




import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 	UpperCamelCase__			(lowerCAmelCase__  ):

    '''simple docstring'''



    lowerCamelCase_   :			Tuple			  =   (KDPMaAncestralDiscreteScheduler,)
    lowerCamelCase_   :			str			  =   1_0







    def 					_lowercase  (					self  ,						**UpperCamelCase__  )  ->   List[str]:
           lowerCamelCase     :   Optional[Any]    				=      {
               "num_train_timesteps": 1100,
               "beta_start": 0.0001,
               "beta_end": 0.02,
               "beta_schedule": "linear",
           }

           config.update(**UpperCamelCase__  )
           return config







    def 					_lowercase  (					self  )  ->   int:
           for timesteps in [10, 50, 100, 1000]:
                  self.check_over_configs(num_train_timesteps=UpperCamelCase__  )







    def 					_lowercase  (					self  )  ->   Dict:
           for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]  ,						[0.0002, 0.002, 0.02]  ):
                  self.check_over_configs(beta_start=UpperCamelCase__  ,						beta_end=UpperCamelCase__  )







    def 					_lowercase  (					self  )  ->   Optional[Any]:
           for schedule in ["linear", "scaled_linear"]:
                  self.check_over_configs(beta_schedule=UpperCamelCase__  )







    def 					_lowercase  (					self  )  ->   str:
           if torch_device == "mps":
                  return
           lowerCamelCase     :   Dict    				=      self.scheduler_classes[0]
           lowerCamelCase     :   List[str]    				=      self.get_scheduler_config()
           lowerCamelCase     :   Optional[Any]    				=      scheduler_class(**UpperCamelCase__  )

           scheduler.set_timesteps(self.num_inference_steps  )

           lowerCamelCase     :   Optional[Any]    				=      torch.manual_seed(0  )

           lowerCamelCase     :   Any    				=      self.dummy_model()
           lowerCamelCase     :   List[str]    				=      self.dummy_sample_deter * scheduler.init_noise_sigma
           lowerCamelCase     :   List[Any]    				=      sample.to(UpperCamelCase__  )

           for i, t in enumerate(scheduler.timesteps  ):
                  lowerCamelCase     :   List[str]    				=      scheduler.scale_model_input(UpperCamelCase__  ,						UpperCamelCase__  )

                  lowerCamelCase     :   Tuple    				=      model(UpperCamelCase__  ,						UpperCamelCase__  )

                  lowerCamelCase     :   Optional[Any]    				=      scheduler.step(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						generator=UpperCamelCase__  )
                  lowerCamelCase     :   List[Any]    				=      output.prev_sample

           lowerCamelCase     :   List[str]    				=      torch.sum(torch.abs(UpperCamelCase__  )  )
           lowerCamelCase     :   Tuple    				=      torch.mean(torch.abs(UpperCamelCase__  )  )

           assert abs(result_sum.item() - 13849.3877  ) < 1e-2
           assert abs(result_mean.item() - 18.0331  ) < 5e-3







    def 					_lowercase  (					self  )  ->   Union[str, Any]:
           for prediction_type in ["epsilon", "v_prediction"]:
                  self.check_over_configs(prediction_type=UpperCamelCase__  )







    def 					_lowercase  (					self  )  ->   Tuple:
           if torch_device == "mps":
                  return
           lowerCamelCase     :   Dict    				=      self.scheduler_classes[0]
           lowerCamelCase     :   List[str]    				=      self.get_scheduler_config(prediction_type="v_prediction"  )
           lowerCamelCase     :   int    				=      scheduler_class(**UpperCamelCase__  )

           scheduler.set_timesteps(self.num_inference_steps  )

           lowerCamelCase     :   Union[str, Any]    				=      self.dummy_model()
           lowerCamelCase     :   Optional[Any]    				=      self.dummy_sample_deter * scheduler.init_noise_sigma
           lowerCamelCase     :   Dict    				=      sample.to(UpperCamelCase__  )

