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     						=			getLogger(__name__)


UpperCAmelCase     						=			4
UpperCAmelCase     						=			7

UpperCAmelCase     						=			{
    '''float16''': torch.floataa,
    '''float32''': torch.floataa,
}


def SCREAMING_SNAKE_CASE			(       lowercase_:		Any			):
       if isinstance(lowercase_						,  torch.Tensor			):
              return image
       elif isinstance(lowercase_						,  PIL.Image.Image			):
              _UpperCamelCase       :    List[Any]					  =					[image]

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

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

       return image





class 				__lowerCAmelCase					(    a_  ):



 '''simple docstring'''

 def __init__(     self    :						Tuple    ,     lowerCamelCase    :						OnnxRuntimeModel    ,     lowerCamelCase    :						OnnxRuntimeModel    ,     lowerCamelCase    :						Any    ,     lowerCamelCase    :						OnnxRuntimeModel    ,     lowerCamelCase    :						DDPMScheduler    ,     lowerCamelCase    :						Any    ,     lowerCamelCase    :						int = 3_5_0    ,     )	->							Optional[Any]:
        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    :						Optional[int]    ,     lowerCamelCase    :						Union[str, List[str]]    ,     lowerCamelCase    :						Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]]    ,     lowerCamelCase    :						int = 7_5    ,     lowerCamelCase    :						float = 9.0    ,     lowerCamelCase    :						int = 2_0    ,     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    ,     )	->							Any:
        self.check_inputs(lowerCamelCase    ,     lowerCamelCase    ,     lowerCamelCase    ,     lowerCamelCase					)

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

        _UpperCamelCase       :    Optional[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.
        _UpperCamelCase       :    int					  =					guidance_scale > 1.0

        # 3. Encode input prompt
        _UpperCamelCase       :    Tuple					  =					self._encode_prompt(
            lowerCamelCase    ,     lowerCamelCase    ,     lowerCamelCase    ,     lowerCamelCase    ,     lowerCamelCase    ,     prompt_embeds=lowerCamelCase    ,     negative_prompt_embeds=lowerCamelCase    ,     )

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

        # 4. Preprocess image
        _UpperCamelCase       :    List[str]					  =					preprocess(lowerCamelCase					)
        _UpperCamelCase       :    int					  =					image.cpu()

        # 5. set timesteps
        self.scheduler.set_timesteps(lowerCamelCase    ,     device=lowerCamelCase					)
        _UpperCamelCase       :    List[str]					  =					self.scheduler.timesteps

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

        _UpperCamelCase       :    Optional[int]					  =					2 if do_classifier_free_guidance else 1
        _UpperCamelCase       :    Dict					  =					np.concatenate([image] * batch_multiplier * num_images_per_prompt					)
        _UpperCamelCase       :    Tuple					  =					np.concatenate([noise_level] * image.shape[0]					)

        # 6. Prepare latent variables
        _UpperCamelCase  , _UpperCamelCase       :    Optional[Any]					  =					image.shape[2:]
        _UpperCamelCase       :    Optional[int]					  =					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       :    Dict					  =					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       :    str					  =					self.prepare_extra_step_kwargs(lowerCamelCase    ,     lowerCamelCase					)

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

        # 9. Denoising loop
        _UpperCamelCase       :    Union[str, Any]					  =					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       :    Tuple					  =					np.concatenate([latents] * 2					) if do_classifier_free_guidance else latents

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

                      # timestep to tensor
                      _UpperCamelCase       :    List[Any]					  =					np.array([t]    ,     dtype=lowerCamelCase					)

                      # predict the noise residual
                      _UpperCamelCase       :    str					  =					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       :    Tuple					  =					np.split(lowerCamelCase    ,     2					)
                      _UpperCamelCase       :    str					  =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                      # compute the previous noisy sample x_t -> x_t-1
                      _UpperCamelCase       :    str					  =					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       :    Optional[Any]					  =					self.decode_latents(latents.float()					)

