


'''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

a_       :     Tuple 							=     logging.get_logger(__name__)






def 						_A					(lowerCAmelCase__	:bytes					,    lowerCAmelCase__	:int		) ->	np.array:




     '''simple docstring'''



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

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

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






@add_end_docstrings(_SCREAMING_SNAKE_CASE							)
class 			a  (  _SCREAMING_SNAKE_CASE							):
 def __init__(			self   ,					*__magic_name__   ,					**__magic_name__    )       ->				Dict:
      # Default, might be overriden by the model.config.
      _a         =				5
      super().__init__(*__magic_name__   ,					**__magic_name__    )

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

      self.check_model_type(__magic_name__    )
 def __call__(			self   ,					__magic_name__   ,					**__magic_name__   ,					)       ->				Any:
      return super().__call__(__magic_name__   ,					**__magic_name__    )
 def 				__UpperCAmelCase		(			self   ,					__magic_name__=None   ,					**__magic_name__    )       ->				List[str]:
      # No parameters on this pipeline right now
      _a         =				{}
      if top_k is not None:
           if top_k > self.model.config.num_labels:
                _a         =				self.model.config.num_labels
           _a         =				top_k
      return {}, {}, postprocess_params
 def 				__UpperCAmelCase		(			self   ,					__magic_name__    )       ->				Optional[Any]:
      if isinstance(__magic_name__   ,					__magic_name__    ):
           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
                _a         =				requests.get(__magic_name__    ).content
           else:
                with open(__magic_name__   ,					'rb'    ) as f:
                     _a         =				f.read()

      if isinstance(__magic_name__   ,					__magic_name__    ):
           _a         =				ffmpeg_read(__magic_name__   ,					self.feature_extractor.sampling_rate    )

      if isinstance(__magic_name__   ,					__magic_name__    ):
           # 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'    )

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

                _a         =				F.resample(
                    torch.from_numpy(__magic_name__    )   ,					__magic_name__   ,					self.feature_extractor.sampling_rate    ).numpy()

      if not isinstance(__magic_name__   ,					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'    )

      _a         =				self.feature_extractor(
          __magic_name__   ,					sampling_rate=self.feature_extractor.sampling_rate   ,					return_tensors='pt'    )
      return processed
 def 				__UpperCAmelCase		(			self   ,					__magic_name__    )       ->				str:
      _a         =				self.model(**__magic_name__    )
      return model_outputs




 def 				__UpperCAmelCase		(			self   ,					__magic_name__   ,					__magic_name__=5    )       ->				Optional[int]:
      _a         =				model_outputs.logits[0].softmax(-1    )
      _a   , _a         =				probs.topk(__magic_name__    )

      _a         =				scores.tolist()
      _a         =				ids.tolist()

      _a         =				[{'score': score, 'label': self.model.config.idalabel[_id]} for score, _id in zip(__magic_name__   ,					__magic_name__    )]

      return labels





'''simple docstring'''






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

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
 from .image_encoder import PaintByExampleImageEncoder
 from .pipeline_paint_by_example import PaintByExamplePipeline

