code: 
  """simple docstring"""
  # DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
  # and https://github.com/hojonathanho/diffusion
  
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import flax
  import jax.numpy as jnp
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from .scheduling_utils_flax import (
      CommonSchedulerState,
      FlaxKarrasDiffusionSchedulers,
      FlaxSchedulerMixin,
      FlaxSchedulerOutput,
      add_noise_common,
      get_velocity_common,
  )
  
  
  @flax.struct.dataclass
  class    _lowerCAmelCase						:
  							lowercase_     :     CommonSchedulerState
  							lowercase_     :     jnp.ndarray
  
  							# setable values
  							lowercase_     :     jnp.ndarray
  							lowercase_     :     jnp.ndarray
  							lowercase_     :     Optional[int]       =							None
  
  
  
  							@classmethod
  							def       _a   (      cls ,						a_ ,						a_ ,						a_ ,						a_ ,						)							->							str:
  								return cls(
  								    common=lowerCamelCase__ ,						final_alpha_cumprod=lowerCamelCase__ ,						init_noise_sigma=lowerCamelCase__ ,						timesteps=lowerCamelCase__ ,						)
  
  
  
  
  
  @dataclass
  class    _lowerCAmelCase						(						UpperCAmelCase__						):
  							lowercase_     :     DDIMSchedulerState
  
  
  
  
  
  class    _lowerCAmelCase						(						UpperCAmelCase__    ,       UpperCAmelCase__						):
  							lowercase_     :     Any       =							[e.name for e in FlaxKarrasDiffusionSchedulers]
  
  							lowercase_     :     jnp.dtype
  
  
  
  							@property
  							def       _a   (      self   )							->							Union[str, Any]:
  								return True
  
  
  
  							@register_to_config
  							def __init__(      self ,						a_ = 1000 ,						a_ = 0.0001 ,						a_ = 0.02 ,						a_ = "linear" ,						a_ = None ,						a_ = True ,						a_ = 0 ,						a_ = "epsilon" ,						a_ = jnp.floataa ,						)							->							List[str]:
  								_UpperCAmelCase =						dtype
  
  
  
  							def       _a   (      self ,						a_ = None   )							->							Union[str, Any]:
  								if common is None:
  									_UpperCAmelCase =						CommonSchedulerState.create(self   )
  
  								# At every step in ddim, we are looking into the previous alphas_cumprod
  								# For the final step, there is no previous alphas_cumprod because we are already at 0
  								# `set_alpha_to_one` decides whether we set this parameter simply to one or
  								# whether we use the final alpha of the "non-previous" one.
  								_UpperCAmelCase =						(
  								    jnp.array(1.0 ,						dtype=self.dtype   ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
  								)
  
  								# standard deviation of the initial noise distribution
  								_UpperCAmelCase =						jnp.array(1.0 ,						dtype=self.dtype   )
  
  								_UpperCAmelCase =						jnp.arange(0 ,						self.config.num_train_timesteps   ).round()[::-1]
  
  								return DDIMSchedulerState.create(
  								    common=lowerCamelCase__ ,						final_alpha_cumprod=lowerCamelCase__ ,						init_noise_sigma=lowerCamelCase__ ,						timesteps=lowerCamelCase__ ,						)
  
  
  
  							def       _a   (      self ,						a_ ,						a_ ,						a_ = None   )							->							List[Any]:
  								return sample
  
  
  
  							def       _a   (      self ,						a_ ,						a_ ,						a_ = ()   )							->							Dict:
  								_UpperCAmelCase =						self.config.num_train_timesteps // num_inference_steps
  								# creates integer timesteps by multiplying by ratio
  								# rounding to avoid issues when num_inference_step is power of 3
  								_UpperCAmelCase =						(jnp.arange(0 ,						lowerCamelCase__   ) * step_ratio).round()[::-1] + self.config.steps_offset
  
  								return state.replace(
  								    num_inference_steps=lowerCamelCase__ ,						timesteps=lowerCamelCase__ ,						)
  
