"""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


__UpperCamelCase       :    str         =		getLogger(__name__)


__UpperCamelCase       :    Optional[Any]         =		4
__UpperCamelCase       :    Dict         =		7

__UpperCamelCase       :    Union[str, Any]         =		{
    "float16": torch.floataa,
    "float32": torch.floataa,
}


def   __UpperCAmelCase     (	_snake_case		:       List[Any]       ):
   if isinstance(a_,				torch.Tensor       ):
      return image
   elif isinstance(a_,				PIL.Image.Image       ):
      _lowercase							     =       [image]

   if isinstance(image[0],				PIL.Image.Image       ):
      _lowercase     ,  _lowercase							     =       image[0].size
      _lowercase     ,  _lowercase							     =       (x - x % 6_4 for x in (w, h))  # resize to integer multiple of 32

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

   return image
class   UpperCAmelCase_	(				lowerCAmelCase__   ):
    def __init__(	self			:       Dict   ,						_lowercase			:       OnnxRuntimeModel   ,						_lowercase			:       OnnxRuntimeModel   ,						_lowercase			:       Any   ,						_lowercase			:       OnnxRuntimeModel   ,						_lowercase			:       DDPMScheduler   ,						_lowercase			:       Any   ,						_lowercase			:       int = 3_5_0   ,						)						->    Tuple:
       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			:       List[Any]   ,						_lowercase			:       Union[str, List[str]]   ,						_lowercase			:       Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]]   ,						_lowercase			:       int = 7_5   ,						_lowercase			:       float = 9.0   ,						_lowercase			:       int = 2_0   ,						_lowercase			:       Optional[Union[str, List[str]]] = None   ,						_lowercase			:       Optional[int] = 1   ,						_lowercase			:       float = 0.0   ,						_lowercase			:       Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,						_lowercase			:       Optional[torch.FloatTensor] = None   ,						_lowercase			:       Optional[np.ndarray] = None   ,						_lowercase			:       Optional[np.ndarray] = None   ,						_lowercase			:       Optional[str] = "pil"   ,						_lowercase			:       bool = True   ,						_lowercase			:       Optional[Callable[[int, int, torch.FloatTensor], None]] = None   ,						_lowercase			:       Optional[int] = 1   ,						)						->    Any:
       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__		):
          _lowercase							     =       1
       elif prompt is not None and isinstance(snake_case__   ,						snake_case__		):
          _lowercase							     =       len(snake_case__		)
       else:
          _lowercase							     =       prompt_embeds.shape[0]

       _lowercase							     =       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							     =       guidance_scale > 1.0

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

       _lowercase							     =       ORT_TO_PT_TYPE[str(text_embeddings.dtype		)]

       # 4. Preprocess image
       _lowercase							     =       preprocess(snake_case__		)
       _lowercase							     =       image.cpu()

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

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

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

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

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

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

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

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

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

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

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

       if not return_dict:
          return (image,)

       return ImagePipelineOutput(images=snake_case__		)
    def 		_lowerCamelCase      (	self			:       List[str]   ,						_lowercase			:       Any		)						->    List[Any]:
       _lowercase							     =       1 / 0.0_83_33 * latents
       _lowercase							     =       self.vae(latent_sample=snake_case__		)[0]
       _lowercase							     =       np.clip(image / 2 + 0.5   ,						0   ,						1		)
       _lowercase							     =       image.transpose((0, 2, 3, 1)		)
       return image




    def 		_lowerCamelCase      (	self			:       int   ,						_lowercase			:       Union[str, List[str]]   ,						_lowercase			:       List[str]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       bool   ,						_lowercase			:       Optional[str]   ,						_lowercase			:       Optional[torch.FloatTensor] = None   ,						_lowercase			:       Optional[torch.FloatTensor] = None   ,						)						->    List[Any]:





       if prompt is not None and isinstance(snake_case__   ,						snake_case__		):
          _lowercase							     =       1
       elif prompt is not None and isinstance(snake_case__   ,						snake_case__		):
          _lowercase							     =       len(snake_case__		)
       else:
          _lowercase							     =       prompt_embeds.shape[0]

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

       _lowercase     ,  _lowercase     ,  _lowercase							     =       prompt_embeds.shape
       # duplicate text embeddings for each generation per prompt, using mps friendly method
       _lowercase							     =       prompt_embeds.repeat(1   ,						snake_case__		)
       _lowercase							     =       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:
          _lowercase							     =       4_2
          if negative_prompt is None:
             _lowercase							     =       [""] * 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__		):
             _lowercase							     =       [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:
             _lowercase							     =       negative_prompt

          _lowercase							     =       text_input_ids.shape[-1]
          _lowercase							     =       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

          _lowercase							     =       self.text_encoder(
              input_ids=uncond_input.input_ids.int().to(snake_case__		)   ,						)
          _lowercase							     =       uncond_embeddings[0]

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

       return prompt_embeds

from __future__ import annotations

import math
import random

def    UpperCamelCase_ (	a_  ,				a_  ,				a_		)     ->list[int]:
		A				=d * e - 1
		A				=0
		A				=0
		while p == 0:
				A				=random.randint(2  ,				n - 1		)
				A				=k
				while True:
						if t % 2 == 0:
								A				=t // 2
								A				=(g**t) % n
								A				=math.gcd(x - 1  ,				a_		)
								if x > 1 and y > 1:
										A				=y
										A				=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()




