

from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


_a	 =     getLogger(__name__)


_a	 =     4
_a	 =     7

_a	 =     {
    '''float16''': torch.floataa,
    '''float32''': torch.floataa,
}







def _a				( SCREAMING_SNAKE_CASE	:    Tuple	)					->					List[Any]:

 """simple docstring"""






 if isinstance(UpperCamelCase__					,      torch.Tensor	):
  return image
 elif isinstance(UpperCamelCase__					,      PIL.Image.Image	):
  __lowerCAmelCase:     Dict     			=   [image]

 if isinstance(image[0]					,      PIL.Image.Image	):
  __lowerCAmelCase:     List[Any]     			=   image[0].size
  __lowerCAmelCase:     Optional[Any]     			=   (x - x % 64 for x in (w, h))  # resize to integer multiple of 32

  __lowerCAmelCase:     str     			=   [np.array(i.resize((w, h)	)	)[None, :] for i in image]
  __lowerCAmelCase:     Union[str, Any]     			=   np.concatenate(UpperCamelCase__					,      axis=0	)
  __lowerCAmelCase:     Dict     			=   np.array(UpperCamelCase__	).astype(np.floataa	) / 2_55.0
  __lowerCAmelCase:     Any     			=   image.transpose(0					,      3					,      1					,      2	)
  __lowerCAmelCase:     Tuple     			=   2.0 * image - 1.0
  __lowerCAmelCase:     List[str]     			=   torch.from_numpy(UpperCamelCase__	)
 elif isinstance(image[0]					,      torch.Tensor	):
  __lowerCAmelCase:     Optional[Any]     			=   torch.cat(UpperCamelCase__					,      dim=0	)

 return image






class 							A_				(	snake_case__      ):


     def __init__(							self    :	List[Any]    ,						UpperCAmelCase    :	Dict    ,						UpperCAmelCase    :	Union[str, Any]    ,						UpperCAmelCase    :	List[Any]    ,						UpperCAmelCase    :	Optional[int]    ,						UpperCAmelCase    :	Optional[Any]    ,						UpperCAmelCase    :	Optional[int]    ,						UpperCAmelCase    :	List[str] = 3_5_0    ,						)							->			Tuple:
      super().__init__(
          vae=__lowerCamelCase    ,						text_encoder=__lowerCamelCase    ,						tokenizer=__lowerCamelCase    ,						unet=__lowerCamelCase    ,						low_res_scheduler=__lowerCamelCase    ,						scheduler=__lowerCamelCase    ,						safety_checker=__lowerCamelCase    ,						feature_extractor=__lowerCamelCase    ,						watermarker=__lowerCamelCase    ,						max_noise_level=__lowerCamelCase    ,						)


     def __call__(							self    :	Dict    ,						UpperCAmelCase    :	Optional[Any]    ,						UpperCAmelCase    :	Any    ,						UpperCAmelCase    :	Union[str, Any] = 7_5    ,						UpperCAmelCase    :	List[str] = 9.0    ,						UpperCAmelCase    :	Optional[Any] = 2_0    ,						UpperCAmelCase    :	Optional[int] = None    ,						UpperCAmelCase    :	List[str] = 1    ,						UpperCAmelCase    :	Dict = 0.0    ,						UpperCAmelCase    :	int = None    ,						UpperCAmelCase    :	Union[str, Any] = None    ,						UpperCAmelCase    :	Optional[Any] = None    ,						UpperCAmelCase    :	Dict = None    ,						UpperCAmelCase    :	int = "pil"    ,						UpperCAmelCase    :	Optional[Any] = True    ,						UpperCAmelCase    :	int = None    ,						UpperCAmelCase    :	int = 1    ,						)							->			Dict:
      self.check_inputs(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase      )

      # 2. Define call parameters
      if prompt is not None and isinstance(__lowerCamelCase    ,						__lowerCamelCase      ):
       __lowerCAmelCase:     Optional[Any]     			=   1
      elif prompt is not None and isinstance(__lowerCamelCase    ,						__lowerCamelCase      ):
       __lowerCAmelCase:     Tuple     			=   len(__lowerCamelCase      )
      else:
       __lowerCAmelCase:     Optional[Any]     			=   prompt_embeds.shape[0]

