code: 
  """simple docstring"""
  from typing import Any, Callable, Dict, List, Optional, Union
  
  import PIL.Image
  import torch
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      DDIMScheduler,
      DiffusionPipeline,
      LMSDiscreteScheduler,
      PNDMScheduler,
      StableDiffusionImgaImgPipeline,
      StableDiffusionInpaintPipelineLegacy,
      StableDiffusionPipeline,
      UNetaDConditionModel,
  )
  from diffusers.configuration_utils import FrozenDict
  from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
  from diffusers.utils import deprecate, logging
  
  
  __magic_name__		       =				logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  class    _lowerCAmelCase						(						__UpperCAmelCase						):
  							lowercase_     :     Union[str, Any]       =							["""safety_checker""", """feature_extractor"""]
  
  
  
  							def __init__(      self ,						a_ ,						a_ ,						a_ ,						a_ ,						a_ ,						a_ ,						a_ ,						a_ = True ,						)							->							Tuple:
  								super().__init__()
  								if hasattr(scheduler.config ,						"steps_offset"   ) and scheduler.config.steps_offset != 1:
  									_UpperCAmelCase =						(
  									    f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
  									    f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
  									    "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
  									    " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
  									    " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
  									    " file"
  									)
  									deprecate("steps_offset!=1" ,						"1.0.0" ,						lowerCamelCase__ ,						standard_warn=lowerCamelCase__   )
  									_UpperCAmelCase =						dict(scheduler.config   )
  									_UpperCAmelCase =						1
  									_UpperCAmelCase =						FrozenDict(lowerCamelCase__   )
  
  								self.register_modules(
  								    vae=lowerCamelCase__ ,						text_encoder=lowerCamelCase__ ,						tokenizer=lowerCamelCase__ ,						unet=lowerCamelCase__ ,						scheduler=lowerCamelCase__ ,						safety_checker=lowerCamelCase__ ,						feature_extractor=lowerCamelCase__ ,						)
  								self.register_to_config(requires_safety_checker=lowerCamelCase__   )
  
  
  
  							@property
  							def       _a   (      self   )							->							Dict[str, Any]:
  								return {k: getattr(self ,						lowerCamelCase__   ) for k in self.config.keys() if not k.startswith("_"   )}
  
  
  
  							def       _a   (      self ,						a_ = "auto"   )							->							Union[str, Any]:
  
  
  
  								if slice_size == "auto":
  									# half the attention head size is usually a good trade-off between
  									# speed and memory
  									_UpperCAmelCase =						self.unet.config.attention_head_dim // 2
  								self.unet.set_attention_slice(lowerCamelCase__   )
  
  
  
  							def       _a   (      self   )							->							str:
  								self.enable_attention_slicing(lowerCamelCase__   )
  
  
  
  							@torch.no_grad()
  							def       _a   (      self ,						a_ ,						a_ ,						a_ ,						a_ = 0.8 ,						a_ = 50 ,						a_ = 7.5 ,						a_ = None ,						a_ = 1 ,						a_ = 0.0 ,						a_ = None ,						a_ = "pil" ,						a_ = True ,						a_ = None ,						a_ = 1 ,						)							->							str:
  								return StableDiffusionInpaintPipelineLegacy(**self.components   )(
  								    prompt=lowerCamelCase__ ,						image=lowerCamelCase__ ,						mask_image=lowerCamelCase__ ,						strength=lowerCamelCase__ ,						num_inference_steps=lowerCamelCase__ ,						guidance_scale=lowerCamelCase__ ,						negative_prompt=lowerCamelCase__ ,						num_images_per_prompt=lowerCamelCase__ ,						eta=lowerCamelCase__ ,						generator=lowerCamelCase__ ,						output_type=lowerCamelCase__ ,						return_dict=lowerCamelCase__ ,						callback=lowerCamelCase__ ,						)
  
  
  
