code: 
  
  
  
  
  
  
  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
  
  
  __snake_case							      =						getLogger(__name__)
  
  
  __snake_case							      =						4
  __snake_case							      =						7
  
  __snake_case							      =						{
      "float16": torch.floataa,
      "float32": torch.floataa,
  }
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			Tuple ):
  
  
  
  
  
  
  
      if isinstance(SCREAMING_SNAKE_CASE__  ,						torch.Tensor ):
          return image
      elif isinstance(SCREAMING_SNAKE_CASE__  ,						PIL.Image.Image ):
          UpperCamelCase :Any										=     [image]
  
      if isinstance(image[0]  ,						PIL.Image.Image ):
          UpperCamelCase							,     UpperCamelCase :Dict										=     image[0].size
          UpperCamelCase							,     UpperCamelCase :List[str]										=     (x - x % 64 for x in (w, h))  # resize to integer multiple of 32
  
          UpperCamelCase :Any										=     [np.array(i.resize((w, h) ) )[None, :] for i in image]
          UpperCamelCase :Optional[Any]										=     np.concatenate(SCREAMING_SNAKE_CASE__  ,						axis=0 )
          UpperCamelCase :List[str]										=     np.array(SCREAMING_SNAKE_CASE__ ).astype(np.floataa ) / 2_55.0
          UpperCamelCase :Union[str, Any]										=     image.transpose(0  ,						3  ,						1  ,						2 )
          UpperCamelCase :List[Any]										=     2.0 * image - 1.0
          UpperCamelCase :Union[str, Any]										=     torch.from_numpy(SCREAMING_SNAKE_CASE__ )
      elif isinstance(image[0]  ,						torch.Tensor ):
          UpperCamelCase :str										=     torch.cat(SCREAMING_SNAKE_CASE__  ,						dim=0 )
  
      return image
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      SCREAMING_SNAKE_CASE__				):
  
  
  
        """simple docstring"""
        def __init__(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_ = 350					,       )       ->   int:
            super().__init__(
                vae=SCREAMING_SNAKE_CASE_					,       text_encoder=SCREAMING_SNAKE_CASE_					,       tokenizer=SCREAMING_SNAKE_CASE_					,       unet=SCREAMING_SNAKE_CASE_					,       low_res_scheduler=SCREAMING_SNAKE_CASE_					,       scheduler=SCREAMING_SNAKE_CASE_					,       safety_checker=SCREAMING_SNAKE_CASE_					,       feature_extractor=SCREAMING_SNAKE_CASE_					,       watermarker=SCREAMING_SNAKE_CASE_					,       max_noise_level=SCREAMING_SNAKE_CASE_					,       )
        def __call__(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_ = 75					,       SCREAMING_SNAKE_CASE_ = 9.0					,       SCREAMING_SNAKE_CASE_ = 20					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = 1					,       SCREAMING_SNAKE_CASE_ = 0.0					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = "pil"					,       SCREAMING_SNAKE_CASE_ = True					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = 1					,       )       ->   List[Any]:
            self.check_inputs(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
  
            # 2. Define call parameters
            if prompt is not None and isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                UpperCamelCase :Optional[int]										=     1
            elif prompt is not None and isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                UpperCamelCase :List[Any]										=     len(SCREAMING_SNAKE_CASE_				)
            else:
                UpperCamelCase :Dict										=     prompt_embeds.shape[0]
  
            UpperCamelCase :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.
            UpperCamelCase :Union[str, Any]										=     guidance_scale > 1.0
  
            # 3. Encode input prompt
            UpperCamelCase :Tuple										=     self._encode_prompt(
                SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       prompt_embeds=SCREAMING_SNAKE_CASE_					,       negative_prompt_embeds=SCREAMING_SNAKE_CASE_					,       )
  
            UpperCamelCase :List[str]										=     ORT_TO_PT_TYPE[str(text_embeddings.dtype				)]
  
            # 4. Preprocess image
            UpperCamelCase :List[Any]										=     preprocess(SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :List[Any]										=     image.cpu()
  
            # 5. set timesteps
            self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_					,       device=SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :Optional[int]										=     self.scheduler.timesteps
  
