






import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class        lowercase__       (     _UpperCAmelCase		):
		a_             =(DDIMScheduler,)
		a_             =(('eta', 0.0), ('num_inference_steps', 50))

		def 						UpperCAmelCase					(							self						,      **__UpperCAmelCase    )->   int:







								'''simple docstring'''





								lowerCAmelCase__	 =  {
								    "num_train_timesteps": 1000,
								    "beta_start": 0.0_001,
								    "beta_end": 0.02,
								    "beta_schedule": "linear",
								    "clip_sample": True,
								}

								config.update(**__UpperCAmelCase    )
								return config

		def 						UpperCAmelCase					(							self						,      **__UpperCAmelCase    )->   int:







								'''simple docstring'''





								lowerCAmelCase__	 =  self.scheduler_classes[0]
								lowerCAmelCase__	 =  self.get_scheduler_config(**__UpperCAmelCase    )
								lowerCAmelCase__	 =  scheduler_class(**__UpperCAmelCase    )

								lowerCAmelCase__	 =  10, 0.0

								lowerCAmelCase__	 =  self.dummy_model()
								lowerCAmelCase__	 =  self.dummy_sample_deter

								scheduler.set_timesteps(__UpperCAmelCase    )

								for t in scheduler.timesteps:
														lowerCAmelCase__	 =  model(__UpperCAmelCase						,      __UpperCAmelCase    )
														lowerCAmelCase__	 =  scheduler.step(__UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    ).prev_sample

								return sample

		def 						UpperCAmelCase					(							self    )->   Optional[int]:







								'''simple docstring'''





								for timesteps in [100, 500, 1000]:
														self.check_over_configs(num_train_timesteps=__UpperCAmelCase    )

		def 						UpperCAmelCase					(							self    )->   Dict:







								'''simple docstring'''





								for steps_offset in [0, 1]:
														self.check_over_configs(steps_offset=__UpperCAmelCase    )

								lowerCAmelCase__	 =  self.scheduler_classes[0]
								lowerCAmelCase__	 =  self.get_scheduler_config(steps_offset=1    )
								lowerCAmelCase__	 =  scheduler_class(**__UpperCAmelCase    )
								scheduler.set_timesteps(5    )
								assert torch.equal(scheduler.timesteps						,      torch.LongTensor([801, 601, 401, 201, 1]    )    )

		def 						UpperCAmelCase					(							self    )->   str:







								'''simple docstring'''





								for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]						,      [0.002, 0.02, 0.2, 2]    ):
														self.check_over_configs(beta_start=__UpperCAmelCase						,      beta_end=__UpperCAmelCase    )

		def 						UpperCAmelCase					(							self    )->   int:







								'''simple docstring'''





								for schedule in ["linear", "squaredcos_cap_v2"]:
														self.check_over_configs(beta_schedule=__UpperCAmelCase    )

		def 						UpperCAmelCase					(							self    )->   int:







								'''simple docstring'''





								for prediction_type in ["epsilon", "v_prediction"]:
														self.check_over_configs(prediction_type=__UpperCAmelCase    )

		def 						UpperCAmelCase					(							self    )->   Optional[Any]:







								'''simple docstring'''





								for clip_sample in [True, False]:
														self.check_over_configs(clip_sample=__UpperCAmelCase    )

		def 						UpperCAmelCase					(							self    )->   Union[str, Any]:







								'''simple docstring'''





								for timestep_spacing in ["trailing", "leading"]:
														self.check_over_configs(timestep_spacing=__UpperCAmelCase    )

		def 						UpperCAmelCase					(							self    )->   Optional[int]:







								'''simple docstring'''





								for rescale_betas_zero_snr in [True, False]:
														self.check_over_configs(rescale_betas_zero_snr=__UpperCAmelCase    )

		def 						UpperCAmelCase					(							self    )->   Any:







								'''simple docstring'''





								self.check_over_configs(thresholding=__UpperCAmelCase    )
								for threshold in [0.5, 1.0, 2.0]:
														for prediction_type in ["epsilon", "v_prediction"]:
																				self.check_over_configs(
																				    thresholding=__UpperCAmelCase						,      prediction_type=__UpperCAmelCase						,      sample_max_value=__UpperCAmelCase						,      )

		def 						UpperCAmelCase					(							self    )->   int:







								'''simple docstring'''





								for t in [1, 10, 49]:
														self.check_over_forward(time_step=__UpperCAmelCase    )

		def 						UpperCAmelCase					(							self    )->   Any:







								'''simple docstring'''





								for t, num_inference_steps in zip([1, 10, 50]						,      [10, 50, 500]    ):
														self.check_over_forward(time_step=__UpperCAmelCase						,      num_inference_steps=__UpperCAmelCase    )

		def 						UpperCAmelCase					(							self    )->   List[Any]:







								'''simple docstring'''





								for t, eta in zip([1, 10, 49]						,      [0.0, 0.5, 1.0]    ):
														self.check_over_forward(time_step=__UpperCAmelCase						,      eta=__UpperCAmelCase    )

		def 						UpperCAmelCase					(							self    )->   int:







								'''simple docstring'''





								lowerCAmelCase__	 =  self.scheduler_classes[0]
								lowerCAmelCase__	 =  self.get_scheduler_config()
								lowerCAmelCase__	 =  scheduler_class(**__UpperCAmelCase    )

								assert torch.sum(torch.abs(scheduler._get_variance(0						,      0    ) - 0.0    )    ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(420						,      400    ) - 0.14_771    )    ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(980						,      960    ) - 0.32_460    )    ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(0						,      0    ) - 0.0    )    ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(487						,      486    ) - 0.00_979    )    ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(999						,      998    ) - 0.02    )    ) < 1E-5

		def 						UpperCAmelCase					(							self    )->   str:







