





import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


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

class      A       (	__lowercase						):



      def __init__(	self:						Optional[Any]		,  _lowerCAmelCase:						AutoencoderKL		,  _lowerCAmelCase:						CLIPTextModel		,  _lowerCAmelCase:						CLIPTokenizer		,  _lowerCAmelCase:						UNetaDConditionModel		,  _lowerCAmelCase:						Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]		,  _lowerCAmelCase:						StableDiffusionSafetyChecker		,  _lowerCAmelCase:						CLIPImageProcessor		,  )						->    List[Any]:
         '''simple docstring'''

         super().__init__()
         self.register_modules(
             vae=_lowerCAmelCase		,  text_encoder=_lowerCAmelCase		,  tokenizer=_lowerCAmelCase		,  unet=_lowerCAmelCase		,  scheduler=_lowerCAmelCase		,  safety_checker=_lowerCAmelCase		,  feature_extractor=_lowerCAmelCase		,  )



      def 	lowerCAmelCase__    (	self:						Union[str, Any]		,  _lowerCAmelCase:						Optional[Union[str, int]] = "auto"       )						->    Optional[Any]:
         '''simple docstring'''

         if slice_size == "auto":
            # half the attention head size is usually a good trade-off between
            # speed and memory
            UpperCAmelCase_											=self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Optional[Any]:
         '''simple docstring'''

         self.enable_attention_slicing(_lowerCAmelCase       )






      @torch.no_grad()
      def __call__(	self:						Union[str, Any]		,  _lowerCAmelCase:						Union[str, List[str]]		,  _lowerCAmelCase:						int = 512		,  _lowerCAmelCase:						int = 512		,  _lowerCAmelCase:						int = 50		,  _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:						Optional[torch.FloatTensor] = None		,  **_lowerCAmelCase:						Union[str, Any]		,  )						->    Optional[int]:
         '''simple docstring'''

         if isinstance(_lowerCAmelCase		,  _lowerCAmelCase       ):
            UpperCAmelCase_											=1
         elif isinstance(_lowerCAmelCase		,  _lowerCAmelCase       ):
            UpperCAmelCase_											=len(_lowerCAmelCase       )
         else:
            raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(_lowerCAmelCase       )}'       )

         if height % 8 != 0 or width % 8 != 0:
            raise ValueError(F'`height` and `width` have to be divisible by 8 but are {height} and {width}.'       )

         if (callback_steps is None) or (
             callback_steps is not None and (not isinstance(_lowerCAmelCase		,  _lowerCAmelCase       ) or callback_steps <= 0)
         ):
            raise ValueError(
                F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
                F' {type(_lowerCAmelCase       )}.'       )

         # get prompt text embeddings
         UpperCAmelCase_											=self.tokenizer(
             _lowerCAmelCase		,  padding="max_length"		,  max_length=self.tokenizer.model_max_length		,  return_tensors="pt"		,  )
         UpperCAmelCase_											=text_inputs.input_ids

         if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
            UpperCAmelCase_											=self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]       )
            logger.warning(
                "The following part of your input was truncated because CLIP can only handle sequences up to"
                F' {self.tokenizer.model_max_length} tokens: {removed_text}'       )
            UpperCAmelCase_											=text_input_ids[:, : self.tokenizer.model_max_length]

         if text_embeddings is None:
            UpperCAmelCase_											=self.text_encoder(text_input_ids.to(self.device       )       )[0]

         # duplicate text embeddings for each generation per prompt, using mps friendly method
         UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_											=text_embeddings.shape
         UpperCAmelCase_											=text_embeddings.repeat(1		,  _lowerCAmelCase		,  1       )
         UpperCAmelCase_											=text_embeddings.view(bs_embed * num_images_per_prompt		,  _lowerCAmelCase		,  -1       )

         # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
         # corresponds to doing no classifier free guidance.
         UpperCAmelCase_											=guidance_scale > 1.0
         # get unconditional embeddings for classifier free guidance
         if do_classifier_free_guidance:
            UpperCAmelCase_											=42
            if negative_prompt is None:
               UpperCAmelCase_											=[""]
            elif type(_lowerCAmelCase       ) is not type(_lowerCAmelCase       ):
               raise TypeError(
                   F'`negative_prompt` should be the same type to `prompt`, but got {type(_lowerCAmelCase       )} !='
                   F' {type(_lowerCAmelCase       )}.'       )
            elif isinstance(_lowerCAmelCase		,  _lowerCAmelCase       ):
               UpperCAmelCase_											=[negative_prompt]
            elif batch_size != len(_lowerCAmelCase       ):
               raise ValueError(
                   F'`negative_prompt`: {negative_prompt} has batch size {len(_lowerCAmelCase       )}, but `prompt`:'
                   F' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
                   " the batch size of `prompt`."       )
            else:
               UpperCAmelCase_											=negative_prompt

            UpperCAmelCase_											=text_input_ids.shape[-1]
            UpperCAmelCase_											=self.tokenizer(
                _lowerCAmelCase		,  padding="max_length"		,  max_length=_lowerCAmelCase		,  truncation=_lowerCAmelCase		,  return_tensors="pt"		,  )
            UpperCAmelCase_											=self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]

            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            UpperCAmelCase_											=uncond_embeddings.shape[1]
            UpperCAmelCase_											=uncond_embeddings.repeat(_lowerCAmelCase		,  _lowerCAmelCase		,  1       )
            UpperCAmelCase_											=uncond_embeddings.view(batch_size * num_images_per_prompt		,  _lowerCAmelCase		,  -1       )

