


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   a__	(						lowerCAmelCase_    ):

       def __init__(   self      :     List[str]      ,							lowerCamelCase_      :     CLIPSegForImageSegmentation      ,							lowerCamelCase_      :     CLIPSegProcessor      ,							lowerCamelCase_      :     AutoencoderKL      ,							lowerCamelCase_      :     CLIPTextModel      ,							lowerCamelCase_      :     CLIPTokenizer      ,							lowerCamelCase_      :     UNetaDConditionModel      ,							lowerCamelCase_      :     Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]      ,							lowerCamelCase_      :     StableDiffusionSafetyChecker      ,							lowerCamelCase_      :     CLIPImageProcessor      ,							):
            super().__init__()

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                     F'''The configuration file of this scheduler: {scheduler} is outdated.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

            if hasattr(scheduler.config      ,							"""skip_prk_steps"""    ) and scheduler.config.skip_prk_steps is False:
                 a_    :     List[str]										=    (
                     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_    :     List[str]										=    dict(scheduler.config    )
                 a_    :     List[str]										=    True
                 a_    :     str										=    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 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     Optional[Union[str, int]] = "auto"    ):
            if slice_size == "auto":
                 # half the attention head size is usually a good trade-off between
                 # speed and memory
                 a_    :     Any										=    self.unet.config.attention_head_dim // 2
            self.unet.set_attention_slice(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     str    ):
            self.enable_attention_slicing(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Any    ):
            if is_accelerate_available():
                 from accelerate import cpu_offload
            else:
                 raise ImportError("""Please install accelerate via `pip install accelerate`"""    )

            a_    :     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(lowerCamelCase_      ,							lowerCamelCase_    )

       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 						UpperCAmelCase(   self      :     int    ):
            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      :     Any      ,							lowerCamelCase_      :     Union[str, List[str]]      ,							lowerCamelCase_      :     Union[torch.FloatTensor, PIL.Image.Image]      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     int = 5_1_2      ,							lowerCamelCase_      :     int = 5_1_2      ,							lowerCamelCase_      :     int = 5_0      ,							lowerCamelCase_      :     float = 7.5      ,							lowerCamelCase_      :     Optional[Union[str, List[str]]] = None      ,							lowerCamelCase_      :     Optional[int] = 1      ,							lowerCamelCase_      :     float = 0.0      ,							lowerCamelCase_      :     Optional[torch.Generator] = None      ,							lowerCamelCase_      :     Optional[torch.FloatTensor] = None      ,							lowerCamelCase_      :     Optional[str] = "pil"      ,							lowerCamelCase_      :     bool = True      ,							lowerCamelCase_      :     Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,							lowerCamelCase_      :     int = 1      ,							**lowerCamelCase_      :     Any      ,							):
            a_    :     str										=    self.segmentation_processor(
                text=[text]      ,							images=[image]      ,							padding="""max_length"""      ,							return_tensors="""pt"""    ).to(self.device    )
            a_    :     List[str]										=    self.segmentation_model(**lowerCamelCase_    )
            a_    :     str										=    torch.sigmoid(outputs.logits    ).cpu().detach().unsqueeze(-1    ).numpy()
            a_    :     Optional[int]										=    self.numpy_to_pil(lowerCamelCase_    )[0].resize(image.size    )

            # Run inpainting pipeline with the generated mask
            a_    :     Any										=    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 math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class   a__	(						lowerCAmelCase_    ):
       lowerCamelCase__:     torch.FloatTensor
       lowerCamelCase__:     Optional[torch.FloatTensor]							    =     None



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase=0.9_99  ,  __UpperCamelCase="cosine"  ,  ):
     if alpha_transform_type == "cosine":

          def alpha_bar_fn(__UpperCamelCase ):
               return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(__UpperCamelCase ):
               return math.exp(t * -12.0 )

     else:
          raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )

     a_    :     List[Any]										=    []
     for i in range(__UpperCamelCase ):
          a_    :     str										=    i / num_diffusion_timesteps
          a_    :     str										=    (i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(__UpperCamelCase ) / alpha_bar_fn(__UpperCamelCase )  ,  __UpperCamelCase ) )
     return torch.tensor(__UpperCamelCase  ,  dtype=torch.floataa )


class   a__	(						lowerCAmelCase_						,       lowerCAmelCase_    ):

