

"""simple docstring"""





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    (    UpperCamelCase_	:			List[Any])      ->		str:
   '''simple docstring'''


   if isinstance(UpperCamelCase_,     torch.Tensor):
      return image
   elif isinstance(UpperCamelCase_,     PIL.Image.Image):
      __lowercase								=		[image]

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

      __lowercase								=		[np.array(i.resize((w, h)))[None, :] for i in image]
      __lowercase								=		np.concatenate(UpperCamelCase_,     axis=0)
      __lowercase								=		np.array(UpperCamelCase_).astype(np.floataa) / 255.0
      __lowercase								=		image.transpose(0,     3,     1,     2)
      __lowercase								=		2.0 * image - 1.0
      __lowercase								=		torch.from_numpy(UpperCamelCase_)
   elif isinstance(image[0],     torch.Tensor):
      __lowercase								=		torch.cat(UpperCamelCase_,     dim=0)

   return image




class    _lowerCAmelCase    (      lowercase  ):







 """simple docstring"""







 def __init__(     self				:     Dict,   UpperCAmelCase__				:     OnnxRuntimeModel,   UpperCAmelCase__				:     OnnxRuntimeModel,   UpperCAmelCase__				:     Any,   UpperCAmelCase__				:     OnnxRuntimeModel,   UpperCAmelCase__				:     DDPMScheduler,   UpperCAmelCase__				:     Any,   UpperCAmelCase__				:     int = 3_5_0,   ):
    super().__init__(
        vae=UpperCAmelCase__,   text_encoder=UpperCAmelCase__,   tokenizer=UpperCAmelCase__,   unet=UpperCAmelCase__,   low_res_scheduler=UpperCAmelCase__,   scheduler=UpperCAmelCase__,   safety_checker=UpperCAmelCase__,   feature_extractor=UpperCAmelCase__,   watermarker=UpperCAmelCase__,   max_noise_level=UpperCAmelCase__,   )


 def __call__(     self				:     Optional[Any],   UpperCAmelCase__				:     Union[str, List[str]],   UpperCAmelCase__				:     Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]],   UpperCAmelCase__				:     int = 7_5,   UpperCAmelCase__				:     float = 9.0,   UpperCAmelCase__				:     int = 2_0,   UpperCAmelCase__				:     Optional[Union[str, List[str]]] = None,   UpperCAmelCase__				:     Optional[int] = 1,   UpperCAmelCase__				:     float = 0.0,   UpperCAmelCase__				:     Optional[Union[torch.Generator, List[torch.Generator]]] = None,   UpperCAmelCase__				:     Optional[torch.FloatTensor] = None,   UpperCAmelCase__				:     Optional[np.ndarray] = None,   UpperCAmelCase__				:     Optional[np.ndarray] = None,   UpperCAmelCase__				:     Optional[str] = "pil",   UpperCAmelCase__				:     bool = True,   UpperCAmelCase__				:     Optional[Callable[[int, int, torch.FloatTensor], None]] = None,   UpperCAmelCase__				:     Optional[int] = 1,   ):
    self.check_inputs(UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__      )

    # 2. Define call parameters
    if prompt is not None and isinstance(UpperCAmelCase__,   UpperCAmelCase__      ):
       __lowercase								=		1
    elif prompt is not None and isinstance(UpperCAmelCase__,   UpperCAmelCase__      ):
       __lowercase								=		len(UpperCAmelCase__      )
    else:
       __lowercase								=		prompt_embeds.shape[0]

    __lowercase								=		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.
    __lowercase								=		guidance_scale > 1.0

    # 3. Encode input prompt
    __lowercase								=		self._encode_prompt(
        UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   prompt_embeds=UpperCAmelCase__,   negative_prompt_embeds=UpperCAmelCase__,   )

    __lowercase								=		ORT_TO_PT_TYPE[str(text_embeddings.dtype      )]

    # 4. Preprocess image
    __lowercase								=		preprocess(UpperCAmelCase__      )
    __lowercase								=		image.cpu()

    # 5. set timesteps
    self.scheduler.set_timesteps(UpperCAmelCase__,   device=UpperCAmelCase__      )
    __lowercase								=		self.scheduler.timesteps

