


"""simple docstring"""


import argparse
from collections import defaultdict

import yaml


UpperCamelCase						:				Tuple             =      "docs/source/en/_toctree.yml"







def  __snake_case       (   UpperCamelCase__	)      ->		List[str]:







		"""simple docstring"""





		A					       = defaultdict(UpperCamelCase__	)
		A					       = []
		A					       = []
		for doc in doc_list:
				if "local" in doc:
						counts[doc["local"]] += 1

				if doc["title"].lower() == "overview":
						overview_doc.append({'local': doc['local'], 'title': doc['title']}	)
				else:
						new_doc_list.append(UpperCamelCase__	)

		A					       = new_doc_list
		A					       = [key for key, value in counts.items() if value > 1]

		A					       = []
		for duplicate_key in duplicates:
				A					       = list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}	)
				if len(UpperCamelCase__	) > 1:
						raise ValueError(
						    f'{duplicate_key} is present several times in the documentation table of content at '
						    '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
						    'others.'	)
				# Only add this once
				new_doc.append({'local': duplicate_key, 'title': titles[0]}	)

		# Add none duplicate-keys
		new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]	)
		A					       = sorted(UpperCamelCase__ ,			key=lambda UpperCamelCase__	: s["title"].lower()	)

		# "overview" gets special treatment and is always first
		if len(UpperCamelCase__	) > 1:
				raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'	)

		overview_doc.extend(UpperCamelCase__	)

		# Sort
		return overview_doc







def  __snake_case       (   UpperCamelCase__=False	)      ->		Any:







		"""simple docstring"""





		with open(UpperCamelCase__ ,			encoding='utf-8'	) as f:
				A					       = yaml.safe_load(f.read()	)

		# Get to the API doc
		A					       = 0
		while content[api_idx]["title"] != "API":
				api_idx += 1
		A					       = content[api_idx]['sections']

		# Then to the model doc
		A					       = 0
		while api_doc[scheduler_idx]["title"] != "Schedulers":
				scheduler_idx += 1

		A					       = api_doc[scheduler_idx]['sections']
		A					       = clean_doc_toc(UpperCamelCase__	)

		A					       = False
		if new_scheduler_doc != scheduler_doc:
				A					       = True
				if overwrite:
						A					       = new_scheduler_doc

		if diff:
				if overwrite:
						A					       = api_doc
						with open(UpperCamelCase__ ,			'w' ,			encoding='utf-8'	) as f:
								f.write(yaml.dump(UpperCamelCase__ ,			allow_unicode=UpperCamelCase__	)	)
				else:
						raise ValueError(
						    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'	)







def  __snake_case       (   UpperCamelCase__=False	)      ->		List[str]:







		"""simple docstring"""





		with open(UpperCamelCase__ ,			encoding='utf-8'	) as f:
				A					       = yaml.safe_load(f.read()	)

		# Get to the API doc
		A					       = 0
		while content[api_idx]["title"] != "API":
				api_idx += 1
		A					       = content[api_idx]['sections']

		# Then to the model doc
		A					       = 0
		while api_doc[pipeline_idx]["title"] != "Pipelines":
				pipeline_idx += 1

		A					       = False
		A					       = api_doc[pipeline_idx]['sections']
		A					       = []

		# sort sub pipeline docs
		for pipeline_doc in pipeline_docs:
				if "section" in pipeline_doc:
						A					       = pipeline_doc['section']
						A					       = clean_doc_toc(UpperCamelCase__	)
						if overwrite:
								A					       = new_sub_pipeline_doc
				new_pipeline_docs.append(UpperCamelCase__	)

		# sort overall pipeline doc
		A					       = clean_doc_toc(UpperCamelCase__	)

		if new_pipeline_docs != pipeline_docs:
				A					       = True
				if overwrite:
						A					       = new_pipeline_docs

		if diff:
				if overwrite:
						A					       = api_doc
						with open(UpperCamelCase__ ,			'w' ,			encoding='utf-8'	) as f:
								f.write(yaml.dump(UpperCamelCase__ ,			allow_unicode=UpperCamelCase__	)	)
				else:
						raise ValueError(
						    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'	)


if __name__ == "__main__":
	UpperCamelCase						:				List[str]             =      argparse.ArgumentParser()
	parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
	UpperCamelCase						:				Tuple             =      parser.parse_args()

	check_scheduler_doc(args.fix_and_overwrite)
	check_pipeline_doc(args.fix_and_overwrite)









"""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.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 650, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_ddp.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf_dist.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.6, """eval_loss""": 0.7},
        },
    ]			)
class 			lowerCamelCase__						(   unittest.TestCase			):







						def __a						(							self   :    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=_lowercase			,			)
								assert hasattr(self			,			'env'		)







						def __a						(							self   :    Optional[int]			,			_lowercase   :    int		):
								A					       = f'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
								# distributed data settings
								A					       = {'smdistributed': {'dataparallel': {'enabled': True}}} if self.script != 'run_ddp.py' else None

								# 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=_lowercase			,			instance_count=_lowercase			,			instance_type=self.instance_type			,			debugger_hook_config=_lowercase			,			hyperparameters={**self.env.distributed_hyperparameters, 'model_name_or_path': self.model_name_or_path}			,			metric_definitions=self.env.metric_definitions			,			distribution=_lowercase			,			py_version='py36'			,			)







						def __a						(							self   :    Tuple			,			_lowercase   :    List[str]		):
								TrainingJobAnalytics(_lowercase		).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv'		)







						@parameterized.expand([(2,)]		)
						def __a						(							self   :    List[Any]			,			_lowercase   :    Union[str, Any]		):
								# create estimator
								A					       = self.create_estimator(_lowercase		)

								# run training
								estimator.fit()

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

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





