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''': 1_6_0_0, '''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''': 1_6_0_0, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
    ]  )
class UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						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   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				{
						    """enabled""": True,
						    """processes_per_host""": 8,
						}
						A_		:    Dict         =				{
						    """enabled""": True,
						    """parameters""": {
						        """microbatches""": 4,
						        """placement_strategy""": """spread""",
						        """pipeline""": """interleaved""",
						        """optimize""": """speed""",
						        """partitions""": 4,
						        """ddp""": True,
						    },
						}

						A_		:    int         =				{"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

						A_		:    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=lowercase		,     instance_type=self.instance_type		,     debugger_hook_config=lowercase		,     hyperparameters={
						        **self.env.hyperparameters,
						        'model_name_or_path': self.model_name_or_path,
						        'max_steps': 5_0_0,
						    }		,     metric_definitions=self.env.metric_definitions		,     distribution=lowercase		,     py_version='py36'		,     )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						TrainingJobAnalytics(lowercase ).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv''' )







				@parameterized.expand([(1,)] )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Dict         =				self.create_estimator(lowercase )

						# run training
						estimator.fit()

						# result dataframe
						A_		:    Any         =				TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()

						# extract kpis
						A_		:    List[str]         =				list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value'] )
						A_		:    Optional[int]         =				list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value'] )
						# get train time from SageMaker job, this includes starting, preprocessing, stopping
						A_		:    Dict         =				(
						    Session().describe_training_job(estimator.latest_training_job.name ).get('TrainingTimeInSeconds'		,     9_9_9_9_9_9 )
						)

						# 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 )


from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
	if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
	from .pipeline_unclip import UnCLIPPipeline
	from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
	from .text_proj import UnCLIPTextProjModel






