





'''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


_SCREAMING_SNAKE_CASE			=							getLogger(__name__)


_SCREAMING_SNAKE_CASE			=							4
_SCREAMING_SNAKE_CASE			=							7

_SCREAMING_SNAKE_CASE			=							{
    "float16": torch.floataa,
    "float32": torch.floataa,
}


def 		__a(SCREAMING_SNAKE_CASE_					:   Any      ):



    '''simple docstring'''





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

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

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

    return image





class 				lowerCAmelCase_						(						__magic_name__						):




 def __init__(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase = 350     ,       )      ->							List[str]:
     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     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase = 75     ,       _lowerCAmelCase = 9.0     ,       _lowerCAmelCase = 20     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = 1     ,       _lowerCAmelCase = 0.0     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = "pil"     ,       _lowerCAmelCase = True     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = 1     ,       )      ->							str:
     self.check_inputs(_lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)

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

     _lowerCAmelCase		     = 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		     = guidance_scale > 1.0

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

     _lowerCAmelCase		     = ORT_TO_PT_TYPE[str(text_embeddings.dtype						)]

     # 4. Preprocess image
     _lowerCAmelCase		     = preprocess(_lowerCAmelCase						)
     _lowerCAmelCase		     = image.cpu()

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

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

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

     # 6. Prepare latent variables
     _lowerCAmelCase     ,     _lowerCAmelCase		     = image.shape[2:]
     _lowerCAmelCase		     = 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		     = 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		     = self.prepare_extra_step_kwargs(_lowerCAmelCase     ,       _lowerCAmelCase						)

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

     # 9. Denoising loop
     _lowerCAmelCase		     = 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		     = np.concatenate([latents] * 2						) if do_classifier_free_guidance else latents

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

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

             # predict the noise residual
             _lowerCAmelCase		     = 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     ,     _lowerCAmelCase		     = np.split(_lowerCAmelCase     ,       2						)
             _lowerCAmelCase		     = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

             # compute the previous noisy sample x_t -> x_t-1
             _lowerCAmelCase		     = 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		     = self.decode_latents(latents.float()						)

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

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=_lowerCAmelCase						)




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							int:
     _lowerCAmelCase		     = 1 / 0.08333 * latents
     _lowerCAmelCase		     = self.vae(latent_sample=_lowerCAmelCase						)[0]
     _lowerCAmelCase		     = np.clip(image / 2 + 0.5     ,       0     ,       1						)
     _lowerCAmelCase		     = image.transpose((0, 2, 3, 1)						)
     return image





 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       )      ->							int:
     if prompt is not None and isinstance(_lowerCAmelCase     ,       _lowerCAmelCase						):
         _lowerCAmelCase		     = 1
     elif prompt is not None and isinstance(_lowerCAmelCase     ,       _lowerCAmelCase						):
         _lowerCAmelCase		     = len(_lowerCAmelCase						)
     else:
         _lowerCAmelCase		     = prompt_embeds.shape[0]

     if prompt_embeds is None:
         _lowerCAmelCase		     = self.tokenizer(
             _lowerCAmelCase     ,       padding="max_length"     ,       max_length=self.tokenizer.model_max_length     ,       truncation=_lowerCAmelCase     ,       return_tensors="pt"     ,       )
         _lowerCAmelCase		     = text_inputs.input_ids
         _lowerCAmelCase		     = 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		     = 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		     = self.text_encoder(
             input_ids=text_input_ids.int().to(_lowerCAmelCase						)     ,       )
         _lowerCAmelCase		     = prompt_embeds[0]

     _lowerCAmelCase     ,     _lowerCAmelCase     ,     _lowerCAmelCase		     = prompt_embeds.shape
     # duplicate text embeddings for each generation per prompt, using mps friendly method
     _lowerCAmelCase		     = prompt_embeds.repeat(1     ,       _lowerCAmelCase						)
     _lowerCAmelCase		     = 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		     = 42
         if negative_prompt is None:
             _lowerCAmelCase		     = [""] * 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		     = [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		     = negative_prompt

         _lowerCAmelCase		     = text_input_ids.shape[-1]
         _lowerCAmelCase		     = 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		     = self.text_encoder(
             input_ids=uncond_input.input_ids.int().to(_lowerCAmelCase						)     ,       )
         _lowerCAmelCase		     = uncond_embeddings[0]

     if do_classifier_free_guidance:
         _lowerCAmelCase		     = uncond_embeddings.shape[1]
         # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
         _lowerCAmelCase		     = uncond_embeddings.repeat(1     ,       _lowerCAmelCase						)
         _lowerCAmelCase		     = 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		     = np.concatenate([uncond_embeddings, prompt_embeds]						)

     return prompt_embeds









'''simple docstring'''


def 		__a(SCREAMING_SNAKE_CASE_					:   int = 1000      ):



    '''simple docstring'''





    return sum(i for i in range(SCREAMING_SNAKE_CASE_      ) if i % 3 == 0 or i % 5 == 0      )


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


