



from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


__snake_case						:List[Any]      		=     logging.get_logger(__name__)

@add_end_docstrings(__UpperCAmelCase    )
class     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     Any     ,		**__SCREAMING_SNAKE_CASE							:     Union[str, Any]):


           '''simple docstring'''

           super().__init__(**__SCREAMING_SNAKE_CASE)

           if self.framework != "pt":
               raise ValueError(F'The {self.__class__} is only available in PyTorch.')
           # No specific FOR_XXX available yet



       def __call__(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Union[np.ndarray, bytes, str]     ,		**__SCREAMING_SNAKE_CASE							:     int):


           '''simple docstring'''

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



       def 		_lowerCamelCase			(		self							:     Tuple     ,		**__SCREAMING_SNAKE_CASE							:     Any):


           '''simple docstring'''

           __a						       =				{}
           if "candidate_labels" in kwargs:
               __a						       =				kwargs['''candidate_labels''']
           if "hypothesis_template" in kwargs:
               __a						       =				kwargs['''hypothesis_template''']

           return preprocess_params, {}, {}



       def 		_lowerCamelCase			(		self							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     List[str]=None     ,		__SCREAMING_SNAKE_CASE							:     Tuple="This is a sound of {}."):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE):
               if audio.startswith('''http://''') or audio.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(__SCREAMING_SNAKE_CASE).content
               else:
                   with open(__SCREAMING_SNAKE_CASE     ,		'''rb''') as f:
                       __a						       =				f.read()

           if isinstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE):
               __a						       =				ffmpeg_read(__SCREAMING_SNAKE_CASE     ,		self.feature_extractor.sampling_rate)

           if not isinstance(__SCREAMING_SNAKE_CASE     ,		np.ndarray):
               raise ValueError('''We expect a numpy ndarray as input''')
           if len(audio.shape) != 1:
               raise ValueError('''We expect a single channel audio input for ZeroShotAudioClassificationPipeline''')

           __a						       =				self.feature_extractor(
               [audio]     ,		sampling_rate=self.feature_extractor.sampling_rate     ,		return_tensors='''pt''')
           __a						       =				candidate_labels
           __a						       =				[hypothesis_template.format(__SCREAMING_SNAKE_CASE) for x in candidate_labels]
           __a						       =				self.tokenizer(__SCREAMING_SNAKE_CASE     ,		return_tensors=self.framework     ,		padding=__SCREAMING_SNAKE_CASE)
           __a						       =				[text_inputs]
           return inputs



       def 		_lowerCamelCase			(		self							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]):


           '''simple docstring'''

           __a						       =				model_inputs.pop('''candidate_labels''')
           __a						       =				model_inputs.pop('''text_inputs''')
           if isinstance(text_inputs[0]     ,		__SCREAMING_SNAKE_CASE):
               __a						       =				text_inputs[0]
           else:
               # Batching case.
               __a						       =				text_inputs[0][0]

           __a						       =				self.model(**__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)

           __a						       =				{
               '''candidate_labels''': candidate_labels,
               '''logits''': outputs.logits_per_audio,
           }
           return model_outputs





       def 		_lowerCamelCase			(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]):


           '''simple docstring'''

           __a						       =				model_outputs.pop('''candidate_labels''')
           __a						       =				model_outputs['''logits'''][0]

           if self.framework == "pt":
               __a						       =				logits.softmax(dim=0)
               __a						       =				probs.tolist()
           else:
               raise ValueError('''`tf` framework not supported.''')

           __a						       =				[
               {'''score''': score, '''label''': candidate_label}
               for score, candidate_label in sorted(zip(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)     ,		key=lambda __SCREAMING_SNAKE_CASE: -x[0])
           ]
           return result










from json import JSONDecodeError  # Workaround for requests.exceptions.JSONDecodeError

import requests





def      __snake_case	(			_UpperCAmelCase = "isbn/0140328726"				):
    __a						       =				olid.strip().strip('''/'''				)  # Remove leading/trailing whitespace & slashes
    if new_olid.count('''/'''				) != 1:
        __a						       =				f'{olid} is not a valid Open Library olid'
        raise ValueError(_UpperCAmelCase				)
    return requests.get(f'https://openlibrary.org/{new_olid}.json'				).json()





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				{
        '''title''': '''Title''',
        '''publish_date''': '''Publish date''',
        '''authors''': '''Authors''',
        '''number_of_pages''': '''Number of pages:''',
        '''first_sentence''': '''First sentence''',
        '''isbn_10''': '''ISBN (10)''',
        '''isbn_13''': '''ISBN (13)''',
    }
    __a						       =				{better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
    __a						       =				[
        get_openlibrary_data(author['''key''']				)['''name'''] for author in data['''Authors''']
    ]
    __a						       =				data['''First sentence''']['''value''']
    for key, value in data.items():
        if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
            __a						       =				''', '''.join(_UpperCAmelCase				)
    return data


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 while True:
  __snake_case						:List[Any]      		=     input('''\nEnter the ISBN code to search (or \'quit\' to stop): ''').strip()
  if isbn.lower() in ("", "q", "quit", "exit", "stop"):
   break

  if len(isbn) not in (10, 13) or not isbn.isdigit():
   print(f'Sorry, {isbn} is not a valid ISBN.  Please, input a valid ISBN.')
   continue

  print(f'\nSearching Open Library for ISBN: {isbn}...\n')

  try:
   __snake_case						:Optional[Any]      		=     summarize_book(get_openlibrary_data(f'isbn/{isbn}'))
   print('''\n'''.join(f'{key}: {value}' for key, value in book_summary.items()))
  except JSONDecodeError:  # Workaround for requests.exceptions.RequestException:
   print(f'Sorry, there are no results for ISBN: {isbn}.')





