


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

__UpperCamelCase							:							List[str]    							=					logging.get_logger(__name__)

def       _a	(					SCREAMING_SNAKE_CASE						:					Union[str, Any]     ,			SCREAMING_SNAKE_CASE						:					List[str]  ):







 """simple docstring"""



 UpperCamelCase__       :     Dict 							= F"{sampling_rate}"
 UpperCamelCase__       :     List[str] 							= '''1'''
 UpperCamelCase__       :     Dict 							= '''f32le'''
 UpperCamelCase__       :     Any 							= [
     '''ffmpeg''',
     '''-i''',
     '''pipe:0''',
     '''-ac''',
     ac,
     '''-ar''',
     ar,
     '''-f''',
     format_for_conversion,
     '''-hide_banner''',
     '''-loglevel''',
     '''quiet''',
     '''pipe:1''',
 ]

 try:
  UpperCamelCase__       :     Dict 							= subprocess.Popen(lowercase_     ,			stdin=subprocess.PIPE     ,			stdout=subprocess.PIPE  )
 except FileNotFoundError:
  raise ValueError('''ffmpeg was not found but is required to load audio files from filename'''  )
 UpperCamelCase__       :     Tuple 							= ffmpeg_process.communicate(lowercase_  )
 UpperCamelCase__       :     Optional[int] 							= output_stream[0]

 UpperCamelCase__       :     Dict 							= np.frombuffer(lowercase_     ,			np.floataa  )
 if audio.shape[0] == 0:
  raise ValueError('''Malformed soundfile'''  )
 return audio






@add_end_docstrings(_SCREAMING_SNAKE_CASE)
class    __magic_name__						(  _SCREAMING_SNAKE_CASE):


     def __init__(   self				:  int				,						*lowerCamelCase__				:  Optional[Any]				,						**lowerCamelCase__				:  Union[str, Any]      )	->   Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__       :     Optional[int] 							= 5
      super().__init__(*lowerCamelCase__				,						**lowerCamelCase__      )

      if self.framework != "pt":
       raise ValueError(F"The {self.__class__} is only available in PyTorch."      )

      self.check_model_type(lowerCamelCase__      )


     def __call__(   self				:  Dict				,						lowerCamelCase__				:  Union[np.ndarray, bytes, str]				,						**lowerCamelCase__				:  int				,						)	->   Optional[int]:





      '''simple docstring'''






      return super().__call__(lowerCamelCase__				,						**lowerCamelCase__      )


     def   UpperCAmelCase__						(   self				:  str				,						lowerCamelCase__				:  str=None				,						**lowerCamelCase__				:  List[Any]      )	->   List[str]:





      '''simple docstring'''






      UpperCamelCase__       :     Union[str, Any] 							= {}
      if top_k is not None:
       if top_k > self.model.config.num_labels:
        UpperCamelCase__       :     List[Any] 							= self.model.config.num_labels
       UpperCamelCase__       :     int 							= top_k
      return {}, {}, postprocess_params


     def   UpperCAmelCase__						(   self				:  List[str]				,						lowerCamelCase__				:  Union[str, Any]      )	->   Optional[int]:





      '''simple docstring'''






      if isinstance(lowerCamelCase__				,						lowerCamelCase__      ):
       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__       :     int 							= requests.get(lowerCamelCase__      ).content
       else:
        with open(lowerCamelCase__				,						'''rb'''      ) as f:
         UpperCamelCase__       :     Optional[int] 							= f.read()

      if isinstance(lowerCamelCase__				,						lowerCamelCase__      ):
       UpperCamelCase__       :     List[str] 							= ffmpeg_read(lowerCamelCase__				,						self.feature_extractor.sampling_rate      )

      if isinstance(lowerCamelCase__				,						lowerCamelCase__      ):
       # 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__       :     Optional[int] 							= inputs.pop('''raw'''				,						lowerCamelCase__      )
       if _inputs is None:
        # Remove path which will not be used from `datasets`.
        inputs.pop('''path'''				,						lowerCamelCase__      )
        UpperCamelCase__       :     Tuple 							= inputs.pop('''array'''				,						lowerCamelCase__      )
       UpperCamelCase__       :     Tuple 							= inputs.pop('''sampling_rate'''      )
       UpperCamelCase__       :     str 							= _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__       :     Optional[Any] 							= F.resample(
            torch.from_numpy(lowerCamelCase__      )				,						lowerCamelCase__				,						self.feature_extractor.sampling_rate      ).numpy()

