
"""simple docstring"""




import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
					import onnxruntime as ort



class 	lowerCAmelCase_						(a__ ,					unittest.TestCase  ):


				"""simple docstring"""



				pass






@nightly
@require_onnxruntime
@require_torch_gpu
class 	lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



				@property
				def 	__magic_name__    (self    )					->		Optional[int]:



											"""simple docstring"""




											return (
											    "CUDAExecutionProvider",
											    {
											        "gpu_mem_limit": "15000000000",  # 15GB
											        "arena_extend_strategy": "kSameAsRequested",
											    },
											)



				@property
				def 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Dict           =   ort.SessionOptions()
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   False
											return options



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/in_paint/overture-creations-5sI6fQgYIuo.png"""    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   OnnxStableDiffusionInpaintPipeline.from_pretrained(
											    """runwayml/stable-diffusion-inpainting"""   ,       revision="""onnx"""   ,       safety_checker=SCREAMING_SNAKE_CASE__   ,       feature_extractor=SCREAMING_SNAKE_CASE__   ,       provider=self.gpu_provider   ,       sess_options=self.gpu_options   ,       )
											pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   """A red cat sitting on a park bench"""

											SCREAMING_SNAKE_CASE__						:       int           =   np.random.RandomState(0    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   pipe(
											    prompt=SCREAMING_SNAKE_CASE__   ,       image=SCREAMING_SNAKE_CASE__   ,       mask_image=SCREAMING_SNAKE_CASE__   ,       guidance_scale=7.5   ,       num_inference_steps=10   ,       generator=SCREAMING_SNAKE_CASE__   ,       output_type="""np"""   ,       )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   output.images
											SCREAMING_SNAKE_CASE__						:       Dict           =   images[0, 2_55:2_58, 2_55:2_58, -1]

											assert images.shape == (1, 5_12, 5_12, 3)
											SCREAMING_SNAKE_CASE__						:       Any           =   np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]    )

											assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-3



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/in_paint/overture-creations-5sI6fQgYIuo.png"""    )
											SCREAMING_SNAKE_CASE__						:       Tuple           =   load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   LMSDiscreteScheduler.from_pretrained(
											    """runwayml/stable-diffusion-inpainting"""   ,       subfolder="""scheduler"""   ,       revision="""onnx"""    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   OnnxStableDiffusionInpaintPipeline.from_pretrained(
											    """runwayml/stable-diffusion-inpainting"""   ,       revision="""onnx"""   ,       scheduler=SCREAMING_SNAKE_CASE__   ,       safety_checker=SCREAMING_SNAKE_CASE__   ,       feature_extractor=SCREAMING_SNAKE_CASE__   ,       provider=self.gpu_provider   ,       sess_options=self.gpu_options   ,       )
											pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   """A red cat sitting on a park bench"""

											SCREAMING_SNAKE_CASE__						:       Dict           =   np.random.RandomState(0    )
											SCREAMING_SNAKE_CASE__						:       str           =   pipe(
											    prompt=SCREAMING_SNAKE_CASE__   ,       image=SCREAMING_SNAKE_CASE__   ,       mask_image=SCREAMING_SNAKE_CASE__   ,       guidance_scale=7.5   ,       num_inference_steps=20   ,       generator=SCREAMING_SNAKE_CASE__   ,       output_type="""np"""   ,       )
											SCREAMING_SNAKE_CASE__						:       Any           =   output.images
											SCREAMING_SNAKE_CASE__						:       int           =   images[0, 2_55:2_58, 2_55:2_58, -1]

											assert images.shape == (1, 5_12, 5_12, 3)
											SCREAMING_SNAKE_CASE__						:       List[str]           =   np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]    )

											assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-3








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.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 650, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "pytorch",
            "script": "run_ddp.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "tensorflow",
            "script": "run_tf_dist.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.6, "eval_loss": 0.7},
        },
    ])
class      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


     def  _lowerCamelCase (						self)			->				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=__lowerCamelCase ,						)
            assert hasattr(self ,						"env")


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Tuple:
            _A      :       Dict				    = F"{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"
            # distributed data settings
            _A      :       Optional[Any]				    = {"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None

            # 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=__lowerCamelCase ,						instance_count=__lowerCamelCase ,						instance_type=self.instance_type ,						debugger_hook_config=__lowerCamelCase ,						hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path} ,						metric_definitions=self.env.metric_definitions ,						distribution=__lowerCamelCase ,						py_version="py36" ,						)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Optional[Any]:
            TrainingJobAnalytics(__lowerCamelCase).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv")


     @parameterized.expand([(2,)])
     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Any:
            # create estimator
            _A      :       Union[str, Any]				    = self.create_estimator(__lowerCamelCase)

            # run training
            estimator.fit()

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

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