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


_SCREAMING_SNAKE_CASE   		=			getLogger(__name__)


_SCREAMING_SNAKE_CASE   		=			4
_SCREAMING_SNAKE_CASE   		=			7

_SCREAMING_SNAKE_CASE   		=			{
    """float16""": torch.floataa,
    """float32""": torch.floataa,
}





def  lowercase(  UpperCamelCase_					)		->  Tuple:







 '''simple docstring'''
 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					) / 2_5_5.0
  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 SCREAMING_SNAKE_CASE_		(			__lowerCAmelCase		):







   def __init__(    self	:	List[str]      ,      lowerCamelCase_	:	OnnxRuntimeModel      ,      lowerCamelCase_	:	OnnxRuntimeModel      ,      lowerCamelCase_	:	Any      ,      lowerCamelCase_	:	OnnxRuntimeModel      ,      lowerCamelCase_	:	DDPMScheduler      ,      lowerCamelCase_	:	Any      ,      lowerCamelCase_	:	int = 350      ,      ):





    """simple docstring"""


    super().__init__(
        vae=lowerCamelCase_      ,      text_encoder=lowerCamelCase_      ,      tokenizer=lowerCamelCase_      ,      unet=lowerCamelCase_      ,      low_res_scheduler=lowerCamelCase_      ,      scheduler=lowerCamelCase_      ,      safety_checker=lowerCamelCase_      ,      feature_extractor=lowerCamelCase_      ,      watermarker=lowerCamelCase_      ,      max_noise_level=lowerCamelCase_      ,      )







   def __call__(    self	:	Tuple      ,      lowerCamelCase_	:	Union[str, List[str]]      ,      lowerCamelCase_	:	Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]]      ,      lowerCamelCase_	:	int = 75      ,      lowerCamelCase_	:	float = 9.0      ,      lowerCamelCase_	:	int = 20      ,      lowerCamelCase_	:	Optional[Union[str, List[str]]] = None      ,      lowerCamelCase_	:	Optional[int] = 1      ,      lowerCamelCase_	:	float = 0.0      ,      lowerCamelCase_	:	Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,      lowerCamelCase_	:	Optional[torch.FloatTensor] = None      ,      lowerCamelCase_	:	Optional[np.ndarray] = None      ,      lowerCamelCase_	:	Optional[np.ndarray] = None      ,      lowerCamelCase_	:	Optional[str] = "pil"      ,      lowerCamelCase_	:	bool = True      ,      lowerCamelCase_	:	Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,      lowerCamelCase_	:	Optional[int] = 1      ,      ):





    """simple docstring"""


    self.check_inputs(lowerCamelCase_      ,      lowerCamelCase_      ,      lowerCamelCase_      ,      lowerCamelCase_							)

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

    UpperCamelCase      =			ORT_TO_PT_TYPE[str(text_embeddings.dtype							)]

    # 4. Preprocess image
    UpperCamelCase      =			preprocess(lowerCamelCase_							)
    UpperCamelCase      =			image.cpu()

    # 5. set timesteps
    self.scheduler.set_timesteps(lowerCamelCase_      ,      device=lowerCamelCase_							)
    UpperCamelCase      =			self.scheduler.timesteps

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

    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      ,      lowerCamelCase_      ,      lowerCamelCase_      ,      lowerCamelCase_      ,      lowerCamelCase_      ,      lowerCamelCase_      ,      lowerCamelCase_      ,      lowerCamelCase_      ,      )

    # 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(lowerCamelCase_      ,      lowerCamelCase_							)

    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(lowerCamelCase_							) - num_inference_steps * self.scheduler.order
    with self.progress_bar(total=lowerCamelCase_							) as progress_bar:
     for i, t in enumerate(lowerCamelCase_							):
      # 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(lowerCamelCase_      ,      lowerCamelCase_							)
      UpperCamelCase      =			np.concatenate([latent_model_input, image]      ,      axis=1							)

      # timestep to tensor
      UpperCamelCase      =			np.array([t]      ,      dtype=lowerCamelCase_							)

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

      # perform guidance
      if do_classifier_free_guidance:
       UpperCamelCase     ,    UpperCamelCase      =			np.split(lowerCamelCase_      ,      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(lowerCamelCase_							)      ,      lowerCamelCase_      ,      lowerCamelCase_      ,      **lowerCamelCase_							).prev_sample

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

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

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

    if not return_dict:
     return (image,)

    return ImagePipelineOutput(images=lowerCamelCase_							)







   def 	lowerCamelCase_  (    self	:	Dict      ,      lowerCamelCase_	:	List[Any]							):