           lowerCamelCase     :   Dict    				=      torch.manual_seed(0  )

           for i, t in enumerate(scheduler.timesteps  ):
                  lowerCamelCase     :   Union[str, Any]    				=      scheduler.scale_model_input(UpperCamelCase__  ,						UpperCamelCase__  )

                  lowerCamelCase     :   Optional[int]    				=      model(UpperCamelCase__  ,						UpperCamelCase__  )

                  lowerCamelCase     :   List[Any]    				=      scheduler.step(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						generator=UpperCamelCase__  )
                  lowerCamelCase     :   Optional[Any]    				=      output.prev_sample

           lowerCamelCase     :   List[Any]    				=      torch.sum(torch.abs(UpperCamelCase__  )  )
           lowerCamelCase     :   Optional[int]    				=      torch.mean(torch.abs(UpperCamelCase__  )  )

           assert abs(result_sum.item() - 328.9970  ) < 1e-2
           assert abs(result_mean.item() - 0.4284  ) < 1e-3







    def 					_lowercase  (					self  )  ->   Optional[int]:
           if torch_device == "mps":
                  return
           lowerCamelCase     :   Optional[Any]    				=      self.scheduler_classes[0]
           lowerCamelCase     :   List[str]    				=      self.get_scheduler_config()
           lowerCamelCase     :   List[Any]    				=      scheduler_class(**UpperCamelCase__  )

           scheduler.set_timesteps(self.num_inference_steps  ,						device=UpperCamelCase__  )
           lowerCamelCase     :   Optional[int]    				=      torch.manual_seed(0  )

           lowerCamelCase     :   Dict    				=      self.dummy_model()
           lowerCamelCase     :   Tuple    				=      self.dummy_sample_deter.to(UpperCamelCase__  ) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
                  lowerCamelCase     :   Any    				=      scheduler.scale_model_input(UpperCamelCase__  ,						UpperCamelCase__  )

                  lowerCamelCase     :   str    				=      model(UpperCamelCase__  ,						UpperCamelCase__  )

                  lowerCamelCase     :   Tuple    				=      scheduler.step(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						generator=UpperCamelCase__  )
                  lowerCamelCase     :   Any    				=      output.prev_sample

           lowerCamelCase     :   Any    				=      torch.sum(torch.abs(UpperCamelCase__  )  )
           lowerCamelCase     :   List[Any]    				=      torch.mean(torch.abs(UpperCamelCase__  )  )

           assert abs(result_sum.item() - 13849.3818  ) < 1e-1
           assert abs(result_mean.item() - 18.0331  ) < 1e-3












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

SCREAMING_SNAKE_CASE__ :   Union[str, Any]						=     logging.get_logger(__name__)

def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)			->				np.array:
       lowerCamelCase     :   Optional[Any]    				=      f'''{sampling_rate}'''
       lowerCamelCase     :   Optional[Any]    				=      "1"
       lowerCamelCase     :   Tuple    				=      "f32le"
       lowerCamelCase     :   Any    				=      [
           "ffmpeg",
           "-i",
           "pipe:0",
           "-ac",
           ac,
           "-ar",
           ar,
           "-f",
           format_for_conversion,
           "-hide_banner",
           "-loglevel",
           "quiet",
           "pipe:1",
       ]

       try:
              lowerCamelCase     :   Optional[int]    				=      subprocess.Popen(_SCREAMING_SNAKE_CASE				,stdin=subprocess.PIPE				,stdout=subprocess.PIPE					)
       except FileNotFoundError:
              raise ValueError("ffmpeg was not found but is required to load audio files from filename"					)
       lowerCamelCase     :   Optional[int]    				=      ffmpeg_process.communicate(_SCREAMING_SNAKE_CASE					)
       lowerCamelCase     :   Union[str, Any]    				=      output_stream[0]

       lowerCamelCase     :   Dict    				=      np.frombuffer(_SCREAMING_SNAKE_CASE				,np.floataa					)
       if audio.shape[0] == 0:
              raise ValueError("Malformed soundfile"					)
       return audio




