


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


UpperCAmelCase_   :				List[str]						      = logging.get_logger(__name__)  # pylint: disable=invalid-name
class 				SCREAMING_SNAKE_CASE__						(       _UpperCamelCase    ):
      def __init__( self       : Any  ,							SCREAMING_SNAKE_CASE__       : CLIPSegForImageSegmentation  ,							SCREAMING_SNAKE_CASE__       : CLIPSegProcessor  ,							SCREAMING_SNAKE_CASE__       : AutoencoderKL  ,							SCREAMING_SNAKE_CASE__       : CLIPTextModel  ,							SCREAMING_SNAKE_CASE__       : CLIPTokenizer  ,							SCREAMING_SNAKE_CASE__       : UNetaDConditionModel  ,							SCREAMING_SNAKE_CASE__       : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]  ,							SCREAMING_SNAKE_CASE__       : StableDiffusionSafetyChecker  ,							SCREAMING_SNAKE_CASE__       : CLIPImageProcessor  ,							)	->    List[str]:
             super().__init__()

             if hasattr(scheduler.config  ,							'steps_offset'  ) and scheduler.config.steps_offset != 1:
                    a_      :	Optional[Any]							=						(
                        F"""The configuration file of this scheduler: {scheduler} is outdated.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'  ,							SCREAMING_SNAKE_CASE__  ,							standard_warn=SCREAMING_SNAKE_CASE__  )
                    a_      :	int							=						dict(scheduler.config  )
                    a_      :	Any							=						1
                    a_      :	Union[str, Any]							=						FrozenDict(SCREAMING_SNAKE_CASE__  )

             if hasattr(scheduler.config  ,							'skip_prk_steps'  ) and scheduler.config.skip_prk_steps is False:
                    a_      :	Optional[Any]							=						(
                        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'  ,							SCREAMING_SNAKE_CASE__  ,							standard_warn=SCREAMING_SNAKE_CASE__  )
                    a_      :	Tuple							=						dict(scheduler.config  )
                    a_      :	Union[str, Any]							=						True
                    a_      :	int							=						FrozenDict(SCREAMING_SNAKE_CASE__  )

             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=SCREAMING_SNAKE_CASE__  ,							segmentation_processor=SCREAMING_SNAKE_CASE__  ,							vae=SCREAMING_SNAKE_CASE__  ,							text_encoder=SCREAMING_SNAKE_CASE__  ,							tokenizer=SCREAMING_SNAKE_CASE__  ,							unet=SCREAMING_SNAKE_CASE__  ,							scheduler=SCREAMING_SNAKE_CASE__  ,							safety_checker=SCREAMING_SNAKE_CASE__  ,							feature_extractor=SCREAMING_SNAKE_CASE__  ,							)
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Optional[Union[str, int]] = "auto"  )	->    Optional[int]:
             if slice_size == "auto":
                    # half the attention head size is usually a good trade-off between
                    # speed and memory
                    a_      :	List[str]							=						self.unet.config.attention_head_dim // 2
             self.unet.set_attention_slice(SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    str:
             self.enable_attention_slicing(SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    int:
             if is_accelerate_available():
                    from accelerate import cpu_offload
             else:
                    raise ImportError('Please install accelerate via `pip install accelerate`'  )

             a_      :	Union[str, Any]							=						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(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Tuple:
             if self.device != torch.device('meta'  ) or not hasattr(self.unet  ,							'_hf_hook'  ):
                    return self.device
             for module in self.unet.modules():
                    if (
                        hasattr(SCREAMING_SNAKE_CASE__  ,							'_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       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Union[str, List[str]]  ,							SCREAMING_SNAKE_CASE__       : Union[torch.FloatTensor, PIL.Image.Image]  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : int = 5_1_2  ,							SCREAMING_SNAKE_CASE__       : int = 5_1_2  ,							SCREAMING_SNAKE_CASE__       : int = 5_0  ,							SCREAMING_SNAKE_CASE__       : float = 7.5  ,							SCREAMING_SNAKE_CASE__       : Optional[Union[str, List[str]]] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[int] = 1  ,							SCREAMING_SNAKE_CASE__       : float = 0.0  ,							SCREAMING_SNAKE_CASE__       : Optional[torch.Generator] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[torch.FloatTensor] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[str] = "pil"  ,							SCREAMING_SNAKE_CASE__       : bool = True  ,							SCREAMING_SNAKE_CASE__       : Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,							SCREAMING_SNAKE_CASE__       : int = 1  ,							**SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							)	->    Union[str, Any]:
             a_      :	Dict							=						self.segmentation_processor(
                 text=[text]  ,							images=[image]  ,							padding='max_length'  ,							return_tensors='pt'  ).to(self.device  )
             a_      :	int							=						self.segmentation_model(**SCREAMING_SNAKE_CASE__  )
             a_      :	int							=						torch.sigmoid(outputs.logits  ).cpu().detach().unsqueeze(-1  ).numpy()
             a_      :	List[str]							=						self.numpy_to_pil(SCREAMING_SNAKE_CASE__  )[0].resize(image.size  )