            # 5. Add noise to image
            UpperCamelCase :Tuple										=     torch.tensor([noise_level]					,       dtype=torch.long					,       device=SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :Union[str, Any]										=     torch.randn(image.shape					,       generator=SCREAMING_SNAKE_CASE_					,       device=SCREAMING_SNAKE_CASE_					,       dtype=SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :Tuple										=     self.low_res_scheduler.add_noise(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
  
            UpperCamelCase :List[str]										=     2 if do_classifier_free_guidance else 1
            UpperCamelCase :Union[str, Any]										=     np.concatenate([image] * batch_multiplier * num_images_per_prompt				)
            UpperCamelCase :Dict										=     np.concatenate([noise_level] * image.shape[0]				)
  
            # 6. Prepare latent variables
            UpperCamelCase							,     UpperCamelCase :str										=     image.shape[2:]
            UpperCamelCase :Dict										=     self.prepare_latents(
                batch_size * num_images_per_prompt					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       )
  
            # 7. Check that sizes of image and latents match
            UpperCamelCase :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
            UpperCamelCase :int										=     self.prepare_extra_step_kwargs(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
  
            UpperCamelCase :int										=     next(
                (input.type for input in self.unet.model.get_inputs() if input.name == '''timestep''')					,       '''tensor(float)'''				)
            UpperCamelCase :Dict										=     ORT_TO_NP_TYPE[timestep_dtype]
  
            # 9. Denoising loop
            UpperCamelCase :Optional[Any]										=     len(SCREAMING_SNAKE_CASE_				) - num_inference_steps * self.scheduler.order
            with self.progress_bar(total=SCREAMING_SNAKE_CASE_				) as progress_bar:
                for i, t in enumerate(SCREAMING_SNAKE_CASE_				):
                    # expand the latents if we are doing classifier free guidance
                    UpperCamelCase :int										=     np.concatenate([latents] * 2				) if do_classifier_free_guidance else latents
  
                    # concat latents, mask, masked_image_latents in the channel dimension
                    UpperCamelCase :str										=     self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
                    UpperCamelCase :Tuple										=     np.concatenate([latent_model_input, image]					,       axis=1				)
  
                    # timestep to tensor
                    UpperCamelCase :Optional[Any]										=     np.array([t]					,       dtype=SCREAMING_SNAKE_CASE_				)
  
                    # predict the noise residual
                    UpperCamelCase :List[str]										=     self.unet(
                        sample=SCREAMING_SNAKE_CASE_					,       timestep=SCREAMING_SNAKE_CASE_					,       encoder_hidden_states=SCREAMING_SNAKE_CASE_					,       class_labels=noise_level.astype(np.intaa				)					,       )[0]
  
                    # perform guidance
                    if do_classifier_free_guidance:
                        UpperCamelCase							,     UpperCamelCase :List[Any]										=     np.split(SCREAMING_SNAKE_CASE_					,       2				)
                    UpperCamelCase :int										=     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
  
                    # compute the previous noisy sample x_t -> x_t-1
                    UpperCamelCase :List[str]										=     self.scheduler.step(
                        torch.from_numpy(SCREAMING_SNAKE_CASE_				)					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				).prev_sample
  
                    # call the callback, if provided
                    if i == len(SCREAMING_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(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
  
          # 10. Post-processing
            UpperCamelCase :Optional[Any]										=     self.decode_latents(latents.float()				)
  
            # 11. Convert to PIL
            if output_type == "pil":
                UpperCamelCase :Dict										=     self.numpy_to_pil(SCREAMING_SNAKE_CASE_				)
  
            if not return_dict:
                return (image,)
  
            return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_				)       ->   Any:
            UpperCamelCase :Union[str, Any]										=     1 / 0.0_8333 * latents
            UpperCamelCase :Optional[int]										=     self.vae(latent_sample=SCREAMING_SNAKE_CASE_				)[0]
            UpperCamelCase :Union[str, Any]										=     np.clip(image / 2 + 0.5					,       0					,       1				)
            UpperCamelCase :Dict										=     image.transpose((0, 2, 3, 1)				)
            return image
  
  
  
  
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       )       ->   Optional[int]:
  