    # 5. Add noise to image
    __lowercase								=		torch.tensor([noise_level],   dtype=torch.long,   device=UpperCAmelCase__      )
    __lowercase								=		torch.randn(image.shape,   generator=UpperCAmelCase__,   device=UpperCAmelCase__,   dtype=UpperCAmelCase__      )
    __lowercase								=		self.low_res_scheduler.add_noise(UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__      )

    __lowercase								=		2 if do_classifier_free_guidance else 1
    __lowercase								=		np.concatenate([image] * batch_multiplier * num_images_per_prompt      )
    __lowercase								=		np.concatenate([noise_level] * image.shape[0]      )

    # 6. Prepare latent variables
    __lowercase						,__lowercase								=		image.shape[2:]
    __lowercase								=		self.prepare_latents(
        batch_size * num_images_per_prompt,   UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   )

    # 7. Check that sizes of image and latents match
    __lowercase								=		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
    __lowercase								=		self.prepare_extra_step_kwargs(UpperCAmelCase__,   UpperCAmelCase__      )

    __lowercase								=		next(
        (input.type for input in self.unet.model.get_inputs() if input.name == "timestep"),   "tensor(float)"      )
    __lowercase								=		ORT_TO_NP_TYPE[timestep_dtype]

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

          # concat latents, mask, masked_image_latents in the channel dimension
          __lowercase								=		self.scheduler.scale_model_input(UpperCAmelCase__,   UpperCAmelCase__      )
          __lowercase								=		np.concatenate([latent_model_input, image],   axis=1      )

          # timestep to tensor
          __lowercase								=		np.array([t],   dtype=UpperCAmelCase__      )

          # predict the noise residual
          __lowercase								=		self.unet(
              sample=UpperCAmelCase__,   timestep=UpperCAmelCase__,   encoder_hidden_states=UpperCAmelCase__,   class_labels=noise_level.astype(np.intaa      ),   )[0]

          # perform guidance
          if do_classifier_free_guidance:
             __lowercase						,__lowercase								=		np.split(UpperCAmelCase__,   2      )
          __lowercase								=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

          # compute the previous noisy sample x_t -> x_t-1
          __lowercase								=		self.scheduler.step(
              torch.from_numpy(UpperCAmelCase__      ),   UpperCAmelCase__,   UpperCAmelCase__,   **UpperCAmelCase__      ).prev_sample

          # call the callback, if provided
          if i == len(UpperCAmelCase__      ) - 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(UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__      )

        # 10. Post-processing
    __lowercase								=		self.decode_latents(latents.float()      )

    # 11. Convert to PIL
    if output_type == "pil":
       __lowercase								=		self.numpy_to_pil(UpperCAmelCase__      )

    if not return_dict:
       return (image,)

    return ImagePipelineOutput(images=UpperCAmelCase__      )


 def    _lowercase					(     self				:     Optional[int],   UpperCAmelCase__				:     Tuple      ):
    __lowercase								=		1 / 0.08_333 * latents
    __lowercase								=		self.vae(latent_sample=UpperCAmelCase__      )[0]
    __lowercase								=		np.clip(image / 2 + 0.5,   0,   1      )
    __lowercase								=		image.transpose((0, 2, 3, 1)      )
    return image



 def    _lowercase					(     self				:     str,   UpperCAmelCase__				:     Union[str, List[str]],   UpperCAmelCase__				:     List[Any],   UpperCAmelCase__				:     Optional[int],   UpperCAmelCase__				:     bool,   UpperCAmelCase__				:     Optional[str],   UpperCAmelCase__				:     Optional[torch.FloatTensor] = None,   UpperCAmelCase__				:     Optional[torch.FloatTensor] = None,   ):
    if prompt is not None and isinstance(UpperCAmelCase__,   UpperCAmelCase__      ):
       __lowercase								=		1
    elif prompt is not None and isinstance(UpperCAmelCase__,   UpperCAmelCase__      ):
       __lowercase								=		len(UpperCAmelCase__      )
    else:
       __lowercase								=		prompt_embeds.shape[0]

