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''': 16_00, '''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''': 16_00, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
    ]       )
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=_lowerCAmelCase					, )
						assert hasattr(self					, """env"""    )






					def 						__A    (				self					, A__    ):
						# configuration for running training on smdistributed Model Parallel
						A__							:					Union[str, Any]       =	{
						    """enabled""": True,
						    """processes_per_host""": 8,
						}
						A__							:					Any       =	{
						    """enabled""": True,
						    """parameters""": {
						        """microbatches""": 4,
						        """placement_strategy""": """spread""",
						        """pipeline""": """interleaved""",
						        """optimize""": """speed""",
						        """partitions""": 4,
						        """ddp""": True,
						    },
						}

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

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






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






					@parameterized.expand([(1,)]    )
					def 						__A    (				self					, A__    ):
						# create estimator
						A__							:					int       =	self.create_estimator(_lowerCAmelCase    )

						# run training
						estimator.fit()

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

						# extract kpis
						A__							:					Dict       =	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__							:					Tuple       =	(
						    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}					, _lowerCAmelCase    )






# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import doctest
import sys
import warnings
from os.path import abspath, dirname, join

import _pytest

from transformers.testing_utils import HfDoctestModule, HfDocTestParser


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
A_ :				Optional[int]						 =    abspath(join(dirname(__file__), 'src'))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)

def 		UpperCamelCase      (lowercase_:				List[str]      )  ->   Any:
	config.addinivalue_line(
	    """markers"""					,   """is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested"""      )
	config.addinivalue_line(
	    """markers"""					,   """is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested"""      )
	config.addinivalue_line("""markers"""					,   """is_pipeline_test: mark test to run only when pipelines are tested"""      )
	config.addinivalue_line("""markers"""					,   """is_staging_test: mark test to run only in the staging environment"""      )
	config.addinivalue_line("""markers"""					,   """accelerate_tests: mark test that require accelerate"""      )
	config.addinivalue_line("""markers"""					,   """tool_tests: mark the tool tests that are run on their specific schedule"""      )

def 		UpperCamelCase      (lowercase_:				Optional[int]      )  ->   Optional[Any]:
	from transformers.testing_utils import pytest_addoption_shared

	pytest_addoption_shared(lowercase_      )

def 		UpperCamelCase      (lowercase_:				List[str]      )  ->   Optional[Any]:
	from transformers.testing_utils import pytest_terminal_summary_main

	A__							:					List[Any]       =	terminalreporter.config.getoption("""--make-reports"""      )
	if make_reports:
		pytest_terminal_summary_main(lowercase_					,   id=lowercase_      )

def 		UpperCamelCase      (lowercase_:				Union[str, Any]					,   lowercase_:				int      )  ->   List[str]:
	# If no tests are collected, pytest exists with code 5, which makes the CI fail.
	if exitstatus == 5:
		A__							:					Tuple       =	0


# Doctest custom flag to ignore output.
A_ :				Tuple						 =    doctest.register_optionflag('IGNORE_RESULT')

A_ :				Dict						 =    doctest.OutputChecker



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					def 						__A    (				self					, A__					, A__					, A__    ):
						if IGNORE_RESULT & optionflags:
							return True
						return OutputChecker.check_output(self					, A__					, A__					, A__    )



A_ :				str						 =    CustomOutputChecker
A_ :				Dict						 =    HfDoctestModule


A_ :				Optional[int]						 =    HfDocTestParser



