




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": 6_50, "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": 6_00, "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": 6_00, "eval_accuracy": 0.6, "eval_loss": 0.7},
        },
    ] )
class    lowercase_		(unittest.TestCase ):



						def 	__a					(			self	:   Optional[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=snake_case__					,							)
								assert hasattr(self					,							'env'  )



						def 	__a					(			self	:   List[str]					,							snake_case__	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							f'''{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'''
								# distributed data settings
								SCREAMING_SNAKE_CASE_					      =							{'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	:   int					,							snake_case__	:   Tuple  ):

								"""simple docstring"""

								TrainingJobAnalytics(snake_case__  ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''  )






						@parameterized.expand([(2,)]  )
						def 	__a					(			self	:   List[str]					,							snake_case__	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.create_estimator(snake_case__  )

								# run training
								estimator.fit()

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

								# extract kpis
								SCREAMING_SNAKE_CASE_					      =							list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']  )
								SCREAMING_SNAKE_CASE_					      =							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_					      =							(
								    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}					,							snake_case__  )





from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
SCREAMING_SNAKE_CASE:     Optional[int]									= 2_9_9_7_9_2_4_5_8

# Symbols
SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE:     Tuple									= symbols('''ct x y z''')


def 					_a							(					lowerCAmelCase )->	float:
		if velocity > c:
				raise ValueError('Speed must not exceed light speed 299,792,458 [m/s]!' )
		elif velocity < 1:
				# Usually the speed should be much higher than 1 (c order of magnitude)
				raise ValueError('Speed must be greater than or equal to 1!' )

		return velocity / c


def 					_a							(					lowerCAmelCase )->	float:
		return 1 / sqrt(1 - beta(lowerCAmelCase ) ** 2 )


def 					_a							(					lowerCAmelCase )->	np.ndarray:
		return np.array(
		    [
		        [gamma(lowerCAmelCase ), -gamma(lowerCAmelCase ) * beta(lowerCAmelCase ), 0, 0],
		        [-gamma(lowerCAmelCase ) * beta(lowerCAmelCase ), gamma(lowerCAmelCase ), 0, 0],
		        [0, 0, 1, 0],
		        [0, 0, 0, 1],
		    ] )


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase = None )->	np.ndarray:
		# Ensure event is not empty
		if event is None:
				SCREAMING_SNAKE_CASE_					      =							np.array([ct, x, y, z] )  # Symbolic four vector
		else:
				event[0] *= c  # x0 is ct (speed of light * time)

		return transformation_matrix(lowerCAmelCase ) @ event


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						# Example of symbolic vector:
						SCREAMING_SNAKE_CASE:     int									= transform(2_9_9_7_9_2_4_5)
						print('''Example of four vector: ''')
						print(f"""ct' = {four_vector[0]}""")
						print(f"""x' = {four_vector[1]}""")
						print(f"""y' = {four_vector[2]}""")
						print(f"""z' = {four_vector[3]}""")

						# Substitute symbols with numerical values
						SCREAMING_SNAKE_CASE:     List[Any]									= {ct: c, x: 1, y: 1, z: 1}
						SCREAMING_SNAKE_CASE:     Optional[Any]									= [four_vector[i].subs(sub_dict) for i in range(4)]

						print(f"""\n{numerical_vector}""")