






'''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 		UpperCamelCase__    (unittest.TestCase			):




		'''simple docstring'''





		def 			UpperCamelCase_					(      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=_lowerCAmelCase   ,)
								assert hasattr(self   ,"""env"""       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								# configuration for running training on smdistributed Model Parallel
								lowerCamelCase__         =  {
								    """enabled""": True,
								    """processes_per_host""": 8,
								}
								lowerCamelCase__         =  {
								    """enabled""": True,
								    """parameters""": {
								        """microbatches""": 4,
								        """placement_strategy""": """spread""",
								        """pipeline""": """interleaved""",
								        """optimize""": """speed""",
								        """partitions""": 4,
								        """ddp""": True,
								    },
								}

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

								lowerCamelCase__         =  """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=_lowerCAmelCase   ,instance_type=self.instance_type   ,debugger_hook_config=_lowerCAmelCase   ,hyperparameters={
								        **self.env.hyperparameters,
								        """model_name_or_path""": self.model_name_or_path,
								        """max_steps""": 5_00,
								    }   ,metric_definitions=self.env.metric_definitions   ,distribution=_lowerCAmelCase   ,py_version="""py36"""   ,)






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								TrainingJobAnalytics(_lowerCAmelCase       ).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''       )






		@parameterized.expand([(1,)]       )
		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								# create estimator
								lowerCamelCase__         =  self.create_estimator(_lowerCAmelCase       )

								# run training
								estimator.fit()

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

								# extract kpis
								lowerCamelCase__         =  list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]       )
								lowerCamelCase__         =  list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]       )
								# get train time from SageMaker job, this includes starting, preprocessing, stopping
								lowerCamelCase__         =  (
								    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}   ,_lowerCAmelCase       )









'''simple docstring'''







from manim import *




class 		UpperCamelCase__    (a			):




		'''simple docstring'''





		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  Rectangle(height=0.5   ,width=0.5       )
								lowerCamelCase__         =  Rectangle(height=0.46   ,width=0.46       ).set_stroke(width=0       )

								lowerCamelCase__         =  [mem.copy() for i in range(6       )]
								lowerCamelCase__         =  [mem.copy() for i in range(6       )]
								lowerCamelCase__         =  VGroup(*_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0       )
								lowerCamelCase__         =  VGroup(*_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0       )
								lowerCamelCase__         =  VGroup(_lowerCAmelCase   ,_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0       )
								lowerCamelCase__         =  Text("""CPU"""   ,font_size=24       )
								lowerCamelCase__         =  Group(_lowerCAmelCase   ,_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0.5   ,aligned_edge=_lowerCAmelCase       )
								cpu.move_to([-2.5, -0.5, 0]       )
								self.add(_lowerCAmelCase       )

								lowerCamelCase__         =  [mem.copy() for i in range(1       )]
								lowerCamelCase__         =  VGroup(*_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0       )
								lowerCamelCase__         =  Text("""GPU"""   ,font_size=24       )
								lowerCamelCase__         =  Group(_lowerCAmelCase   ,_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0.5   ,aligned_edge=_lowerCAmelCase       )
								gpu.align_to(_lowerCAmelCase   ,_lowerCAmelCase       )
								gpu.set_x(gpu.get_x() - 1       )

								self.add(_lowerCAmelCase       )

								lowerCamelCase__         =  [mem.copy() for i in range(6       )]
								lowerCamelCase__         =  VGroup(*_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0       )

								lowerCamelCase__         =  Text("""Model"""   ,font_size=24       )
								lowerCamelCase__         =  Group(_lowerCAmelCase   ,_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0.5   ,aligned_edge=_lowerCAmelCase       )
								model.move_to([3, -1.0, 0]       )

								self.play(
								    Create(_lowerCAmelCase   ,run_time=1       )   ,Create(_lowerCAmelCase   ,run_time=1       )   ,Create(_lowerCAmelCase   ,run_time=1       )   ,)

								lowerCamelCase__         =  MarkupText(
								    F'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.'''   ,font_size=24   ,)

								lowerCamelCase__         =  Square(side_length=2.2       )
								key.move_to([-5, 2, 0]       )

								lowerCamelCase__         =  MarkupText(
								    F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''   ,font_size=18   ,)

								key_text.move_to([-5, 2.4, 0]       )

								step_a.move_to([2, 2, 0]       )
								self.play(Write(_lowerCAmelCase   ,run_time=2.5       )   ,Write(_lowerCAmelCase       )   ,Write(_lowerCAmelCase       )       )

								self.add(_lowerCAmelCase       )

								lowerCamelCase__         =  []
								lowerCamelCase__         =  []
								lowerCamelCase__         =  []
								for i, rect in enumerate(_lowerCAmelCase       ):
														lowerCamelCase__         =  Rectangle(height=0.46   ,width=0.46       ).set_stroke(width=0.0       ).set_fill(_lowerCAmelCase   ,opacity=0.7       )
														cpu_target.move_to(_lowerCAmelCase       )
														cpu_target.generate_target()
														lowerCamelCase__         =  0.46 / 4
														lowerCamelCase__         =  0.46 / 3

														if i == 0:
																				cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT       )   ,buff=0.02   ,direction=_lowerCAmelCase       )
																				cpu_target.target.set_x(cpu_target.target.get_x() + 0.1       )
														elif i == 3:
																				cpu_target.target.next_to(cpu_targs[0].target   ,direction=_lowerCAmelCase   ,buff=0.0       )
														else:
																				cpu_target.target.next_to(cpu_targs[i - 1].target   ,direction=_lowerCAmelCase   ,buff=0.0       )
														cpu_targs.append(_lowerCAmelCase       )

														first_animations.append(rect.animate(run_time=0.5       ).set_stroke(_lowerCAmelCase       )       )
														second_animations.append(MoveToTarget(_lowerCAmelCase   ,run_time=1.5       )       )

								self.play(*_lowerCAmelCase       )
								self.play(*_lowerCAmelCase       )

								self.wait()

