





"""simple docstring"""




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 			_a						(				unittest.TestCase    ):






				def     snake_case     (			self		:   Dict							)     ->				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=lowerCAmelCase__,			)
										assert hasattr(self,			'''env'''							)






				def     snake_case     (			self		:   Union[str, Any],			lowerCAmelCase__		:   Optional[Any]=1							)     ->				Any:




										'''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=lowerCAmelCase__,			instance_type=self.instance_type,			debugger_hook_config=lowerCAmelCase__,			hyperparameters={**self.env.hyperparameters, '''model_name_or_path''': self.model_name_or_path},			metric_definitions=self.env.metric_definitions,			py_version='''py36''',			)






				def     snake_case     (			self		:   int,			lowerCAmelCase__		:   List[str]							)     ->				str:




										'''simple docstring'''



										TrainingJobAnalytics(lowerCAmelCase__							).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""							)






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




										'''simple docstring'''



										_UpperCamelCase      : Tuple         =   self.create_estimator()

										# run training
										estimator.fit()

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

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










"""simple docstring"""




import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
UpperCamelCase_									=0b1_0_1_1_0_0_1_1_1_1_1_0_1_1_0_0_1_0_0_1_0_0_0_0_0_1_1_1_1_0_1_1_1_0_1_1_0_0_0_1_1_0_0_1_1_1_1_0
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
UpperCamelCase_									=[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]




class 			_a						:






				def __init__(			self		:   str							)     ->				str:




										'''simple docstring'''



										_UpperCamelCase      : str         =   WATERMARK_BITS
										_UpperCamelCase      : Optional[int]         =   WatermarkEncoder()

										self.encoder.set_watermark('''bits''',			self.watermark							)






				def     snake_case     (			self		:   Dict,			lowerCAmelCase__		:   torch.FloatTensor							)     ->				int:




										'''simple docstring'''



										if images.shape[-1] < 2_5_6:
																return images

										_UpperCamelCase      : Union[str, Any]         =   (2_5_5 * (images / 2 + 0.5)).cpu().permute(0,			2,			3,			1							).float().numpy()

										_UpperCamelCase      : List[str]         =   [self.encoder.encode(lowerCAmelCase__,			'''dwtDct'''							) for image in images]

										_UpperCamelCase      : Dict         =   torch.from_numpy(np.array(lowerCAmelCase__							)							).permute(0,			3,			1,			2							)

										_UpperCamelCase      : Optional[int]         =   torch.clamp(2 * (images / 2_5_5 - 0.5),			min=-1.0,			max=1.0							)
										return images



