



'''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.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 650, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_ddp.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf_dist.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.6, """eval_loss""": 0.7},
        },
    ]			)
class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	def      A	(						self			:     int		)			->		List[str]:
								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=__snake_case		,			)
								assert hasattr(self		,			'''env'''		)







	def      A	(						self			:     int		,			__snake_case			:     List[Any]		)			->		Union[str, Any]:
								UpperCAmelCase :       List[Any] 							=		F"""{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}"""
								# distributed data settings
								UpperCAmelCase :       Optional[Any] 							=		{'''smdistributed''': {'''dataparallel''': {'''enabled''': True}}} if self.script != '''run_ddp.py''' else None

								# 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=__snake_case		,			instance_count=__snake_case		,			instance_type=self.instance_type		,			debugger_hook_config=__snake_case		,			hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path}		,			metric_definitions=self.env.metric_definitions		,			distribution=__snake_case		,			py_version='''py36'''		,			)







	def      A	(						self			:     Dict		,			__snake_case			:     Optional[Any]		)			->		List[str]:
								TrainingJobAnalytics(__snake_case		).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""		)







	@parameterized.expand([(2,)]		)
	def      A	(						self			:     Tuple		,			__snake_case			:     int		)			->		Any:
								# create estimator
								UpperCAmelCase :       Dict 							=		self.create_estimator(__snake_case		)

								# run training
								estimator.fit()

								# result dataframe
								UpperCAmelCase :       List[str] 							=		TrainingJobAnalytics(estimator.latest_training_job.name		).dataframe()

								# extract kpis
								UpperCAmelCase :       str 							=		list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']		)
								UpperCAmelCase :       Union[str, Any] 							=		list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']		)
								# get train time from SageMaker job, this includes starting, preprocessing, stopping
								UpperCAmelCase :       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}		,			__snake_case		)










'''simple docstring'''






from manim import *

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	def      A	(						self			:     Union[str, Any]		)			->		List[str]:
								UpperCAmelCase :       Optional[Any] 							=		Rectangle(height=0.5		,			width=0.5		)
								UpperCAmelCase :       str 							=		Rectangle(height=0.46		,			width=0.46		).set_stroke(width=0		)

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

								UpperCAmelCase :       Optional[Any] 							=		[mem.copy() for i in range(4		)]
								UpperCAmelCase :       Union[str, Any] 							=		VGroup(*__snake_case		).arrange(__snake_case		,			buff=0		)
								UpperCAmelCase :       List[str] 							=		Text('''GPU'''		,			font_size=24		)
								UpperCAmelCase :       Dict 							=		Group(__snake_case		,			__snake_case		).arrange(__snake_case		,			buff=0.5		,			aligned_edge=__snake_case		)
								gpu.move_to([-1, -1, 0]		)
								self.add(__snake_case		)

								UpperCAmelCase :       int 							=		[mem.copy() for i in range(6		)]
								UpperCAmelCase :       Union[str, Any] 							=		VGroup(*__snake_case		).arrange(__snake_case		,			buff=0		)

								UpperCAmelCase :       List[str] 							=		Text('''Model'''		,			font_size=24		)
								UpperCAmelCase :       Tuple 							=		Group(__snake_case		,			__snake_case		).arrange(__snake_case		,			buff=0.5		,			aligned_edge=__snake_case		)
								model.move_to([3, -1.0, 0]		)
								self.add(__snake_case		)

								UpperCAmelCase :       Any 							=		[]
								for i, rect in enumerate(__snake_case		):
															rect.set_stroke(__snake_case		)
															# target = fill.copy().set_fill(YELLOW, opacity=0.7)
															# target.move_to(rect)
															# self.add(target)

															UpperCAmelCase :       Dict 							=		Rectangle(height=0.46 / 4		,			width=0.46 / 3		).set_stroke(width=0.0		).set_fill(__snake_case		,			opacity=0.7		)

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

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

								UpperCAmelCase :       Any 							=		Text('''Loaded Checkpoint'''		,			font_size=24		)
								UpperCAmelCase :       Union[str, Any] 							=		Group(__snake_case		,			__snake_case		).arrange(__snake_case		,			aligned_edge=__snake_case		,			buff=0.4		)
								checkpoint.move_to([3, 0.5, 0]		)

								UpperCAmelCase :       Optional[int] 							=		Square(side_length=2.2		)
								key.move_to([-5, 2, 0]		)

								UpperCAmelCase :       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]		)

								self.add(__snake_case		,			__snake_case		)

								UpperCAmelCase :       Tuple 							=		MarkupText(
								    F"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""		,			font_size=18		,			)

								blue_text.next_to(__snake_case		,			DOWN * 2.4		,			aligned_edge=key_text.get_left()		)

								UpperCAmelCase :       List[Any] 							=		MarkupText(
								    F"""Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>."""		,			font_size=24		,			)
								step_a.move_to([2, 2, 0]		)
								self.play(Write(__snake_case		)		,			Write(__snake_case		)		)

								self.play(Write(__snake_case		,			run_time=1		)		,			Create(__snake_case		,			run_time=1		)		)

								UpperCAmelCase :       Tuple 							=		[]
								UpperCAmelCase :       int 							=		[]
								for i, rect in enumerate(__snake_case		):
															UpperCAmelCase :       Any 							=		fill.copy().set_fill(__snake_case		,			opacity=0.7		)
															target.move_to(__snake_case		)
															first_animations.append(GrowFromCenter(__snake_case		,			run_time=1		)		)

															UpperCAmelCase :       List[str] 							=		target.copy()
															cpu_target.generate_target()
															if i < 5:
																						cpu_target.target.move_to(cpu_left_col_base[i + 1]		)
															else:
																						cpu_target.target.move_to(cpu_right_col_base[i - 5]		)
															second_animations.append(MoveToTarget(__snake_case		,			run_time=1.5		)		)

								self.play(*__snake_case		)
								self.play(*__snake_case		)
								self.wait()





