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					:List[Any]  =      getLogger(__name__)
  
  
  _lowerCAmelCase					:List[Any]  =      4
  _lowerCAmelCase					:List[str]  =      7
  
  _lowerCAmelCase					:Any  =      {
      "float16": torch.floataa,
      "float32": torch.floataa,
  }
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	str							):
  
  
  
  
  
  
  
      if isinstance(UpperCamelCase__  , torch.Tensor							):
          return image
      elif isinstance(UpperCamelCase__  , PIL.Image.Image							):
          _UpperCAmelCase							:						Tuple       							=					[image]
  
      if isinstance(image[0]  , PIL.Image.Image							):
          _UpperCAmelCase							,       _UpperCAmelCase							:						Any       							=					image[0].size
          _UpperCAmelCase							,       _UpperCAmelCase							:						List[Any]       							=					(x - x % 64 for x in (w, h))  # resize to integer multiple of 32
  
          _UpperCAmelCase							:						List[str]       							=					[np.array(i.resize((w, h)							)							)[None, :] for i in image]
          _UpperCAmelCase							:						Dict       							=					np.concatenate(UpperCamelCase__  , axis=0							)
          _UpperCAmelCase							:						str       							=					np.array(UpperCamelCase__							).astype(np.floataa							) / 255.0
          _UpperCAmelCase							:						List[Any]       							=					image.transpose(0  , 3  , 1  , 2							)
          _UpperCAmelCase							:						Optional[Any]       							=					2.0 * image - 1.0
          _UpperCAmelCase							:						Optional[Any]       							=					torch.from_numpy(UpperCamelCase__							)
      elif isinstance(image[0]  , torch.Tensor							):
          _UpperCAmelCase							:						Union[str, Any]       							=					torch.cat(UpperCamelCase__  , dim=0							)
  
      return image
  
  
  class 							_UpperCAmelCase							(				SCREAMING_SNAKE_CASE__   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A ,			A ,			A ,			A ,			A ,			A ,			A = 3_5_0 ,			)							->      int:
           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 = 7_5 ,			A = 9.0 ,			A = 2_0 ,			A = None ,			A = 1 ,			A = 0.0 ,			A = None ,			A = None ,			A = None ,			A = None ,			A = "pil" ,			A = True ,			A = None ,			A = 1 ,			)							->      List[Any]:
           self.check_inputs(A ,			A ,			A ,			A	)
  
           # 2. Define call parameters
           if prompt is not None and isinstance(A ,			A	):
               _UpperCAmelCase							:						Dict       							=					1
           elif prompt is not None and isinstance(A ,			A	):
               _UpperCAmelCase							:						List[str]       							=					len(A	)
           else:
               _UpperCAmelCase							:						Dict       							=					prompt_embeds.shape[0]
  
           _UpperCAmelCase							:						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							:						Optional[Any]       							=					guidance_scale > 1.0
  
           # 3. Encode input prompt
           _UpperCAmelCase							:						Union[str, Any]       							=					self._encode_prompt(
               A ,			A ,			A ,			A ,			A ,			prompt_embeds=A ,			negative_prompt_embeds=A ,			)
  
           _UpperCAmelCase							:						str       							=					ORT_TO_PT_TYPE[str(text_embeddings.dtype	)]
  
           # 4. Preprocess image
           _UpperCAmelCase							:						Union[str, Any]       							=					preprocess(A	)
           _UpperCAmelCase							:						Union[str, Any]       							=					image.cpu()
  
           # 5. set timesteps
           self.scheduler.set_timesteps(A ,			device=A	)
           _UpperCAmelCase							:						Optional[Any]       							=					self.scheduler.timesteps
  
           # 5. Add noise to image
           _UpperCAmelCase							:						Any       							=					torch.tensor([noise_level] ,			dtype=torch.long ,			device=A	)
           _UpperCAmelCase							:						Union[str, Any]       							=					torch.randn(image.shape ,			generator=A ,			device=A ,			dtype=A	)
           _UpperCAmelCase							:						Optional[int]       							=					self.low_res_scheduler.add_noise(A ,			A ,			A	)
  
           _UpperCAmelCase							:						Any       							=					2 if do_classifier_free_guidance else 1
           _UpperCAmelCase							:						Any       							=					np.concatenate([image] * batch_multiplier * num_images_per_prompt	)
           _UpperCAmelCase							:						Optional[Any]       							=					np.concatenate([noise_level] * image.shape[0]	)
  
           # 6. Prepare latent variables
           _UpperCAmelCase							,       _UpperCAmelCase							:						str       							=					image.shape[2:]
           _UpperCAmelCase							:						Optional[int]       							=					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
           _UpperCAmelCase							:						int       							=					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							:						Dict       							=					self.prepare_extra_step_kwargs(A ,			A	)
  
           _UpperCAmelCase							:						Dict       							=					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							:						Any       							=					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
                   _UpperCAmelCase							:						Any       							=					np.concatenate([latents] * 2	) if do_classifier_free_guidance else latents
  
                   # concat latents, mask, masked_image_latents in the channel dimension
                   _UpperCAmelCase							:						Tuple       							=					self.scheduler.scale_model_input(A ,			A	)
                   _UpperCAmelCase							:						int       							=					np.concatenate([latent_model_input, image] ,			axis=1	)
  
                   # timestep to tensor
                   _UpperCAmelCase							:						Union[str, Any]       							=					np.array([t] ,			dtype=A	)
  
                   # predict the noise residual
                   _UpperCAmelCase							:						Optional[int]       							=					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:
                       _UpperCAmelCase							,       _UpperCAmelCase							:						Tuple       							=					np.split(A ,			2	)
                   _UpperCAmelCase							:						Union[str, Any]       							=					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
  
