
"""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_model_parallelism.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1600, "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": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]							)
class 			_SCREAMING_SNAKE_CASE					(			unittest.TestCase							):






			def  __lowerCAmelCase (					self       )			->     Dict:
						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  __lowerCAmelCase (					self       ,   __A       )			->     Any:
						# configuration for running training on smdistributed Model Parallel
						lowerCAmelCase_    :Union[str, Any]			     =  {
						    """enabled""": True,
						    """processes_per_host""": 8,
						}
						lowerCAmelCase_    :Tuple			     =  {
						    """enabled""": True,
						    """parameters""": {
						        """microbatches""": 4,
						        """placement_strategy""": """spread""",
						        """pipeline""": """interleaved""",
						        """optimize""": """speed""",
						        """partitions""": 4,
						        """ddp""": True,
						    },
						}

						lowerCAmelCase_    :Any			     =  {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

						lowerCAmelCase_    :Any			     =  """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  __lowerCAmelCase (					self       ,   __A       )			->     List[Any]:
						TrainingJobAnalytics(__A       ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""       )






			@parameterized.expand([(1,)]       )
			def  __lowerCAmelCase (					self       ,   __A       )			->     List[str]:
						# create estimator
						lowerCAmelCase_    :Any			     =  self.create_estimator(__A       )

						# run training
						estimator.fit()

						# result dataframe
						lowerCAmelCase_    :Optional[Any]			     =  TrainingJobAnalytics(estimator.latest_training_job.name       ).dataframe()

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










"""simple docstring"""



import json
import os
import re
import sys
import urllib.request

import requests
from bsa import BeautifulSoup

__snake_case   			=    {
    """User-Agent""": """Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"""
    """ (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"""
}







def   __lowerCAmelCase						(						lowercase  :  str = "dhaka"						,							lowercase  :  int = 5 )	->						int:


    """simple docstring"""





    snake_case    :      List[Any]      					=    min(lowercase						,							50 )  # Prevent abuse!
    snake_case    :      Optional[Any]      					=    {
        "q": query,
        "tbm": "isch",
        "hl": "en",
        "ijn": "0",
    }

    snake_case    :      str      					=    requests.get("https://www.google.com/search"						,							params=lowercase						,							headers=lowercase )
    snake_case    :      List[str]      					=    BeautifulSoup(html.text						,							"html.parser" )
    snake_case    :      List[Any]      					=    "".join(
        re.findall(R"AF_initDataCallback\(([^<]+)\);"						,							str(soup.select("script" ) ) ) )

    snake_case    :      Optional[Any]      					=    json.dumps(lowercase )
    snake_case    :      str      					=    json.loads(lowercase )

    snake_case    :      List[str]      					=    re.findall(
        R"\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\","						,							lowercase						,							)
    if not matched_google_image_data:
        return 0

    snake_case    :      List[str]      					=    re.sub(
        R"\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]"						,							""						,							str(lowercase )						,							)

    snake_case    :      Dict      					=    re.findall(
        R"(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]"						,							lowercase						,							)
    for index, fixed_full_res_image in enumerate(lowercase ):
        if index >= max_images:
            return index
        snake_case    :      List[str]      					=    bytes(lowercase						,							"ascii" ).decode(
            "unicode-escape" )
        snake_case    :      Dict      					=    bytes(lowercase						,							"ascii" ).decode(
            "unicode-escape" )
        snake_case    :      int      					=    urllib.request.build_opener()
        snake_case    :      int      					=    [
            (
                "User-Agent",
                "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
                " (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582",
            )
        ]
        urllib.request.install_opener(lowercase )
        snake_case    :      Optional[int]      					=    F'query_{query.replace(" "						,							"_" )}'
        if not os.path.exists(lowercase ):
            os.makedirs(lowercase )
        urllib.request.urlretrieve(  # noqa: S310
            lowercase						,							F'{path_name}/original_size_img_{index}.jpg' )
    return index


if __name__ == "__main__":
  try:
    __snake_case   			=    download_images_from_google_query(sys.argv[1])
    print(F'''{image_count} images were downloaded to disk.''')
  except IndexError:
    print("""Please provide a search term.""")
    raise





