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  							=						logging.get_logger(__name__)



class   snake_case_       (		__A       ):
   __A	:		Union[str, Any]         =				["input_features", "attention_mask"]






   def __init__(      self				:     Dict	,						lowercase_				:     Union[str, Any]=80	,						lowercase_				:     int=1_60_00	,						lowercase_				:     Dict=0.0	,						lowercase_				:     Optional[Any]=10	,						lowercase_				:     int=25	,						lowercase_				:     List[str]="hamming_window"	,						lowercase_				:     Optional[int]=3_27_68.0	,						lowercase_				:     List[Any]=0.97	,						lowercase_				:     Optional[Any]=1.0	,						lowercase_				:     List[str]=True	,						lowercase_				:     Optional[int]=True	,						lowercase_				:     str=False	,						**lowercase_				:     List[Any]	,						)					->						str:
       super().__init__(feature_size=lowercase_	,						sampling_rate=lowercase_	,						padding_value=lowercase_	,						**lowercase_  )

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

       lowercase__ :   Union[str, Any]    					=				win_length * sampling_rate // 10_00
       lowercase__ :   Optional[int]    					=				hop_length * sampling_rate // 10_00

       lowercase__ :   Tuple    					=				optimal_fft_length(self.sample_size  )
       lowercase__ :   str    					=				(self.n_fft // 2) + 1






   def       __UpperCamelCase     (      self				:     List[str]	,						lowercase_				:     np.array  )					->						np.ndarray:
       if self.win_function == "hamming_window":
           lowercase__ :   Union[str, Any]    					=				window_function(window_length=self.sample_size	,						name=self.win_function	,						periodic=lowercase_  )
       else:
           lowercase__ :   Optional[Any]    					=				window_function(window_length=self.sample_size	,						name=self.win_function  )

       lowercase__ :   Tuple    					=				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	,						)

       lowercase__ :   Any    					=				spectrogram(
           one_waveform * self.frame_signal_scale	,						window=lowercase_	,						frame_length=self.sample_size	,						hop_length=self.sample_stride	,						fft_length=self.n_fft	,						center=lowercase_	,						preemphasis=self.preemphasis_coeff	,						mel_filters=lowercase_	,						mel_floor=self.mel_floor	,						log_mel="log"	,						)
       return msfc_features.T






   def       __UpperCamelCase     (      self				:     Union[str, Any]	,						lowercase_				:     Union[str, Any]	,						lowercase_				:     Union[str, Any]	,						lowercase_				:     Union[str, Any]  )					->						int:
       # make sure we normalize float32 arrays
       if self.normalize_means:
           lowercase__ :   int    					=				x[:input_length].mean(axis=0  )
           lowercase__ :   str    					=				np.subtract(lowercase_	,						lowercase_  )
       if self.normalize_vars:
           lowercase__ :   int    					=				x[:input_length].std(axis=0  )
           lowercase__ :   Optional[int]    					=				np.divide(lowercase_	,						lowercase_  )

       if input_length < x.shape[0]:
           lowercase__ :   List[Any]    					=				padding_value

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

       return x






   def       __UpperCamelCase     (      self				:     Tuple	,						lowercase_				:     List[np.ndarray]	,						lowercase_				:     Optional[np.ndarray] = None  )					->						List[np.ndarray]:
       lowercase__ :   str    					=				attention_mask.sum(-1  ) if attention_mask is not None else [x.shape[0] for x in input_features]
       return [self._normalize_one(lowercase_	,						lowercase_	,						self.padding_value  ) for x, n in zip(lowercase_	,						lowercase_  )]







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

       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."  )

       lowercase__ :   Dict    					=				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}'''  )
       lowercase__ :   Optional[Any]    					=				is_batched_numpy or (
           isinstance(lowercase_	,						(list, tuple)  ) and (isinstance(raw_speech[0]	,						(np.ndarray, tuple, list)  ))
       )

       if is_batched:
           lowercase__ :   List[str]    					=				[np.asarray(lowercase_	,						dtype=np.floataa  ) for speech in raw_speech]
       elif not is_batched and not isinstance(lowercase_	,						np.ndarray  ):
           lowercase__ :   Tuple    					=				np.asarray(lowercase_	,						dtype=np.floataa  )
       elif isinstance(lowercase_	,						np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
           lowercase__ :   Optional[Any]    					=				raw_speech.astype(np.floataa  )

       # always return batch
       if not is_batched:
           lowercase__ :   Dict    					=				[raw_speech]

       # extract fbank features
       lowercase__ :   Union[str, Any]    					=				[self._extract_mfsc_features(lowercase_  ) for one_waveform in raw_speech]

       # convert into correct format for padding
       lowercase__ :   str    					=				BatchFeature({"input_features": features}  )

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

       lowercase__ :   Tuple    					=				padded_inputs.get("attention_mask"  )
       if attention_mask is not None:
           lowercase__ :   int    					=				[np.asarray(lowercase_	,						dtype=np.intaa  ) for array in attention_mask]

       if self.normalize_means or self.normalize_vars:
           lowercase__ :   Dict    					=				(
               np.array(lowercase_	,						dtype=np.intaa  )
               if self._get_padding_strategies(lowercase_	,						max_length=lowercase_  ) is not PaddingStrategy.DO_NOT_PAD
               and padding
               else None
           )
           lowercase__ :   str    					=				self.normalize(
               padded_inputs["input_features"]	,						attention_mask=lowercase_  )

       if return_tensors is not None:
           lowercase__ :   Tuple    					=				padded_inputs.convert_to_tensors(lowercase_  )

       return padded_inputs




from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
   __lowerCamelCase			: str							   =		input('''Enter image url: ''').strip()
   print(F"""Downloading image from {url} ...""")
   __lowerCamelCase			: Any							   =		BeautifulSoup(requests.get(url).content, '''html.parser''')
   # The image URL is in the content field of the first meta tag with property og:image
   __lowerCamelCase			: List[Any]							   =		soup.find('''meta''', {'''property''': '''og:image'''})['''content''']
   __lowerCamelCase			: Tuple							   =		requests.get(image_url).content
   __lowerCamelCase			: Union[str, Any]							   =		F"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
   with open(file_name, '''wb''') as fp:
      fp.write(image_data)
   print(F"""Done. Image saved to disk as {file_name}.""")