        # 11. Convert to PIL
        if output_type == "pil":
               _UpperCamelCase       :    str					  =					self.numpy_to_pil(lowerCamelCase					)

        if not return_dict:
               return (image,)

        return ImagePipelineOutput(images=lowerCamelCase					)

 def    __snake_case   (     self    :						List[Any]    ,     lowerCamelCase    :						List[Any]					)	->							List[Any]:
        _UpperCamelCase       :    int					  =					1 / 0.0_8_3_3_3 * latents
        _UpperCamelCase       :    List[Any]					  =					self.vae(latent_sample=lowerCamelCase					)[0]
        _UpperCamelCase       :    List[Any]					  =					np.clip(image / 2 + 0.5    ,     0    ,     1					)
        _UpperCamelCase       :    List[Any]					  =					image.transpose((0, 2, 3, 1)					)
        return image

 def    __snake_case   (     self    :						List[str]    ,     lowerCamelCase    :						Union[str, List[str]]    ,     lowerCamelCase    :						Optional[Any]    ,     lowerCamelCase    :						Optional[int]    ,     lowerCamelCase    :						bool    ,     lowerCamelCase    :						Optional[str]    ,     lowerCamelCase    :						Optional[torch.FloatTensor] = None    ,     lowerCamelCase    :						Optional[torch.FloatTensor] = None    ,     )	->							int:
        if prompt is not None and isinstance(lowerCamelCase    ,     lowerCamelCase					):
               _UpperCamelCase       :    List[Any]					  =					1
        elif prompt is not None and isinstance(lowerCamelCase    ,     lowerCamelCase					):
               _UpperCamelCase       :    Any					  =					len(lowerCamelCase					)
        else:
               _UpperCamelCase       :    Union[str, Any]					  =					prompt_embeds.shape[0]

        if prompt_embeds is None:
               _UpperCamelCase       :    Optional[Any]					  =					self.tokenizer(
                   lowerCamelCase    ,     padding='max_length'    ,     max_length=self.tokenizer.model_max_length    ,     truncation=lowerCamelCase    ,     return_tensors='pt'    ,     )
               _UpperCamelCase       :    Optional[Any]					  =					text_inputs.input_ids
               _UpperCamelCase       :    Any					  =					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       :    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
               _UpperCamelCase       :    Tuple					  =					self.text_encoder(
                   input_ids=text_input_ids.int().to(lowerCamelCase					)    ,     )
               _UpperCamelCase       :    Union[str, Any]					  =					prompt_embeds[0]

        _UpperCamelCase  , _UpperCamelCase  , _UpperCamelCase       :    List[str]					  =					prompt_embeds.shape
        # duplicate text embeddings for each generation per prompt, using mps friendly method
        _UpperCamelCase       :    Dict					  =					prompt_embeds.repeat(1    ,     lowerCamelCase					)
        _UpperCamelCase       :    Any					  =					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       :    List[str]
               if negative_prompt is None:
                      _UpperCamelCase       :    Tuple					  =					[''] * 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       :    List[str]					  =					[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       :    List[str]					  =					negative_prompt

               _UpperCamelCase       :    Optional[Any]					  =					text_input_ids.shape[-1]
               _UpperCamelCase       :    Optional[int]					  =					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       :    Optional[Any]					  =					self.text_encoder(
                   input_ids=uncond_input.input_ids.int().to(lowerCamelCase					)    ,     )
               _UpperCamelCase       :    Tuple					  =					uncond_embeddings[0]

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

        return prompt_embeds




def SCREAMING_SNAKE_CASE			(       lowercase_:		int			):
       if not isinstance(lowercase_						,  lowercase_			):
              _UpperCamelCase       :    Optional[int]					  =					(
                  'operation can not be conducted on a object of type '
                  F"{type(lowercase_			).__name__}"
              )
              raise TypeError(lowercase_			)
       _UpperCamelCase       :    List[Any]					  =					''
       for _ in range(0						,  32			):
              bit_string += str(number % 2			)
              _UpperCamelCase       :    Dict					  =					number >> 1
       return bit_string


def SCREAMING_SNAKE_CASE			(       lowercase_:		int			):
       if number < 0:
              raise ValueError('the value of input must be positive'			)
       elif isinstance(lowercase_						,  lowercase_			):
              raise TypeError('Input value must be a \'int\' type'			)
       elif isinstance(lowercase_						,  lowercase_			):
              raise TypeError('\'<\' not supported between instances of \'str\' and \'int\''			)
       _UpperCamelCase       :    List[Any]					  =					0
       # iterator over [1 to 32],since we are dealing with 32 bit integer
       for _ in range(1						,  33			):
              # left shift the bits by unity
              _UpperCamelCase       :    Tuple					  =					result << 1
              # get the end bit
              _UpperCamelCase       :    List[Any]					  =					number % 2
              # right shift the bits by unity
              _UpperCamelCase       :    Dict					  =					number >> 1
              # add that bit to our ans
              _UpperCamelCase       :    int					  =					result | end_bit
       return get_reverse_bit_string(lowercase_			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()



