code: 
  
  
  '''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
  
  
  lowerCAmelCase__   	=				getLogger(__name__)
  
  
  lowerCAmelCase__   	=				4
  lowerCAmelCase__   	=				7
  
  lowerCAmelCase__   	=				{
      'float16': torch.floataa,
      'float32': torch.floataa,
  }
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Tuple		):
         if isinstance(UpperCamelCase__       ,							torch.Tensor		):
                return image
         elif isinstance(UpperCamelCase__       ,							PIL.Image.Image		):
                _A   : Dict       					=				[image]
  
         if isinstance(image[0]       ,							PIL.Image.Image		):
                _A   : List[Any]       					=				image[0].size
                _A   : Optional[Any]       					=				(x - x % 64 for x in (w, h))  # resize to integer multiple of 32
  
                _A   : str       					=				[np.array(i.resize((w, h)		)		)[None, :] for i in image]
                _A   : Union[str, Any]       					=				np.concatenate(UpperCamelCase__       ,							axis=0		)
                _A   : Dict       					=				np.array(UpperCamelCase__		).astype(np.floataa		) / 255.0
                _A   : Any       					=				image.transpose(0       ,							3       ,							1       ,							2		)
                _A   : Tuple       					=				2.0 * image - 1.0
                _A   : List[str]       					=				torch.from_numpy(UpperCamelCase__		)
         elif isinstance(image[0]       ,							torch.Tensor		):
                _A   : Optional[Any]       					=				torch.cat(UpperCamelCase__       ,							dim=0		)
  
         return image
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       a):
  
  
  
  
       '''simple docstring'''
  
  
       def __init__(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase = 3_5_0				,		)    ->       Tuple:
              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				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase = 7_5				,		__lowerCamelCase = 9.0				,		__lowerCamelCase = 2_0				,		__lowerCamelCase = None				,		__lowerCamelCase = 1				,		__lowerCamelCase = 0.0				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = "pil"				,		__lowerCamelCase = True				,		__lowerCamelCase = None				,		__lowerCamelCase = 1				,		)    ->       Dict:
              self.check_inputs(__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)
  
              # 2. Define call parameters
              if prompt is not None and isinstance(__lowerCamelCase				,		__lowerCamelCase):
                     _A   : Optional[Any]       					=				1
              elif prompt is not None and isinstance(__lowerCamelCase				,		__lowerCamelCase):
                     _A   : Tuple       					=				len(__lowerCamelCase)
              else:
                     _A   : Optional[Any]       					=				prompt_embeds.shape[0]
  
              _A   : Optional[int]       					=				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.
              _A   : Optional[Any]       					=				guidance_scale > 1.0
  
              # 3. Encode input prompt
              _A   : Tuple       					=				self._encode_prompt(
                  __lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		prompt_embeds=__lowerCamelCase				,		negative_prompt_embeds=__lowerCamelCase				,		)
  
              _A   : List[str]       					=				ORT_TO_PT_TYPE[str(text_embeddings.dtype)]
  
              # 4. Preprocess image
              _A   : int       					=				preprocess(__lowerCamelCase)
              _A   : Tuple       					=				image.cpu()
  
              # 5. set timesteps
              self.scheduler.set_timesteps(__lowerCamelCase				,		device=__lowerCamelCase)
              _A   : str       					=				self.scheduler.timesteps
  
              # 5. Add noise to image
              _A   : int       					=				torch.tensor([noise_level]				,		dtype=torch.long				,		device=__lowerCamelCase)
              _A   : List[str]       					=				torch.randn(image.shape				,		generator=__lowerCamelCase				,		device=__lowerCamelCase				,		dtype=__lowerCamelCase)
              _A   : List[str]       					=				self.low_res_scheduler.add_noise(__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)
  
              _A   : Union[str, Any]       					=				2 if do_classifier_free_guidance else 1
              _A   : Optional[Any]       					=				np.concatenate([image] * batch_multiplier * num_images_per_prompt)
              _A   : Tuple       					=				np.concatenate([noise_level] * image.shape[0])
  
              # 6. Prepare latent variables
              _A   : Any       					=				image.shape[2:]
              _A   : Dict       					=				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
              _A   : List[str]       					=				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
              _A   : Any       					=				self.prepare_extra_step_kwargs(__lowerCamelCase				,		__lowerCamelCase)
  
