





import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import 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.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 650, '''eval_accuracy''': 0.6, '''eval_loss''': 0.9},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 600, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]     )
class 					__lowerCAmelCase       (			unittest.TestCase     ):






			def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''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=__magic_name__					,							)
							assert hasattr(self					,							'''env'''    )






			def 			lowerCamelCase  (self					,							__magic_name__=1    )     ->   Tuple:


							'''simple docstring'''



							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}-single'''					,							instance_count=__magic_name__					,							instance_type=self.instance_type					,							debugger_hook_config=__magic_name__					,							hyperparameters={**self.env.hyperparameters, '''model_name_or_path''': self.model_name_or_path}					,							metric_definitions=self.env.metric_definitions					,							py_version='''py36'''					,							)






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   List[str]:


							'''simple docstring'''



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






			def 			lowerCamelCase  (self    )     ->   List[Any]:


							'''simple docstring'''



							snake_case_						:    Optional[Any]				=  self.create_estimator()

							# run training
							estimator.fit()

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

							# extract kpis
							snake_case_						:    Optional[int]				=  list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']    )
							snake_case_						:    str				=  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_						:    Union[str, Any]				=  (
							    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}					,							__magic_name__    )







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
