
'''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

_snake_case					: str     			=					logging.get_logger(__name__)



def       _a		(						_SCREAMING_SNAKE_CASE		: bytes						,      _SCREAMING_SNAKE_CASE		: int	):
						_SCREAMING_SNAKE_CASE					=			F'{sampling_rate}'
						_SCREAMING_SNAKE_CASE					=			"1"
						_SCREAMING_SNAKE_CASE					=			"f32le"
						_SCREAMING_SNAKE_CASE					=			[
						    "ffmpeg",
						    "-i",
						    "pipe:0",
						    "-ac",
						    ac,
						    "-ar",
						    ar,
						    "-f",
						    format_for_conversion,
						    "-hide_banner",
						    "-loglevel",
						    "quiet",
						    "pipe:1",
						]

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

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




@add_end_docstrings(__UpperCAmelCase      )
class       lowerCAmelCase			(					__UpperCAmelCase      ):




		def __init__(					self       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								# Default, might be overriden by the model.config.
								_SCREAMING_SNAKE_CASE					=			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       ,    ):
								return super().__call__(UpperCamelCase       ,    **UpperCamelCase   )




		def      lowercase							(					self       ,    UpperCamelCase=None       ,    **UpperCamelCase   ):
								# No parameters on this pipeline right now
								_SCREAMING_SNAKE_CASE					=			{}
								if top_k is not None:
														if top_k > self.model.config.num_labels:
																				_SCREAMING_SNAKE_CASE					=			self.model.config.num_labels
														_SCREAMING_SNAKE_CASE					=			top_k
								return {}, {}, postprocess_params




		def      lowercase							(					self       ,    UpperCamelCase   ):
								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
																				_SCREAMING_SNAKE_CASE					=			requests.get(UpperCamelCase   ).content
														else:
																				with open(UpperCamelCase       ,    "rb"   ) as f:
																										_SCREAMING_SNAKE_CASE					=			f.read()

								if isinstance(UpperCamelCase       ,    UpperCamelCase   ):
														_SCREAMING_SNAKE_CASE					=			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"   )

														_SCREAMING_SNAKE_CASE					=			inputs.pop("raw"       ,    UpperCamelCase   )
														if _inputs is None:
																				# Remove path which will not be used from `datasets`.
																				inputs.pop("path"       ,    UpperCamelCase   )
																				_SCREAMING_SNAKE_CASE					=			inputs.pop("array"       ,    UpperCamelCase   )
														_SCREAMING_SNAKE_CASE					=			inputs.pop("sampling_rate"   )
														_SCREAMING_SNAKE_CASE					=			_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`."   )

																				_SCREAMING_SNAKE_CASE					=			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"   )

								_SCREAMING_SNAKE_CASE					=			self.feature_extractor(
								    UpperCamelCase       ,    sampling_rate=self.feature_extractor.sampling_rate       ,    return_tensors="pt"   )
								return processed




		def      lowercase							(					self       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			self.model(**UpperCamelCase   )
								return model_outputs




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase=5   ):
								_SCREAMING_SNAKE_CASE					=			model_outputs.logits[0].softmax(-1   )
								_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			probs.topk(UpperCamelCase   )

								_SCREAMING_SNAKE_CASE					=			scores.tolist()
								_SCREAMING_SNAKE_CASE					=			ids.tolist()

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

								return labels

'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class       lowerCAmelCase			(					__UpperCAmelCase      ):




		def __init__(					self       ,    UpperCamelCase       ,    UpperCamelCase   ):
								super().__init__()
								self.register_modules(unet=UpperCamelCase       ,    scheduler=UpperCamelCase   )




		@torch.no_grad()
		def __call__(					self       ,    UpperCamelCase = 1       ,    UpperCamelCase = None       ,    UpperCamelCase = 1_000       ,    UpperCamelCase = "pil"       ,    UpperCamelCase = True       ,    ):
								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size       ,    UpperCamelCase   ):
														_SCREAMING_SNAKE_CASE					=			(
														    batch_size,
														    self.unet.config.in_channels,
														    self.unet.config.sample_size,
														    self.unet.config.sample_size,
														)
								else:
														_SCREAMING_SNAKE_CASE					=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								if self.device.type == "mps":
														# randn does not work reproducibly on mps
														_SCREAMING_SNAKE_CASE					=			randn_tensor(UpperCamelCase       ,    generator=UpperCamelCase   )
														_SCREAMING_SNAKE_CASE					=			image.to(self.device   )
								else:
														_SCREAMING_SNAKE_CASE					=			randn_tensor(UpperCamelCase       ,    generator=UpperCamelCase       ,    device=self.device   )

								# set step values
								self.scheduler.set_timesteps(UpperCamelCase   )

								for t in self.progress_bar(self.scheduler.timesteps   ):
														# 1. predict noise model_output
														_SCREAMING_SNAKE_CASE					=			self.unet(UpperCamelCase       ,    UpperCamelCase   ).sample

														# 2. compute previous image: x_t -> x_t-1
														_SCREAMING_SNAKE_CASE					=			self.scheduler.step(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    generator=UpperCamelCase   ).prev_sample

								_SCREAMING_SNAKE_CASE					=			(image / 2 + 0.5).clamp(0       ,    1   )
								_SCREAMING_SNAKE_CASE					=			image.cpu().permute(0       ,    2       ,    3       ,    1   ).numpy()
								if output_type == "pil":
														_SCREAMING_SNAKE_CASE					=			self.numpy_to_pil(UpperCamelCase   )

								if not return_dict:
														return (image,)

								return ImagePipelineOutput(images=UpperCamelCase   )