


'''simple docstring'''
import warnings
from typing import Any, Dict, 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 ...utils import PaddingStrategy, TensorType, logging


__snake_case							   =  logging.get_logger(__name__)
class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       ['input_values', 'attention_mask']




		def __init__(	self					,     UpperCamelCase_ = 1					,     UpperCamelCase_ = 16000					,     UpperCamelCase_ = 0.0					,     UpperCamelCase_ = False					,     UpperCamelCase_ = 80					,     UpperCamelCase_ = 16					,     UpperCamelCase_ = 64					,     UpperCamelCase_ = "hann_window"					,     UpperCamelCase_ = 1.0					,     UpperCamelCase_ = 80					,     UpperCamelCase_ = 7600					,     UpperCamelCase_ = 1e-10					,     UpperCamelCase_ = 2					,     UpperCamelCase_ = True					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							super().__init__(feature_size=UpperCamelCase_					,     sampling_rate=UpperCamelCase_					,     padding_value=UpperCamelCase_					,     **UpperCamelCase_							)
							UpperCamelCase__     :Union[str, Any]							      =				do_normalize
							UpperCamelCase__     :Dict							      =				return_attention_mask

							UpperCamelCase__     :List[Any]							      =				num_mel_bins
							UpperCamelCase__     :List[str]							      =				hop_length
							UpperCamelCase__     :List[Any]							      =				win_length
							UpperCamelCase__     :str							      =				win_function
							UpperCamelCase__     :List[Any]							      =				frame_signal_scale
							UpperCamelCase__     :Optional[int]							      =				fmin
							UpperCamelCase__     :List[str]							      =				fmax
							UpperCamelCase__     :Optional[Any]							      =				mel_floor
							UpperCamelCase__     :Tuple							      =				reduction_factor

							UpperCamelCase__     :Optional[int]							      =				win_length * sampling_rate // 1000
							UpperCamelCase__     :Optional[int]							      =				hop_length * sampling_rate // 1000
							UpperCamelCase__     :Tuple							      =				optimal_fft_length(self.sample_size							)
							UpperCamelCase__     :Optional[Any]							      =				(self.n_fft // 2) + 1

							UpperCamelCase__     :Optional[int]							      =				window_function(window_length=self.sample_size					,     name=self.win_function					,     periodic=UpperCamelCase_							)

							UpperCamelCase__     :List[Any]							      =				mel_filter_bank(
							    num_frequency_bins=self.n_freqs					,     num_mel_filters=self.num_mel_bins					,     min_frequency=self.fmin					,     max_frequency=self.fmax					,     sampling_rate=self.sampling_rate					,     norm='''slaney'''					,     mel_scale='''slaney'''					,     )

							if frame_signal_scale != 1.0:
												warnings.warn(
												    '''The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers'''					,     UpperCamelCase_					,     )
							if reduction_factor != 2.0:
												warnings.warn(
												    '''The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers'''					,     UpperCamelCase_					,     )




		@staticmethod
		# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
		def 					lowerCAmelCase__       (	UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_ = 0.0							):

							'''simple docstring'''


							if attention_mask is not None:
												UpperCamelCase__     :str							      =				np.array(UpperCamelCase_					,     np.intaa							)
												UpperCamelCase__     :List[Any]							      =				[]

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

																	normed_input_values.append(UpperCamelCase_							)
							else:
												UpperCamelCase__     :List[Any]							      =				[(x - x.mean()) / np.sqrt(x.var() + 1e-7							) for x in input_values]

							return normed_input_values




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     ):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				spectrogram(
							    UpperCamelCase_					,     window=self.window					,     frame_length=self.sample_size					,     hop_length=self.sample_stride					,     fft_length=self.n_fft					,     mel_filters=self.mel_filters					,     mel_floor=self.mel_floor					,     log_mel='''log10'''					,     )
							return log_mel_spec.T




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

							'''simple docstring'''


							if audio is None and audio_target is None:
												raise ValueError('''You must provide either `audio` or `audio_target` values.'''							)

