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
  
  
  UpperCAmelCase__      =							getLogger(__name__)
  
  
  UpperCAmelCase__      =							4
  UpperCAmelCase__      =							7
  
  UpperCAmelCase__      =							{
      'float16': torch.floataa,
      'float32': torch.floataa,
  }
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Optional[Any]   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			if isinstance(__A,torch.Tensor   ):
  						return image
  			elif isinstance(__A,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(__A,axis=0   )
  						__A=  np.array(__A   ).astype(np.floataa   ) / 2_55.0
  						__A=  image.transpose(0,3,1,2   )
  						__A=  2.0 * image - 1.0
  						__A=  torch.from_numpy(__A   )
  			elif isinstance(image[0],torch.Tensor   ):
  						__A=  torch.cat(__A,dim=0   )
  
  			return image
  
  
  
  
  
  
  class 	a__				(				UpperCAmelCase_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			def __init__(     self       : List[str] ,						lowerCAmelCase_       : List[str] ,						lowerCAmelCase_       : List[str] ,						lowerCAmelCase_       : Optional[Any] ,						lowerCAmelCase_       : List[Any] ,						lowerCAmelCase_       : Dict ,						lowerCAmelCase_       : Optional[int] ,						lowerCAmelCase_       : Dict = 350 ,						)			->		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       : List[str] ,						lowerCAmelCase_       : Dict ,						lowerCAmelCase_       : Optional[Any] ,						lowerCAmelCase_       : List[Any] = 75 ,						lowerCAmelCase_       : List[Any] = 9.0 ,						lowerCAmelCase_       : Any = 20 ,						lowerCAmelCase_       : Union[str, Any] = None ,						lowerCAmelCase_       : Union[str, Any] = 1 ,						lowerCAmelCase_       : Tuple = 0.0 ,						lowerCAmelCase_       : List[str] = None ,						lowerCAmelCase_       : int = None ,						lowerCAmelCase_       : Dict = None ,						lowerCAmelCase_       : List[Any] = None ,						lowerCAmelCase_       : str = "pil" ,						lowerCAmelCase_       : Optional[Any] = True ,						lowerCAmelCase_       : Dict = None ,						lowerCAmelCase_       : Union[str, Any] = 1 ,						)			->		Dict:
  						self.check_inputs(lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_  )
  
  						# 2. Define call parameters
  						if prompt is not None and isinstance(lowerCAmelCase_ ,						lowerCAmelCase_  ):
  									__A=  1
  						elif prompt is not None and isinstance(lowerCAmelCase_ ,						lowerCAmelCase_  ):
  									__A=  len(lowerCAmelCase_  )
  						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(
  						    lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						prompt_embeds=lowerCAmelCase_ ,						negative_prompt_embeds=lowerCAmelCase_ ,						)
  
  						__A=  ORT_TO_PT_TYPE[str(text_embeddings.dtype  )]
  
  						# 4. Preprocess image
  						__A=  preprocess(lowerCAmelCase_  )
  						__A=  image.cpu()
  
  						# 5. set timesteps
  						self.scheduler.set_timesteps(lowerCAmelCase_ ,						device=lowerCAmelCase_  )
  						__A=  self.scheduler.timesteps
  
  						# 5. Add noise to image
  						__A=  torch.tensor([noise_level] ,						dtype=torch.long ,						device=lowerCAmelCase_  )
  						__A=  torch.randn(image.shape ,						generator=lowerCAmelCase_ ,						device=lowerCAmelCase_ ,						dtype=lowerCAmelCase_  )
  						__A=  self.low_res_scheduler.add_noise(lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_  )
  
  						__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 ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						)
  
  						# 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(lowerCAmelCase_ ,						lowerCAmelCase_  )
  
  						__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(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=  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(lowerCAmelCase_ ,						lowerCAmelCase_  )
  												__A=  np.concatenate([latent_model_input, image] ,						axis=1  )
  
  												# timestep to tensor
  												__A=  np.array([t] ,						dtype=lowerCAmelCase_  )
  
  												# predict the noise residual
  												__A=  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,  __A=  np.split(lowerCAmelCase_ ,						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(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=  self.decode_latents(latents.float()  )
  
