'''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		:      Tuple						   =getLogger(__name__)


_A		:      List[Any]						   =4
_A		:      int						   =7

_A		:      List[str]						   ={
    '''float16''': torch.floataa,
    '''float32''': torch.floataa,
}







def 				__UpperCamelCase (					__UpperCAmelCase  :					str			)							->	Union[str, Any]:
  if isinstance(__UpperCAmelCase ,   torch.Tensor			):
    return image
  elif isinstance(__UpperCAmelCase ,   PIL.Image.Image			):
    lowercase				:							Union[str, Any]									=   [image]

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

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

  return image






class _lowerCAmelCase			(	snake_case					):







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







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

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

        lowercase				:							Any									=   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				:							Optional[Any]									=   guidance_scale > 1.0

        # 3. Encode input prompt
        lowercase				:							Optional[Any]									=   self._encode_prompt(
            lowercase  ,    lowercase  ,    lowercase  ,    lowercase  ,    lowercase  ,    prompt_embeds=lowercase  ,    negative_prompt_embeds=lowercase  ,    )

        lowercase				:							Any									=   ORT_TO_PT_TYPE[str(text_embeddings.dtype     )]

        # 4. Preprocess image
        lowercase				:							int									=   preprocess(lowercase     )
        lowercase				:							Optional[Any]									=   image.cpu()

        # 5. set timesteps
        self.scheduler.set_timesteps(lowercase  ,    device=lowercase     )
        lowercase				:							Any									=   self.scheduler.timesteps

        # 5. Add noise to image
        lowercase				:							List[str]									=   torch.tensor([noise_level]  ,    dtype=torch.long  ,    device=lowercase     )
        lowercase				:							Tuple									=   torch.randn(image.shape  ,    generator=lowercase  ,    device=lowercase  ,    dtype=lowercase     )
        lowercase				:							Any									=   self.low_res_scheduler.add_noise(lowercase  ,    lowercase  ,    lowercase     )

        lowercase				:							List[Any]									=   2 if do_classifier_free_guidance else 1
        lowercase				:							Any									=   np.concatenate([image] * batch_multiplier * num_images_per_prompt     )
        lowercase				:							Dict									=   np.concatenate([noise_level] * image.shape[0]     )

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

        # 7. Check that sizes of image and latents match
        lowercase				:							Tuple									=   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				:							List[str]									=   self.prepare_extra_step_kwargs(lowercase  ,    lowercase     )

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

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

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

            # timestep to tensor
            lowercase				:							Tuple									=   np.array([t]  ,    dtype=lowercase     )

            # predict the noise residual
            lowercase				:							Union[str, Any]									=   self.unet(
                sample=lowercase  ,    timestep=lowercase  ,    encoder_hidden_states=lowercase  ,    class_labels=noise_level.astype(np.intaa     )  ,    )[0]

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

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

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

        # 10. Post-processing
        lowercase				:							Tuple									=   self.decode_latents(latents.float()     )

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

        if not return_dict:
          return (image,)

        return ImagePipelineOutput(images=lowercase     )







      def      lowerCamelCase__      (  self  ,    lowercase     ):
        lowercase				:							int									=   1 / 0.0_8333 * latents
        lowercase				:							Any									=   self.vae(latent_sample=lowercase     )[0]
        lowercase				:							Union[str, Any]									=   np.clip(image / 2 + 0.5  ,    0  ,    1     )
        lowercase				:							Tuple									=   image.transpose((0, 2, 3, 1)     )
        return image







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase  ,    lowercase  ,    lowercase  ,    lowercase = None  ,    lowercase = None  ,    ):
        if prompt is not None and isinstance(lowercase  ,    lowercase     ):
          lowercase				:							List[str]									=   1
        elif prompt is not None and isinstance(lowercase  ,    lowercase     ):
          lowercase				:							str									=   len(lowercase     )
        else:
          lowercase				:							List[Any]									=   prompt_embeds.shape[0]

        if prompt_embeds is None:
          lowercase				:							Optional[Any]									=   self.tokenizer(
              lowercase  ,    padding='''max_length'''  ,    max_length=self.tokenizer.model_max_length  ,    truncation=lowercase  ,    return_tensors='''pt'''  ,    )
          lowercase				:							Any									=   text_inputs.input_ids
          lowercase				:							Dict									=   self.tokenizer(lowercase  ,    padding='''longest'''  ,    return_tensors='''pt'''     ).input_ids

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

        lowercase				,      lowercase				,      lowercase				:							Any									=   prompt_embeds.shape
        # duplicate text embeddings for each generation per prompt, using mps friendly method
        lowercase				:							Union[str, Any]									=   prompt_embeds.repeat(1  ,    lowercase     )
        lowercase				:							Tuple									=   prompt_embeds.reshape(bs_embed * num_images_per_prompt  ,    lowercase  ,    -1     )

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

          lowercase				:							int									=   text_input_ids.shape[-1]
          lowercase				:							List[Any]									=   self.tokenizer(
              lowercase  ,    padding='''max_length'''  ,    max_length=lowercase  ,    truncation=lowercase  ,    return_tensors='''pt'''  ,    )

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

          lowercase				:							Any									=   self.text_encoder(
              input_ids=uncond_input.input_ids.int().to(lowercase     )  ,    )
          lowercase				:							List[Any]									=   uncond_embeddings[0]

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

        return prompt_embeds
'''simple docstring'''
from __future__ import annotations

import math
import random







def 				__UpperCamelCase (					__UpperCAmelCase  :					int ,   __UpperCAmelCase  :					int ,   __UpperCAmelCase  :					int			)							->	list[int]:
  lowercase				:							Union[str, Any]									=   d * e - 1
  lowercase				:							List[str]									=   0
  lowercase				:							Tuple									=   0
  while p == 0:
    lowercase				:							Optional[Any]									=   random.randint(2 ,   n - 1			)
    lowercase				:							Dict									=   k
    while True:
      if t % 2 == 0:
        lowercase				:							List[Any]									=   t // 2
        lowercase				:							Union[str, Any]									=   (g**t) % n
        lowercase				:							Optional[Any]									=   math.gcd(x - 1 ,   __UpperCAmelCase			)
        if x > 1 and y > 1:
          lowercase				:							Dict									=   y
          lowercase				:							Any									=   n // y
          break  # find the correct factors
      else:
        break  # t is not divisible by 2, break and choose another g
  return sorted([p, q]			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()