							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 audio 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.'''							)

							if audio is not None:
												UpperCamelCase__     :List[str]							      =				self._process_audio(
												    UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_					,     )
							else:
												UpperCamelCase__     :List[str]							      =				None

							if audio_target is not None:
												UpperCamelCase__     :Tuple							      =				self._process_audio(
												    UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_					,     )

												if inputs is None:
																	return inputs_target
												else:
																	UpperCamelCase__     :Dict							      =				inputs_target['''input_values''']
																	UpperCamelCase__     :Optional[Any]							      =				inputs_target.get('''attention_mask'''							)
																	if decoder_attention_mask is not None:
																						UpperCamelCase__     :int							      =				decoder_attention_mask

							return inputs




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = False					,     UpperCamelCase_ = False					,     UpperCamelCase_ = None					,     UpperCamelCase_ = False					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


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

							if is_batched:
												UpperCamelCase__     :Optional[int]							      =				[np.asarray(UpperCamelCase_					,     dtype=np.floataa							) for speech in speech]
							elif not is_batched and not isinstance(UpperCamelCase_					,     np.ndarray							):
												UpperCamelCase__     :str							      =				np.asarray(UpperCamelCase_					,     dtype=np.floataa							)
							elif isinstance(UpperCamelCase_					,     np.ndarray							) and speech.dtype is np.dtype(np.floataa							):
												UpperCamelCase__     :Tuple							      =				speech.astype(np.floataa							)

							# always return batch
							if not is_batched:
												UpperCamelCase__     :Any							      =				[speech]

							# needed to make pad() work on spectrogram inputs
							UpperCamelCase__     :List[Any]							      =				self.feature_size

							# convert into correct format for padding
							if is_target:
												UpperCamelCase__     :int							      =				[self._extract_mel_features(UpperCamelCase_							) for waveform in speech]
												UpperCamelCase__     :Union[str, Any]							      =				BatchFeature({'''input_values''': features}							)
												UpperCamelCase__     :int							      =				self.num_mel_bins
							else:
												UpperCamelCase__     :List[str]							      =				BatchFeature({'''input_values''': speech}							)

							UpperCamelCase__     :Optional[Any]							      =				self.pad(
							    UpperCamelCase_					,     padding=UpperCamelCase_					,     max_length=UpperCamelCase_					,     truncation=UpperCamelCase_					,     pad_to_multiple_of=UpperCamelCase_					,     return_attention_mask=UpperCamelCase_					,     **UpperCamelCase_					,     )

							UpperCamelCase__     :Tuple							      =				feature_size_hack

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

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

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

							if return_tensors is not None:
												UpperCamelCase__     :Optional[Any]							      =				padded_inputs.convert_to_tensors(UpperCamelCase_							)

							return padded_inputs




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				super().to_dict()

							# Don't serialize these as they are derived from the other properties.
							UpperCamelCase__     :int							      =				['''window''', '''mel_filters''', '''sample_size''', '''sample_stride''', '''n_fft''', '''n_freqs''']
							for name in names:
												if name in output:
																	del output[name]

							return output



'''simple docstring'''
from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup


def     a   (    __a = ""     )						->			dict[str, float]:



					'''simple docstring'''






					UpperCamelCase__     :Optional[Any]							      =				url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
					UpperCamelCase__     :Optional[int]							      =				BeautifulSoup(requests.get(__a     ).text				,      '''html.parser'''     )
					UpperCamelCase__     :Tuple							      =				soup.find_all('''td'''				,      attrs='''titleColumn'''     )
					UpperCamelCase__     :Union[str, Any]							      =				soup.find_all('''td'''				,      class_='''ratingColumn imdbRating'''     )
					return {
					    title.a.text: float(rating.strong.text     )
					    for title, rating in zip(__a				,      __a     )
					}


def     a   (    __a = "IMDb_Top_250_Movies.csv"     )						->			None:



					'''simple docstring'''






					UpperCamelCase__     :Optional[Any]							      =				get_imdb_top_aaa_movies()
					with open(__a				,      '''w'''				,      newline=''''''     ) as out_file:
										UpperCamelCase__     :int							      =				csv.writer(__a     )
										writer.writerow(['''Movie title''', '''IMDb rating''']     )
										for title, rating in movies.items():
															writer.writerow([title, rating]     )


if __name__ == "__main__":
		write_movies()