  						# 11. Convert to PIL
  						if output_type == "pil":
  									__A=  self.numpy_to_pil(lowerCAmelCase_  )
  
  						if not return_dict:
  									return (image,)
  
  						return ImagePipelineOutput(images=lowerCAmelCase_  )
  			def 				lowerCAmelCase    (     self       : Dict ,						lowerCAmelCase_       : Tuple  )			->		Any:
  						__A=  1 / 0.0_83_33 * latents
  						__A=  self.vae(latent_sample=lowerCAmelCase_  )[0]
  						__A=  np.clip(image / 2 + 0.5 ,						0 ,						1  )
  						__A=  image.transpose((0, 2, 3, 1)  )
  						return image
  
  			def 				lowerCAmelCase    (     self       : List[Any] ,						lowerCAmelCase_       : List[Any] ,						lowerCAmelCase_       : Tuple ,						lowerCAmelCase_       : Dict ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : List[str] ,						lowerCAmelCase_       : List[Any] = None ,						lowerCAmelCase_       : List[Any] = None ,						)			->		Dict:
  						if prompt is not None and isinstance(lowerCAmelCase_ ,						lowerCAmelCase_  ):
  									__A=  1
  						elif prompt is not None and isinstance(lowerCAmelCase_ ,						lowerCAmelCase_  ):
  									__A=  len(lowerCAmelCase_  )
  						else:
  									__A=  prompt_embeds.shape[0]
  
  						if prompt_embeds is None:
  									__A=  self.tokenizer(
  									    lowerCAmelCase_ ,						padding='max_length' ,						max_length=self.tokenizer.model_max_length ,						truncation=lowerCAmelCase_ ,						return_tensors='pt' ,						)
  									__A=  text_inputs.input_ids
  									__A=  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=  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(lowerCAmelCase_  ) ,						)
  									__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 ,						lowerCAmelCase_  )
  						__A=  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=  42
  									if negative_prompt is None:
  												__A=  [''] * 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=  [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=  negative_prompt
  
  									__A=  text_input_ids.shape[-1]
  									__A=  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=  self.text_encoder(
  									    input_ids=uncond_input.input_ids.int().to(lowerCAmelCase_  ) ,						)
  									__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 ,						lowerCAmelCase_  )
  									__A=  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=  np.concatenate([uncond_embeddings, prompt_embeds]  )
  
  						return prompt_embeds
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  UpperCAmelCase__      =							logging.get_logger(__name__)
  
  UpperCAmelCase__      =							{
      '''google/mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'''
  }
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							int							= MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
  			A	:							Optional[int]							= '''mobilebert'''
  			def __init__(     self       : Optional[int] ,						lowerCAmelCase_       : str=30_522 ,						lowerCAmelCase_       : List[Any]=512 ,						lowerCAmelCase_       : Optional[int]=24 ,						lowerCAmelCase_       : str=4 ,						lowerCAmelCase_       : List[str]=512 ,						lowerCAmelCase_       : List[str]="relu" ,						lowerCAmelCase_       : Dict=0.0 ,						lowerCAmelCase_       : List[Any]=0.1 ,						lowerCAmelCase_       : Optional[Any]=512 ,						lowerCAmelCase_       : str=2 ,						lowerCAmelCase_       : Dict=0.02 ,						lowerCAmelCase_       : Any=1E-12 ,						lowerCAmelCase_       : List[Any]=0 ,						lowerCAmelCase_       : Union[str, Any]=128 ,						lowerCAmelCase_       : Any=True ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : List[Any]=128 ,						lowerCAmelCase_       : Dict=False ,						lowerCAmelCase_       : Tuple=True ,						lowerCAmelCase_       : Any=4 ,						lowerCAmelCase_       : Optional[Any]="no_norm" ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : Union[str, Any]=None ,						**lowerCAmelCase_       : Union[str, Any] ,						)			->		List[Any]:
  						super().__init__(pad_token_id=lowerCAmelCase_ ,						**lowerCAmelCase_  )
  
  						__A=  vocab_size
  						__A=  hidden_size
  						__A=  num_hidden_layers
  						__A=  num_attention_heads
  						__A=  hidden_act
  						__A=  intermediate_size
  						__A=  hidden_dropout_prob
  						__A=  attention_probs_dropout_prob
  						__A=  max_position_embeddings
  						__A=  type_vocab_size
  						__A=  initializer_range
  						__A=  layer_norm_eps
  						__A=  embedding_size
  						__A=  trigram_input
  						__A=  use_bottleneck
  						__A=  intra_bottleneck_size
  						__A=  use_bottleneck_attention
  						__A=  key_query_shared_bottleneck
  						__A=  num_feedforward_networks
  						__A=  normalization_type
  						__A=  classifier_activation
  
  						if self.use_bottleneck:
  									__A=  intra_bottleneck_size
  						else:
  									__A=  hidden_size
  
  						__A=  classifier_dropout
  
  
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			@property
  			def 				lowerCAmelCase    (     self       : List[str]  )			->		Mapping[str, Mapping[int, str]]:
  						if self.task == "multiple-choice":
  									__A=  {0: 'batch', 1: 'choice', 2: 'sequence'}
  						else:
  									__A=  {0: 'batch', 1: 'sequence'}
  						return OrderedDict(
  						    [
  						        ('input_ids', dynamic_axis),
  						        ('attention_mask', dynamic_axis),
  						        ('token_type_ids', dynamic_axis),
  						    ]  )
  
  
  
  