





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_    :				Union[str, Any]					=			getLogger(__name__)


a_    :				int					=			4
a_    :				Optional[Any]					=			7

a_    :				Optional[Any]					=			{
    'float16': torch.floataa,
    'float32': torch.floataa,
}




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						Union[str, Any]						):
     if isinstance(snake_case_ ,     torch.Tensor						):
          return image
     elif isinstance(snake_case_ ,     PIL.Image.Image						):
          __magic_name__				=       [image]

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

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

     return image



class  SCREAMING_SNAKE_CASE_  ( SCREAMING_SNAKE_CASE__			):


  """simple docstring"""





  def __init__(  self	,  A	,  A	,  A	,  A	,  A	,  A	,  A = 3_50	,  )		->   Union[str, Any]:





       '''simple docstring'''







       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	,  )		->   Tuple:





       '''simple docstring'''







       self.check_inputs(A	,  A	,  A	,  A						)

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

       __magic_name__				=       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.
       __magic_name__				=       guidance_scale > 1.0

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

       __magic_name__				=       ORT_TO_PT_TYPE[str(text_embeddings.dtype						)]

       # 4. Preprocess image
       __magic_name__				=       preprocess(A						)
       __magic_name__				=       image.cpu()

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

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

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

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

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

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

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

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

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

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

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

       if not return_dict:
            return (image,)

       return ImagePipelineOutput(images=A						)





  def 			__A      (  self	,  A						)		->   List[Any]:





       '''simple docstring'''







       __magic_name__				=       1 / 0.0_83_33 * latents
       __magic_name__				=       self.vae(latent_sample=A						)[0]
       __magic_name__				=       np.clip(image / 2 + 0.5	,  0	,  1						)
       __magic_name__				=       image.transpose((0, 2, 3, 1)						)
       return image







  def 			__A      (  self	,  A	,  A	,  A	,  A	,  A	,  A = None	,  A = None	,  )		->   Optional[int]:





       '''simple docstring'''







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

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

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

            __magic_name__				=       text_input_ids.shape[-1]
            __magic_name__				=       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

            __magic_name__				=       self.text_encoder(
                input_ids=uncond_input.input_ids.int().to(A						)	,  )
            __magic_name__				=       uncond_embeddings[0]

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

       return prompt_embeds






def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						int						):
     if not isinstance(snake_case_ ,     snake_case_						):
          __magic_name__				=       f'Input value of [number={number}] must be an integer'
          raise TypeError(snake_case_						)
     if number < 0 or number % 2 == 1:
          return False
     __magic_name__				=       int(number**0.5						)
     return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
    import doctest

    doctest.testmod()