      __lowerCAmelCase:     Optional[int]     			=   self._execution_device
      # 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.
      __lowerCAmelCase:     Optional[Any]     			=   guidance_scale > 1.0

      # 3. Encode input prompt
      __lowerCAmelCase:     Tuple     			=   self._encode_prompt(
          __lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						prompt_embeds=__lowerCamelCase    ,						negative_prompt_embeds=__lowerCamelCase    ,						)

      __lowerCAmelCase:     List[str]     			=   ORT_TO_PT_TYPE[str(text_embeddings.dtype      )]

      # 4. Preprocess image
      __lowerCAmelCase:     int     			=   preprocess(__lowerCamelCase      )
      __lowerCAmelCase:     Tuple     			=   image.cpu()

      # 5. set timesteps
      self.scheduler.set_timesteps(__lowerCamelCase    ,						device=__lowerCamelCase      )
      __lowerCAmelCase:     str     			=   self.scheduler.timesteps

      # 5. Add noise to image
      __lowerCAmelCase:     int     			=   torch.tensor([noise_level]    ,						dtype=torch.long    ,						device=__lowerCamelCase      )
      __lowerCAmelCase:     List[str]     			=   torch.randn(image.shape    ,						generator=__lowerCamelCase    ,						device=__lowerCamelCase    ,						dtype=__lowerCamelCase      )
      __lowerCAmelCase:     List[str]     			=   self.low_res_scheduler.add_noise(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase      )

      __lowerCAmelCase:     Union[str, Any]     			=   2 if do_classifier_free_guidance else 1
      __lowerCAmelCase:     Optional[Any]     			=   np.concatenate([image] * batch_multiplier * num_images_per_prompt      )
      __lowerCAmelCase:     Tuple     			=   np.concatenate([noise_level] * image.shape[0]      )

      # 6. Prepare latent variables
      __lowerCAmelCase:     Any     			=   image.shape[2:]
      __lowerCAmelCase:     Dict     			=   self.prepare_latents(
          batch_size * num_images_per_prompt    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						)

      # 7. Check that sizes of image and latents match
      __lowerCAmelCase:     List[str]     			=   image.shape[1]
      if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
       raise ValueError(
           'Incorrect configuration settings! The config of `pipeline.unet` expects'
           F''' {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +'''
           F''' `num_channels_image`: {num_channels_image} '''
           F''' = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of'''
           ' `pipeline.unet` or your `image` input.'      )

      # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
      __lowerCAmelCase:     Any     			=   self.prepare_extra_step_kwargs(__lowerCamelCase    ,						__lowerCamelCase      )

      __lowerCAmelCase:     List[str]     			=   next(
          (input.type for input in self.unet.model.get_inputs() if input.name == 'timestep')    ,						'tensor(float)'      )
      __lowerCAmelCase:     int     			=   ORT_TO_NP_TYPE[timestep_dtype]

      # 9. Denoising loop
      __lowerCAmelCase:     str     			=   len(__lowerCamelCase      ) - num_inference_steps * self.scheduler.order
      with self.progress_bar(total=__lowerCamelCase      ) as progress_bar:
       for i, t in enumerate(__lowerCamelCase      ):
        # expand the latents if we are doing classifier free guidance
        __lowerCAmelCase:     Dict     			=   np.concatenate([latents] * 2      ) if do_classifier_free_guidance else latents

        # concat latents, mask, masked_image_latents in the channel dimension
        __lowerCAmelCase:     Optional[int]     			=   self.scheduler.scale_model_input(__lowerCamelCase    ,						__lowerCamelCase      )
        __lowerCAmelCase:     Optional[int]     			=   np.concatenate([latent_model_input, image]    ,						axis=1      )

        # timestep to tensor
        __lowerCAmelCase:     Optional[int]     			=   np.array([t]    ,						dtype=__lowerCamelCase      )

