






"""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""": 1_600, """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_600, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
    ]     )
class      UpperCamelCase		(unittest.TestCase     ):






						def      __snake_case      (		self  :str				)      ->Optional[int]:
										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=__magic_name__			, )
										assert hasattr(self			, """env"""				)






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

										lowercase    :      Optional[Any]       = {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

										lowercase    :      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=__magic_name__			, instance_type=self.instance_type			, debugger_hook_config=__magic_name__			, hyperparameters={
										        **self.env.hyperparameters,
										        """model_name_or_path""": self.model_name_or_path,
										        """max_steps""": 500,
										    }			, metric_definitions=self.env.metric_definitions			, distribution=__magic_name__			, py_version="""py36"""			, )






						def      __snake_case      (		self  :Optional[Any]			, __magic_name__  :Any				)      ->str:
										TrainingJobAnalytics(__magic_name__				).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""				)






						@parameterized.expand([(1,)]				)
						def      __snake_case      (		self  :int			, __magic_name__  :int				)      ->str:
										# create estimator
										lowercase    :      List[str]       = self.create_estimator(__magic_name__				)

										# run training
										estimator.fit()

										# result dataframe
										lowercase    :      Any       = TrainingJobAnalytics(estimator.latest_training_job.name				).dataframe()

										# extract kpis
										lowercase    :      Optional[Any]       = list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]				)
										lowercase    :      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
										lowercase    :      Dict       = (
										    Session().describe_training_job(estimator.latest_training_job.name				).get("""TrainingTimeInSeconds"""			, 999_999				)
										)

										# 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}			, __magic_name__				)








"""simple docstring"""







from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame


def      UpperCamelCase       ( _A = "laptop" )     ->       DataFrame:
				lowercase    :      List[str]       = F"""https://www.amazon.in/laptop/s?k={product}"""
				lowercase    :      str       = {
				    """User-Agent""": """Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36""",
				    """Accept-Language""": """en-US, en;q=0.5""",
				}
				lowercase    :      str       = BeautifulSoup(requests.get(_A  ,				headers=_A ).text )
				# Initialize a Pandas dataframe with the column titles
				lowercase    :      Optional[Any]       = DataFrame(
				    columns=[
				        """Product Title""",
				        """Product Link""",
				        """Current Price of the product""",
				        """Product Rating""",
				        """MRP of the product""",
				        """Discount""",
				    ] )
				# Loop through each entry and store them in the dataframe
				for item, _ in zip_longest(
				    soup.find_all(
				        """div"""  ,				attrs={"""class""": """s-result-item""", """data-component-type""": """s-search-result"""}  ,				)  ,				soup.find_all("""div"""  ,				attrs={"""class""": """a-row a-size-base a-color-base"""} )  ,				):
								try:
												lowercase    :      str       = item.ha.text
												lowercase    :      Tuple       = """https://www.amazon.in/""" + item.ha.a["""href"""]
												lowercase    :      Tuple       = item.find("""span"""  ,				attrs={"""class""": """a-offscreen"""} ).text
												try:
																lowercase    :      Any       = item.find("""span"""  ,				attrs={"""class""": """a-icon-alt"""} ).text
												except AttributeError:
																lowercase    :      Tuple       = """Not available"""
												try:
																lowercase    :      Any       = (
																    """₹"""
																    + item.find(
																        """span"""  ,				attrs={"""class""": """a-price a-text-price"""} ).text.split("""₹""" )[1]
																)
												except AttributeError:
																lowercase    :      Any       = """"""
												try:
																lowercase    :      List[str]       = float(
																    (
																        (
																            float(product_mrp.strip("""₹""" ).replace(""","""  ,				"""""" ) )
																            - float(product_price.strip("""₹""" ).replace(""","""  ,				"""""" ) )
																        )
																        / float(product_mrp.strip("""₹""" ).replace(""","""  ,				"""""" ) )
																    )
																    * 100 )
												except ValueError:
																lowercase    :      Tuple       = float("""nan""" )
								except AttributeError:
												pass
								lowercase    :      Union[str, Any]       = [
								    product_title,
								    product_link,
								    product_price,
								    product_rating,
								    product_mrp,
								    discount,
								]
				lowercase    :      Tuple       = """ """
				lowercase    :      List[str]       = """ """
				data_frame.index += 1
				return data_frame


if __name__ == "__main__":
				_lowerCAmelCase            =	'headphones'
				get_amazon_product_data(product).to_csv(F'Amazon Product Data for {product}.csv')