                   # compute the previous noisy sample x_t -> x_t-1
                   _UpperCAmelCase							:						Union[str, Any]       							=					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
           _UpperCAmelCase							:						Dict       							=					self.decode_latents(latents.float()	)
  
           # 11. Convert to PIL
           if output_type == "pil":
               _UpperCAmelCase							:						Any       							=					self.numpy_to_pil(A	)
  
           if not return_dict:
               return (image,)
  
           return ImagePipelineOutput(images=A	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Any:
           _UpperCAmelCase							:						Union[str, Any]       							=					1 / 0.08_333 * latents
           _UpperCAmelCase							:						Tuple       							=					self.vae(latent_sample=A	)[0]
           _UpperCAmelCase							:						List[str]       							=					np.clip(image / 2 + 0.5 ,			0 ,			1	)
           _UpperCAmelCase							:						int       							=					image.transpose((0, 2, 3, 1)	)
           return image
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A ,			A ,			A ,			A ,			A = None ,			A = None ,			)							->      Optional[int]:
  
  
  
           if prompt is not None and isinstance(A ,			A	):
               _UpperCAmelCase							:						Tuple       							=					1
           elif prompt is not None and isinstance(A ,			A	):
               _UpperCAmelCase							:						Tuple       							=					len(A	)
           else:
               _UpperCAmelCase							:						List[str]       							=					prompt_embeds.shape[0]
  
           if prompt_embeds is None:
               _UpperCAmelCase							:						List[Any]       							=					self.tokenizer(
                   A ,			padding='''max_length''' ,			max_length=self.tokenizer.model_max_length ,			truncation=A ,			return_tensors='''pt''' ,			)
               _UpperCAmelCase							:						str       							=					text_inputs.input_ids
               _UpperCAmelCase							:						int       							=					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	):
                   _UpperCAmelCase							:						Optional[int]       							=					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							:						Any       							=					self.text_encoder(
                   input_ids=text_input_ids.int().to(A	) ,			)
               _UpperCAmelCase							:						Union[str, Any]       							=					prompt_embeds[0]
  
           _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							:						Dict       							=					prompt_embeds.shape
           # duplicate text embeddings for each generation per prompt, using mps friendly method
           _UpperCAmelCase							:						Union[str, Any]       							=					prompt_embeds.repeat(1 ,			A	)
           _UpperCAmelCase							:						Any       							=					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:
               _UpperCAmelCase							:						Any       							=					4_2
               if negative_prompt is None:
                   _UpperCAmelCase							:						Any       							=					[''''''] * 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	):
                   _UpperCAmelCase							:						Any       							=					[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:
                   _UpperCAmelCase							:						Any       							=					negative_prompt
  
               _UpperCAmelCase							:						Dict       							=					text_input_ids.shape[-1]
               _UpperCAmelCase							:						str       							=					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
  
               _UpperCAmelCase							:						Optional[int]       							=					self.text_encoder(
                   input_ids=uncond_input.input_ids.int().to(A	) ,			)
               _UpperCAmelCase							:						int       							=					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							:						int       							=					uncond_embeddings.repeat(1 ,			A	)
               _UpperCAmelCase							:						List[Any]       							=					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
               _UpperCAmelCase							:						Union[str, Any]       							=					np.concatenate([uncond_embeddings, prompt_embeds]	)
  
           return prompt_embeds
  
  
  
style_context: 
  
  
  
  """simple docstring"""
  import os
  
  from apache_beam.io.filesystems import FileSystems
  from apache_beam.pipeline import Pipeline
  
  from .logging import get_logger
  
  
  _lowerCAmelCase					:Optional[int]  =      2 << 20  # 2mb
  _lowerCAmelCase					:Union[str, Any]  =      get_logger(__name__)
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      Any:
           _UpperCAmelCase							:						str       							=					self._options.get_all_options().get('''runner'''	)
           return runner in [None, "DirectRunner", "PortableRunner"]
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	List[str]  , UpperCamelCase__	:	str  , UpperCamelCase__	:	Dict=False							):
      _UpperCAmelCase							:						Union[str, Any]       							=					FileSystems
      if fs.exists(UpperCamelCase__							):
          if force_upload:
              logger.info(F'Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.'							)
          else:
              logger.info(F'Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.'							)
              return
      with fs.create(UpperCamelCase__							) as remote_file:
          with open(UpperCamelCase__  , '''rb'''							) as local_file:
              _UpperCAmelCase							:						Optional[Any]       							=					local_file.read(UpperCamelCase__							)
              while chunk:
                  remote_file.write(UpperCamelCase__							)
                  _UpperCAmelCase							:						List[Any]       							=					local_file.read(UpperCamelCase__							)
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	int  , UpperCamelCase__	:	Dict  , UpperCamelCase__	:	str=False							):
      _UpperCAmelCase							:						int       							=					FileSystems
      if os.path.exists(UpperCamelCase__							):
          if force_download:
              logger.info(F'Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.'							)
          else:
              logger.info(F'Local path already exist: {remote_file_path}. Skipping it as force_upload=False.'							)
              return
      with fs.open(UpperCamelCase__							) as remote_file:
          with open(UpperCamelCase__  , '''wb'''							) as local_file:
              _UpperCAmelCase							:						List[str]       							=					remote_file.read(UpperCamelCase__							)
              while chunk:
                  local_file.write(UpperCamelCase__							)
                  _UpperCAmelCase							:						List[Any]       							=					remote_file.read(UpperCamelCase__							)
  
  
  