        # predict the noise residual
        __lowerCAmelCase:     str     			=   self.unet(
            sample=__lowerCamelCase    ,						timestep=__lowerCamelCase    ,						encoder_hidden_states=__lowerCamelCase    ,						class_labels=noise_level.astype(np.intaa      )    ,						)[0]

        # perform guidance
        if do_classifier_free_guidance:
         __lowerCAmelCase:     Optional[int]     			=   np.split(__lowerCamelCase    ,						2      )
        __lowerCAmelCase:     List[Any]     			=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

        # compute the previous noisy sample x_t -> x_t-1
        __lowerCAmelCase:     Dict     			=   self.scheduler.step(
            torch.from_numpy(__lowerCamelCase      )    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						**__lowerCamelCase      ).prev_sample

        # call the callback, if provided
        if i == len(__lowerCamelCase      ) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
         progress_bar.update()
         if callback is not None and i % callback_steps == 0:
          callback(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase      )

        # 10. Post-processing
      __lowerCAmelCase:     List[Any]     			=   self.decode_latents(latents.float()      )

      # 11. Convert to PIL
      if output_type == "pil":
       __lowerCAmelCase:     List[str]     			=   self.numpy_to_pil(__lowerCamelCase      )

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=__lowerCamelCase      )


     def 						UpperCAmelCase						(							self    :	List[Any]    ,						UpperCAmelCase    :	Dict      )							->			Any:
      __lowerCAmelCase:     Tuple     			=   1 / 0.08333 * latents
      __lowerCAmelCase:     List[Any]     			=   self.vae(latent_sample=__lowerCamelCase      )[0]
      __lowerCAmelCase:     Any     			=   np.clip(image / 2 + 0.5    ,						0    ,						1      )
      __lowerCAmelCase:     Any     			=   image.transpose((0, 2, 3, 1)      )
      return image


     def 						UpperCAmelCase						(							self    :	List[Any]    ,						UpperCAmelCase    :	Optional[int]    ,						UpperCAmelCase    :	Union[str, Any]    ,						UpperCAmelCase    :	Tuple    ,						UpperCAmelCase    :	Optional[Any]    ,						UpperCAmelCase    :	Any    ,						UpperCAmelCase    :	Dict = None    ,						UpperCAmelCase    :	List[Any] = None    ,						)							->			List[Any]:
      if prompt is not None and isinstance(__lowerCamelCase    ,						__lowerCamelCase      ):
       __lowerCAmelCase:     Optional[Any]     			=   1
      elif prompt is not None and isinstance(__lowerCamelCase    ,						__lowerCamelCase      ):
       __lowerCAmelCase:     Dict     			=   len(__lowerCamelCase      )
      else:
       __lowerCAmelCase:     Union[str, Any]     			=   prompt_embeds.shape[0]

      if prompt_embeds is None:
       __lowerCAmelCase:     List[str]     			=   self.tokenizer(
           __lowerCamelCase    ,						padding='max_length'    ,						max_length=self.tokenizer.model_max_length    ,						truncation=__lowerCamelCase    ,						return_tensors='pt'    ,						)
       __lowerCAmelCase:     Dict     			=   text_inputs.input_ids
       __lowerCAmelCase:     Any     			=   self.tokenizer(__lowerCamelCase    ,						padding='longest'    ,						return_tensors='pt'      ).input_ids

       if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
           __lowerCamelCase    ,						__lowerCamelCase      ):
        __lowerCAmelCase:     Union[str, Any]     			=   self.tokenizer.batch_decode(
            untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]      )
        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}'''      )

       # no positional arguments to text_encoder
       __lowerCAmelCase:     int     			=   self.text_encoder(
           input_ids=text_input_ids.int().to(__lowerCamelCase      )    ,						)
       __lowerCAmelCase:     List[Any]     			=   prompt_embeds[0]

      __lowerCAmelCase:     Dict     			=   prompt_embeds.shape
      # duplicate text embeddings for each generation per prompt, using mps friendly method
      __lowerCAmelCase:     Optional[Any]     			=   prompt_embeds.repeat(1    ,						__lowerCamelCase      )
      __lowerCAmelCase:     Optional[Any]     			=   prompt_embeds.reshape(bs_embed * num_images_per_prompt    ,						__lowerCamelCase    ,						-1      )

