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

import requests
def       A	(    _lowercase = "isbn/0140328726"						):
						SCREAMING_SNAKE_CASE							:    int							=							olid.strip().strip('''/'''						)  # Remove leading/trailing whitespace & slashes
						if new_olid.count('''/'''						) != 1:
												SCREAMING_SNAKE_CASE							:    Tuple							=							f"""{olid} is not a valid Open Library olid"""
												raise ValueError(_lowercase						)
						return requests.get(f"""https://openlibrary.org/{new_olid}.json"""						).json()
def       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    Tuple							=							{
						    '''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)''',
						}
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							{better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
						SCREAMING_SNAKE_CASE							:    Any							=							[
						    get_openlibrary_data(author['''key''']						)['''name'''] for author in data['''Authors''']
						]
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							data['''First sentence''']['''value''']
						for key, value in data.items():
												if isinstance(_lowercase		,  _lowercase						):
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							''', '''.join(_lowercase						)
						return data


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					while True:
										__UpperCamelCase		:       Optional[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:
															__UpperCamelCase		:       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}.""")






from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


__UpperCamelCase		:       Optional[Any]									=     logging.get_logger(__name__)





class 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		["""input_features""", """attention_mask"""]


						def __init__(       self	:				Any    ,		UpperCamelCase__	:				List[str]=80    ,		UpperCamelCase__	:				Tuple=1_6000    ,		UpperCamelCase__	:				Dict=0.0    ,		UpperCamelCase__	:				Tuple=10    ,		UpperCamelCase__	:				int=25    ,		UpperCamelCase__	:				Optional[Any]="hamming_window"    ,		UpperCamelCase__	:				Tuple=3_2768.0    ,		UpperCamelCase__	:				str=0.97    ,		UpperCamelCase__	:				List[str]=1.0    ,		UpperCamelCase__	:				Optional[int]=True    ,		UpperCamelCase__	:				Dict=True    ,		UpperCamelCase__	:				List[str]=False    ,		**UpperCamelCase__	:				List[str]    ,		):





												'''simple docstring'''





												super().__init__(feature_size=UpperCamelCase__    ,		sampling_rate=UpperCamelCase__    ,		padding_value=UpperCamelCase__    ,		**UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Dict							=							feature_size
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							sampling_rate
												SCREAMING_SNAKE_CASE							:    int							=							padding_value
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							hop_length
												SCREAMING_SNAKE_CASE							:    Tuple							=							win_length
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							frame_signal_scale
												SCREAMING_SNAKE_CASE							:    int							=							preemphasis_coeff
												SCREAMING_SNAKE_CASE							:    List[Any]							=							mel_floor
												SCREAMING_SNAKE_CASE							:    int							=							normalize_means
												SCREAMING_SNAKE_CASE							:    List[str]							=							normalize_vars
												SCREAMING_SNAKE_CASE							:    Any							=							win_function
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							return_attention_mask

												SCREAMING_SNAKE_CASE							:    int							=							win_length * sampling_rate // 1000
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							hop_length * sampling_rate // 1000

												SCREAMING_SNAKE_CASE							:    int							=							optimal_fft_length(self.sample_size						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							(self.n_fft // 2) + 1


						def        __A       (       self	:				str    ,		UpperCamelCase__	:				np.array						):





												'''simple docstring'''





												if self.win_function == "hamming_window":
																		SCREAMING_SNAKE_CASE							:    List[str]							=							window_function(window_length=self.sample_size    ,		name=self.win_function    ,		periodic=UpperCamelCase__						)
												else:
																		SCREAMING_SNAKE_CASE							:    Tuple							=							window_function(window_length=self.sample_size    ,		name=self.win_function						)

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							mel_filter_bank(
												    num_frequency_bins=self.n_freqs    ,		num_mel_filters=self.feature_size    ,		min_frequency=0.0    ,		max_frequency=self.sampling_rate / 2.0    ,		sampling_rate=self.sampling_rate    ,		)

												SCREAMING_SNAKE_CASE							:    Tuple							=							spectrogram(
												    one_waveform * self.frame_signal_scale    ,		window=UpperCamelCase__    ,		frame_length=self.sample_size    ,		hop_length=self.sample_stride    ,		fft_length=self.n_fft    ,		center=UpperCamelCase__    ,		preemphasis=self.preemphasis_coeff    ,		mel_filters=UpperCamelCase__    ,		mel_floor=self.mel_floor    ,		log_mel='''log'''    ,		)
												return msfc_features.T


						def        __A       (       self	:				int    ,		UpperCamelCase__	:				Optional[Any]    ,		UpperCamelCase__	:				List[Any]    ,		UpperCamelCase__	:				Optional[int]						):





