






"""simple docstring"""


_UpperCamelCase				:    str     =						{hex(i)[2:]: i for i in range(16)}  # Use [:2] to strip off the leading '0x'




def 			snake_case  (A_      :str      ):







  '''simple docstring'''

  a     :			List[str]				=   hex_string.strip().lower()
  if not hex_string:
    raise ValueError('Empty string was passed to the function'      )
  a     :			Optional[Any]				=   hex_string[0] == '-'
  if is_negative:
    a     :			Any				=   hex_string[1:]
  if not all(char in hex_table for char in hex_string      ):
    raise ValueError('Non-hexadecimal value was passed to the function'      )
  a     :			str				=   0
  for char in hex_string:
    a     :			Union[str, Any]				=   1_6 * decimal_number + hex_table[char]
  return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
   from doctest import testmod

   testmod()










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


_lowerCamelCase			 =						getLogger(__name__)


_lowerCamelCase			 =						4
_lowerCamelCase			 =						7

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




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						str      )  ->							List[Any]:
    if isinstance(__UpperCamelCase       ,     torch.Tensor      ):
        return image
    elif isinstance(__UpperCamelCase       ,     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(__UpperCamelCase       ,     axis=0      )
        UpperCAmelCase_							 =     np.array(__UpperCamelCase      ).astype(np.floataa      ) / 255.0
        UpperCAmelCase_							 =     image.transpose(0       ,     3       ,     1       ,     2      )
        UpperCAmelCase_							 =     2.0 * image - 1.0
        UpperCAmelCase_							 =     torch.from_numpy(__UpperCamelCase      )
    elif isinstance(image[0]       ,     torch.Tensor      ):
        UpperCAmelCase_							 =     torch.cat(__UpperCamelCase       ,     dim=0      )

    return image



class 			a     (   _A			):

     '''simple docstring'''





     def __init__(	self   :			Dict					,    __snake_case   :			OnnxRuntimeModel					,    __snake_case   :			OnnxRuntimeModel					,    __snake_case   :			Any					,    __snake_case   :			OnnxRuntimeModel					,    __snake_case   :			DDPMScheduler					,    __snake_case   :			Any					,    __snake_case   :			int = 3_50					,    ):
         super().__init__(
             vae=__snake_case					,    text_encoder=__snake_case					,    tokenizer=__snake_case					,    unet=__snake_case					,    low_res_scheduler=__snake_case					,    scheduler=__snake_case					,    safety_checker=__snake_case					,    feature_extractor=__snake_case					,    watermarker=__snake_case					,    max_noise_level=__snake_case					,    )
     def __call__(	self   :			Optional[int]					,    __snake_case   :			Union[str, List[str]]					,    __snake_case   :			Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]]					,    __snake_case   :			int = 75					,    __snake_case   :			float = 9.0					,    __snake_case   :			int = 20					,    __snake_case   :			Optional[Union[str, List[str]]] = None					,    __snake_case   :			Optional[int] = 1					,    __snake_case   :			float = 0.0					,    __snake_case   :			Optional[Union[torch.Generator, List[torch.Generator]]] = None					,    __snake_case   :			Optional[torch.FloatTensor] = None					,    __snake_case   :			Optional[np.ndarray] = None					,    __snake_case   :			Optional[np.ndarray] = None					,    __snake_case   :			Optional[str] = "pil"					,    __snake_case   :			bool = True					,    __snake_case   :			Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,    __snake_case   :			Optional[int] = 1					,    ):
         self.check_inputs(__snake_case					,    __snake_case					,    __snake_case					,    __snake_case				)

         # 2. Define call parameters
         if prompt is not None and isinstance(__snake_case					,    __snake_case				):
             UpperCAmelCase_							 =     1
         elif prompt is not None and isinstance(__snake_case					,    __snake_case				):
             UpperCAmelCase_							 =     len(__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(
             __snake_case					,    __snake_case					,    __snake_case					,    __snake_case					,    __snake_case					,    prompt_embeds=__snake_case					,    negative_prompt_embeds=__snake_case					,    )

         UpperCAmelCase_							 =     ORT_TO_PT_TYPE[str(text_embeddings.dtype				)]

         # 4. Preprocess image
         UpperCAmelCase_							 =     preprocess(__snake_case				)
         UpperCAmelCase_							 =     image.cpu()

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

         # 5. Add noise to image
         UpperCAmelCase_							 =     torch.tensor([noise_level]					,    dtype=torch.long					,    device=__snake_case				)
         UpperCAmelCase_							 =     torch.randn(image.shape					,    generator=__snake_case					,    device=__snake_case					,    dtype=__snake_case				)
         UpperCAmelCase_							 =     self.low_res_scheduler.add_noise(__snake_case					,    __snake_case					,    __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					,    __snake_case					,    __snake_case					,    __snake_case					,    __snake_case					,    __snake_case					,    __snake_case					,    __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(__snake_case					,    __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(__snake_case				) - num_inference_steps * self.scheduler.order
         with self.progress_bar(total=__snake_case				) as progress_bar:
             for i, t in enumerate(__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(__snake_case					,    __snake_case				)
                 UpperCAmelCase_							 =     np.concatenate([latent_model_input, image]					,    axis=1				)

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

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

                 # perform guidance
                 if do_classifier_free_guidance:
                     UpperCAmelCase_ ,							UpperCAmelCase_							 =     np.split(__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(__snake_case				)					,    __snake_case					,    __snake_case					,    **__snake_case				).prev_sample

                 # call the callback, if provided
                 if i == len(__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(__snake_case					,    __snake_case					,    __snake_case				)

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

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

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=__snake_case				)
     def   lowerCamelCase_   (	self   :			Any					,    __snake_case   :			str				):
         UpperCAmelCase_							 =     1 / 0.08_333 * latents
         UpperCAmelCase_							 =     self.vae(latent_sample=__snake_case				)[0]
         UpperCAmelCase_							 =     np.clip(image / 2 + 0.5					,    0					,    1				)
         UpperCAmelCase_							 =     image.transpose((0, 2, 3, 1)				)
         return image




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

         if prompt_embeds is None:
             UpperCAmelCase_							 =     self.tokenizer(
                 __snake_case					,    padding='''max_length'''					,    max_length=self.tokenizer.model_max_length					,    truncation=__snake_case					,    return_tensors='''pt'''					,    )
             UpperCAmelCase_							 =     text_inputs.input_ids
             UpperCAmelCase_							 =     self.tokenizer(__snake_case					,    padding='''longest'''					,    return_tensors='''pt'''				).input_ids

             if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                 __snake_case					,    __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(__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					,    __snake_case				)
         UpperCAmelCase_							 =     prompt_embeds.reshape(bs_embed * num_images_per_prompt					,    __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(__snake_case				) is not type(__snake_case				):
                 raise TypeError(
                     F'`negative_prompt` should be the same type to `prompt`, but got {type(__snake_case				)} !='
                     F' {type(__snake_case				)}.'				)
             elif isinstance(__snake_case					,    __snake_case				):
                 UpperCAmelCase_							 =     [negative_prompt]
             elif batch_size != len(__snake_case				):
                 raise ValueError(
                     F'`negative_prompt`: {negative_prompt} has batch size {len(__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(
                 __snake_case					,    padding='''max_length'''					,    max_length=__snake_case					,    truncation=__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(__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					,    __snake_case				)
             UpperCAmelCase_							 =     uncond_embeddings.reshape(batch_size * num_images_per_prompt					,    __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