      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance and negative_prompt_embeds is None:
       __lowerCAmelCase:     List[str]
       if negative_prompt is None:
        __lowerCAmelCase:     int     			=   [""] * batch_size
       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      ):
        __lowerCAmelCase:     Union[str, Any]     			=   [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:
        __lowerCAmelCase:     List[str]     			=   negative_prompt

       __lowerCAmelCase:     str     			=   text_input_ids.shape[-1]
       __lowerCAmelCase:     List[Any]     			=   self.tokenizer(
           __lowerCamelCase    ,						padding='max_length'    ,						max_length=__lowerCamelCase    ,						truncation=__lowerCamelCase    ,						return_tensors='pt'    ,						)

       # if hasattr(uncond_input, "attention_mask"):
       #     attention_mask = uncond_input.attention_mask.to(device)
       # else:
       #     attention_mask = None

       __lowerCAmelCase:     int     			=   self.text_encoder(
           input_ids=uncond_input.input_ids.int().to(__lowerCamelCase      )    ,						)
       __lowerCAmelCase:     Any     			=   uncond_embeddings[0]

      if do_classifier_free_guidance:
       __lowerCAmelCase:     List[Any]     			=   uncond_embeddings.shape[1]
       # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
       __lowerCAmelCase:     Tuple     			=   uncond_embeddings.repeat(1    ,						__lowerCamelCase      )
       __lowerCAmelCase:     List[Any]     			=   uncond_embeddings.reshape(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
       __lowerCAmelCase:     Union[str, Any]     			=   np.concatenate([uncond_embeddings, prompt_embeds]      )

      return prompt_embeds








from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)






class 							A_				(	snake_case__      ):


     def __init__(							self    :	List[Any]    ,						UpperCAmelCase    :	AutoencoderKL    ,						UpperCAmelCase    :	CLIPTextModel    ,						UpperCAmelCase    :	CLIPTokenizer    ,						UpperCAmelCase    :	UNetaDConditionModel    ,						UpperCAmelCase    :	Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]    ,						)							->			Tuple:
      super().__init__()

      self.register_modules(vae=UpperCAmelCase    ,						text_encoder=UpperCAmelCase    ,						tokenizer=UpperCAmelCase    ,						unet=UpperCAmelCase    ,						scheduler=UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	str    ,						UpperCAmelCase    :	Dict      )							->			Any:
      with torch.no_grad():
       __lowerCAmelCase:     Tuple     			=   self.vae.encode(tfms.ToTensor()(UpperCAmelCase      ).unsqueeze(0      ).to(self.device      ) * 2 - 1      )
       __lowerCAmelCase:     Tuple     			=   0.18215 * latent.latent_dist.sample()
      return latent


     def 						UpperCAmelCase						(							self    :	List[Any]    ,						UpperCAmelCase    :	str      )							->			int:
      __lowerCAmelCase:     Optional[Any]     			=   (1 / 0.18215) * latent
      with torch.no_grad():
       __lowerCAmelCase:     str     			=   self.vae.decode(UpperCAmelCase      ).sample
      __lowerCAmelCase:     Any     			=   (img / 2 + 0.5).clamp(0    ,						1      )
      __lowerCAmelCase:     Optional[Any]     			=   img.detach().cpu().permute(0    ,						2    ,						3    ,						1      ).numpy()
      __lowerCAmelCase:     List[str]     			=   (img * 2_5_5).round().astype('uint8'      )
      return Image.fromarray(img[0]      )


     def 						UpperCAmelCase						(							self    :	Optional[Any]    ,						UpperCAmelCase    :	Tuple      )							->			str:
      __lowerCAmelCase:     Dict     			=   self.tokenizer(
          UpperCAmelCase    ,						padding='max_length'    ,						max_length=self.tokenizer.model_max_length    ,						truncation=UpperCAmelCase    ,						return_tensors='pt'    ,						)

