






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 			lowercase__  (				A_:	List[Any]		)  ->							List[Any]:







       """simple docstring"""






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

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

              __UpperCAmelCase    							=[np.array(i.resize((w, h)		)		)[None, :] for i in image]
              __UpperCAmelCase    							=np.concatenate(A_     ,			axis=0		)
              __UpperCAmelCase    							=np.array(A_		).astype(np.floataa		) / 2_5_5.0
              __UpperCAmelCase    							=image.transpose(0     ,			3     ,			1     ,			2		)
              __UpperCAmelCase    							=2.0 * image - 1.0
              __UpperCAmelCase    							=torch.from_numpy(A_		)
       elif isinstance(image[0]     ,			torch.Tensor		):
              __UpperCAmelCase    							=torch.cat(A_     ,			dim=0		)

       return image





class 							_A (    UpperCamelCase							):
    """simple docstring"""

    def __init__(							self			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    OnnxRuntimeModel   ,    __SCREAMING_SNAKE_CASE			:    OnnxRuntimeModel   ,    __SCREAMING_SNAKE_CASE			:    Any   ,    __SCREAMING_SNAKE_CASE			:    OnnxRuntimeModel   ,    __SCREAMING_SNAKE_CASE			:    DDPMScheduler   ,    __SCREAMING_SNAKE_CASE			:    Any   ,    __SCREAMING_SNAKE_CASE			:    int = 350   ,    )			->       Any:
           super().__init__(
               vae=__SCREAMING_SNAKE_CASE   ,    text_encoder=__SCREAMING_SNAKE_CASE   ,    tokenizer=__SCREAMING_SNAKE_CASE   ,    unet=__SCREAMING_SNAKE_CASE   ,    low_res_scheduler=__SCREAMING_SNAKE_CASE   ,    scheduler=__SCREAMING_SNAKE_CASE   ,    safety_checker=__SCREAMING_SNAKE_CASE   ,    feature_extractor=__SCREAMING_SNAKE_CASE   ,    watermarker=__SCREAMING_SNAKE_CASE   ,    max_noise_level=__SCREAMING_SNAKE_CASE   ,    )



    def __call__(							self			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, List[str]]   ,    __SCREAMING_SNAKE_CASE			:    Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]]   ,    __SCREAMING_SNAKE_CASE			:    int = 75   ,    __SCREAMING_SNAKE_CASE			:    float = 9.0   ,    __SCREAMING_SNAKE_CASE			:    int = 20   ,    __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[Union[torch.Generator, List[torch.Generator]]] = None   ,    __SCREAMING_SNAKE_CASE			:    Optional[torch.FloatTensor] = None   ,    __SCREAMING_SNAKE_CASE			:    Optional[np.ndarray] = None   ,    __SCREAMING_SNAKE_CASE			:    Optional[np.ndarray] = 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			:    Optional[int] = 1   ,    )			->       Dict:
           self.check_inputs(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			)

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

           __UpperCAmelCase    							=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.
           __UpperCAmelCase    							=guidance_scale > 1.0

           # 3. Encode input prompt
           __UpperCAmelCase    							=self._encode_prompt(
               __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    prompt_embeds=__SCREAMING_SNAKE_CASE   ,    negative_prompt_embeds=__SCREAMING_SNAKE_CASE   ,    )

           __UpperCAmelCase    							=ORT_TO_PT_TYPE[str(text_embeddings.dtype			)]

           # 4. Preprocess image
           __UpperCAmelCase    							=preprocess(__SCREAMING_SNAKE_CASE			)
           __UpperCAmelCase    							=image.cpu()

           # 5. set timesteps
           self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE   ,    device=__SCREAMING_SNAKE_CASE			)
           __UpperCAmelCase    							=self.scheduler.timesteps

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

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

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

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

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

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

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

                         # timestep to tensor
                         __UpperCAmelCase    							=np.array([t]   ,    dtype=__SCREAMING_SNAKE_CASE			)

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

                         # perform guidance
                         if do_classifier_free_guidance:
                                __UpperCAmelCase							,   __UpperCAmelCase    							=np.split(__SCREAMING_SNAKE_CASE   ,    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(
                             torch.from_numpy(__SCREAMING_SNAKE_CASE			)   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			).prev_sample

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

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

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

           if not return_dict:
                  return (image,)

           return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE			)



    def _a       (							self			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    str			)			->       str:
           __UpperCAmelCase    							=1 / 0.08_333 * latents
           __UpperCAmelCase    							=self.vae(latent_sample=__SCREAMING_SNAKE_CASE			)[0]
           __UpperCAmelCase    							=np.clip(image / 2 + 0.5   ,    0   ,    1			)
           __UpperCAmelCase    							=image.transpose((0, 2, 3, 1)			)
           return image



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

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

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

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

           # get unconditional embeddings for classifier free guidance
           if do_classifier_free_guidance and negative_prompt_embeds is None:
                  __UpperCAmelCase    							=42
                  if negative_prompt is None:
                         __UpperCAmelCase    							=[""""""] * batch_size
                  elif type(__SCREAMING_SNAKE_CASE			) is not type(__SCREAMING_SNAKE_CASE			):
                         raise TypeError(
                             f'''`negative_prompt` should be the same type to `prompt`, but got {type(__SCREAMING_SNAKE_CASE			)} !='''
                             f''' {type(__SCREAMING_SNAKE_CASE			)}.'''			)
                  elif isinstance(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			):
                         __UpperCAmelCase    							=[negative_prompt]
                  elif batch_size != len(__SCREAMING_SNAKE_CASE			):
                         raise ValueError(
                             f'''`negative_prompt`: {negative_prompt} has batch size {len(__SCREAMING_SNAKE_CASE			)}, 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(
                      __SCREAMING_SNAKE_CASE   ,    padding="""max_length"""   ,    max_length=__SCREAMING_SNAKE_CASE   ,    truncation=__SCREAMING_SNAKE_CASE   ,    return_tensors="""pt"""   ,    )

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

                  __UpperCAmelCase    							=self.text_encoder(
                      input_ids=uncond_input.input_ids.int().to(__SCREAMING_SNAKE_CASE			)   ,    )
                  __UpperCAmelCase    							=uncond_embeddings[0]

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

           return prompt_embeds












from collections import defaultdict







def 			lowercase__  (				A_:	int = 5		)  ->							int:







       """simple docstring"""






       __UpperCAmelCase    							=defaultdict(A_		)
       __UpperCAmelCase    							=0

       while True:
              __UpperCAmelCase    							=get_digits(A_		)
              freqs[digits].append(A_		)

              if len(freqs[digits]		) == max_base:
                     __UpperCAmelCase    							=freqs[digits][0] ** 3
                     return base

              num += 1







def 			lowercase__  (				A_:	int		)  ->							str:







       """simple docstring"""






       return "".join(sorted(str(num**3		)		)		)


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




