






'''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     						= logging.get_logger(__name__)




def 							_SCREAMING_SNAKE_CASE	(					A	:							bytes	,		A	:							int	)     ->			np.array:







   """simple docstring"""
   __snake_case					:   List[str]  				=			F"""{sampling_rate}"""
   __snake_case					:   Optional[int]  				=			'1'
   __snake_case					:   List[Any]  				=			'f32le'
   __snake_case					:   List[str]  				=			[
       'ffmpeg',
       '-i',
       'pipe:0',
       '-ac',
       ac,
       '-ar',
       ar,
       '-f',
       format_for_conversion,
       '-hide_banner',
       '-loglevel',
       'quiet',
       'pipe:1',
   ]

   try:
      __snake_case					:   List[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'	)
   __snake_case					:   Dict  				=			ffmpeg_process.communicate(A	)
   __snake_case					:   List[str]  				=			output_stream[0]

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





@add_end_docstrings(UpperCamelCase_				)
class a_     (     UpperCamelCase_				):







   def __init__(self       , *__a       , **__a)      ->   List[Any]:
      """simple docstring"""






      __snake_case					:   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       , )      ->   Dict:
      """simple docstring"""






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







   def      SCREAMING_SNAKE_CASE__       (self       , __a=None       , **__a)      ->   Any:
      """simple docstring"""






      __snake_case					:   List[Any]  				=			{}
      if top_k is not None:
         if top_k > self.model.config.num_labels:
            __snake_case					:   Tuple  				=			self.model.config.num_labels
         __snake_case					:   List[str]  				=			top_k
      return {}, {}, postprocess_params







   def      SCREAMING_SNAKE_CASE__       (self       , __a)      ->   Optional[int]:
      """simple docstring"""






      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
            __snake_case					:   str  				=			requests.get(__a).content
         else:
            with open(__a       , 'rb') as f:
               __snake_case					:   List[str]  				=			f.read()

      if isinstance(__a       , __a):
         __snake_case					:   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')

         __snake_case					:   Optional[Any]  				=			inputs.pop('raw'       , __a)
         if _inputs is None:
            # Remove path which will not be used from `datasets`.
            inputs.pop('path'       , __a)
            __snake_case					:   Tuple  				=			inputs.pop('array'       , __a)
         __snake_case					:   List[Any]  				=			inputs.pop('sampling_rate')
         __snake_case					:   Any  				=			_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`.')

            __snake_case					:   Any  				=			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')

      __snake_case					:   Any  				=			self.feature_extractor(
          __a       , sampling_rate=self.feature_extractor.sampling_rate       , return_tensors='pt')
      return processed







   def      SCREAMING_SNAKE_CASE__       (self       , __a)      ->   Union[str, Any]:
      """simple docstring"""






      __snake_case					:   Dict  				=			self.model(**__a)
      return model_outputs







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a=5)      ->   Optional[Any]:
      """simple docstring"""






      __snake_case					:   int  				=			model_outputs.logits[0].softmax(-1)
      __snake_case				,__snake_case					:   Tuple  				=			probs.topk(__a)

      __snake_case					:   str  				=			scores.tolist()
      __snake_case					:   Dict  				=			ids.tolist()

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

      return labels







'''simple docstring'''






import bsa
import requests




def 							_SCREAMING_SNAKE_CASE	(					A	:							bsa.element.ResultSet	)     ->			dict[str, str]:







   """simple docstring"""
   return {
       "name": soup.ha.a.text,
       "genre": soup.find('span'	,		class_='genre'	).text.strip(),
       "rating": soup.strong.text,
       "page_link": F"""https://www.imdb.com{soup.a.get("href"	)}""",
   }




def 							_SCREAMING_SNAKE_CASE	(					A	:							int = 5	)     ->			tuple:







   """simple docstring"""
   __snake_case					:   Optional[int]  				=			int(float(A	)	)
   if num_movies < 1:
      return ()
   __snake_case					:   Union[str, Any]  				=			(
       'https://www.imdb.com/search/title?title_type='
       F"""feature&sort=num_votes,desc&count={num_movies}"""
   )
   __snake_case					:   List[Any]  				=			bsa.BeautifulSoup(requests.get(A	).content	,		'html.parser'	)
   return tuple(
       get_movie_data_from_soup(A	)
       for movie in source.find_all('div'	,		class_='lister-item mode-advanced'	)	)


if __name__ == "__main__":
      import json

      __A     						= int(input('''How many movies would you like to see? '''))
      print(
          ''', '''.join(
              json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
          )
      )