       @register_to_config
       def __init__(   self      :     str      ,							lowerCamelCase_      :     int = 1_0_0_0      ,							lowerCamelCase_      :     str = "fixed_small_log"      ,							lowerCamelCase_      :     bool = True      ,							lowerCamelCase_      :     Optional[float] = 1.0      ,							lowerCamelCase_      :     str = "epsilon"      ,							lowerCamelCase_      :     str = "squaredcos_cap_v2"      ,							):
            if beta_schedule != "squaredcos_cap_v2":
                 raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"""    )

            a_    :     Dict										=    betas_for_alpha_bar(lowerCamelCase_    )

            a_    :     List[Any]										=    1.0 - self.betas
            a_    :     List[str]										=    torch.cumprod(self.alphas      ,							dim=0    )
            a_    :     List[Any]										=    torch.tensor(1.0    )

            # standard deviation of the initial noise distribution
            a_    :     int										=    1.0

            # setable values
            a_    :     List[str]										=    None
            a_    :     Optional[int]										=    torch.from_numpy(np.arange(0      ,							lowerCamelCase_    )[::-1].copy()    )

            a_    :     List[Any]										=    variance_type

       def 						UpperCAmelCase(   self      :     int      ,							lowerCamelCase_      :     torch.FloatTensor      ,							lowerCamelCase_      :     Optional[int] = None    ):
            return sample

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     Union[str, torch.device] = None    ):
            a_    :     Tuple										=    num_inference_steps
            a_    :     Tuple										=    (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
            a_    :     Tuple										=    (np.arange(0      ,							lowerCamelCase_    ) * step_ratio).round()[::-1].copy().astype(np.intaa    )
            a_    :     Dict										=    torch.from_numpy(lowerCamelCase_    ).to(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     Tuple      ,							lowerCamelCase_      :     List[Any]=None      ,							lowerCamelCase_      :     Tuple=None      ,							lowerCamelCase_      :     str=None    ):
            if prev_timestep is None:
                 a_    :     List[Any]										=    t - 1

            a_    :     Any										=    self.alphas_cumprod[t]
            a_    :     Union[str, Any]										=    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
            a_    :     List[Any]										=    1 - alpha_prod_t
            a_    :     int										=    1 - alpha_prod_t_prev

            if prev_timestep == t - 1:
                 a_    :     int										=    self.betas[t]
            else:
                 a_    :     Optional[int]										=    1 - alpha_prod_t / alpha_prod_t_prev

            # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
            # and sample from it to get previous sample
            # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
            a_    :     int										=    beta_prod_t_prev / beta_prod_t * beta

            if variance_type is None:
                 a_    :     Optional[Any]										=    self.config.variance_type

            # hacks - were probably added for training stability
            if variance_type == "fixed_small_log":
                 a_    :     Dict										=    torch.log(torch.clamp(lowerCamelCase_      ,							min=1E-20    )    )
                 a_    :     List[Any]										=    torch.exp(0.5 * variance    )
            elif variance_type == "learned_range":
                 # NOTE difference with DDPM scheduler
                 a_    :     Dict										=    variance.log()
                 a_    :     Optional[Any]										=    beta.log()

                 a_    :     str										=    (predicted_variance + 1) / 2
                 a_    :     Optional[int]										=    frac * max_log + (1 - frac) * min_log

            return variance

       def 						UpperCAmelCase(   self      :     Union[str, Any]      ,							lowerCamelCase_      :     torch.FloatTensor      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     torch.FloatTensor      ,							lowerCamelCase_      :     Optional[int] = None      ,							lowerCamelCase_      :     List[str]=None      ,							lowerCamelCase_      :     bool = True      ,							):
            a_    :     List[str]										=    timestep

            if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
                 a_			,			a_    :     List[str]										=    torch.split(lowerCamelCase_      ,							sample.shape[1]      ,							dim=1    )
            else:
                 a_    :     Any										=    None

