
"""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






"""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