    """simple docstring"""


    UpperCamelCase      =			1 / 0.0_8_3_3_3 * latents
    UpperCamelCase      =			self.vae(latent_sample=lowerCamelCase_							)[0]
    UpperCamelCase      =			np.clip(image / 2 + 0.5      ,      0      ,      1							)
    UpperCamelCase      =			image.transpose((0, 2, 3, 1)							)
    return image







   def 	lowerCamelCase_  (    self	:	Dict      ,      lowerCamelCase_	:	Union[str, List[str]]      ,      lowerCamelCase_	:	Union[str, Any]      ,      lowerCamelCase_	:	Optional[int]      ,      lowerCamelCase_	:	bool      ,      lowerCamelCase_	:	Optional[str]      ,      lowerCamelCase_	:	Optional[torch.FloatTensor] = None      ,      lowerCamelCase_	:	Optional[torch.FloatTensor] = None      ,      ):





    """simple docstring"""


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

    if prompt_embeds is None:
     UpperCamelCase      =			self.tokenizer(
         lowerCamelCase_      ,      padding="""max_length"""      ,      max_length=self.tokenizer.model_max_length      ,      truncation=lowerCamelCase_      ,      return_tensors="""pt"""      ,      )
     UpperCamelCase      =			text_inputs.input_ids
     UpperCamelCase      =			self.tokenizer(lowerCamelCase_      ,      padding="""longest"""      ,      return_tensors="""pt"""							).input_ids

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

import numpy as np





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)		->  List[str]:







 '''simple docstring'''
 UpperCamelCase      =			n_draws * class_counts / class_counts.sum()
 # floored means we don't overshoot n_samples, but probably undershoot
 UpperCamelCase      =			np.floor(UpperCamelCase_					)
 # we add samples according to how much "left over" probability
 # they had, until we arrive at n_samples
 UpperCamelCase      =			int(n_draws - floored.sum()					)
 if need_to_add > 0:
  UpperCamelCase      =			continuous - floored
  UpperCamelCase      =			np.sort(np.unique(UpperCamelCase_					)					)[::-1]
  # add according to remainder, but break ties
  # randomly to avoid biases
  for value in values:
   ((UpperCamelCase)     ,    )      =			np.where(remainder == value					)
   # if we need_to_add less than what's in inds
   # we draw randomly from them.
   # if we need to add more, we add them all and
   # go to the next value
   UpperCamelCase      =			min(len(UpperCamelCase_					)       ,				UpperCamelCase_					)
   UpperCamelCase      =			rng.choice(UpperCamelCase_       ,				size=UpperCamelCase_       ,				replace=UpperCamelCase_					)
   floored[inds] += 1
   need_to_add -= add_now
   if need_to_add == 0:
    break
 return floored.astype(np.intaa					)





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_=10					)		->  Dict:







 '''simple docstring'''
 UpperCamelCase     ,    UpperCamelCase      =			np.unique(UpperCamelCase_       ,				return_inverse=UpperCamelCase_					)
 UpperCamelCase      =			classes.shape[0]
 UpperCamelCase      =			np.bincount(UpperCamelCase_					)
 if np.min(UpperCamelCase_					) < 2:
  raise ValueError("""Minimum class count error"""					)
 if n_train < n_classes:
  raise ValueError(
      """The train_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_train, n_classes)					)
 if n_test < n_classes:
  raise ValueError(
      """The test_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_test, n_classes)					)
 UpperCamelCase      =			np.split(np.argsort(UpperCamelCase_       ,				kind="""mergesort"""					)       ,				np.cumsum(UpperCamelCase_					)[:-1]					)
 for _ in range(UpperCamelCase_					):
  UpperCamelCase      =			approximate_mode(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)
  UpperCamelCase      =			class_counts - n_i
  UpperCamelCase      =			approximate_mode(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)

  UpperCamelCase      =			[]
  UpperCamelCase      =			[]

  for i in range(UpperCamelCase_					):
   UpperCamelCase      =			rng.permutation(class_counts[i]					)
   UpperCamelCase      =			class_indices[i].take(UpperCamelCase_       ,				mode="""clip"""					)
   train.extend(perm_indices_class_i[: n_i[i]]					)
   test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]					)
  UpperCamelCase      =			rng.permutation(UpperCamelCase_					)
  UpperCamelCase      =			rng.permutation(UpperCamelCase_					)

  yield train, test