												'''simple docstring'''





												if self.normalize_means:
																		SCREAMING_SNAKE_CASE							:    str							=							x[:input_length].mean(axis=0						)
																		SCREAMING_SNAKE_CASE							:    List[str]							=							np.subtract(UpperCamelCase__    ,		UpperCamelCase__						)
												if self.normalize_vars:
																		SCREAMING_SNAKE_CASE							:    str							=							x[:input_length].std(axis=0						)
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							np.divide(UpperCamelCase__    ,		UpperCamelCase__						)

												if input_length < x.shape[0]:
																		SCREAMING_SNAKE_CASE							:    List[str]							=							padding_value

												# make sure array is in float32
												SCREAMING_SNAKE_CASE							:    str							=							x.astype(np.floataa						)

												return x


						def        __A       (       self	:				Union[str, Any]    ,		UpperCamelCase__	:				List[np.ndarray]    ,		UpperCamelCase__	:				Optional[np.ndarray] = None						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							attention_mask.sum(-1						) if attention_mask is not None else [x.shape[0] for x in input_features]
												return [self._normalize_one(UpperCamelCase__    ,		UpperCamelCase__    ,		self.padding_value						) for x, n in zip(UpperCamelCase__    ,		UpperCamelCase__						)]



						def __call__(       self	:				Dict    ,		UpperCamelCase__	:				Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]    ,		UpperCamelCase__	:				Union[bool, str, PaddingStrategy] = False    ,		UpperCamelCase__	:				Optional[int] = None    ,		UpperCamelCase__	:				bool = False    ,		UpperCamelCase__	:				Optional[int] = None    ,		UpperCamelCase__	:				Optional[bool] = None    ,		UpperCamelCase__	:				Optional[Union[str, TensorType]] = None    ,		UpperCamelCase__	:				Optional[int] = None    ,		**UpperCamelCase__	:				Optional[int]    ,		):





												'''simple docstring'''





												if sampling_rate is not None:
																		if sampling_rate != self.sampling_rate:
																								raise ValueError(
																								    f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
																								    f""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
																								    f""" {self.sampling_rate} and not {sampling_rate}."""						)
												else:
																		logger.warning(
																		    '''It is strongly recommended to pass the ``sampling_rate`` argument to this function. '''
																		    '''Failing to do so can result in silent errors that might be hard to debug.'''						)

												SCREAMING_SNAKE_CASE							:    Any							=							isinstance(UpperCamelCase__    ,		np.ndarray						) and len(raw_speech.shape						) > 1
												if is_batched_numpy and len(raw_speech.shape						) > 2:
																		raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""						)
												SCREAMING_SNAKE_CASE							:    str							=							is_batched_numpy or (
												    isinstance(UpperCamelCase__    ,		(list, tuple)						) and (isinstance(raw_speech[0]    ,		(np.ndarray, tuple, list)						))
												)

												if is_batched:
																		SCREAMING_SNAKE_CASE							:    int							=							[np.asarray(UpperCamelCase__    ,		dtype=np.floataa						) for speech in raw_speech]
												elif not is_batched and not isinstance(UpperCamelCase__    ,		np.ndarray						):
																		SCREAMING_SNAKE_CASE							:    Dict							=							np.asarray(UpperCamelCase__    ,		dtype=np.floataa						)
												elif isinstance(UpperCamelCase__    ,		np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							raw_speech.astype(np.floataa						)

												# always return batch
												if not is_batched:
																		SCREAMING_SNAKE_CASE							:    List[str]							=							[raw_speech]

												# extract fbank features
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							[self._extract_mfsc_features(UpperCamelCase__						) for one_waveform in raw_speech]

												# convert into correct format for padding
												SCREAMING_SNAKE_CASE							:    Tuple							=							BatchFeature({'''input_features''': features}						)

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.pad(
												    UpperCamelCase__    ,		padding=UpperCamelCase__    ,		max_length=UpperCamelCase__    ,		truncation=UpperCamelCase__    ,		pad_to_multiple_of=UpperCamelCase__    ,		return_attention_mask=UpperCamelCase__    ,		**UpperCamelCase__    ,		)
												# make sure list is in array format
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							padded_inputs.get('''input_features'''						)
												if isinstance(input_features[0]    ,		UpperCamelCase__						):
																		SCREAMING_SNAKE_CASE							:    Any							=							[np.asarray(UpperCamelCase__    ,		dtype=np.floataa						) for feature in input_features]

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							padded_inputs.get('''attention_mask'''						)
												if attention_mask is not None:
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							[np.asarray(UpperCamelCase__    ,		dtype=np.intaa						) for array in attention_mask]

												if self.normalize_means or self.normalize_vars:
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							(
																		    np.array(UpperCamelCase__    ,		dtype=np.intaa						)
																		    if self._get_padding_strategies(UpperCamelCase__    ,		max_length=UpperCamelCase__						) is not PaddingStrategy.DO_NOT_PAD
																		    and padding
																		    else None
																		)
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							self.normalize(
																		    padded_inputs['''input_features''']    ,		attention_mask=UpperCamelCase__						)

												if return_tensors is not None:
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							padded_inputs.convert_to_tensors(UpperCamelCase__						)

												return padded_inputs





