													=   get_color_coded_rgb(lowerCAmelCase__      )
												else:
																SCREAMING_SNAKE_CASE_   :	Dict													=   get_black_and_white_rgb(lowerCAmelCase__      )

				return img


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				# colored version, full figure
				A        =			get_image()

				# uncomment for colored version, different section, zoomed in
				# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
				# figure_width = 0.8)

				# uncomment for black and white version, full figure
				# img = get_image(use_distance_color_coding = False)

				# uncomment to save the image
				# img.save("mandelbrot.png")

				img.show()