      __lowerCAmelCase:     Any     			=   self.text_encoder(text_input.input_ids.to(self.device      )      )[0]

      __lowerCAmelCase:     List[str]     			=   self.tokenizer(
          ''    ,						padding='max_length'    ,						max_length=self.tokenizer.model_max_length    ,						truncation=UpperCAmelCase    ,						return_tensors='pt'    ,						)

      __lowerCAmelCase:     str     			=   self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

      return torch.cat([uncond_embedding, text_embedding]      )


     def __call__(							self    :	Optional[int]    ,						UpperCAmelCase    :	Image.Image    ,						UpperCAmelCase    :	str    ,						UpperCAmelCase    :	float = 0.3    ,						UpperCAmelCase    :	float = 0.6    ,						UpperCAmelCase    :	float = 0.5    ,						UpperCAmelCase    :	int = 4_2    ,						UpperCAmelCase    :	int = 5_0    ,						UpperCAmelCase    :	float = 7.5    ,						)							->			Image.Image:
      __lowerCAmelCase:     Optional[int]     			=   steps - int(kmin * steps      )
      __lowerCAmelCase:     Dict     			=   steps - int(kmax * steps      )

      __lowerCAmelCase:     Union[str, Any]     			=   self.prep_text(UpperCAmelCase      )

      self.scheduler.set_timesteps(UpperCAmelCase      )

      __lowerCAmelCase			,							__lowerCAmelCase:     str     			=   img.size
      __lowerCAmelCase:     List[Any]     			=   self.encode(UpperCAmelCase      )

      torch.manual_seed(UpperCAmelCase      )
      __lowerCAmelCase:     Optional[int]     			=   torch.randn(
          (1, self.unet.config.in_channels, height // 8, width // 8)    ,						).to(self.device      )

      __lowerCAmelCase:     int     			=   self.scheduler.add_noise(
          UpperCAmelCase    ,						UpperCAmelCase    ,						timesteps=self.scheduler.timesteps[tmax]    ,						)

      __lowerCAmelCase:     Tuple     			=   torch.cat([latents] * 2      )

      __lowerCAmelCase:     int     			=   self.scheduler.scale_model_input(UpperCAmelCase    ,						self.scheduler.timesteps[tmax]      )

      with torch.no_grad():
       __lowerCAmelCase:     List[str]     			=   self.unet(
           UpperCAmelCase    ,						self.scheduler.timesteps[tmax]    ,						encoder_hidden_states=UpperCAmelCase    ,						).sample

      __lowerCAmelCase			,							__lowerCAmelCase:     Optional[Any]     			=   pred.chunk(2      )
      __lowerCAmelCase:     Union[str, Any]     			=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

      __lowerCAmelCase:     List[str]     			=   self.scheduler.step(UpperCAmelCase    ,						self.scheduler.timesteps[tmax]    ,						UpperCAmelCase      ).prev_sample

      for i, t in enumerate(tqdm(self.scheduler.timesteps      )      ):
       if i > tmax:
        if i < tmin:  # layout generation phase
         __lowerCAmelCase:     Optional[Any]     			=   self.scheduler.add_noise(
             UpperCAmelCase    ,						UpperCAmelCase    ,						timesteps=UpperCAmelCase    ,						)

         __lowerCAmelCase:     List[Any]     			=   (mix_factor * latents) + (
             1 - mix_factor
         ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
         __lowerCAmelCase:     Optional[Any]     			=   torch.cat([input] * 2      )

        else:  # content generation phase
         __lowerCAmelCase:     str     			=   torch.cat([latents] * 2      )

        __lowerCAmelCase:     List[Any]     			=   self.scheduler.scale_model_input(UpperCAmelCase    ,						UpperCAmelCase      )

        with torch.no_grad():
         __lowerCAmelCase:     Any     			=   self.unet(
             UpperCAmelCase    ,						UpperCAmelCase    ,						encoder_hidden_states=UpperCAmelCase    ,						).sample

        __lowerCAmelCase			,							__lowerCAmelCase:     Optional[int]     			=   pred.chunk(2      )
        __lowerCAmelCase:     Union[str, Any]     			=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

        __lowerCAmelCase:     List[str]     			=   self.scheduler.step(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      ).prev_sample

      return self.decode(UpperCAmelCase      )





