




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


__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					["input_values", "padding_mask"]


				def __init__(							self:   List[Any]		,					_lowerCamelCase:   int = 1		,					_lowerCamelCase:   int = 2_40_00		,					_lowerCamelCase:   float = 0.0		,					_lowerCamelCase:   float = None		,					_lowerCamelCase:   float = None		,					**_lowerCamelCase:   str		,					):
											super().__init__(feature_size=_lowerCamelCase		,					sampling_rate=_lowerCamelCase		,					padding_value=_lowerCamelCase		,					**_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    chunk_length_s
											SCREAMING_SNAKE_CASE_   	=    overlap


				@property
				def     _A							(							self:   List[str]		):
											if self.chunk_length_s is None:
																		return None
											else:
																		return int(self.chunk_length_s * self.sampling_rate		)


				@property
				def     _A							(							self:   str		):
											if self.chunk_length_s is None or self.overlap is None:
																		return None
											else:
																		return max(1		,					int((1.0 - self.overlap) * self.chunk_length		)		)





				def __call__(							self:   List[str]		,					_lowerCamelCase:   Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]		,					_lowerCamelCase:   Optional[Union[bool, str, PaddingStrategy]] = None		,					_lowerCamelCase:   Optional[bool] = False		,					_lowerCamelCase:   Optional[int] = None		,					_lowerCamelCase:   Optional[Union[str, TensorType]] = None		,					_lowerCamelCase:   Optional[int] = None		,					):
											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 padding and truncation:
																		raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''		)
											elif padding is None:
																		# by default let's pad the inputs
																		SCREAMING_SNAKE_CASE_   	=    True

											SCREAMING_SNAKE_CASE_   	=    bool(
											    isinstance(_lowerCamelCase		,					(list, tuple)		) and (isinstance(raw_audio[0]		,					(np.ndarray, tuple, list)		))		)

											if is_batched:
																		SCREAMING_SNAKE_CASE_   	=    [np.asarray(_lowerCamelCase		,					dtype=np.floataa		).T for audio in raw_audio]
											elif not is_batched and not isinstance(_lowerCamelCase		,					np.ndarray		):
																		SCREAMING_SNAKE_CASE_   	=    np.asarray(_lowerCamelCase		,					dtype=np.floataa		)
											elif isinstance(_lowerCamelCase		,					np.ndarray		) and raw_audio.dtype is np.dtype(np.floataa		):
																		SCREAMING_SNAKE_CASE_   	=    raw_audio.astype(np.floataa		)

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

											# verify inputs are valid
											for idx, example in enumerate(_lowerCamelCase		):
																		if example.ndim > 2:
																									raise ValueError(f"Expected input shape (channels, length) but got shape {example.shape}"		)
																		if self.feature_size == 1 and example.ndim != 1:
																									raise ValueError(f"Expected mono audio but example has {example.shape[-1]} channels"		)
																		if self.feature_size == 2 and example.shape[-1] != 2:
																									raise ValueError(f"Expected stereo audio but example has {example.shape[-1]} channels"		)

											SCREAMING_SNAKE_CASE_   	=    None
											SCREAMING_SNAKE_CASE_   	=    BatchFeature({'''input_values''': raw_audio}		)
											if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
																		if truncation:
																									SCREAMING_SNAKE_CASE_   	=    min(array.shape[0] for array in raw_audio		)
																									SCREAMING_SNAKE_CASE_   	=    int(np.floor(max_length / self.chunk_stride		)		)
																									SCREAMING_SNAKE_CASE_   	=    (nb_step - 1) * self.chunk_stride + self.chunk_length
																		elif padding:
																									SCREAMING_SNAKE_CASE_   	=    max(array.shape[0] for array in raw_audio		)
																									SCREAMING_SNAKE_CASE_   	=    int(np.ceil(max_length / self.chunk_stride		)		)
																									SCREAMING_SNAKE_CASE_   	=    (nb_step - 1) * self.chunk_stride + self.chunk_length
																									SCREAMING_SNAKE_CASE_   	=    '''max_length'''
																		else:
																									SCREAMING_SNAKE_CASE_   	=    input_values

        # normal padding on batch
											if padded_inputs is None:
																		SCREAMING_SNAKE_CASE_   	=    self.pad(
																		    _lowerCamelCase		,					max_length=_lowerCamelCase		,					truncation=_lowerCamelCase		,					padding=_lowerCamelCase		,					return_attention_mask=_lowerCamelCase		,					)
																		if padding:
																									SCREAMING_SNAKE_CASE_   	=    padded_inputs.pop('''attention_mask'''		)

											SCREAMING_SNAKE_CASE_   	=    []
											for example in padded_inputs.pop('''input_values'''		):
																		if self.feature_size == 1:
																									SCREAMING_SNAKE_CASE_   	=    example[..., None]
																		input_values.append(example.T		)

											SCREAMING_SNAKE_CASE_   	=    input_values
											if return_tensors is not None:
																		SCREAMING_SNAKE_CASE_   	=    padded_inputs.convert_to_tensors(_lowerCamelCase		)

											return padded_inputs








import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE 						="""%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Search: """)))

			print("""Googling.....""")

			__SCREAMING_SNAKE_CASE 						=f"""https://www.google.com/search?q={query}&num=100"""

			__SCREAMING_SNAKE_CASE 						=requests.get(
			    url,
			    headers={"""User-Agent""": str(UserAgent().random)},
			)

			try:
						__SCREAMING_SNAKE_CASE 						=(
						    BeautifulSoup(res.text, """html.parser""")
						    .find("""div""", attrs={"""class""": """yuRUbf"""})
						    .find("""a""")
						    .get("""href""")
						)

			except AttributeError:
						__SCREAMING_SNAKE_CASE 						=parse_qs(
						    BeautifulSoup(res.text, """html.parser""")
						    .find("""div""", attrs={"""class""": """kCrYT"""})
						    .find("""a""")
						    .get("""href""")
						)["""url"""][0]

			webbrowser.open(link)


