code: 
  """simple docstring"""
  from typing import Union
  
  import torch
  from PIL import Image
  from torchvision import transforms as tfms
  from tqdm.auto import tqdm
  from transformers import CLIPTextModel, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      DDIMScheduler,
      DiffusionPipeline,
      LMSDiscreteScheduler,
      PNDMScheduler,
      UNetaDConditionModel,
  )
  
  
  class    _lowerCAmelCase						(						lowerCamelCase						):
  
  
  
  							def __init__(      self ,						a_ ,						a_ ,						a_ ,						a_ ,						a_ ,						)							->							Optional[Any]:
  								super().__init__()
  
  								self.register_modules(vae=a_ ,						text_encoder=a_ ,						tokenizer=a_ ,						unet=a_ ,						scheduler=a_   )
  
  
  
  							def       _a   (      self ,						a_   )							->							Union[str, Any]:
  								with torch.no_grad():
  									_UpperCAmelCase =						self.vae.encode(tfms.ToTensor()(a_   ).unsqueeze(0   ).to(self.device   ) * 2 - 1   )
  									_UpperCAmelCase =						0.18215 * latent.latent_dist.sample()
  								return latent
  
  
  
  							def       _a   (      self ,						a_   )							->							Any:
  								_UpperCAmelCase =						(1 / 0.18215) * latent
  								with torch.no_grad():
  									_UpperCAmelCase =						self.vae.decode(a_   ).sample
  								_UpperCAmelCase =						(img / 2 + 0.5).clamp(0 ,						1   )
  								_UpperCAmelCase =						img.detach().cpu().permute(0 ,						2 ,						3 ,						1   ).numpy()
  								_UpperCAmelCase =						(img * 255).round().astype("uint8"   )
  								return Image.fromarray(img[0]   )
  
  
  
  							def       _a   (      self ,						a_   )							->							Dict:
  								_UpperCAmelCase =						self.tokenizer(
  								    a_ ,						padding="max_length" ,						max_length=self.tokenizer.model_max_length ,						truncation=a_ ,						return_tensors="pt" ,						)
  
  								_UpperCAmelCase =						self.text_encoder(text_input.input_ids.to(self.device   )   )[0]
  
  								_UpperCAmelCase =						self.tokenizer(
  								    "" ,						padding="max_length" ,						max_length=self.tokenizer.model_max_length ,						truncation=a_ ,						return_tensors="pt" ,						)
  
  								_UpperCAmelCase =						self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
  
  								return torch.cat([uncond_embedding, text_embedding]   )
  
  
  
  							def __call__(      self ,						a_ ,						a_ ,						a_ = 0.3 ,						a_ = 0.6 ,						a_ = 0.5 ,						a_ = 42 ,						a_ = 50 ,						a_ = 7.5 ,						)							->							Image.Image:
  								_UpperCAmelCase =						steps - int(kmin * steps   )
  								_UpperCAmelCase =						steps - int(kmax * steps   )
  
  								_UpperCAmelCase =						self.prep_text(a_   )
  
  								self.scheduler.set_timesteps(a_   )
  
  								_UpperCAmelCase ,      _UpperCAmelCase =						img.size
  								_UpperCAmelCase =						self.encode(a_   )
  
  								torch.manual_seed(a_   )
  								_UpperCAmelCase =						torch.randn(
  								    (1, self.unet.config.in_channels, height // 8, width // 8) ,						).to(self.device   )
  
  								_UpperCAmelCase =						self.scheduler.add_noise(
  								    a_ ,						a_ ,						timesteps=self.scheduler.timesteps[tmax] ,						)
  
  								_UpperCAmelCase =						torch.cat([latents] * 2   )
  
  								_UpperCAmelCase =						self.scheduler.scale_model_input(a_ ,						self.scheduler.timesteps[tmax]   )
  
  								with torch.no_grad():
  									_UpperCAmelCase =						self.unet(
  									    a_ ,						self.scheduler.timesteps[tmax] ,						encoder_hidden_states=a_ ,						).sample
  
  								_UpperCAmelCase ,      _UpperCAmelCase =						pred.chunk(2   )
  								_UpperCAmelCase =						pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
  								_UpperCAmelCase =						self.scheduler.step(a_ ,						self.scheduler.timesteps[tmax] ,						a_   ).prev_sample
  
  								for i, t in enumerate(tqdm(self.scheduler.timesteps   )   ):
  									if i > tmax:
  										if i < tmin:  # layout generation phase
  											_UpperCAmelCase =						self.scheduler.add_noise(
  											    a_ ,						a_ ,						timesteps=a_ ,						)
  
  											_UpperCAmelCase =						(mix_factor * latents) + (
  											    1 - mix_factor
  											) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
  											_UpperCAmelCase =						torch.cat([input] * 2   )
  
  										else:  # content generation phase
  											_UpperCAmelCase =						torch.cat([latents] * 2   )
  
  										_UpperCAmelCase =						self.scheduler.scale_model_input(a_ ,						a_   )
  
  										with torch.no_grad():
  											_UpperCAmelCase =						self.unet(
  											    a_ ,						a_ ,						encoder_hidden_states=a_ ,						).sample
  
  										_UpperCAmelCase ,      _UpperCAmelCase =						pred.chunk(2   )
  										_UpperCAmelCase =						pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
  										_UpperCAmelCase =						self.scheduler.step(a_ ,						a_ ,						a_   ).prev_sample
  
  								return self.decode(a_   )
  
  
  
  
  
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
  
  
  
  
  