






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_model_parallelism.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1_6_0_0, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1_6_0_0, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]							)
class 						UpperCAmelCase__ (							unittest.TestCase							):







		def  A__      (					self		):
						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  A__      (					self       ,A__		):
						# configuration for running training on smdistributed Model Parallel
						_A	:				int   =      {
						    '''enabled''': True,
						    '''processes_per_host''': 8,
						}
						_A	:				str   =      {
						    '''enabled''': True,
						    '''parameters''': {
						        '''microbatches''': 4,
						        '''placement_strategy''': '''spread''',
						        '''pipeline''': '''interleaved''',
						        '''optimize''': '''speed''',
						        '''partitions''': 4,
						        '''ddp''': True,
						    },
						}

						_A	:				int   =      {'''smdistributed''': {'''modelparallel''': smp_options}, '''mpi''': mpi_options}

						_A	:				Dict   =      '''trainer''' if self.script == '''run_glue.py''' else '''smtrainer'''
						# 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}-{instance_count}-smp-{name_extension}"""       ,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,
						        '''max_steps''': 500,
						    }       ,metric_definitions=self.env.metric_definitions       ,distribution=A__       ,py_version='''py36'''       ,)







		def  A__      (					self       ,A__		):
						TrainingJobAnalytics(A__		).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""		)







		@parameterized.expand([(1,)]		)
		def  A__      (					self       ,A__		):
						# create estimator
						_A	:				List[str]   =      self.create_estimator(A__		)

						# run training
						estimator.fit()

						# result dataframe
						_A	:				Optional[int]   =      TrainingJobAnalytics(estimator.latest_training_job.name		).dataframe()

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











from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

_UpperCamelCase			:	Union[str, Any]								={'UserAgent': UserAgent().random}



def  a__ (__lowercase							:Optional[Any]   )		-> dict:
				_A	:				str   =      script.contents[0]
				_A	:				Dict   =      json.loads(data[data.find('''{"config"'''   ) : -1]   )
				return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]






class 						UpperCAmelCase__ :







		def __init__(					self       ,A__		):
						_A	:				Any   =      f"""https://www.instagram.com/{username}/"""
						_A	:				Optional[Any]   =      self.get_json()







		def  A__      (					self		):
						_A	:				str   =      requests.get(self.url       ,headers=A__		).text
						_A	:				Dict   =      BeautifulSoup(A__       ,'''html.parser'''		).find_all('''script'''		)
						try:
										return extract_user_profile(scripts[4]		)
						except (json.decoder.JSONDecodeError, KeyError):
										return extract_user_profile(scripts[3]		)







		def __repr__(					self		):
						return f"""{self.__class__.__name__}('{self.username}')"""







		def __str__(					self		):
						return f"""{self.fullname} ({self.username}) is {self.biography}"""







		@property
		def  A__      (					self		):
						return self.user_data["username"]







		@property
		def  A__      (					self		):
						return self.user_data["full_name"]







		@property
		def  A__      (					self		):
						return self.user_data["biography"]







		@property
		def  A__      (					self		):
						return self.user_data["business_email"]







		@property
		def  A__      (					self		):
						return self.user_data["external_url"]







		@property
		def  A__      (					self		):
						return self.user_data["edge_followed_by"]["count"]







		@property
		def  A__      (					self		):
						return self.user_data["edge_follow"]["count"]







		@property
		def  A__      (					self		):
						return self.user_data["edge_owner_to_timeline_media"]["count"]







		@property
		def  A__      (					self		):
						return self.user_data["profile_pic_url_hd"]







		@property
		def  A__      (					self		):
						return self.user_data["is_verified"]







		@property
		def  A__      (					self		):
						return self.user_data["is_private"]



def  a__ (__lowercase							:str = "github"   )		-> None:
				import os

				if os.environ.get('''CI'''   ):
								return  # test failing on GitHub Actions
				_A	:				Optional[int]   =      InstagramUser(__lowercase   )
				assert instagram_user.user_data
				assert isinstance(instagram_user.user_data					,  __lowercase   )
				assert instagram_user.username == username
				if username != "github":
								return
				assert instagram_user.fullname == "GitHub"
				assert instagram_user.biography == "Built for developers."
				assert instagram_user.number_of_posts > 150
				assert instagram_user.number_of_followers > 12_0000
				assert instagram_user.number_of_followings > 15
				assert instagram_user.email == "support@github.com"
				assert instagram_user.website == "https://github.com/readme"
				assert instagram_user.profile_picture_url.startswith('''https://instagram.'''   )
				assert instagram_user.is_verified is True
				assert instagram_user.is_private is False


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	_UpperCamelCase			:	List[Any]								=InstagramUser('github')
	print(instagram_user)
	print(f'''{instagram_user.number_of_posts = }''')
	print(f'''{instagram_user.number_of_followers = }''')
	print(f'''{instagram_user.number_of_followings = }''')
	print(f'''{instagram_user.email = }''')
	print(f'''{instagram_user.website = }''')
	print(f'''{instagram_user.profile_picture_url = }''')
	print(f'''{instagram_user.is_verified = }''')
	print(f'''{instagram_user.is_private = }''')



