

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


UpperCamelCase__     :					int								=			getLogger(__name__)


UpperCamelCase__     :					Optional[int]								=			4
UpperCamelCase__     :					int								=			7

UpperCamelCase__     :					Tuple								=			{
    "float16": torch.floataa,
    "float32": torch.floataa,
}


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						Dict				):





    """simple docstring"""


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

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

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

    return image





class 					__snake_case     (			lowerCAmelCase__				):

  def __init__(    self  ,						_A  ,						_A  ,						_A  ,						_A  ,						_A  ,						_A  ,						_A = 350  ,						):
      super().__init__(
          vae=_A  ,						text_encoder=_A  ,						tokenizer=_A  ,						unet=_A  ,						low_res_scheduler=_A  ,						scheduler=_A  ,						safety_checker=_A  ,						feature_extractor=_A  ,						watermarker=_A  ,						max_noise_level=_A  ,						)

  def __call__(    self  ,						_A  ,						_A  ,						_A = 75  ,						_A = 9.0  ,						_A = 20  ,						_A = None  ,						_A = 1  ,						_A = 0.0  ,						_A = None  ,						_A = None  ,						_A = None  ,						_A = None  ,						_A = "pil"  ,						_A = True  ,						_A = None  ,						_A = 1  ,						):
      self.check_inputs(_A  ,						_A  ,						_A  ,						_A)

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

      SCREAMING_SNAKE_CASE_													=  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.
      SCREAMING_SNAKE_CASE_													=  guidance_scale > 1.0

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

      SCREAMING_SNAKE_CASE_													=  ORT_TO_PT_TYPE[str(text_embeddings.dtype)]

      # 4. Preprocess image
      SCREAMING_SNAKE_CASE_													=  preprocess(_A)
      SCREAMING_SNAKE_CASE_													=  image.cpu()

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

      if not return_dict:
          return (image,)

      return ImagePipelineOutput(images=_A)

  def   lowerCAmelCase__   (    self  ,						_A):
      SCREAMING_SNAKE_CASE_													=  1 / 0.0_8_3_3_3 * latents
      SCREAMING_SNAKE_CASE_													=  self.vae(latent_sample=_A)[0]
      SCREAMING_SNAKE_CASE_													=  np.clip(image / 2 + 0.5  ,						0  ,						1)
      SCREAMING_SNAKE_CASE_													=  image.transpose((0, 2, 3, 1))
      return image


  def   lowerCAmelCase__   (    self  ,						_A  ,						_A  ,						_A  ,						_A  ,						_A  ,						_A = None  ,						_A = None  ,						):
      if prompt is not None and isinstance(_A  ,						_A):
          SCREAMING_SNAKE_CASE_													=  1
      elif prompt is not None and isinstance(_A  ,						_A):
          SCREAMING_SNAKE_CASE_													=  len(_A)
      else:
          SCREAMING_SNAKE_CASE_													=  prompt_embeds.shape[0]

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

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

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

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

          SCREAMING_SNAKE_CASE_													=  text_input_ids.shape[-1]
          SCREAMING_SNAKE_CASE_													=  self.tokenizer(
              _A  ,						padding='max_length'  ,						max_length=_A  ,						truncation=_A  ,						return_tensors='pt'  ,						)

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

          SCREAMING_SNAKE_CASE_													=  self.text_encoder(
              input_ids=uncond_input.input_ids.int().to(_A)  ,						)
          SCREAMING_SNAKE_CASE_													=  uncond_embeddings[0]

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

      return prompt_embeds





from itertools import permutations


def     _UpperCAmelCase				(     ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  list(map(''.join       ,      permutations('0123456789'				)				)				)
    return result[999_999]


if __name__ == "__main__":
       print(solution())