@add_end_docstrings(lowerCAmelCase__  )
class 	UpperCamelCase__			(lowerCAmelCase__  ):

    '''simple docstring'''



    def __init__(					self  ,						*UpperCamelCase__  ,						**UpperCamelCase__  )  ->   Dict:
           # Default, might be overriden by the model.config.
           lowerCamelCase     :   Union[str, Any]    				=      5
           super().__init__(*UpperCamelCase__  ,						**UpperCamelCase__  )

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

           self.check_model_type(UpperCamelCase__  )







    def __call__(					self  ,						UpperCamelCase__  ,						**UpperCamelCase__  ,						)  ->   Optional[int]:
           return super().__call__(UpperCamelCase__  ,						**UpperCamelCase__  )







    def 					_lowercase  (					self  ,						UpperCamelCase__=None  ,						**UpperCamelCase__  )  ->   str:
           # No parameters on this pipeline right now
           lowerCamelCase     :   int    				=      {}
           if top_k is not None:
                  if top_k > self.model.config.num_labels:
                         lowerCamelCase     :   Any    				=      self.model.config.num_labels
                  lowerCamelCase     :   Any    				=      top_k
           return {}, {}, postprocess_params







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   Dict:
           if isinstance(UpperCamelCase__  ,						UpperCamelCase__  ):
                  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
                         lowerCamelCase     :   str    				=      requests.get(UpperCamelCase__  ).content
                  else:
                         with open(UpperCamelCase__  ,						"rb"  ) as f:
                                lowerCamelCase     :   int    				=      f.read()

           if isinstance(UpperCamelCase__  ,						UpperCamelCase__  ):
                  lowerCamelCase     :   str    				=      ffmpeg_read(UpperCamelCase__  ,						self.feature_extractor.sampling_rate  )

           if isinstance(UpperCamelCase__  ,						UpperCamelCase__  ):
                  # 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"  )

                  lowerCamelCase     :   Optional[int]    				=      inputs.pop("raw"  ,						UpperCamelCase__  )
                  if _inputs is None:
                         # Remove path which will not be used from `datasets`.
                         inputs.pop("path"  ,						UpperCamelCase__  )
                         lowerCamelCase     :   List[str]    				=      inputs.pop("array"  ,						UpperCamelCase__  )
                  lowerCamelCase     :   str    				=      inputs.pop("sampling_rate"  )
                  lowerCamelCase     :   Union[str, Any]    				=      _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`."  )

                         lowerCamelCase     :   Tuple    				=      F.resample(
                             torch.from_numpy(UpperCamelCase__  )  ,						UpperCamelCase__  ,						self.feature_extractor.sampling_rate  ).numpy()

           if not isinstance(UpperCamelCase__  ,						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"  )

           lowerCamelCase     :   Optional[int]    				=      self.feature_extractor(
               UpperCamelCase__  ,						sampling_rate=self.feature_extractor.sampling_rate  ,						return_tensors="pt"  )
           return processed







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   str:
           lowerCamelCase     :   Optional[Any]    				=      self.model(**UpperCamelCase__  )
           return model_outputs







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__=5  )  ->   Tuple:
           lowerCamelCase     :   Tuple    				=      model_outputs.logits[0].softmax(-1  )
           lowerCamelCase				,   lowerCamelCase     :   str    				=      probs.topk(UpperCamelCase__  )

           lowerCamelCase     :   str    				=      scores.tolist()
           lowerCamelCase     :   Optional[int]    				=      ids.tolist()

           lowerCamelCase     :   Optional[int]    				=      [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(UpperCamelCase__  ,						UpperCamelCase__  )]

           return labels






