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_     :     Optional[Any]  		=						getLogger(__name__)
  
  
  lowerCamelCase_     :     Optional[Any]  		=						4
  lowerCamelCase_     :     Any  		=						7
  
  lowerCamelCase_     :     Optional[int]  		=						{
      """float16""": torch.floataa,
      """float32""": torch.floataa,
  }
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      if isinstance(lowercase ,					torch.Tensor	):
          return image
      elif isinstance(lowercase ,					PIL.Image.Image	):
          a					=[image]
  
      if isinstance(image[0] ,					PIL.Image.Image	):
          a	,   a					=image[0].size
          a	,   a					=(x - x % 64 for x in (w, h))  # resize to integer multiple of 32
  
          a					=[np.array(i.resize((w, h)	)	)[None, :] for i in image]
          a					=np.concatenate(lowercase ,					axis=0	)
          a					=np.array(lowercase	).astype(np.floataa	) / 255.0
          a					=image.transpose(0 ,					3 ,					1 ,					2	)
          a					=2.0 * image - 1.0
          a					=torch.from_numpy(lowercase	)
      elif isinstance(image[0] ,					torch.Tensor	):
          a					=torch.cat(lowercase ,					dim=0	)
  
      return image
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def __init__(	self				,					__A				,					__A				,					__A				,					__A				,					__A				,					__A				,					__A = 350				,					)      ->  Dict:
       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				,					)      ->  Dict:
       self.check_inputs(__A				,					__A				,					__A				,					__A		)
  
       # 2. Define call parameters
       if prompt is not None and isinstance(__A				,					__A		):
           a					=1
       elif prompt is not None and isinstance(__A				,					__A		):
           a					=len(__A		)
       else:
           a					=prompt_embeds.shape[0]
  
       a					=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					=guidance_scale > 1.0
  
       # 3. Encode input prompt
       a					=self._encode_prompt(
           __A				,					__A				,					__A				,					__A				,					__A				,					prompt_embeds=__A				,					negative_prompt_embeds=__A				,					)
  
       a					=ORT_TO_PT_TYPE[str(text_embeddings.dtype		)]
  
       # 4. Preprocess image
       a					=preprocess(__A		)
       a					=image.cpu()
  
       # 5. set timesteps
       self.scheduler.set_timesteps(__A				,					device=__A		)
       a					=self.scheduler.timesteps
  
       # 5. Add noise to image
       a					=torch.tensor([noise_level]				,					dtype=torch.long				,					device=__A		)
       a					=torch.randn(image.shape				,					generator=__A				,					device=__A				,					dtype=__A		)
       a					=self.low_res_scheduler.add_noise(__A				,					__A				,					__A		)
  
       a					=2 if do_classifier_free_guidance else 1
       a					=np.concatenate([image] * batch_multiplier * num_images_per_prompt		)
       a					=np.concatenate([noise_level] * image.shape[0]		)
  
       # 6. Prepare latent variables
       a	,   a					=image.shape[2:]
       a					=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
       a					=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					=self.prepare_extra_step_kwargs(__A				,					__A		)
  
       a					=next(
           (input.type for input in self.unet.model.get_inputs() if input.name == '''timestep''')				,					'''tensor(float)'''		)
       a					=ORT_TO_NP_TYPE[timestep_dtype]
  
       # 9. Denoising loop
       a					=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
               a					=np.concatenate([latents] * 2		) if do_classifier_free_guidance else latents
  
               # concat latents, mask, masked_image_latents in the channel dimension
               a					=self.scheduler.scale_model_input(__A				,					__A		)
               a					=np.concatenate([latent_model_input, image]				,					axis=1		)
  
               # timestep to tensor
               a					=np.array([t]				,					dtype=__A		)
  
               # predict the noise residual
               a					=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:
                   a	,   a					=np.split(__A				,					2		)
               a					=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
  
