


import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

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


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






class     UpperCamelCase(  _a   ):
					snake_case_		:				List[str]										=	["""input_features"""]


					def __init__(						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]=8_0     ,    SCREAMING_SNAKE_CASE	:		Tuple=1_6_0_0_0     ,    SCREAMING_SNAKE_CASE	:		int=1_6_0     ,    SCREAMING_SNAKE_CASE	:		List[Any]=3_0     ,    SCREAMING_SNAKE_CASE	:		Any=4_0_0     ,    SCREAMING_SNAKE_CASE	:		List[Any]=0.0     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=False     ,    **SCREAMING_SNAKE_CASE	:		str     ,    )			->	Optional[int]:




											'''simple docstring'''

											super().__init__(
											    feature_size=SCREAMING_SNAKE_CASE     ,    sampling_rate=SCREAMING_SNAKE_CASE     ,    padding_value=SCREAMING_SNAKE_CASE     ,    return_attention_mask=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE     ,    )
											__snake_case				=		n_fft
											__snake_case				=		hop_length
											__snake_case				=		chunk_length
											__snake_case				=		chunk_length * sampling_rate
											__snake_case				=		self.n_samples // hop_length
											__snake_case				=		sampling_rate
											__snake_case				=		mel_filter_bank(
											    num_frequency_bins=1 + n_fft // 2     ,    num_mel_filters=SCREAMING_SNAKE_CASE     ,    min_frequency=0.0     ,    max_frequency=8000.0     ,    sampling_rate=SCREAMING_SNAKE_CASE     ,    norm="slaney"     ,    mel_scale="slaney"     ,    )


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		np.array						)			->	np.ndarray:




											'''simple docstring'''

											__snake_case				=		spectrogram(
											    SCREAMING_SNAKE_CASE     ,    window_function(self.n_fft     ,    "hann"						)     ,    frame_length=self.n_fft     ,    hop_length=self.hop_length     ,    power=2.0     ,    mel_filters=self.mel_filters     ,    log_mel="log10"     ,    )
											__snake_case				=		log_spec[:, :-1]
											__snake_case				=		np.maximum(SCREAMING_SNAKE_CASE     ,    log_spec.max() - 8.0						)
											__snake_case				=		(log_spec + 4.0) / 4.0
											return log_spec


					@staticmethod
					# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
					def 							SCREAMING_SNAKE_CASE_  (						SCREAMING_SNAKE_CASE	:		List[np.ndarray]     ,    SCREAMING_SNAKE_CASE	:		List[np.ndarray]     ,    SCREAMING_SNAKE_CASE	:		float = 0.0						)			->	List[np.ndarray]:




											'''simple docstring'''

											if attention_mask is not None:
																	__snake_case				=		np.array(SCREAMING_SNAKE_CASE     ,    np.intaa						)
																	__snake_case				=		[]

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

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

											return normed_input_values


					def __call__(						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,    SCREAMING_SNAKE_CASE	:		bool = True     ,    SCREAMING_SNAKE_CASE	:		Optional[int] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[str, TensorType]] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[bool] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[str] = "max_length"     ,    SCREAMING_SNAKE_CASE	:		Optional[int] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[int] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[bool] = None     ,    **SCREAMING_SNAKE_CASE	:		int     ,    )			->	BatchFeature:




											'''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.__class__.__name__} was trained using a'''
																							    f''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
																							    f''' was sampled with {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."						)

											__snake_case				=		isinstance(SCREAMING_SNAKE_CASE     ,    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}'''						)
											__snake_case				=		is_batched_numpy or (
											    isinstance(SCREAMING_SNAKE_CASE     ,    (list, tuple)						) and (isinstance(raw_speech[0]     ,    (np.ndarray, tuple, list)						))
											)

											if is_batched:
																	__snake_case				=		[np.asarray([speech]     ,    dtype=np.floataa						).T for speech in raw_speech]
											elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE     ,    np.ndarray						):
																	__snake_case				=		np.asarray(SCREAMING_SNAKE_CASE     ,    dtype=np.floataa						)
											elif isinstance(SCREAMING_SNAKE_CASE     ,    np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
																	__snake_case				=		raw_speech.astype(np.floataa						)

											# always return batch
											if not is_batched:
																	__snake_case				=		[np.asarray([raw_speech]						).T]

											__snake_case				=		BatchFeature({"input_features": raw_speech}						)

											# convert into correct format for padding

											__snake_case				=		self.pad(
											    SCREAMING_SNAKE_CASE     ,    padding=SCREAMING_SNAKE_CASE     ,    max_length=max_length if max_length else self.n_samples     ,    truncation=SCREAMING_SNAKE_CASE     ,    pad_to_multiple_of=SCREAMING_SNAKE_CASE     ,    return_attention_mask=return_attention_mask or do_normalize     ,    )

											# zero-mean and unit-variance normalization
											if do_normalize:
																	__snake_case				=		self.zero_mean_unit_var_norm(
																	    padded_inputs["input_features"]     ,    attention_mask=padded_inputs["attention_mask"]     ,    padding_value=self.padding_value     ,    )
																	__snake_case				=		np.stack(padded_inputs["input_features"]     ,    axis=0						)

											# make sure list is in array format
											__snake_case				=		padded_inputs.get("input_features"						).transpose(2     ,    0     ,    1						)

											__snake_case				=		[self._np_extract_fbank_features(SCREAMING_SNAKE_CASE						) for waveform in input_features[0]]

											if isinstance(input_features[0]     ,    SCREAMING_SNAKE_CASE						):
																	__snake_case				=		[np.asarray(SCREAMING_SNAKE_CASE     ,    dtype=np.floataa						) for feature in input_features]
											else:
																	__snake_case				=		input_features

											if return_attention_mask:
																	# rescale from sample (48000) to feature (3000)
																	__snake_case				=		padded_inputs["attention_mask"][:, :: self.hop_length]

											if return_tensors is not None:
																	__snake_case				=		padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE						)

											return padded_inputs






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	Dict[str, Any]:




											'''simple docstring'''

											__snake_case				=		copy.deepcopy(self.__dict__						)
											__snake_case				=		self.__class__.__name__
											if "mel_filters" in output:
																	del output["mel_filters"]
											return output









from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup





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


						'''simple docstring'''






						__snake_case				=		url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
						__snake_case				=		BeautifulSoup(requests.get(_lowerCAmelCase ).text      ,	"html.parser" )
						__snake_case				=		soup.find_all("td"      ,	attrs="titleColumn" )
						__snake_case				=		soup.find_all("td"      ,	class_="ratingColumn imdbRating" )
						return {
						    title.a.text: float(rating.strong.text )
						    for title, rating in zip(_lowerCAmelCase      ,	_lowerCAmelCase )
						}





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


						'''simple docstring'''






						__snake_case				=		get_imdb_top_aaa_movies()
						with open(_lowerCAmelCase      ,	"w"      ,	newline="" ) as out_file:
												__snake_case				=		csv.writer(_lowerCAmelCase )
												writer.writerow(["Movie title", "IMDb rating"] )
												for title, rating in movies.items():
																		writer.writerow([title, rating] )


if __name__ == "__main__":
	write_movies()





