




from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


UpperCamelCase__						=		logging.get_logger(__name__)


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):
					_lowerCamelCase						    =					["""input_values""", """attention_mask"""]

					def __init__(							self					,			__lowercase=1					,			__lowercase=16_000					,			__lowercase=0.0					,			__lowercase=False					,			__lowercase=True					,			**__lowercase					,			):







						'''simple docstring'''




						super().__init__(feature_size=__lowercase					,			sampling_rate=__lowercase					,			padding_value=__lowercase					,			**__lowercase   )
						lowerCamelCase_				:						Union[str, Any]				  =				return_attention_mask
						lowerCamelCase_				:						List[Any]				  =				do_normalize

					@staticmethod
					def       lowercase_				(							__lowercase					,			__lowercase					,			__lowercase = 0.0   ):







						'''simple docstring'''




						if attention_mask is not None:
							lowerCamelCase_				:						Union[str, Any]				  =				np.array(__lowercase					,			np.intaa   )
							lowerCamelCase_				:						str				  =				[]

							for vector, length in zip(__lowercase					,			attention_mask.sum(-1   )   ):
								lowerCamelCase_				:						Tuple				  =				(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7   )
								if length < normed_slice.shape[0]:
									lowerCamelCase_				:						List[Any]				  =				padding_value

								normed_input_values.append(__lowercase   )
						else:
							lowerCamelCase_				:						Any				  =				[(x - x.mean()) / np.sqrt(x.var() + 1E-7   ) for x in input_values]

						return normed_input_values





					def __call__(							self					,			__lowercase					,			__lowercase = False					,			__lowercase = None					,			__lowercase = False					,			__lowercase = None					,			__lowercase = None					,			__lowercase = None					,			__lowercase = None					,			**__lowercase					,			):







						'''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.'   )

						lowerCamelCase_				:						int				  =				isinstance(__lowercase					,			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}"   )
						lowerCamelCase_				:						List[str]				  =				is_batched_numpy or (
						    isinstance(__lowercase					,			(list, tuple)   ) and (isinstance(raw_speech[0]					,			(np.ndarray, tuple, list)   ))
						)

						# always return batch
						if not is_batched:
							lowerCamelCase_				:						List[Any]				  =				[raw_speech]

						# convert into correct format for padding
						lowerCamelCase_				:						str				  =				BatchFeature({'input_values': raw_speech}   )

						lowerCamelCase_				:						str				  =				self.pad(
						    __lowercase					,			padding=__lowercase					,			max_length=__lowercase					,			truncation=__lowercase					,			pad_to_multiple_of=__lowercase					,			return_attention_mask=__lowercase					,			)

						# convert input values to correct format
						lowerCamelCase_				:						Tuple				  =				padded_inputs['input_values']
						if not isinstance(input_values[0]					,			np.ndarray   ):
							lowerCamelCase_				:						List[str]				  =				[np.asarray(__lowercase					,			dtype=np.floataa   ) for array in input_values]
						elif (
						    not isinstance(__lowercase					,			np.ndarray   )
						    and isinstance(input_values[0]					,			np.ndarray   )
						    and input_values[0].dtype is np.dtype(np.floataa   )
						):
							lowerCamelCase_				:						int				  =				[array.astype(np.floataa   ) for array in input_values]
						elif isinstance(__lowercase					,			np.ndarray   ) and input_values.dtype is np.dtype(np.floataa   ):
							lowerCamelCase_				:						List[str]				  =				input_values.astype(np.floataa   )

						# convert attention_mask to correct format
						lowerCamelCase_				:						Any				  =				padded_inputs.get('attention_mask'   )
						if attention_mask is not None:
							lowerCamelCase_				:						Optional[int]				  =				[np.asarray(__lowercase					,			dtype=np.intaa   ) for array in attention_mask]

						# zero-mean and unit-variance normalization
						if self.do_normalize:
							lowerCamelCase_				:						Tuple				  =				(
							    attention_mask
							    if self._get_padding_strategies(__lowercase					,			max_length=__lowercase   ) is not PaddingStrategy.DO_NOT_PAD
							    else None
							)
							lowerCamelCase_				:						Any				  =				self.zero_mean_unit_var_norm(
							    padded_inputs['input_values']					,			attention_mask=__lowercase					,			padding_value=self.padding_value   )

						if return_tensors is not None:
							lowerCamelCase_				:						Any				  =				padded_inputs.convert_to_tensors(__lowercase   )

						return padded_inputs







"""simple docstring"""




import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

UpperCamelCase_	 ={"""UserAgent""": UserAgent().random}
UpperCamelCase_	 ="""https://www.mywaifulist.moe/random"""





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Optional[Any]      =       requests.get(_lowercase			,		headers=_lowercase  )
						with open(_lowercase			,		'''wb'''  ) as file:
												file.write(image.content  )





def 			a_							(				):
						_UpperCamelCase     :   Union[str, Any]      =       BeautifulSoup(requests.get(_lowercase			,		headers=_lowercase  ).text			,		'''html.parser'''  )
						_UpperCamelCase     :   List[str]      =       soup.find('''meta'''			,		attrs={'''property''': '''og:title'''}  ).attrs['''content''']
						_UpperCamelCase     :   Optional[int]      =       soup.find('''meta'''			,		attrs={'''property''': '''og:image'''}  ).attrs['''content''']
						_UpperCamelCase     :   Any      =       soup.find('''p'''			,		id='''description'''  ).get_text()
						_UpperCamelCase  ,				_UpperCamelCase     :   Optional[int]      =       os.path.splitext(os.path.basename(_lowercase  )  )
						_UpperCamelCase     :   Dict      =       title.strip().replace(''' '''			,		'''_'''  )
						_UpperCamelCase     :   str      =       F"""{image_title}{image_extension}"""
						save_image(_lowercase			,		_lowercase  )
						return (title, description, image_title)


if __name__ == "__main__":
						UpperCamelCase_ ,      UpperCamelCase_ ,      UpperCamelCase_	 =random_anime_character()
						print(F"{title}\n\n{desc}\n\nImage saved : {image_title}")