      if not isinstance(lowerCamelCase__				,						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__       :     Any 							= self.feature_extractor(
          lowerCamelCase__				,						sampling_rate=self.feature_extractor.sampling_rate				,						return_tensors='''pt'''      )
      return processed


     def   UpperCAmelCase__						(   self				:  int				,						lowerCamelCase__				:  int      )	->   List[str]:





      '''simple docstring'''






      UpperCamelCase__       :     Tuple 							= self.model(**lowerCamelCase__      )
      return model_outputs


     def   UpperCAmelCase__						(   self				:  Optional[int]				,						lowerCamelCase__				:  Tuple				,						lowerCamelCase__				:  Optional[Any]=5      )	->   List[str]:





      '''simple docstring'''






      UpperCamelCase__       :     Tuple 							= model_outputs.logits[0].softmax(-1      )
      UpperCamelCase__							,					UpperCamelCase__       :     Any 							= probs.topk(lowerCamelCase__      )

      UpperCamelCase__       :     Optional[int] 							= scores.tolist()
      UpperCamelCase__       :     List[Any] 							= ids.tolist()

      UpperCamelCase__       :     Optional[Any] 							= [{'''score''': score, '''label''': self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase__				,						lowerCamelCase__      )]

      return labels








from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)

@flax.struct.dataclass
class 			_lowerCamelCase    :

      """simple docstring"""

      lowerCAmelCase__      :			CommonSchedulerState

      # setable values
      lowerCAmelCase__      :			jnp.ndarray
      lowerCAmelCase__      :			jnp.ndarray
      lowerCAmelCase__      :			jnp.ndarray
      lowerCAmelCase__      :			Optional[int]     =       None

      # running values
      lowerCAmelCase__      :			Optional[jnp.ndarray]     =       None







      @classmethod
      def    snake_case						(  cls       :			str			,							snake_case       :			CommonSchedulerState			,							snake_case       :			jnp.ndarray			,							snake_case       :			jnp.ndarray			,							snake_case       :			jnp.ndarray					):
          return cls(common=snake_case			,							init_noise_sigma=snake_case			,							timesteps=snake_case			,							sigmas=snake_case					)







@dataclass
class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			LMSDiscreteSchedulerState







class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE				,      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			List[Any]     =       [e.name for e in FlaxKarrasDiffusionSchedulers]

      lowerCAmelCase__      :			jnp.dtype







      @property
      def    snake_case						(  self       :			Any					):
          return True







      @register_to_config
      def __init__(  self       :			Union[str, Any]			,							snake_case       :			int = 1000			,							snake_case       :			float = 0.00_01			,							snake_case       :			float = 0.02			,							snake_case       :			str = "linear"			,							snake_case       :			Optional[jnp.ndarray] = None			,							snake_case       :			str = "epsilon"			,							snake_case       :			jnp.dtype = jnp.floataa			,							):
          __UpperCamelCase			=    dtype







      def    snake_case						(  self       :			int			,							snake_case       :			Optional[CommonSchedulerState] = None					):
          if common is None:
              __UpperCamelCase			=    CommonSchedulerState.create(self					)

          __UpperCamelCase			=    jnp.arange(0			,							self.config.num_train_timesteps					).round()[::-1]
          __UpperCamelCase			=    ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

          # standard deviation of the initial noise distribution
          __UpperCamelCase			=    sigmas.max()

          return LMSDiscreteSchedulerState.create(
              common=snake_case			,							init_noise_sigma=snake_case			,							timesteps=snake_case			,							sigmas=snake_case			,							)







      def    snake_case						(  self       :			str			,							snake_case       :			LMSDiscreteSchedulerState			,							snake_case       :			jnp.ndarray			,							snake_case       :			int					):
          ((__UpperCamelCase)		,   )			=    jnp.where(state.timesteps == timestep			,							size=1					)
          __UpperCamelCase			=    step_index[0]