  
  
            if prompt is not None and isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                UpperCamelCase :List[str]										=     1
            elif prompt is not None and isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                UpperCamelCase :int										=     len(SCREAMING_SNAKE_CASE_				)
            else:
                UpperCamelCase :Union[str, Any]										=     prompt_embeds.shape[0]
  
            if prompt_embeds is None:
                UpperCamelCase :Optional[Any]										=     self.tokenizer(
                    SCREAMING_SNAKE_CASE_					,       padding='''max_length'''					,       max_length=self.tokenizer.model_max_length					,       truncation=SCREAMING_SNAKE_CASE_					,       return_tensors='''pt'''					,       )
                UpperCamelCase :List[str]										=     text_inputs.input_ids
                UpperCamelCase :Tuple										=     self.tokenizer(SCREAMING_SNAKE_CASE_					,       padding='''longest'''					,       return_tensors='''pt'''				).input_ids
  
                if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                    SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                    UpperCamelCase :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 :Optional[int]										=     self.text_encoder(
                    input_ids=text_input_ids.int().to(SCREAMING_SNAKE_CASE_				)					,       )
                UpperCamelCase :int										=     prompt_embeds[0]
  
            UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase :int										=     prompt_embeds.shape
            # duplicate text embeddings for each generation per prompt, using mps friendly method
            UpperCamelCase :Any										=     prompt_embeds.repeat(1					,       SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :Any										=     prompt_embeds.reshape(bs_embed * num_images_per_prompt					,       SCREAMING_SNAKE_CASE_					,       -1				)
  
            # get unconditional embeddings for classifier free guidance
            if do_classifier_free_guidance and negative_prompt_embeds is None:
                UpperCamelCase :Tuple										=     42
                if negative_prompt is None:
                    UpperCamelCase :List[str]										=     [''''''] * batch_size
                elif type(SCREAMING_SNAKE_CASE_				) is not type(SCREAMING_SNAKE_CASE_				):
                    raise TypeError(
                        F'''`negative_prompt` should be the same type to `prompt`, but got {type(SCREAMING_SNAKE_CASE_				)} !='''
                        F''' {type(SCREAMING_SNAKE_CASE_				)}.'''				)
                elif isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                    UpperCamelCase :Optional[Any]										=     [negative_prompt]
                elif batch_size != len(SCREAMING_SNAKE_CASE_				):
                    raise ValueError(
                        F'''`negative_prompt`: {negative_prompt} has batch size {len(SCREAMING_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 :int										=     negative_prompt
  
                UpperCamelCase :List[str]										=     text_input_ids.shape[-1]
                UpperCamelCase :Union[str, Any]										=     self.tokenizer(
                    SCREAMING_SNAKE_CASE_					,       padding='''max_length'''					,       max_length=SCREAMING_SNAKE_CASE_					,       truncation=SCREAMING_SNAKE_CASE_					,       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(SCREAMING_SNAKE_CASE_				)					,       )
                UpperCamelCase :Optional[int]										=     uncond_embeddings[0]
  
            if do_classifier_free_guidance:
                UpperCamelCase :Optional[Any]										=     uncond_embeddings.shape[1]
                # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                UpperCamelCase :Optional[Any]										=     uncond_embeddings.repeat(1					,       SCREAMING_SNAKE_CASE_				)
                UpperCamelCase :str										=     uncond_embeddings.reshape(batch_size * num_images_per_prompt					,       SCREAMING_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 :Dict										=     np.concatenate([uncond_embeddings, prompt_embeds]				)
  
            return prompt_embeds
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __snake_case							      =						{
      """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
  }
  
  try:
   if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __snake_case							      =						["""LlamaTokenizer"""]
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __snake_case							      =						["""LlamaTokenizerFast"""]
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __snake_case							      =						[
       """OpenLlamaForCausalLM""",
       """OpenLlamaModel""",
       """OpenLlamaPreTrainedModel""",
       """OpenLlamaForSequenceClassification""",
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig
  
   try:
    if not is_sentencepiece_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from transformers import LlamaTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from transformers import LlamaTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_open_llama import (
        OpenLlamaForCausalLM,
        OpenLlamaForSequenceClassification,
        OpenLlamaModel,
        OpenLlamaPreTrainedModel,
    )
  
  
  else:
   import sys
  
   __snake_case							      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  