  							@torch.no_grad()
  							def       _a   (      self ,						a_ ,						a_ ,						a_ = 0.8 ,						a_ = 50 ,						a_ = 7.5 ,						a_ = None ,						a_ = 1 ,						a_ = 0.0 ,						a_ = None ,						a_ = "pil" ,						a_ = True ,						a_ = None ,						a_ = 1 ,						**a_ ,						)							->							Any:
  								return StableDiffusionImgaImgPipeline(**self.components   )(
  								    prompt=lowerCamelCase__ ,						image=lowerCamelCase__ ,						strength=lowerCamelCase__ ,						num_inference_steps=lowerCamelCase__ ,						guidance_scale=lowerCamelCase__ ,						negative_prompt=lowerCamelCase__ ,						num_images_per_prompt=lowerCamelCase__ ,						eta=lowerCamelCase__ ,						generator=lowerCamelCase__ ,						output_type=lowerCamelCase__ ,						return_dict=lowerCamelCase__ ,						callback=lowerCamelCase__ ,						callback_steps=lowerCamelCase__ ,						)
  
  
  
  							@torch.no_grad()
  							def       _a   (      self ,						a_ ,						a_ = 512 ,						a_ = 512 ,						a_ = 50 ,						a_ = 7.5 ,						a_ = None ,						a_ = 1 ,						a_ = 0.0 ,						a_ = None ,						a_ = None ,						a_ = "pil" ,						a_ = True ,						a_ = None ,						a_ = 1 ,						)							->							Dict:
  								return StableDiffusionPipeline(**self.components   )(
  								    prompt=lowerCamelCase__ ,						height=lowerCamelCase__ ,						width=lowerCamelCase__ ,						num_inference_steps=lowerCamelCase__ ,						guidance_scale=lowerCamelCase__ ,						negative_prompt=lowerCamelCase__ ,						num_images_per_prompt=lowerCamelCase__ ,						eta=lowerCamelCase__ ,						generator=lowerCamelCase__ ,						latents=lowerCamelCase__ ,						output_type=lowerCamelCase__ ,						return_dict=lowerCamelCase__ ,						callback=lowerCamelCase__ ,						callback_steps=lowerCamelCase__ ,						)
  
  
  
  
  
style_context: 
  """simple docstring"""
  import subprocess
  from typing import Union
  
  import numpy as np
  import requests
  
  from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
  from .base import PIPELINE_INIT_ARGS, Pipeline
  
  
  if is_torch_available():
  			from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
  
  __magic_name__		       =				logging.get_logger(__name__)
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						f"{sampling_rate}"
  	_UpperCAmelCase =						"1"
  	_UpperCAmelCase =						"f32le"
  	_UpperCAmelCase =						[
  	    "ffmpeg",
  	    "-i",
  	    "pipe:0",
  	    "-ac",
  	    ac,
  	    "-ar",
  	    ar,
  	    "-f",
  	    format_for_conversion,
  	    "-hide_banner",
  	    "-loglevel",
  	    "quiet",
  	    "pipe:1",
  	]
  
  	try:
  		_UpperCAmelCase =						subprocess.Popen(UpperCamelCase__				,     stdin=subprocess.PIPE				,     stdout=subprocess.PIPE      )
  	except FileNotFoundError:
  		raise ValueError("ffmpeg was not found but is required to load audio files from filename"      )
  	_UpperCAmelCase =						ffmpeg_process.communicate(UpperCamelCase__      )
  	_UpperCAmelCase =						output_stream[0]
  
  	_UpperCAmelCase =						np.frombuffer(UpperCamelCase__				,     np.floataa      )
  	if audio.shape[0] == 0:
  		raise ValueError("Malformed soundfile"      )
  	return audio
  
  
  
  @add_end_docstrings(lowerCamelCase						)
  class    _lowerCAmelCase						(						lowerCamelCase						):
  
