


import argparse
from collections import defaultdict

import yaml


lowerCAmelCase       			=						'docs/source/en/_toctree.yml'







def 			_lowerCamelCase(      lowercase__      )		->      List[Any]:

		'''simple docstring'''







		__lowercase=     defaultdict(lowercase_      )
		__lowercase=     []
		__lowercase=     []
		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(lowercase_      )

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

		__lowercase=     []
		for duplicate_key in duplicates:
				__lowercase=     list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}      )
				if len(lowercase_      ) > 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]      )
		__lowercase=     sorted(lowercase_  ,					key=lambda lowercase__      : s["title"].lower()      )

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

		overview_doc.extend(lowercase_      )

		# Sort
		return overview_doc







def 			_lowerCamelCase(      lowercase__=False      )		->      List[Any]:

		'''simple docstring'''







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

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

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

		__lowercase=     api_doc[scheduler_idx]["""sections"""]
		__lowercase=     clean_doc_toc(lowercase_      )

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

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







def 			_lowerCamelCase(      lowercase__=False      )		->      int:

		'''simple docstring'''







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

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

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

		__lowercase=     False
		__lowercase=     api_doc[pipeline_idx]["""sections"""]
		__lowercase=     []

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

		# sort overall pipeline doc
		__lowercase=     clean_doc_toc(lowercase_      )

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

		if diff:
				if overwrite:
						__lowercase=     api_doc
						with open(lowercase_  ,					'w'  ,					encoding='utf-8'      ) as f:
								f.write(yaml.dump(lowercase_  ,					allow_unicode=lowercase_      )      )
				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__":
	lowerCAmelCase       			=						argparse.ArgumentParser()
	parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
	lowerCAmelCase       			=						parser.parse_args()

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









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''': 6_50, '''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''': 6_00, '''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''': 6_00, '''eval_accuracy''': 0.6, '''eval_loss''': 0.7},
        },
    ]							)
class        _a		(unittest.TestCase							):


					'''simple docstring'''






					def     __A     (					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=A__       ,			)
						assert hasattr(self       ,			"""env"""     )






					def     __A     (					self       ,			A__     ):
						A__				:       int	  =						F"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
						# distributed data settings
						A__				:       str	  =						{"""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=A__       ,			instance_count=A__       ,			instance_type=self.instance_type       ,			debugger_hook_config=A__       ,			hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path}       ,			metric_definitions=self.env.metric_definitions       ,			distribution=A__       ,			py_version="""py36"""       ,			)






					def     __A     (					self       ,			A__     ):
						TrainingJobAnalytics(A__     ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""     )






					@parameterized.expand([(2,)]     )
					def     __A     (					self       ,			A__     ):
						# create estimator
						A__				:       str	  =						self.create_estimator(A__     )

						# run training
						estimator.fit()

						# result dataframe
						A__				:       str	  =						TrainingJobAnalytics(estimator.latest_training_job.name     ).dataframe()

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