            # 1. compute alphas, betas
            if prev_timestep is None:
                 a_    :     Optional[Any]										=    t - 1

            a_    :     Tuple										=    self.alphas_cumprod[t]
            a_    :     Any										=    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
            a_    :     int										=    1 - alpha_prod_t
            a_    :     int										=    1 - alpha_prod_t_prev

            if prev_timestep == t - 1:
                 a_    :     Optional[int]										=    self.betas[t]
                 a_    :     int										=    self.alphas[t]
            else:
                 a_    :     int										=    1 - alpha_prod_t / alpha_prod_t_prev
                 a_    :     Dict										=    1 - beta

            # 2. compute predicted original sample from predicted noise also called
            # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
            if self.config.prediction_type == "epsilon":
                 a_    :     Optional[int]										=    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
            elif self.config.prediction_type == "sample":
                 a_    :     Optional[Any]										=    model_output
            else:
                 raise ValueError(
                     F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
                     """ for the UnCLIPScheduler."""    )

            # 3. Clip "predicted x_0"
            if self.config.clip_sample:
                 a_    :     Dict										=    torch.clamp(
                     lowerCamelCase_      ,							-self.config.clip_sample_range      ,							self.config.clip_sample_range    )

            # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
            # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
            a_    :     Dict										=    (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
            a_    :     Any										=    alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

            # 5. Compute predicted previous sample µ_t
            # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
            a_    :     Optional[Any]										=    pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

            # 6. Add noise
            a_    :     Optional[int]										=    0
            if t > 0:
                 a_    :     Any										=    randn_tensor(
                     model_output.shape      ,							dtype=model_output.dtype      ,							generator=lowerCamelCase_      ,							device=model_output.device    )

                 a_    :     Tuple										=    self._get_variance(
                     lowerCamelCase_      ,							predicted_variance=lowerCamelCase_      ,							prev_timestep=lowerCamelCase_      ,							)

                 if self.variance_type == "fixed_small_log":
                      a_    :     str										=    variance
                 elif self.variance_type == "learned_range":
                      a_    :     Optional[Any]										=    (0.5 * variance).exp()
                 else:
                      raise ValueError(
                          F'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
                          """ for the UnCLIPScheduler."""    )

                 a_    :     List[str]										=    variance * variance_noise

            a_    :     List[Any]										=    pred_prev_sample + variance

            if not return_dict:
                 return (pred_prev_sample,)

            return UnCLIPSchedulerOutput(prev_sample=lowerCamelCase_      ,							pred_original_sample=lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     torch.FloatTensor      ,							lowerCamelCase_      :     torch.FloatTensor      ,							lowerCamelCase_      :     torch.IntTensor      ,							):
            # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
            a_    :     Tuple										=    self.alphas_cumprod.to(device=original_samples.device      ,							dtype=original_samples.dtype    )
            a_    :     Union[str, Any]										=    timesteps.to(original_samples.device    )

            a_    :     List[str]										=    alphas_cumprod[timesteps] ** 0.5
            a_    :     Tuple										=    sqrt_alpha_prod.flatten()
            while len(sqrt_alpha_prod.shape    ) < len(original_samples.shape    ):
                 a_    :     Dict										=    sqrt_alpha_prod.unsqueeze(-1    )

            a_    :     int										=    (1 - alphas_cumprod[timesteps]) ** 0.5
            a_    :     Any										=    sqrt_one_minus_alpha_prod.flatten()
            while len(sqrt_one_minus_alpha_prod.shape    ) < len(original_samples.shape    ):
                 a_    :     Tuple										=    sqrt_one_minus_alpha_prod.unsqueeze(-1    )

            a_    :     List[str]										=    sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
            return noisy_samples