    if prompt_embeds is None:
       __lowercase								=		self.tokenizer(
           UpperCAmelCase__,   padding="max_length",   max_length=self.tokenizer.model_max_length,   truncation=UpperCAmelCase__,   return_tensors="pt",   )
       __lowercase								=		text_inputs.input_ids
       __lowercase								=		self.tokenizer(UpperCAmelCase__,   padding="longest",   return_tensors="pt"      ).input_ids

       if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
           UpperCAmelCase__,   UpperCAmelCase__      ):
          __lowercase								=		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
       __lowercase								=		self.text_encoder(
           input_ids=text_input_ids.int().to(UpperCAmelCase__      ),   )
       __lowercase								=		prompt_embeds[0]

    __lowercase						,__lowercase						,__lowercase								=		prompt_embeds.shape
    # duplicate text embeddings for each generation per prompt, using mps friendly method
    __lowercase								=		prompt_embeds.repeat(1,   UpperCAmelCase__      )
    __lowercase								=		prompt_embeds.reshape(bs_embed * num_images_per_prompt,   UpperCAmelCase__,   -1      )

    # get unconditional embeddings for classifier free guidance
    if do_classifier_free_guidance and negative_prompt_embeds is None:
       __lowercase								=		42
       if negative_prompt is None:
          __lowercase								=		[""] * batch_size
       elif type(UpperCAmelCase__      ) is not type(UpperCAmelCase__      ):
          raise TypeError(
              F"""`negative_prompt` should be the same type to `prompt`, but got {type(UpperCAmelCase__      )} !="""
              F""" {type(UpperCAmelCase__      )}."""      )
       elif isinstance(UpperCAmelCase__,   UpperCAmelCase__      ):
          __lowercase								=		[negative_prompt]
       elif batch_size != len(UpperCAmelCase__      ):
          raise ValueError(
              F"""`negative_prompt`: {negative_prompt} has batch size {len(UpperCAmelCase__      )}, but `prompt`:"""
              F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
              " the batch size of `prompt`."      )
       else:
          __lowercase								=		negative_prompt

       __lowercase								=		text_input_ids.shape[-1]
       __lowercase								=		self.tokenizer(
           UpperCAmelCase__,   padding="max_length",   max_length=UpperCAmelCase__,   truncation=UpperCAmelCase__,   return_tensors="pt",   )

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

       __lowercase								=		self.text_encoder(
           input_ids=uncond_input.input_ids.int().to(UpperCAmelCase__      ),   )
       __lowercase								=		uncond_embeddings[0]

    if do_classifier_free_guidance:
       __lowercase								=		uncond_embeddings.shape[1]
       # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
       __lowercase								=		uncond_embeddings.repeat(1,   UpperCAmelCase__      )
       __lowercase								=		uncond_embeddings.reshape(batch_size * num_images_per_prompt,   UpperCAmelCase__,   -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
       __lowercase								=		np.concatenate([uncond_embeddings, prompt_embeds]      )

    return prompt_embeds







"""simple docstring"""





import math
from decimal import Decimal, getcontext




def     _A    (    UpperCamelCase_	:			int = 4000000)      ->		int:
   '''simple docstring'''


   try:
      __lowercase								=		int(UpperCamelCase_)
   except (TypeError, ValueError):
      raise TypeError("Parameter n must be int or castable to int.")
   if n <= 0:
      raise ValueError("Parameter n must be greater than or equal to one.")
   __lowercase								=		100
   __lowercase								=		(Decimal(5) ** Decimal(0.5) + 1) / Decimal(2)

   __lowercase								=		(math.floor(math.log(n * (phi + 2),     UpperCamelCase_) - 1) // 3) * 3 + 2
   __lowercase								=		Decimal(round(phi ** Decimal(index + 1))) / (phi + 2)
   __lowercase								=		num // 2
   return int(UpperCamelCase_)


if __name__ == "__main__":
   print(F"{solution() = }")