  
  
  							def       _a   (      self ,						a_ ,						a_ ,						a_   )							->							List[str]:
  								_UpperCAmelCase =						state.common.alphas_cumprod[timestep]
  								_UpperCAmelCase =						jnp.where(
  								    prev_timestep >= 0 ,						state.common.alphas_cumprod[prev_timestep] ,						state.final_alpha_cumprod   )
  								_UpperCAmelCase =						1 - alpha_prod_t
  								_UpperCAmelCase =						1 - alpha_prod_t_prev
  
  								_UpperCAmelCase =						(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
  								return variance
  
  
  
  							def       _a   (      self ,						a_ ,						a_ ,						a_ ,						a_ ,						a_ = 0.0 ,						a_ = True ,						)							->							Any:
  								if state.num_inference_steps is None:
  									raise ValueError(
  									    "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"   )
  
  								# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
  								# Ideally, read DDIM paper in-detail understanding
  
  								# Notation (<variable name> -> <name in paper>
  								# - pred_noise_t -> e_theta(x_t, t)
  								# - pred_original_sample -> f_theta(x_t, t) or x_0
  								# - std_dev_t -> sigma_t
  								# - eta -> η
  								# - pred_sample_direction -> "direction pointing to x_t"
  								# - pred_prev_sample -> "x_t-1"
  
  								# 1. get previous step value (=t-1)
  								_UpperCAmelCase =						timestep - self.config.num_train_timesteps // state.num_inference_steps
  
  								_UpperCAmelCase =						state.common.alphas_cumprod
  								_UpperCAmelCase =						state.final_alpha_cumprod
  
  								# 2. compute alphas, betas
  								_UpperCAmelCase =						alphas_cumprod[timestep]
  								_UpperCAmelCase =						jnp.where(prev_timestep >= 0 ,						alphas_cumprod[prev_timestep] ,						lowerCamelCase__   )
  
  								_UpperCAmelCase =						1 - alpha_prod_t
  
  								# 3. compute predicted original sample from predicted noise also called
  								# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  								if self.config.prediction_type == "epsilon":
  									_UpperCAmelCase =						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  									_UpperCAmelCase =						model_output
  								elif self.config.prediction_type == "sample":
  									_UpperCAmelCase =						model_output
  									_UpperCAmelCase =						(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
  								elif self.config.prediction_type == "v_prediction":
  									_UpperCAmelCase =						(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
  									_UpperCAmelCase =						(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
  								else:
  									raise ValueError(
  									    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
  									    " `v_prediction`"   )
  
  								# 4. compute variance: "sigma_t(η)" -> see formula (16)
  								# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
  								_UpperCAmelCase =						self._get_variance(lowerCamelCase__ ,						lowerCamelCase__ ,						lowerCamelCase__   )
  								_UpperCAmelCase =						eta * variance ** 0.5
  
  								# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  								_UpperCAmelCase =						(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon
  
  								# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  								_UpperCAmelCase =						alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
  
  								if not return_dict:
  									return (prev_sample, state)
  
  								return FlaxDDIMSchedulerOutput(prev_sample=lowerCamelCase__ ,						state=lowerCamelCase__   )
  
  
  
  							def       _a   (      self ,						a_ ,						a_ ,						a_ ,						a_ ,						)							->							Dict:
  								return add_noise_common(state.common ,						lowerCamelCase__ ,						lowerCamelCase__ ,						lowerCamelCase__   )
  
  
  
  							def       _a   (      self ,						a_ ,						a_ ,						a_ ,						a_ ,						)							->							int:
  								return get_velocity_common(state.common ,						lowerCamelCase__ ,						lowerCamelCase__ ,						lowerCamelCase__   )
  
  
  
  							def __len__(      self   )							->							int:
  								return self.config.num_train_timesteps
  
  
  
  
  
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
  
  
  
  
  