





from manim import *

class    lowerCAmelCase							(					__UpperCamelCase			):
 def      A_	(      self  :      Dict	)			->	List[str]:
     lowerCamelCase__    :      List[Any]					 =		Rectangle(height=0.5			,     width=0.5	)
     lowerCamelCase__    :      Optional[Any]					 =		Rectangle(height=0.4_6			,     width=0.4_6	).set_stroke(width=0	)

     lowerCamelCase__    :      Optional[int]					 =		[mem.copy() for i in range(6	)]
     lowerCamelCase__    :      Union[str, Any]					 =		[mem.copy() for i in range(6	)]
     lowerCamelCase__    :      Any					 =		VGroup(*UpperCAmelCase	).arrange(UpperCAmelCase			,     buff=0	)
     lowerCamelCase__    :      Any					 =		VGroup(*UpperCAmelCase	).arrange(UpperCAmelCase			,     buff=0	)
     lowerCamelCase__    :      int					 =		VGroup(UpperCAmelCase			,     UpperCAmelCase	).arrange(UpperCAmelCase			,     buff=0	)
     lowerCamelCase__    :      List[Any]					 =		Text('CPU'			,     font_size=24	)
     lowerCamelCase__    :      Union[str, Any]					 =		Group(UpperCAmelCase			,     UpperCAmelCase	).arrange(UpperCAmelCase			,     buff=0.5			,     aligned_edge=UpperCAmelCase	)
     cpu.move_to([-2.5, -0.5, 0]	)
     self.add(UpperCAmelCase	)

     lowerCamelCase__    :      List[Any]					 =		[mem.copy() for i in range(1	)]
     lowerCamelCase__    :      str					 =		VGroup(*UpperCAmelCase	).arrange(UpperCAmelCase			,     buff=0	)
     lowerCamelCase__    :      int					 =		Text('GPU'			,     font_size=24	)
     lowerCamelCase__    :      Optional[Any]					 =		Group(UpperCAmelCase			,     UpperCAmelCase	).arrange(UpperCAmelCase			,     buff=0.5			,     aligned_edge=UpperCAmelCase	)
     gpu.align_to(UpperCAmelCase			,     UpperCAmelCase	)
     gpu.set_x(gpu.get_x() - 1	)

     self.add(UpperCAmelCase	)

     lowerCamelCase__    :      Optional[int]					 =		[mem.copy() for i in range(6	)]
     lowerCamelCase__    :      Tuple					 =		VGroup(*UpperCAmelCase	).arrange(UpperCAmelCase			,     buff=0	)

     lowerCamelCase__    :      Dict					 =		Text('Model'			,     font_size=24	)
     lowerCamelCase__    :      Dict					 =		Group(UpperCAmelCase			,     UpperCAmelCase	).arrange(UpperCAmelCase			,     buff=0.5			,     aligned_edge=UpperCAmelCase	)
     model.move_to([3, -1.0, 0]	)

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

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

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

     lowerCamelCase__    :      str					 =		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(UpperCAmelCase			,     run_time=2.5	)			,     Write(UpperCAmelCase	)			,     Write(UpperCAmelCase	)	)

     self.add(UpperCAmelCase	)

     lowerCamelCase__    :      int					 =		[]
     lowerCamelCase__    :      str					 =		[]
     lowerCamelCase__    :      Dict					 =		[]
     for i, rect in enumerate(UpperCAmelCase	):
         lowerCamelCase__    :      Union[str, Any]					 =		Rectangle(height=0.4_6			,     width=0.4_6	).set_stroke(width=0.0	).set_fill(UpperCAmelCase			,     opacity=0.7	)
         cpu_target.move_to(UpperCAmelCase	)
         cpu_target.generate_target()
         lowerCamelCase__    :      Dict					 =		0.4_6 / 4
         lowerCamelCase__    :      List[str]					 =		0.4_6 / 3

         if i == 0:
             cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT	)			,     buff=0.0_2			,     direction=UpperCAmelCase	)
             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=UpperCAmelCase			,     buff=0.0	)
         else:
             cpu_target.target.next_to(cpu_targs[i - 1].target			,     direction=UpperCAmelCase			,     buff=0.0	)
         cpu_targs.append(UpperCAmelCase	)

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

     self.play(*UpperCAmelCase	)
     self.play(*UpperCAmelCase	)

     self.wait()










import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import 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.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 650, '''eval_accuracy''': 0.6, '''eval_loss''': 0.9},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 600, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]						)
class 				__snake_case						(     unittest.TestCase						):




							def    UpperCAmelCase__			(       self     :       int):
									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     :       Union[str, Any]				,				A_     :       str=1):
									# 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}-single"""				,				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}				,				metric_definitions=self.env.metric_definitions				,				py_version='''py36'''				,				)




							def    UpperCAmelCase__			(       self     :       List[str]				,				A_     :       Optional[Any]):
									TrainingJobAnalytics(A_).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""")







							def    UpperCAmelCase__			(       self     :       int):
									# create estimator
									lowerCAmelCase_			:     List[Any]									= self.create_estimator()

									# run training
									estimator.fit()

									# result dataframe
									lowerCAmelCase_			:     Union[str, Any]									= TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

									# extract kpis
									lowerCAmelCase_			:     Optional[Any]									= list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value'''])
									lowerCAmelCase_			:     Optional[int]									= 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'''				,				9_9_9_9_9_9)
									)

									# 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_)





