'''simple docstring'''


import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation


def  _lowerCamelCase   (				lowerCamelCase_ :							Dict = 8       ):



					"""simple docstring"""





					UpperCAmelCase_							:       str       =				ascii_letters + digits + punctuation
					return "".join(secrets.choice(__lowerCAmelCase       ) for _ in range(__lowerCAmelCase       )       )


def  _lowerCamelCase   (				lowerCamelCase_ :							Union[str, Any]     ,					lowerCamelCase_ :							int       ):



					"""simple docstring"""





					i -= len(__lowerCAmelCase       )
					UpperCAmelCase_							:       Tuple       =				i // 3
					UpperCAmelCase_							:       Tuple       =				i % 3
					# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
					#     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
					UpperCAmelCase_							:       Optional[int]       =				(
					    chars_incl
					    + random(__lowerCAmelCase     ,					quotient + remainder       )
					    + random(__lowerCAmelCase     ,					__lowerCAmelCase       )
					    + random(__lowerCAmelCase     ,					__lowerCAmelCase       )
					)
					UpperCAmelCase_							:       List[Any]       =				list(__lowerCAmelCase       )
					shuffle(__lowerCAmelCase       )
					return "".join(__lowerCAmelCase       )

					# random is a generalised function for letters, characters and numbers


def  _lowerCamelCase   (				lowerCamelCase_ :							str     ,					lowerCamelCase_ :							Tuple       ):



					"""simple docstring"""





					return "".join(secrets.choice(__lowerCAmelCase       ) for _ in range(__lowerCAmelCase       )       )


def  _lowerCamelCase   (				lowerCamelCase_ :							Optional[int]     ,					lowerCamelCase_ :							Any       ):



					"""simple docstring"""





					pass  # Put your code here...


def  _lowerCamelCase   (				lowerCamelCase_ :							Optional[Any]     ,					lowerCamelCase_ :							Optional[int]       ):



					"""simple docstring"""





					pass  # Put your code here...


def  _lowerCamelCase   (				lowerCamelCase_ :							Dict     ,					lowerCamelCase_ :							Tuple       ):



					"""simple docstring"""





					pass  # Put your code here...


def  _lowerCamelCase   (				lowerCamelCase_ :							str     ,					lowerCamelCase_ :							Tuple = 8       ):



					"""simple docstring"""





					if len(__lowerCAmelCase       ) < min_length:
										# Your Password must be at least 8 characters long
										return False

					UpperCAmelCase_							:       Optional[Any]       =				any(char in ascii_uppercase for char in password       )
					UpperCAmelCase_							:       str       =				any(char in ascii_lowercase for char in password       )
					UpperCAmelCase_							:       Tuple       =				any(char in digits for char in password       )
					UpperCAmelCase_							:       List[str]       =				any(char in punctuation for char in password       )

					return upper and lower and num and spec_char
					# Passwords should contain UPPERCASE, lowerase
					# numbers, and special characters


def  _lowerCamelCase   (				):



					"""simple docstring"""





					UpperCAmelCase_							:       Dict       =				int(input('Please indicate the max length of your password: '       ).strip()       )
					UpperCAmelCase_							:       str       =				input(
					    'Please indicate the characters that must be in your password: '       ).strip()
					print('Password generated:'     ,					password_generator(__lowerCAmelCase       )       )
					print(
					    'Alternative Password generated:'     ,					alternative_password_generator(__lowerCAmelCase     ,					__lowerCAmelCase       )     ,					)
					print('[If you are thinking of using this passsword, You better save it.]'       )


if __name__ == "__main__":
	main()







'''simple docstring'''


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 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  def _UpperCamelCase					(			self						):




       '''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 _UpperCamelCase					(			self				,		snake_case_=1						):




       '''simple docstring'''

       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=snake_case_				,		instance_type=self.instance_type				,		debugger_hook_config=snake_case_				,		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				,		snake_case_						):




       '''simple docstring'''

       TrainingJobAnalytics(snake_case_						).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''						)






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				self.create_estimator()

       # run training
       estimator.fit()

       # result dataframe
       UpperCAmelCase_							:       int       =				TrainingJobAnalytics(estimator.latest_training_job.name						).dataframe()

       # extract kpis
       UpperCAmelCase_							:       Dict       =				list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']						)
       UpperCAmelCase_							:       List[str]       =				list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']						)
       # get train time from SageMaker job, this includes starting, preprocessing, stopping
       UpperCAmelCase_							:       int       =				(
           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}				,		snake_case_						)