              _A   : List[str]       					=				next(
                  (input.type for input in self.unet.model.get_inputs() if input.name == "timestep")				,		"tensor(float)")
              _A   : int       					=				ORT_TO_NP_TYPE[timestep_dtype]
  
              # 9. Denoising loop
              _A   : str       					=				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
                            _A   : Dict       					=				np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
  
                            # concat latents, mask, masked_image_latents in the channel dimension
                            _A   : Optional[int]       					=				self.scheduler.scale_model_input(__lowerCamelCase				,		__lowerCamelCase)
                            _A   : Optional[int]       					=				np.concatenate([latent_model_input, image]				,		axis=1)
  
                            # timestep to tensor
                            _A   : Optional[int]       					=				np.array([t]				,		dtype=__lowerCamelCase)
  
                            # predict the noise residual
                            _A   : 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:
                                   _A   : Optional[int]       					=				np.split(__lowerCamelCase				,		2)
                            _A   : List[Any]       					=				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
  
                            # compute the previous noisy sample x_t -> x_t-1
                            _A   : Dict       					=				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
              _A   : List[Any]       					=				self.decode_latents(latents.float())
  
              # 11. Convert to PIL
              if output_type == "pil":
                     _A   : List[str]       					=				self.numpy_to_pil(__lowerCamelCase)
  
              if not return_dict:
                     return (image,)
  
              return ImagePipelineOutput(images=__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       Any:
              _A   : Tuple       					=				1 / 0.0_8_3_3_3 * latents
              _A   : List[Any]       					=				self.vae(latent_sample=__lowerCamelCase)[0]
              _A   : Any       					=				np.clip(image / 2 + 0.5				,		0				,		1)
              _A   : Any       					=				image.transpose((0, 2, 3, 1))
              return image
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		)    ->       List[Any]:
              if prompt is not None and isinstance(__lowerCamelCase				,		__lowerCamelCase):
                     _A   : Optional[Any]       					=				1
              elif prompt is not None and isinstance(__lowerCamelCase				,		__lowerCamelCase):
                     _A   : Dict       					=				len(__lowerCamelCase)
              else:
                     _A   : Union[str, Any]       					=				prompt_embeds.shape[0]
  
              if prompt_embeds is None:
                     _A   : List[str]       					=				self.tokenizer(
                         __lowerCamelCase				,		padding="max_length"				,		max_length=self.tokenizer.model_max_length				,		truncation=__lowerCamelCase				,		return_tensors="pt"				,		)
                     _A   : Dict       					=				text_inputs.input_ids
                     _A   : 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):
                            _A   : 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
                     _A   : int       					=				self.text_encoder(
                         input_ids=text_input_ids.int().to(__lowerCamelCase)				,		)
                     _A   : List[Any]       					=				prompt_embeds[0]
  
              _A   : Dict       					=				prompt_embeds.shape
              # duplicate text embeddings for each generation per prompt, using mps friendly method
              _A   : Optional[Any]       					=				prompt_embeds.repeat(1				,		__lowerCamelCase)
              _A   : Optional[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:
                     _A   : List[str]
                     if negative_prompt is None:
                            _A   : int       					=				[""] * 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):
                            _A   : Union[str, Any]       					=				[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:
                            _A   : List[str]       					=				negative_prompt
  
                     _A   : str       					=				text_input_ids.shape[-1]
                     _A   : List[Any]       					=				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
  
                     _A   : int       					=				self.text_encoder(
                         input_ids=uncond_input.input_ids.int().to(__lowerCamelCase)				,		)
                     _A   : Any       					=				uncond_embeddings[0]
  
              if do_classifier_free_guidance:
                     _A   : List[Any]       					=				uncond_embeddings.shape[1]
                     # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                     _A   : Tuple       					=				uncond_embeddings.repeat(1				,		__lowerCamelCase)
                     _A   : 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
                     _A   : Union[str, Any]       					=				np.concatenate([uncond_embeddings, prompt_embeds])
  
              return prompt_embeds
  
style_context: 
  
  
  lowerCAmelCase__   	=				'\n# Transformers installation\n! pip install transformers datasets evaluate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
  
  lowerCAmelCase__   	=				[{'type': 'code', 'content': INSTALL_CONTENT}]
  lowerCAmelCase__   	=				{
      '{processor_class}': 'FakeProcessorClass',
      '{model_class}': 'FakeModelClass',
      '{object_class}': 'FakeObjectClass',
  }
  