								'''simple docstring'''





								lowerCAmelCase__	 =  self.full_loop()

								lowerCAmelCase__	 =  torch.sum(torch.abs(__UpperCAmelCase    )    )
								lowerCAmelCase__	 =  torch.mean(torch.abs(__UpperCAmelCase    )    )

								assert abs(result_sum.item() - 172.0_067    ) < 1E-2
								assert abs(result_mean.item() - 0.223_967    ) < 1E-3

		def 						UpperCAmelCase					(							self    )->   Tuple:







								'''simple docstring'''





								lowerCAmelCase__	 =  self.full_loop(prediction_type="v_prediction"    )

								lowerCAmelCase__	 =  torch.sum(torch.abs(__UpperCAmelCase    )    )
								lowerCAmelCase__	 =  torch.mean(torch.abs(__UpperCAmelCase    )    )

								assert abs(result_sum.item() - 52.5_302    ) < 1E-2
								assert abs(result_mean.item() - 0.0_684    ) < 1E-3

		def 						UpperCAmelCase					(							self    )->   Union[str, Any]:







								'''simple docstring'''





								lowerCAmelCase__	 =  self.full_loop(set_alpha_to_one=__UpperCAmelCase						,      beta_start=0.01    )
								lowerCAmelCase__	 =  torch.sum(torch.abs(__UpperCAmelCase    )    )
								lowerCAmelCase__	 =  torch.mean(torch.abs(__UpperCAmelCase    )    )

								assert abs(result_sum.item() - 149.8_295    ) < 1E-2
								assert abs(result_mean.item() - 0.1_951    ) < 1E-3


		def 						UpperCAmelCase					(							self    )->   Optional[int]:







								'''simple docstring'''





								lowerCAmelCase__	 =  self.full_loop(set_alpha_to_one=__UpperCAmelCase						,      beta_start=0.01    )
								lowerCAmelCase__	 =  torch.sum(torch.abs(__UpperCAmelCase    )    )
								lowerCAmelCase__	 =  torch.mean(torch.abs(__UpperCAmelCase    )    )

								assert abs(result_sum.item() - 149.0_784    ) < 1E-2
								assert abs(result_mean.item() - 0.1_941    ) < 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

a_							=      logging.get_logger(__name__)


def     _a					(       UpperCamelCase_   :			bytes      ,				UpperCamelCase_   :			int				)	->				np.array:
						"""simple docstring"""







						lowerCAmelCase__	 =  F"{sampling_rate}"
						lowerCAmelCase__	 =  "1"
						lowerCAmelCase__	 =  "f32le"
						lowerCAmelCase__	 =  [
						    "ffmpeg",
						    "-i",
						    "pipe:0",
						    "-ac",
						    ac,
						    "-ar",
						    ar,
						    "-f",
						    format_for_conversion,
						    "-hide_banner",
						    "-loglevel",
						    "quiet",
						    "pipe:1",
						]

						try:
												lowerCAmelCase__	 =  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"				)
						lowerCAmelCase__	 =  ffmpeg_process.communicate(UpperCamelCase_				)
						lowerCAmelCase__	 =  output_stream[0]

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





@add_end_docstrings(_UpperCAmelCase		)
class        lowercase__       (     _UpperCAmelCase		):

	def __init__(							self						,      *__UpperCAmelCase						,      **__UpperCAmelCase    )->   Any:







							'''simple docstring'''





							lowerCAmelCase__	 =  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						,      )->   List[str]:







							'''simple docstring'''





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

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase=None						,      **__UpperCAmelCase    )->   Dict:







							'''simple docstring'''





							lowerCAmelCase__	 =  {}
							if top_k is not None:
													if top_k > self.model.config.num_labels:
																			lowerCAmelCase__	 =  self.model.config.num_labels
													lowerCAmelCase__	 =  top_k
							return {}, {}, postprocess_params

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   str:







							'''simple docstring'''





							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__	 =  requests.get(__UpperCAmelCase    ).content
													else:
																			with open(__UpperCAmelCase						,      "rb"    ) as f:
																									lowerCAmelCase__	 =  f.read()

							if isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ):
													lowerCAmelCase__	 =  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__	 =  inputs.pop("raw"						,      __UpperCAmelCase    )
													if _inputs is None:
																			# Remove path which will not be used from `datasets`.
																			inputs.pop("path"						,      __UpperCAmelCase    )
																			lowerCAmelCase__	 =  inputs.pop("array"						,      __UpperCAmelCase    )
													lowerCAmelCase__	 =  inputs.pop("sampling_rate"    )
													lowerCAmelCase__	 =  _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__	 =  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__	 =  self.feature_extractor(
							    __UpperCAmelCase						,      sampling_rate=self.feature_extractor.sampling_rate						,      return_tensors="pt"    )
							return processed

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   Tuple:







							'''simple docstring'''





							lowerCAmelCase__	 =  self.model(**__UpperCAmelCase    )
							return model_outputs


	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase=5    )->   int:







							'''simple docstring'''





							lowerCAmelCase__	 =  model_outputs.logits[0].softmax(-1    )
							lowerCAmelCase__ ,							lowerCAmelCase__	 =  probs.topk(__UpperCAmelCase    )

							lowerCAmelCase__	 =  scores.tolist()
							lowerCAmelCase__	 =  ids.tolist()

							lowerCAmelCase__	 =  [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(__UpperCAmelCase						,      __UpperCAmelCase    )]

							return labels






