
import html

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends


if is_bsa_available():
		import bsa
		from bsa import BeautifulSoup


A		: Optional[Any]								=     logging.get_logger(__name__)



class    a_ (			_a    ):






			def __init__(      self				,			**__UpperCamelCase  ):
									requires_backends(self				,			["""bs4"""]  )
									super().__init__(**__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									_lowercase					       =					[]
									_lowercase					       =					[]
									_lowercase					       =					element if element.name else element.parent
									for parent in child.parents:  # type: bs4.element.Tag
															_lowercase					       =					parent.find_all(child.name				,			recursive=__UpperCamelCase  )
															xpath_tags.append(child.name  )
															xpath_subscripts.append(
															    0 if 1 == len(__UpperCamelCase  ) else next(i for i, s in enumerate(__UpperCamelCase				,			1  ) if s is child  )  )
															_lowercase					       =					parent
									xpath_tags.reverse()
									xpath_subscripts.reverse()
									return xpath_tags, xpath_subscripts






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									_lowercase					       =					BeautifulSoup(__UpperCamelCase				,			"""html.parser"""  )

									_lowercase					       =					[]
									_lowercase					       =					[]
									_lowercase					       =					[]

									for element in html_code.descendants:
															if type(__UpperCamelCase  ) == bsa.element.NavigableString:
																					if type(element.parent  ) != bsa.element.Tag:
																											continue

																					_lowercase					       =					html.unescape(__UpperCamelCase  ).strip()
																					if not text_in_this_tag:
																											continue

																					all_doc_strings.append(__UpperCamelCase  )

																					_lowercase       ,						_lowercase					       =					self.xpath_soup(__UpperCamelCase  )
																					stringaxtag_seq.append(__UpperCamelCase  )
																					stringaxsubs_seq.append(__UpperCamelCase  )

									if len(__UpperCamelCase  ) != len(__UpperCamelCase  ):
															raise ValueError("""Number of doc strings and xtags does not correspond"""  )
									if len(__UpperCamelCase  ) != len(__UpperCamelCase  ):
															raise ValueError("""Number of doc strings and xsubs does not correspond"""  )

									return all_doc_strings, stringaxtag_seq, stringaxsubs_seq






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase  ):
									_lowercase					       =					""""""
									for tagname, subs in zip(__UpperCamelCase				,			__UpperCamelCase  ):
															xpath += f"""/{tagname}"""
															if subs != 0:
																					xpath += f"""[{subs}]"""
									return xpath






			def __call__(      self				,			__UpperCamelCase  ):
									_lowercase					       =					False

									# Check that strings has a valid type
									if isinstance(__UpperCamelCase				,			__UpperCamelCase  ):
															_lowercase					       =					True
									elif isinstance(__UpperCamelCase				,			(list, tuple)  ):
															if len(__UpperCamelCase  ) == 0 or isinstance(html_strings[0]				,			__UpperCamelCase  ):
																					_lowercase					       =					True

									if not valid_strings:
															raise ValueError(
															    """HTML strings must of type `str`, `List[str]` (batch of examples), """
															    f"""but is of type {type(__UpperCamelCase  )}."""  )

									_lowercase					       =					bool(isinstance(__UpperCamelCase				,			(list, tuple)  ) and (isinstance(html_strings[0]				,			__UpperCamelCase  ))  )

									if not is_batched:
															_lowercase					       =					[html_strings]

									# Get nodes + xpaths
									_lowercase					       =					[]
									_lowercase					       =					[]
									for html_string in html_strings:
															_lowercase       ,						_lowercase       ,						_lowercase					       =					self.get_three_from_single(__UpperCamelCase  )
															nodes.append(__UpperCamelCase  )
															_lowercase					       =					[]
															for node, tag_list, sub_list in zip(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  ):
																					_lowercase					       =					self.construct_xpath(__UpperCamelCase				,			__UpperCamelCase  )
																					xpath_strings.append(__UpperCamelCase  )
															xpaths.append(__UpperCamelCase  )

									# return as Dict
									_lowercase					       =					{"""nodes""": nodes, """xpaths""": xpaths}
									_lowercase					       =					BatchFeature(data=__UpperCamelCase				,			tensor_type=__UpperCamelCase  )

									return encoded_inputs

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    a_ (			unittest.TestCase    ):






			def    UpperCamelCase_				(      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=__UpperCamelCase				,			)
									assert hasattr(self				,			"""env"""  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									# configuration for running training on smdistributed Model Parallel
									_lowercase					       =					{
									    """enabled""": True,
									    """processes_per_host""": 8,
									}
									_lowercase					       =					{
									    """enabled""": True,
									    """parameters""": {
									        """microbatches""": 4,
									        """placement_strategy""": """spread""",
									        """pipeline""": """interleaved""",
									        """optimize""": """speed""",
									        """partitions""": 4,
									        """ddp""": True,
									    },
									}

									_lowercase					       =					{"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

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






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






			@parameterized.expand([(1,)]  )
			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									# create estimator
									_lowercase					       =					self.create_estimator(__UpperCamelCase  )

									# run training
									estimator.fit()

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

									# extract kpis
									_lowercase					       =					list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]  )
									_lowercase					       =					list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]  )
									# get train time from SageMaker job, this includes starting, preprocessing, stopping
									_lowercase					       =					(
									    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}				,			__UpperCamelCase  )