             # Run inpainting pipeline with the generated mask
             a_      :	int							=						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=SCREAMING_SNAKE_CASE__  ,							image=SCREAMING_SNAKE_CASE__  ,							mask_image=SCREAMING_SNAKE_CASE__  ,							height=SCREAMING_SNAKE_CASE__  ,							width=SCREAMING_SNAKE_CASE__  ,							num_inference_steps=SCREAMING_SNAKE_CASE__  ,							guidance_scale=SCREAMING_SNAKE_CASE__  ,							negative_prompt=SCREAMING_SNAKE_CASE__  ,							num_images_per_prompt=SCREAMING_SNAKE_CASE__  ,							eta=SCREAMING_SNAKE_CASE__  ,							generator=SCREAMING_SNAKE_CASE__  ,							latents=SCREAMING_SNAKE_CASE__  ,							output_type=SCREAMING_SNAKE_CASE__  ,							return_dict=SCREAMING_SNAKE_CASE__  ,							callback=SCREAMING_SNAKE_CASE__  ,							callback_steps=SCREAMING_SNAKE_CASE__  ,							)



import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      snake_case__       :  Optional[Any]       =		(DDIMParallelScheduler,)
      snake_case__       :  Union[str, Any]       =		(('''eta''', 0.0), ('''num_inference_steps''', 50))
      def  SCREAMING_SNAKE_CASE							( self       : str  ,							**SCREAMING_SNAKE_CASE__       : List[Any]  )	->    List[Any]:
             a_      :	Union[str, Any]							=						{
                 'num_train_timesteps': 1_0_0_0,
                 'beta_start': 0.0001,
                 'beta_end': 0.02,
                 'beta_schedule': 'linear',
                 'clip_sample': True,
             }

             config.update(**SCREAMING_SNAKE_CASE__  )
             return config
      def  SCREAMING_SNAKE_CASE							( self       : str  ,							**SCREAMING_SNAKE_CASE__       : Tuple  )	->    int:
             a_      :	Any							=						self.scheduler_classes[0]
             a_      :	str							=						self.get_scheduler_config(**SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[Any]							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )

             a_							,	a_      :	Optional[Any]							=						1_0, 0.0

             a_      :	str							=						self.dummy_model()
             a_      :	Dict							=						self.dummy_sample_deter

             scheduler.set_timesteps(SCREAMING_SNAKE_CASE__  )

             for t in scheduler.timesteps:
                    a_      :	List[Any]							=						model(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                    a_      :	Optional[Any]							=						scheduler.step(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ).prev_sample

             return sample
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Any:
             for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
                    self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    List[Any]:
             for steps_offset in [0, 1]:
                    self.check_over_configs(steps_offset=SCREAMING_SNAKE_CASE__  )