               # compute the previous noisy sample x_t -> x_t-1
               a					=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
       a					=self.decode_latents(latents.float()		)
  
       # 11. Convert to PIL
       if output_type == "pil":
           a					=self.numpy_to_pil(__A		)
  
       if not return_dict:
           return (image,)
  
       return ImagePipelineOutput(images=__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  Optional[int]:
       a					=1 / 0.08_333 * latents
       a					=self.vae(latent_sample=__A		)[0]
       a					=np.clip(image / 2 + 0.5				,					0				,					1		)
       a					=image.transpose((0, 2, 3, 1)		)
       return image
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A				,					__A				,					__A				,					__A = None				,					__A = None				,					)      ->  int:
       if prompt is not None and isinstance(__A				,					__A		):
           a					=1
       elif prompt is not None and isinstance(__A				,					__A		):
           a					=len(__A		)
       else:
           a					=prompt_embeds.shape[0]
  
       if prompt_embeds is None:
           a					=self.tokenizer(
               __A				,					padding='''max_length'''				,					max_length=self.tokenizer.model_max_length				,					truncation=__A				,					return_tensors='''pt'''				,					)
           a					=text_inputs.input_ids
           a					=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		):
               a					=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					=self.text_encoder(
               input_ids=text_input_ids.int().to(__A		)				,					)
           a					=prompt_embeds[0]
  
       a	,   a	,   a					=prompt_embeds.shape
       # duplicate text embeddings for each generation per prompt, using mps friendly method
       a					=prompt_embeds.repeat(1				,					__A		)
       a					=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:
           a					=42
           if negative_prompt is None:
               a					=[''''''] * 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		):
               a					=[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:
               a					=negative_prompt
  
           a					=text_input_ids.shape[-1]
           a					=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
  
           a					=self.text_encoder(
               input_ids=uncond_input.input_ids.int().to(__A		)				,					)
           a					=uncond_embeddings[0]
  
       if do_classifier_free_guidance:
           a					=uncond_embeddings.shape[1]
           # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
           a					=uncond_embeddings.repeat(1				,					__A		)
           a					=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
           a					=np.concatenate([uncond_embeddings, prompt_embeds]		)
  
       return prompt_embeds
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import copy
  
  from transformers.configuration_utils import PretrainedConfig
  from transformers.utils import logging
  
  
  lowerCamelCase_     :     int    		=						logging.get_logger(__name__)
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						"hybrid-clip"
   __lowerCAmelCase				      =						True
  
  
   def __init__(	self				,					__A=512				,					**__A		)      ->  Any:
       super().__init__(**__A		)
  
       if "text_config" not in kwargs:
           raise ValueError('''`text_config` can not be `None`.'''		)
  
       if "vision_config" not in kwargs:
           raise ValueError('''`vision_config` can not be `None`.'''		)
  
       a					=kwargs.pop('''text_config'''		)
       a					=kwargs.pop('''vision_config'''		)
  
       a					=text_config.pop('''model_type'''		)
       a					=vision_config.pop('''model_type'''		)
  
       from transformers import AutoConfig
  
       a					=AutoConfig.for_model(__A				,					**__A		)
  
       if vision_model_type == "clip":
           a					=AutoConfig.for_model(__A				,					**__A		).vision_config
       elif vision_model_type == "clip_vision_model":
           from transformers import CLIPVisionConfig
  
           a					=CLIPVisionConfig(**__A		)
       else:
           a					=AutoConfig.for_model(__A				,					**__A		)
  
       a					=projection_dim
       a					=1.0
  
  
   @classmethod
   def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					__A				,					**__A		)      ->  Tuple:
       return cls(text_config=text_config.to_dict()				,					vision_config=vision_config.to_dict()				,					**__A		)
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
       a					=copy.deepcopy(self.__dict__		)
       a					=self.text_config.to_dict()
       a					=self.vision_config.to_dict()
       a					=self.__class__.model_type
       return output