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

												UpperCamelCase_		:							Tuple  =		{"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

												UpperCamelCase_		:							Optional[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=__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        A_							(self			,     __UpperCamelCase						)				->				List[Any]:
												TrainingJobAnalytics(__UpperCamelCase						).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''						)





						@parameterized.expand([(1,)]						)
						def        A_							(self			,     __UpperCamelCase						)				->				int:
												# create estimator
												UpperCamelCase_		:							Tuple  =		self.create_estimator(__UpperCamelCase						)

												# run training
												estimator.fit()

												# result dataframe
												UpperCamelCase_		:							Optional[Any]  =		TrainingJobAnalytics(estimator.latest_training_job.name						).dataframe()

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	{
    "configuration_bridgetower": [
        "BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BridgeTowerConfig",
        "BridgeTowerTextConfig",
        "BridgeTowerVisionConfig",
    ],
    "processing_bridgetower": ["BridgeTowerProcessor"],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	SCREAMING_SNAKE_CASE		:     Optional[Any]         =	["BridgeTowerImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	SCREAMING_SNAKE_CASE		:     Tuple         =	[
	    "BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "BridgeTowerForContrastiveLearning",
	    "BridgeTowerForImageAndTextRetrieval",
	    "BridgeTowerForMaskedLM",
	    "BridgeTowerModel",
	    "BridgeTowerPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_bridgetower import (
	    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BridgeTowerConfig,
	    BridgeTowerTextConfig,
	    BridgeTowerVisionConfig,
	)
	from .processing_bridgetower import BridgeTowerProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_bridgetower import BridgeTowerImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_bridgetower import (
		    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BridgeTowerForContrastiveLearning,
		    BridgeTowerForImageAndTextRetrieval,
		    BridgeTowerForMaskedLM,
		    BridgeTowerModel,
		    BridgeTowerPreTrainedModel,
		)


else:
	import sys

	SCREAMING_SNAKE_CASE		:     List[Any]         =	_LazyModule(__name__, globals()["__file__"], _import_structure)