             a_      :	Tuple							=						self.scheduler_classes[0]
             a_      :	int							=						self.get_scheduler_config(steps_offset=1  )
             a_      :	str							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )
             scheduler.set_timesteps(5  )
             assert torch.equal(scheduler.timesteps  ,							torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]  )  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    List[str]:
             for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]  ,							[0.002, 0.02, 0.2, 2]  ):
                    self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__  ,							beta_end=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    List[str]:
             for schedule in ["linear", "squaredcos_cap_v2"]:
                    self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Dict:
             for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Tuple:
             for clip_sample in [True, False]:
                    self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Union[str, Any]:
             for timestep_spacing in ["trailing", "leading"]:
                    self.check_over_configs(timestep_spacing=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    int:
             for rescale_betas_zero_snr in [True, False]:
                    self.check_over_configs(rescale_betas_zero_snr=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Tuple:
             self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__  )
             for threshold in [0.5, 1.0, 2.0]:
                    for prediction_type in ["epsilon", "v_prediction"]:
                           self.check_over_configs(
                               thresholding=SCREAMING_SNAKE_CASE__  ,							prediction_type=SCREAMING_SNAKE_CASE__  ,							sample_max_value=SCREAMING_SNAKE_CASE__  ,							)
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Tuple:
             for t in [1, 1_0, 4_9]:
                    self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Dict:
             for t, num_inference_steps in zip([1, 1_0, 5_0]  ,							[1_0, 5_0, 5_0_0]  ):
                    self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__  ,							num_inference_steps=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Optional[Any]:
             for t, eta in zip([1, 1_0, 4_9]  ,							[0.0, 0.5, 1.0]  ):
                    self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__  ,							eta=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Tuple:
             a_      :	Union[str, Any]							=						self.scheduler_classes[0]
             a_      :	List[Any]							=						self.get_scheduler_config()
             a_      :	Dict							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )

             assert torch.sum(torch.abs(scheduler._get_variance(0  ,							0  ) - 0.0  )  ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(4_2_0  ,							4_0_0  ) - 0.14771  )  ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(9_8_0  ,							9_6_0  ) - 0.32460  )  ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(0  ,							0  ) - 0.0  )  ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(4_8_7  ,							4_8_6  ) - 0.00979  )  ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(9_9_9  ,							9_9_8  ) - 0.02  )  ) < 1E-5
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    Dict:
             a_      :	Dict							=						self.scheduler_classes[0]
             a_      :	int							=						self.get_scheduler_config()
             a_      :	List[str]							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )

             a_							,	a_      :	int							=						1_0, 0.0
             scheduler.set_timesteps(SCREAMING_SNAKE_CASE__  )

             a_      :	Union[str, Any]							=						self.dummy_model()
             a_      :	Optional[int]							=						self.dummy_sample_deter
             a_      :	Optional[Any]							=						self.dummy_sample_deter + 0.1
             a_      :	Any							=						self.dummy_sample_deter - 0.1

             a_      :	List[Any]							=						samplea.shape[0]
             a_      :	List[str]							=						torch.stack([samplea, samplea, samplea]  ,							dim=0  )
             a_      :	Union[str, Any]							=						torch.arange(SCREAMING_SNAKE_CASE__  )[0:3, None].repeat(1  ,							SCREAMING_SNAKE_CASE__  )

             a_      :	int							=						model(samples.flatten(0  ,							1  )  ,							timesteps.flatten(0  ,							1  )  )
             a_      :	Optional[Any]							=						scheduler.batch_step_no_noise(SCREAMING_SNAKE_CASE__  ,							timesteps.flatten(0  ,							1  )  ,							samples.flatten(0  ,							1  )  ,							SCREAMING_SNAKE_CASE__  )

             a_      :	Optional[int]							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	Optional[int]							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             assert abs(result_sum.item() - 1147.7904  ) < 1E-2
             assert abs(result_mean.item() - 0.4982  ) < 1E-3
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Tuple:
             a_      :	Tuple							=						self.full_loop()

             a_      :	List[str]							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	Any							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             assert abs(result_sum.item() - 172.0067  ) < 1E-2
             assert abs(result_mean.item() - 0.223967  ) < 1E-3
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    List[str]:
             a_      :	Optional[int]							=						self.full_loop(prediction_type='v_prediction'  )

             a_      :	str							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	Union[str, Any]							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             assert abs(result_sum.item() - 52.5302  ) < 1E-2
             assert abs(result_mean.item() - 0.0684  ) < 1E-3
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Optional[Any]:
             # We specify different beta, so that the first alpha is 0.99
             a_      :	Dict							=						self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE__  ,							beta_start=0.01  )
             a_      :	int							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	str							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             assert abs(result_sum.item() - 149.8295  ) < 1E-2
             assert abs(result_mean.item() - 0.1951  ) < 1E-3

      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Union[str, Any]:
             # We specify different beta, so that the first alpha is 0.99
             a_      :	Tuple							=						self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE__  ,							beta_start=0.01  )
             a_      :	Dict							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	str							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             assert abs(result_sum.item() - 149.0784  ) < 1E-2
             assert abs(result_mean.item() - 0.1941  ) < 1E-3


