

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_      (__A     :						bytes					,   __A     :						int     )			->	np.array:
   __lowerCAmelCase  :			Dict					    =						f'''{sampling_rate}'''
   __lowerCAmelCase  :			Optional[int]					    =						"""1"""
   __lowerCAmelCase  :			Union[str, Any]					    =						"""f32le"""
   __lowerCAmelCase  :			Dict					    =						[
       """ffmpeg""",
       """-i""",
       """pipe:0""",
       """-ac""",
       ac,
       """-ar""",
       ar,
       """-f""",
       format_for_conversion,
       """-hide_banner""",
       """-loglevel""",
       """quiet""",
       """pipe:1""",
   ]

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

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



@add_end_docstrings(a_ )
class SCREAMING_SNAKE_CASE	(  a_ ):
  """simple docstring"""






  def __init__(			self :    Union[str, Any]       ,							*lowerCAmelCase :    str       ,							**lowerCAmelCase :    str    )							->	int:






     """simple docstring"""







     __lowerCAmelCase  :			int					    =						5
     super().__init__(*lowerCAmelCase       ,							**lowerCAmelCase    )

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

     self.check_model_type(lowerCAmelCase    )







  def __call__(			self :    Any       ,							lowerCAmelCase :    Union[np.ndarray, bytes, str]       ,							**lowerCAmelCase :    Dict       ,							)							->	Union[str, Any]:






     """simple docstring"""







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







  def        SCREAMING_SNAKE_CASE    (			self :    List[Any]       ,							lowerCAmelCase :    Union[str, Any]=None       ,							**lowerCAmelCase :    List[str]    )							->	List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :			Any					    =						{}
     if top_k is not None:
        if top_k > self.model.config.num_labels:
           __lowerCAmelCase  :			Dict					    =						self.model.config.num_labels
        __lowerCAmelCase  :			Optional[int]					    =						top_k
     return {}, {}, postprocess_params







  def        SCREAMING_SNAKE_CASE    (			self :    Any       ,							lowerCAmelCase :    int    )							->	str:






     """simple docstring"""







     if isinstance(lowerCAmelCase       ,							lowerCAmelCase    ):
        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[Any]					    =						requests.get(lowerCAmelCase    ).content
        else:
           with open(lowerCAmelCase       ,							"""rb"""    ) as f:
              __lowerCAmelCase  :			Optional[int]					    =						f.read()

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

     if isinstance(lowerCAmelCase       ,							lowerCAmelCase    ):
        # 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  :			int					    =						inputs.pop("""raw"""       ,							lowerCAmelCase    )
        if _inputs is None:
           # Remove path which will not be used from `datasets`.
           inputs.pop("""path"""       ,							lowerCAmelCase    )
           __lowerCAmelCase  :			List[str]					    =						inputs.pop("""array"""       ,							lowerCAmelCase    )
        __lowerCAmelCase  :			List[Any]					    =						inputs.pop("""sampling_rate"""    )
        __lowerCAmelCase  :			Optional[int]					    =						_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  :			Dict					    =						F.resample(
               torch.from_numpy(lowerCAmelCase    )       ,							lowerCAmelCase       ,							self.feature_extractor.sampling_rate    ).numpy()

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







  def        SCREAMING_SNAKE_CASE    (			self :    int       ,							lowerCAmelCase :    Dict    )							->	List[str]:






     """simple docstring"""







     __lowerCAmelCase  :			Optional[Any]					    =						self.model(**lowerCAmelCase    )
     return model_outputs







  def        SCREAMING_SNAKE_CASE    (			self :    List[str]       ,							lowerCAmelCase :    int       ,							lowerCAmelCase :    List[str]=5    )							->	Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :			Any					    =						model_outputs.logits[0].softmax(-1    )
     __lowerCAmelCase    ,__lowerCAmelCase  :			List[str]					    =						probs.topk(lowerCAmelCase    )

     __lowerCAmelCase  :			List[str]					    =						scores.tolist()
     __lowerCAmelCase  :			Dict					    =						ids.tolist()

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

     return labels









"""simple docstring"""


import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

A_     :      Any								=1_8_0





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Dict						,  snake_case   :					Any		)->		Tuple:
       _lowerCamelCase    	=					k_size // 2
       _lowerCamelCase	,	_lowerCamelCase    	=					np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       _lowerCamelCase    	=					(
           1
           / (2 * np.pi * sigma)
           * np.exp(-(np.square(snake_case		) + np.square(snake_case		)) / (2 * np.square(snake_case		))		)
       )
       return g





