
"""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

__UpperCAmelCase 					=    logging.get_logger(__name__)






def 		_snake_case			(      lowercase__						:    Optional[Any] ,	lowercase__						:    Dict						)    ->							Union[str, Any]:


			'''simple docstring'''





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

			try:
						lowerCAmelCase_       :Optional[int]     	=    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"""						)
			lowerCAmelCase_       :List[str]     	=    ffmpeg_process.communicate(__lowerCamelCase						)
			lowerCAmelCase_       :Dict     	=    output_stream[0]

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




@add_end_docstrings(__SCREAMING_SNAKE_CASE				)
class      _SCREAMING_SNAKE_CASE   (     __SCREAMING_SNAKE_CASE				):






			def __init__(   self			,  *__A			,  **__A			)     ->	str:
						# Default, might be overriden by the model.config.
						lowerCAmelCase_       :Optional[int]     	=    5
						super().__init__(*_A			,  **_A			)

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

						self.check_model_type(_A			)






			def __call__(   self			,  __A			,  **__A			,  )     ->	int:
						return super().__call__(_A			,  **_A			)






			def 					__lowerCAmelCase					(   self			,  __A=None			,  **__A			)     ->	Union[str, Any]:
						# No parameters on this pipeline right now
						lowerCAmelCase_       :Dict     	=    {}
						if top_k is not None:
									if top_k > self.model.config.num_labels:
												lowerCAmelCase_       :Tuple     	=    self.model.config.num_labels
									lowerCAmelCase_       :Optional[int]     	=    top_k
						return {}, {}, postprocess_params






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	Optional[Any]:
						if isinstance(_A			,  _A			):
									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_       :Optional[int]     	=    requests.get(_A			).content
									else:
												with open(_A			,  """rb"""			) as f:
															lowerCAmelCase_       :Dict     	=    f.read()

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

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

												lowerCAmelCase_       :str     	=    F.resample(
												    torch.from_numpy(_A			)			,  _A			,  self.feature_extractor.sampling_rate			).numpy()

						if not isinstance(_A			,  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(
						    _A			,  sampling_rate=self.feature_extractor.sampling_rate			,  return_tensors="""pt"""			)
						return processed






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	int:
						lowerCAmelCase_       :Optional[int]     	=    self.model(**_A			)
						return model_outputs






			def 					__lowerCAmelCase					(   self			,  __A			,  __A=5			)     ->	Tuple:
						lowerCAmelCase_       :Optional[Any]     	=    model_outputs.logits[0].softmax(-1			)
						lowerCAmelCase_	,			lowerCAmelCase_       :Any     	=    probs.topk(_A			)

						lowerCAmelCase_       :int     	=    scores.tolist()
						lowerCAmelCase_       :Union[str, Any]     	=    ids.tolist()

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

						return labels








import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig





class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       =CustomConfig



       def __init__(							self ,     _A	)				->				int:
          super().__init__(_A	)
          SCREAMING_SNAKE_CASE_	=      torch.nn.Linear(config.hidden_size ,     config.hidden_size	)



       def     _UpperCamelCase	(							self ,     _A	)				->				Optional[int]:
          return self.linear(_A	)




       def     _UpperCamelCase	(							self ,     _A	)				->				int:
          pass







class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       =NoSuperInitConfig



       def __init__(							self ,     _A	)				->				Optional[int]:
          super().__init__(_A	)
          SCREAMING_SNAKE_CASE_	=      torch.nn.Linear(config.attribute ,     config.attribute	)



       def     _UpperCamelCase	(							self ,     _A	)				->				Any:
          return self.linear(_A	)




       def     _UpperCamelCase	(							self ,     _A	)				->				Tuple:
          pass