  
  
  							def __init__(      self ,						*a_ ,						**a_   )							->							Union[str, Any]:
  								# Default, might be overriden by the model.config.
  								_UpperCAmelCase =						5
  								super().__init__(*a_ ,						**a_   )
  
  								if self.framework != "pt":
  									raise ValueError(f"The {self.__class__} is only available in PyTorch."   )
  
  								self.check_model_type(a_   )
  
  
  
  							def __call__(      self ,						a_ ,						**a_ ,						)							->							int:
  								return super().__call__(a_ ,						**a_   )
  
  
  
  							def       _a   (      self ,						a_=None ,						**a_   )							->							Dict:
  								# No parameters on this pipeline right now
  								_UpperCAmelCase =						{}
  								if top_k is not None:
  									if top_k > self.model.config.num_labels:
  										_UpperCAmelCase =						self.model.config.num_labels
  									_UpperCAmelCase =						top_k
  								return {}, {}, postprocess_params
  
  
  
  							def       _a   (      self ,						a_   )							->							str:
  								if isinstance(a_ ,						a_   ):
  									if inputs.startswith("http://"   ) or inputs.startswith("https://"   ):
  										# We need to actually check for a real protocol, otherwise it's impossible to use a local file
  										# like http_huggingface_co.png
  										_UpperCAmelCase =						requests.get(a_   ).content
  									else:
  										with open(a_ ,						"rb"   ) as f:
  											_UpperCAmelCase =						f.read()
  
  								if isinstance(a_ ,						a_   ):
  									_UpperCAmelCase =						ffmpeg_read(a_ ,						self.feature_extractor.sampling_rate   )
  
  								if isinstance(a_ ,						a_   ):
  									# Accepting `"array"` which is the key defined in `datasets` for
  									# better integration
  									if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
  										raise ValueError(
  										    "When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a "
  										    "\"raw\" key containing the numpy array representing the audio and a \"sampling_rate\" key, "
  										    "containing the sampling_rate associated with that array"   )
  
  									_UpperCAmelCase =						inputs.pop("raw" ,						a_   )
  									if _inputs is None:
  										# Remove path which will not be used from `datasets`.
  										inputs.pop("path" ,						a_   )
  										_UpperCAmelCase =						inputs.pop("array" ,						a_   )
  									_UpperCAmelCase =						inputs.pop("sampling_rate"   )
  									_UpperCAmelCase =						_inputs
  									if in_sampling_rate != self.feature_extractor.sampling_rate:
  										import torch
  
  										if is_torchaudio_available():
  											from torchaudio import functional as F
  										else:
  											raise ImportError(
  											    "torchaudio is required to resample audio samples in AudioClassificationPipeline. "
  											    "The torchaudio package can be installed through: `pip install torchaudio`."   )
  
  										_UpperCAmelCase =						F.resample(
  										    torch.from_numpy(a_   ) ,						a_ ,						self.feature_extractor.sampling_rate   ).numpy()
  
  								if not isinstance(a_ ,						np.ndarray   ):
  									raise ValueError("We expect a numpy ndarray as input"   )
  								if len(inputs.shape   ) != 1:
  									raise ValueError("We expect a single channel audio input for AudioClassificationPipeline"   )
  
  								_UpperCAmelCase =						self.feature_extractor(
  								    a_ ,						sampling_rate=self.feature_extractor.sampling_rate ,						return_tensors="pt"   )
  								return processed
  
  
  
  							def       _a   (      self ,						a_   )							->							List[str]:
  								_UpperCAmelCase =						self.model(**a_   )
  								return model_outputs
  
  
  
  							def       _a   (      self ,						a_ ,						a_=5   )							->							int:
  								_UpperCAmelCase =						model_outputs.logits[0].softmax(-1   )
  								_UpperCAmelCase ,      _UpperCAmelCase =						probs.topk(a_   )
  
  								_UpperCAmelCase =						scores.tolist()
  								_UpperCAmelCase =						ids.tolist()
  
  								_UpperCAmelCase =						[{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(a_ ,						a_   )]
  
  								return labels
  
  
  
  
  