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Optional[int]						,  snake_case   :					int						,  snake_case   :					Dict		)->		Optional[Any]:
       _lowerCamelCase    	=					np.zeros(snake_case		)

       for row in range(1						,  image_shape[0] - 1		):
              for col in range(1						,  image_shape[1] - 1		):
                     _lowerCamelCase    	=					gradient_direction[row, col]

                     if (
                         0 <= direction < PI / 8
                         or 15 * PI / 8 <= direction <= 2 * PI
                         or 7 * PI / 8 <= direction <= 9 * PI / 8
                     ):
                            _lowerCamelCase    	=					sobel_grad[row, col - 1]
                            _lowerCamelCase    	=					sobel_grad[row, col + 1]
                            if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                                   _lowerCamelCase    	=					sobel_grad[row, col]

                     elif (
                         PI / 8 <= direction < 3 * PI / 8
                         or 9 * PI / 8 <= direction < 11 * PI / 8
                     ):
                            _lowerCamelCase    	=					sobel_grad[row + 1, col - 1]
                            _lowerCamelCase    	=					sobel_grad[row - 1, col + 1]
                            if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                                   _lowerCamelCase    	=					sobel_grad[row, col]

                     elif (
                         3 * PI / 8 <= direction < 5 * PI / 8
                         or 11 * PI / 8 <= direction < 13 * PI / 8
                     ):
                            _lowerCamelCase    	=					sobel_grad[row - 1, col]
                            _lowerCamelCase    	=					sobel_grad[row + 1, col]
                            if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                                   _lowerCamelCase    	=					sobel_grad[row, col]

                     elif (
                         5 * PI / 8 <= direction < 7 * PI / 8
                         or 13 * PI / 8 <= direction < 15 * PI / 8
                     ):
                            _lowerCamelCase    	=					sobel_grad[row - 1, col - 1]
                            _lowerCamelCase    	=					sobel_grad[row + 1, col + 1]
                            if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                                   _lowerCamelCase    	=					sobel_grad[row, col]

       return destination





def SCREAMING_SNAKE_CASE_   (       snake_case   :					List[Any]						,  snake_case   :					List[Any]						,  snake_case   :					int						,  snake_case   :					int						,  snake_case   :					List[str]						,  snake_case   :					str		)->		Tuple:
       for row in range(1						,  image_shape[0] - 1		):
              for col in range(1						,  image_shape[1] - 1		):
                     if destination[row, col] >= threshold_high:
                            _lowerCamelCase    	=					strong
                     elif destination[row, col] <= threshold_low:
                            _lowerCamelCase    	=					0
                     else:
                            _lowerCamelCase    	=					weak





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Union[str, Any]						,  snake_case   :					Optional[int]						,  snake_case   :					Dict						,  snake_case   :					Tuple		)->		Optional[int]:
       for row in range(1						,  image_shape[0]		):
              for col in range(1						,  image_shape[1]		):
                     if destination[row, col] == weak:
                            if 255 in (
                                destination[row, col + 1],
                                destination[row, col - 1],
                                destination[row - 1, col],
                                destination[row + 1, col],
                                destination[row - 1, col - 1],
                                destination[row + 1, col - 1],
                                destination[row - 1, col + 1],
                                destination[row + 1, col + 1],
                            ):
                                   _lowerCamelCase    	=					strong
                            else:
                                   _lowerCamelCase    	=					0





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Dict						,  snake_case   :					Union[str, Any]=15						,  snake_case   :					Optional[int]=30						,  snake_case   :					int=128						,  snake_case   :					str=255		)->		List[Any]:
       # gaussian_filter
       _lowerCamelCase    	=					img_convolve(snake_case						,  gen_gaussian_kernel(9						,  sigma=1.4		)		)
       # get the gradient and degree by sobel_filter
       _lowerCamelCase	,	_lowerCamelCase    	=					sobel_filter(snake_case		)
       _lowerCamelCase    	=					PI + np.radadeg(snake_case		)

       _lowerCamelCase    	=					suppress_non_maximum(image.shape						,  snake_case						,  snake_case		)

       detect_high_low_threshold(
           image.shape						,  snake_case						,  snake_case						,  snake_case						,  snake_case						,  snake_case		)

       track_edge(image.shape						,  snake_case						,  snake_case						,  snake_case		)

       return destination


if __name__ == "__main__":
      # read original image in gray mode
      A_     :      Optional[Any]								=cva.imread(R"""../image_data/lena.jpg""", 0)
      # canny edge detection
      A_     :      Optional[Any]								=canny(lena)
      cva.imshow("""canny""", canny_destination)
      cva.waitKey(0)