          __UpperCamelCase			=    state.sigmas[step_index]
          __UpperCamelCase			=    sample / ((sigma**2 + 1) ** 0.5)
          return sample







      def    snake_case						(  self       :			Optional[Any]			,							snake_case       :			LMSDiscreteSchedulerState			,							snake_case       :			str			,							snake_case       :			Tuple			,							snake_case       :			int					):

          def lms_derivative(snake_case       :			int					):
              __UpperCamelCase			=    1.0
              for k in range(snake_case					):
                  if current_order == k:
                      continue
                  prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
              return prod

          __UpperCamelCase			=    integrate.quad(snake_case			,							state.sigmas[t]			,							state.sigmas[t + 1]			,							epsrel=1E-4					)[0]

          return integrated_coeff







      def    snake_case						(  self       :			int			,							snake_case       :			LMSDiscreteSchedulerState			,							snake_case       :			int			,							snake_case       :			Tuple = ()					):
          __UpperCamelCase			=    jnp.linspace(self.config.num_train_timesteps - 1			,							0			,							snake_case			,							dtype=self.dtype					)

          __UpperCamelCase			=    jnp.floor(snake_case					).astype(jnp.intaa					)
          __UpperCamelCase			=    jnp.ceil(snake_case					).astype(jnp.intaa					)

          __UpperCamelCase			=    jnp.mod(snake_case			,							1.0					)

          __UpperCamelCase			=    ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
          __UpperCamelCase			=    (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
          __UpperCamelCase			=    jnp.concatenate([sigmas, jnp.array([0.0]			,							dtype=self.dtype					)]					)

          __UpperCamelCase			=    timesteps.astype(jnp.intaa					)

          # initial running values
          __UpperCamelCase			=    jnp.zeros((0,) + shape			,							dtype=self.dtype					)

          return state.replace(
              timesteps=snake_case			,							sigmas=snake_case			,							num_inference_steps=snake_case			,							derivatives=snake_case			,							)







      def    snake_case						(  self       :			Tuple			,							snake_case       :			LMSDiscreteSchedulerState			,							snake_case       :			jnp.ndarray			,							snake_case       :			int			,							snake_case       :			jnp.ndarray			,							snake_case       :			int = 4			,							snake_case       :			bool = True			,							):
          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'''					)

          __UpperCamelCase			=    state.sigmas[timestep]

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
              __UpperCamelCase			=    sample - sigma * model_output
          elif self.config.prediction_type == "v_prediction":
              # * c_out + input * c_skip
              __UpperCamelCase			=    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
          else:
              raise ValueError(
                  F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"					)

          # 2. Convert to an ODE derivative
          __UpperCamelCase			=    (sample - pred_original_sample) / sigma
          __UpperCamelCase			=    state.replace(derivatives=jnp.append(state.derivatives			,							snake_case					)					)
          if len(state.derivatives					) > order:
              __UpperCamelCase			=    state.replace(derivatives=jnp.delete(state.derivatives			,							0					)					)

          # 3. Compute linear multistep coefficients
          __UpperCamelCase			=    min(timestep + 1			,							snake_case					)
          __UpperCamelCase			=    [self.get_lms_coefficient(snake_case			,							snake_case			,							snake_case			,							snake_case					) for curr_order in range(snake_case					)]

          # 4. Compute previous sample based on the derivatives path
          __UpperCamelCase			=    sample + sum(
              coeff * derivative for coeff, derivative in zip(snake_case			,							reversed(state.derivatives					)					)					)

          if not return_dict:
              return (prev_sample, state)

          return FlaxLMSSchedulerOutput(prev_sample=snake_case			,							state=snake_case					)







      def    snake_case						(  self       :			Dict			,							snake_case       :			LMSDiscreteSchedulerState			,							snake_case       :			jnp.ndarray			,							snake_case       :			jnp.ndarray			,							snake_case       :			jnp.ndarray			,							):
          __UpperCamelCase			=    state.sigmas[timesteps].flatten()
          __UpperCamelCase			=    broadcast_to_shape_from_left(snake_case			,							noise.shape					)

          __UpperCamelCase			=    original_samples + noise * sigma

          return noisy_samples







      def __len__(  self       :			Optional[Any]					):
          return self.config.num_train_timesteps






