"""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 	UpperCamelCase    (					unittest.TestCase		):



				def       _lowercase  (self       :   List[str])					->			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=_A			,						)
					assert hasattr(self			,						'env')



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

					__snake_case    :							List[str]   	=				{'smdistributed': {'modelparallel': smp_options}, 'mpi': mpi_options}

					__snake_case    :							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': 5_00,
					    }			,						metric_definitions=self.env.metric_definitions			,						distribution=_A			,						py_version='py36'			,						)



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






				@parameterized.expand([(1,)])
				def       _lowercase  (self       :   Optional[Any]			,						_A       :   Dict)					->			str:
					# create estimator
					__snake_case    :							List[str]   	=				self.create_estimator(_A)

					# run training
					estimator.fit()

					# result dataframe
					__snake_case    :							int   	=				TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

					# extract kpis
					__snake_case    :							Union[str, Any]   	=				list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value'])
					__snake_case    :							Dict   	=				list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value'])
					# get train time from SageMaker job, this includes starting, preprocessing, stopping
					__snake_case    :							Tuple   	=				(
					    Session().describe_training_job(estimator.latest_training_job.name).get('TrainingTimeInSeconds'			,						99_99_99)
					)

					# 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 argparse
from collections import defaultdict

import yaml


_a  :  Tuple=				"docs/source/en/_toctree.yml"
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Optional[Any]						)							->    Dict:

	'''simple docstring'''





	__snake_case    :							Union[str, Any]   	=				defaultdict(UpperCAmelCase_						)
	__snake_case    :							List[str]   	=				[]
	__snake_case    :							Tuple   	=				[]
	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_						)

	__snake_case    :							Any   	=				new_doc_list
	__snake_case    :							Dict   	=				[key for key, value in counts.items() if value > 1]

	__snake_case    :							Optional[int]   	=				[]
	for duplicate_key in duplicates:
		__snake_case    :							Tuple   	=				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]						)
	__snake_case    :							List[Any]   	=				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 						__UpperCAmelCase       (					UpperCAmelCase_					:       Optional[int]=False						)							->    str:

	'''simple docstring'''





	with open(UpperCAmelCase_    ,							encoding='utf-8'						) as f:
		__snake_case    :							List[Any]   	=				yaml.safe_load(f.read()						)

	# Get to the API doc
	__snake_case    :							str   	=				0
	while content[api_idx]["title"] != "API":
		api_idx += 1
	__snake_case    :							Union[str, Any]   	=				content[api_idx]['sections']

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

	__snake_case    :							Optional[Any]   	=				api_doc[scheduler_idx]['sections']
	__snake_case    :							Tuple   	=				clean_doc_toc(UpperCAmelCase_						)

	__snake_case    :							Tuple   	=				False
	if new_scheduler_doc != scheduler_doc:
		__snake_case    :							Optional[int]   	=				True
		if overwrite:
			__snake_case    :							Tuple   	=				new_scheduler_doc

	if diff:
		if overwrite:
			__snake_case    :							Optional[int]   	=				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 						__UpperCAmelCase       (					UpperCAmelCase_					:       Any=False						)							->    Union[str, Any]:

	'''simple docstring'''





	with open(UpperCAmelCase_    ,							encoding='utf-8'						) as f:
		__snake_case    :							Dict   	=				yaml.safe_load(f.read()						)

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

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

	__snake_case    :							Optional[int]   	=				False
	__snake_case    :							Optional[Any]   	=				api_doc[pipeline_idx]['sections']
	__snake_case    :							Tuple   	=				[]

	# sort sub pipeline docs
	for pipeline_doc in pipeline_docs:
		if "section" in pipeline_doc:
			__snake_case    :							List[str]   	=				pipeline_doc['section']
			__snake_case    :							Optional[int]   	=				clean_doc_toc(UpperCAmelCase_						)
			if overwrite:
				__snake_case    :							List[Any]   	=				new_sub_pipeline_doc
		new_pipeline_docs.append(UpperCAmelCase_						)

	# sort overall pipeline doc
	__snake_case    :							List[Any]   	=				clean_doc_toc(UpperCAmelCase_						)

	if new_pipeline_docs != pipeline_docs:
		__snake_case    :							Dict   	=				True
		if overwrite:
			__snake_case    :							Tuple   	=				new_pipeline_docs

	if diff:
		if overwrite:
			__snake_case    :							Union[str, Any]   	=				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__":
	_a  :  Optional[int]=				argparse.ArgumentParser()
	parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
	_a  :  List[str]=				parser.parse_args()

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





