from math import factorial





class       lowercase__		:


						def __init__( self			:       List[Any]					,	UpperCamelCase__			:       Any					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							real
												if isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Dict           =							[1] * rank
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							rank


						def __repr__( self			:       int		):





												'''simple docstring'''





												return (
												    f"""{self.real}+"""
												    f"""{'+'.join(str(UpperCamelCase__		)+'E'+str(n+1		)for n,dual in enumerate(self.duals		)		)}"""
												)


						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							self.duals.copy()
												while cur[-1] == 0:
																		cur.pop(-1		)
												return Dual(self.real					,	UpperCamelCase__		)


						def __add__( self			:       List[Any]					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												if not isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																		return Dual(self.real + other					,	self.duals		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.duals.copy()
												SCREAMING_SNAKE_CASE							:					List[str]           =							other.duals.copy()
												if len(UpperCamelCase__		) > len(UpperCamelCase__		):
																		o_dual.extend([1] * (len(UpperCamelCase__		) - len(UpperCamelCase__		))		)
												elif len(UpperCamelCase__		) < len(UpperCamelCase__		):
																		s_dual.extend([1] * (len(UpperCamelCase__		) - len(UpperCamelCase__		))		)
												SCREAMING_SNAKE_CASE							:					Dict           =							[]
												for i in range(len(UpperCamelCase__		)		):
																		new_duals.append(s_dual[i] + o_dual[i]		)
												return Dual(self.real + other.real					,	UpperCamelCase__		)

						UpperCamelCase_  =	__add__


						def __sub__( self			:       Optional[int]					,	UpperCamelCase__			:       Union[str, Any]		):





												'''simple docstring'''





												return self + other * -1


						def __mul__( self			:       Any					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												if not isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[]
																		for i in self.duals:
																								new_duals.append(i * other		)
																		return Dual(self.real * other					,	UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							[0] * (len(self.duals		) + len(other.duals		) + 1)
												for i, item in enumerate(self.duals		):
																		for j, jtem in enumerate(other.duals		):
																								new_duals[i + j + 1] += item * jtem
												for k in range(len(self.duals		)		):
																		new_duals[k] += self.duals[k] * other.real
												for index in range(len(other.duals		)		):
																		new_duals[index] += other.duals[index] * self.real
												return Dual(self.real * other.real					,	UpperCamelCase__		)

						UpperCamelCase_  =	__mul__


						def __truediv__( self			:       Any					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												if not isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Any           =							[]
																		for i in self.duals:
																								new_duals.append(i / other		)
																		return Dual(self.real / other					,	UpperCamelCase__		)
												raise ValueError


						def __floordiv__( self			:       Optional[int]					,	UpperCamelCase__			:       Union[str, Any]		):





												'''simple docstring'''





												if not isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[]
																		for i in self.duals:
																								new_duals.append(i // other		)
																		return Dual(self.real // other					,	UpperCamelCase__		)
												raise ValueError



						def __pow__( self			:       Optional[int]					,	UpperCamelCase__			:       int		):





												'''simple docstring'''





												if n < 0 or isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																		raise ValueError('''power must be a positive integer'''		)
												if n == 0:
																		return 1
												if n == 1:
																		return self
												SCREAMING_SNAKE_CASE							:					str           =							self
												for _ in range(n - 1		):
																		x *= self
												return x






def    A			(					_lowercase	,						_lowercase	,						_lowercase		):
						if not callable(_lowercase		):
												raise ValueError('''differentiate() requires a function as input for func'''		)
						if not isinstance(_lowercase	,						(float, int)		):
												raise ValueError('''differentiate() requires a float as input for position'''		)
						if not isinstance(_lowercase	,						_lowercase		):
												raise ValueError('''differentiate() requires an int as input for order'''		)
						SCREAMING_SNAKE_CASE							:					List[Any]           =							Dual(_lowercase	,						1		)
						SCREAMING_SNAKE_CASE							:					Dict           =							func(_lowercase		)
						if order == 0:
												return result.real
						return result.duals[order - 1] * factorial(_lowercase		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					def    A			(					_lowercase		):
											return y**2 * y**4



					print(differentiate(f, 9, 2))






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       lowercase__		(     unittest.TestCase):


						def  __A ( self			:       Any		):





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


						def  __A ( self			:       str					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							f"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
												# distributed data settings
												SCREAMING_SNAKE_CASE							:					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=UpperCamelCase__					,	instance_count=UpperCamelCase__					,	instance_type=self.instance_type					,	debugger_hook_config=UpperCamelCase__					,	hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path}					,	metric_definitions=self.env.metric_definitions					,	distribution=UpperCamelCase__					,	py_version='''py36'''					,	)


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       List[str]		):





												'''simple docstring'''





												TrainingJobAnalytics(UpperCamelCase__		).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""		)



						@parameterized.expand([(2,)]		)
						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							self.create_estimator(UpperCamelCase__		)

												# run training
												estimator.fit()

												# result dataframe
												SCREAMING_SNAKE_CASE							:					Optional[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							:					Dict           =							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[Any]           =							(
												    Session().describe_training_job(estimator.latest_training_job.name		).get('''TrainingTimeInSeconds'''					,	99_9999		)
												)

												# 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__		)





