



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       lowerCamelCase_  (    _a       :						Dict    ):



    '''simple docstring'''




    if isinstance(snake_case_   ,		torch.Tensor    ):
        return image
    elif isinstance(snake_case_   ,		PIL.Image.Image    ):
        UpperCAmelCase_     :       List[str]  		=					[image]

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

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

    return image







class       _snake_case				(     UpperCAmelCase__ ):






   '''simple docstring'''






   def __init__(    self:     Optional[int]  ,lowerCamelCase_:     OnnxRuntimeModel  ,lowerCamelCase_:     OnnxRuntimeModel  ,lowerCamelCase_:     Any  ,lowerCamelCase_:     OnnxRuntimeModel  ,lowerCamelCase_:     DDPMScheduler  ,lowerCamelCase_:     Any  ,lowerCamelCase_:     int = 350  ,)	->      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:     Any  ,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  ,)	->      Union[str, 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__							):
           UpperCAmelCase_     :       str  		=					1
       elif prompt is not None and isinstance(snake_case__  ,snake_case__							):
           UpperCAmelCase_     :       Any  		=					len(snake_case__							)
       else:
           UpperCAmelCase_     :       Dict  		=					prompt_embeds.shape[0]

       UpperCAmelCase_     :       List[str]  		=					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_     :       Any  		=					guidance_scale > 1.0

       # 3. Encode input prompt
       UpperCAmelCase_     :       Union[str, Any]  		=					self._encode_prompt(
           snake_case__  ,snake_case__  ,snake_case__  ,snake_case__  ,snake_case__  ,prompt_embeds=snake_case__  ,negative_prompt_embeds=snake_case__  ,)

       UpperCAmelCase_     :       Optional[int]  		=					ORT_TO_PT_TYPE[str(text_embeddings.dtype							)]

       # 4. Preprocess image
       UpperCAmelCase_     :       Tuple  		=					preprocess(snake_case__							)
       UpperCAmelCase_     :       str  		=					image.cpu()

       # 5. set timesteps
       self.scheduler.set_timesteps(snake_case__  ,device=snake_case__							)
       UpperCAmelCase_     :       Optional[Any]  		=					self.scheduler.timesteps

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

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

       # 6. Prepare latent variables
       UpperCAmelCase_		, UpperCAmelCase_     :       List[Any]  		=					image.shape[2:]
       UpperCAmelCase_     :       int  		=					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
       UpperCAmelCase_     :       Any  		=					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_     :       Optional[int]  		=					self.prepare_extra_step_kwargs(snake_case__  ,snake_case__							)

       UpperCAmelCase_     :       Optional[Any]  		=					next(
           (input.type for input in self.unet.model.get_inputs() if input.name == """timestep""")  ,"""tensor(float)"""							)
       UpperCAmelCase_     :       Union[str, Any]  		=					ORT_TO_NP_TYPE[timestep_dtype]

       # 9. Denoising loop
       UpperCAmelCase_     :       str  		=					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
               UpperCAmelCase_     :       Dict  		=					np.concatenate([latents] * 2							) if do_classifier_free_guidance else latents

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

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

               # predict the noise residual
               UpperCAmelCase_     :       str  		=					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:
                   UpperCAmelCase_		, UpperCAmelCase_     :       Union[str, Any]  		=					np.split(snake_case__  ,2							)
               UpperCAmelCase_     :       Optional[int]  		=					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

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

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

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=snake_case__							)


   def       A__    (    self:     Union[str, Any]  ,lowerCamelCase_:     Dict							)	->      str:
       UpperCAmelCase_     :       Optional[Any]  		=					1 / 0.0_8_3_3_3 * latents
       UpperCAmelCase_     :       Dict  		=					self.vae(latent_sample=snake_case__							)[0]
       UpperCAmelCase_     :       Any  		=					np.clip(image / 2 + 0.5  ,0  ,1							)
       UpperCAmelCase_     :       List[Any]  		=					image.transpose((0, 2, 3, 1)							)
       return image






   def       A__    (    self:     List[Any]  ,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(snake_case__  ,snake_case__							):
           UpperCAmelCase_     :       Optional[Any]  		=					1
       elif prompt is not None and isinstance(snake_case__  ,snake_case__							):
           UpperCAmelCase_     :       Union[str, Any]  		=					len(snake_case__							)
       else:
           UpperCAmelCase_     :       Union[str, Any]  		=					prompt_embeds.shape[0]

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

       UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_     :       Optional[Any]  		=					prompt_embeds.shape
       # duplicate text embeddings for each generation per prompt, using mps friendly method
       UpperCAmelCase_     :       List[Any]  		=					prompt_embeds.repeat(1  ,snake_case__							)
       UpperCAmelCase_     :       Dict  		=					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:
           UpperCAmelCase_     :       Union[str, Any]  		=					42
           if negative_prompt is None:
               UpperCAmelCase_     :       Optional[Any]  		=					[""""""] * 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__							):
               UpperCAmelCase_     :       Optional[int]  		=					[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:
               UpperCAmelCase_     :       Optional[Any]  		=					negative_prompt

           UpperCAmelCase_     :       Dict  		=					text_input_ids.shape[-1]
           UpperCAmelCase_     :       int  		=					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

           UpperCAmelCase_     :       Tuple  		=					self.text_encoder(
               input_ids=uncond_input.input_ids.int().to(snake_case__							)  ,)
           UpperCAmelCase_     :       Any  		=					uncond_embeddings[0]

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

       return prompt_embeds






def 				__SCREAMING_SNAKE_CASE    (     snake_case_		):

						'''simple docstring'''


						return int(snake_case_		) if x - int(snake_case_		) >= 0 else int(snake_case_		) - 1


if __name__ == "__main__":
				import doctest

				doctest.testmod()




