


from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


lowerCAmelCase__      		= logging.get_logger(__name__)  # pylint: disable=invalid-name







class      lowerCAmelCase__       (				a):




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						)			->				List[Any]:
            super().__init__()

            if hasattr(scheduler.config ,						"steps_offset") and scheduler.config.steps_offset != 1:
                   _A      :       int				    = (
                       F"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                       F" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
                       "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
                       " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
                       " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
                       " file"
                   )
                   deprecate("steps_offset!=1" ,						"1.0.0" ,						__lowerCamelCase ,						standard_warn=__lowerCamelCase)
                   _A      :       List[str]				    = dict(scheduler.config)
                   _A      :       Union[str, Any]				    = 1
                   _A      :       List[Any]				    = FrozenDict(__lowerCamelCase)

            if hasattr(scheduler.config ,						"skip_prk_steps") and scheduler.config.skip_prk_steps is False:
                   _A      :       Optional[int]				    = (
                       F"The configuration file of this scheduler: {scheduler} has not set the configuration"
                       " `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
                       " sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
                       " incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
                       " Hub, it would be very nice if you could open a Pull request for the"
                       " `scheduler/scheduler_config.json` file"
                   )
                   deprecate("skip_prk_steps not set" ,						"1.0.0" ,						__lowerCamelCase ,						standard_warn=__lowerCamelCase)
                   _A      :       Optional[Any]				    = dict(scheduler.config)
                   _A      :       List[str]				    = True
                   _A      :       Tuple				    = FrozenDict(__lowerCamelCase)

            if safety_checker is None:
                   logger.warning(
                       F"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
                       " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
                       " results in services or applications open to the public. Both the diffusers team and Hugging Face"
                       " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
                       " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
                       " information, please have a look at https://github.com/huggingface/diffusers/pull/254 .")

            self.register_modules(
                segmentation_model=__lowerCamelCase ,						segmentation_processor=__lowerCamelCase ,						vae=__lowerCamelCase ,						text_encoder=__lowerCamelCase ,						tokenizer=__lowerCamelCase ,						unet=__lowerCamelCase ,						scheduler=__lowerCamelCase ,						safety_checker=__lowerCamelCase ,						feature_extractor=__lowerCamelCase ,						)


     def  _lowerCamelCase (						self ,						__lowerCamelCase = "auto")			->				Optional[int]:
            if slice_size == "auto":
                   # half the attention head size is usually a good trade-off between
                   # speed and memory
                   _A      :       Optional[Any]				    = self.unet.config.attention_head_dim // 2
            self.unet.set_attention_slice(__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				List[str]:
            self.enable_attention_slicing(__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				List[Any]:
            if is_accelerate_available():
                   from accelerate import cpu_offload
            else:
                   raise ImportError("Please install accelerate via `pip install accelerate`")

            _A      :       str				    = torch.device("cuda")

            for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
                   if cpu_offloaded_model is not None:
                          cpu_offload(__lowerCamelCase ,						__lowerCamelCase)


     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def  _lowerCamelCase (						self)			->				List[Any]:
            if self.device != torch.device("meta") or not hasattr(self.unet ,						"_hf_hook"):
                   return self.device
            for module in self.unet.modules():
                   if (
                       hasattr(__lowerCamelCase ,						"_hf_hook")
                       and hasattr(module._hf_hook ,						"execution_device")
                       and module._hf_hook.execution_device is not None
                   ):
                          return torch.device(module._hf_hook.execution_device)
            return self.device


     @torch.no_grad()
     def __call__(						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase = 5_1_2 ,						__lowerCamelCase = 5_1_2 ,						__lowerCamelCase = 5_0 ,						__lowerCamelCase = 7.5 ,						__lowerCamelCase = None ,						__lowerCamelCase = 1 ,						__lowerCamelCase = 0.0 ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = "pil" ,						__lowerCamelCase = True ,						__lowerCamelCase = None ,						__lowerCamelCase = 1 ,						**__lowerCamelCase ,						)			->				Dict:
            _A      :       Union[str, Any]				    = self.segmentation_processor(
                text=[text] ,						images=[image] ,						padding="max_length" ,						return_tensors="pt").to(self.device)
            _A      :       Optional[Any]				    = self.segmentation_model(**__lowerCamelCase)
            _A      :       Union[str, Any]				    = torch.sigmoid(outputs.logits).cpu().detach().unsqueeze(-1).numpy()
            _A      :       int				    = self.numpy_to_pil(__lowerCamelCase)[0].resize(image.size)

            # Run inpainting pipeline with the generated mask
            _A      :       str				    = StableDiffusionInpaintPipeline(
                vae=self.vae ,						text_encoder=self.text_encoder ,						tokenizer=self.tokenizer ,						unet=self.unet ,						scheduler=self.scheduler ,						safety_checker=self.safety_checker ,						feature_extractor=self.feature_extractor ,						)
            return inpainting_pipeline(
                prompt=__lowerCamelCase ,						image=__lowerCamelCase ,						mask_image=__lowerCamelCase ,						height=__lowerCamelCase ,						width=__lowerCamelCase ,						num_inference_steps=__lowerCamelCase ,						guidance_scale=__lowerCamelCase ,						negative_prompt=__lowerCamelCase ,						num_images_per_prompt=__lowerCamelCase ,						eta=__lowerCamelCase ,						generator=__lowerCamelCase ,						latents=__lowerCamelCase ,						output_type=__lowerCamelCase ,						return_dict=__lowerCamelCase ,						callback=__lowerCamelCase ,						callback_steps=__lowerCamelCase ,						)




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)