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
            UpperCAmelCase_											=torch.cat([uncond_embeddings, text_embeddings]       )

         # get the initial random noise unless the user supplied it

         # Unlike in other pipelines, latents need to be generated in the target device
         # for 1-to-1 results reproducibility with the CompVis implementation.
         # However this currently doesn't work in `mps`.
         UpperCAmelCase_											=(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
         UpperCAmelCase_											=(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
         UpperCAmelCase_											=text_embeddings.dtype
         if latents is None:
            if self.device.type == "mps":
               # randn does not exist on mps
               UpperCAmelCase_											=torch.randn(
                   _lowerCAmelCase		,  generator=_lowerCAmelCase		,  device="cpu"		,  dtype=_lowerCAmelCase       ).to(self.device       )
               UpperCAmelCase_											=torch.randn(_lowerCAmelCase		,  generator=_lowerCAmelCase		,  device="cpu"		,  dtype=_lowerCAmelCase       ).to(
                   self.device       )
            else:
               UpperCAmelCase_											=torch.randn(
                   _lowerCAmelCase		,  generator=_lowerCAmelCase		,  device=self.device		,  dtype=_lowerCAmelCase       )
               UpperCAmelCase_											=torch.randn(_lowerCAmelCase		,  generator=_lowerCAmelCase		,  device=self.device		,  dtype=_lowerCAmelCase       )
         else:
            if latents_reference.shape != latents_shape:
               raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'       )
            UpperCAmelCase_											=latents_reference.to(self.device       )
            UpperCAmelCase_											=latents.to(self.device       )

         # This is the key part of the pipeline where we
         # try to ensure that the generated images w/ the same seed
         # but different sizes actually result in similar images
         UpperCAmelCase_											=(latents_shape[3] - latents_shape_reference[3]) // 2
         UpperCAmelCase_											=(latents_shape[2] - latents_shape_reference[2]) // 2
         UpperCAmelCase_											=latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
         UpperCAmelCase_											=latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
         UpperCAmelCase_											=0 if dx < 0 else dx
         UpperCAmelCase_											=0 if dy < 0 else dy
         UpperCAmelCase_											=max(-dx		,  0       )
         UpperCAmelCase_											=max(-dy		,  0       )
         # import pdb
         # pdb.set_trace()
         UpperCAmelCase_											=latents_reference[:, :, dy : dy + h, dx : dx + w]

         # set timesteps
         self.scheduler.set_timesteps(_lowerCAmelCase       )

         # Some schedulers like PNDM have timesteps as arrays
         # It's more optimized to move all timesteps to correct device beforehand
         UpperCAmelCase_											=self.scheduler.timesteps.to(self.device       )

         # scale the initial noise by the standard deviation required by the scheduler
         UpperCAmelCase_											=latents * self.scheduler.init_noise_sigma

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
         # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
         # and should be between [0, 1]
         UpperCAmelCase_											="eta" in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )
         UpperCAmelCase_											={}
         if accepts_eta:
            UpperCAmelCase_											=eta

         for i, t in enumerate(self.progress_bar(_lowerCAmelCase       )       ):
            # expand the latents if we are doing classifier free guidance
            UpperCAmelCase_											=torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents
            UpperCAmelCase_											=self.scheduler.scale_model_input(_lowerCAmelCase		,  _lowerCAmelCase       )

            # predict the noise residual
            UpperCAmelCase_											=self.unet(_lowerCAmelCase		,  _lowerCAmelCase		,  encoder_hidden_states=_lowerCAmelCase       ).sample

            # perform guidance
            if do_classifier_free_guidance:
               UpperCAmelCase_						,       UpperCAmelCase_											=noise_pred.chunk(2       )
               UpperCAmelCase_											=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

            # compute the previous noisy sample x_t -> x_t-1
            UpperCAmelCase_											=self.scheduler.step(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  **_lowerCAmelCase       ).prev_sample

            # call the callback, if provided
            if callback is not None and i % callback_steps == 0:
               callback(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       )

         UpperCAmelCase_											=1 / 0.1_82_15 * latents
         UpperCAmelCase_											=self.vae.decode(_lowerCAmelCase       ).sample

         UpperCAmelCase_											=(image / 2 + 0.5).clamp(0		,  1       )

         # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
         UpperCAmelCase_											=image.cpu().permute(0		,  2		,  3		,  1       ).float().numpy()

         if self.safety_checker is not None:
            UpperCAmelCase_											=self.feature_extractor(self.numpy_to_pil(_lowerCAmelCase       )		,  return_tensors="pt"       ).to(
                self.device       )
            UpperCAmelCase_						,       UpperCAmelCase_											=self.safety_checker(
                images=_lowerCAmelCase		,  clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype       )       )
         else:
            UpperCAmelCase_											=None

         if output_type == "pil":
            UpperCAmelCase_											=self.numpy_to_pil(_lowerCAmelCase       )

         if not return_dict:
            return (image, has_nsfw_concept)

         return StableDiffusionPipelineOutput(images=_lowerCAmelCase		,  nsfw_content_detected=_lowerCAmelCase       )











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase  :   Any						      ={
    """configuration_upernet""": ["""UperNetConfig"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __lowercase  :   Tuple						      =[
         """UperNetForSemanticSegmentation""",
         """UperNetPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_upernet import UperNetConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
     import sys

     __lowercase  :   Any						      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




