

'''simple docstring'''



import html

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends


if is_bsa_available():
		import bsa
		from bsa import BeautifulSoup


__snake_case			:     int										=      logging.get_logger(__name__)



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			def __init__(				self	:						Union[str, Any]		,      **lowerCAmelCase_	:						str			)      ->		Optional[Any]:





									'''simple docstring'''
									requires_backends(self		,      ["""bs4"""]			)
									super().__init__(**lowerCAmelCase_			)


			def lowercase__						(				self	:						int		,      lowerCAmelCase_	:						Tuple			)      ->		Any:





									'''simple docstring'''
									A__					:       Union[str, Any]     =[]
									A__					:       List[Any]     =[]
									A__					:       List[str]     =element if element.name else element.parent
									for parent in child.parents:  # type: bs4.element.Tag
															A__					:       int     =parent.find_all(child.name		,      recursive=lowerCAmelCase_			)
															xpath_tags.append(child.name			)
															xpath_subscripts.append(
															    0 if 1 == len(lowerCAmelCase_			) else next(i for i, s in enumerate(lowerCAmelCase_		,      1			) if s is child			)			)
															A__					:       str     =parent
									xpath_tags.reverse()
									xpath_subscripts.reverse()
									return xpath_tags, xpath_subscripts


			def lowercase__						(				self	:						Tuple		,      lowerCAmelCase_	:						str			)      ->		Optional[Any]:





									'''simple docstring'''
									A__					:       int     =BeautifulSoup(lowerCAmelCase_		,      """html.parser"""			)

									A__					:       Optional[int]     =[]
									A__					:       Optional[int]     =[]
									A__					:       Tuple     =[]

									for element in html_code.descendants:
															if type(lowerCAmelCase_			) == bsa.element.NavigableString:
																					if type(element.parent			) != bsa.element.Tag:
																											continue

																					A__					:       Any     =html.unescape(lowerCAmelCase_			).strip()
																					if not text_in_this_tag:
																											continue

																					all_doc_strings.append(lowerCAmelCase_			)

																					A__   ,						A__					:       Optional[int]     =self.xpath_soup(lowerCAmelCase_			)
																					stringaxtag_seq.append(lowerCAmelCase_			)
																					stringaxsubs_seq.append(lowerCAmelCase_			)

									if len(lowerCAmelCase_			) != len(lowerCAmelCase_			):
															raise ValueError("""Number of doc strings and xtags does not correspond"""			)
									if len(lowerCAmelCase_			) != len(lowerCAmelCase_			):
															raise ValueError("""Number of doc strings and xsubs does not correspond"""			)

									return all_doc_strings, stringaxtag_seq, stringaxsubs_seq


			def lowercase__						(				self	:						Optional[Any]		,      lowerCAmelCase_	:						Optional[Any]		,      lowerCAmelCase_	:						str			)      ->		Dict:





									'''simple docstring'''
									A__					:       int     =""""""
									for tagname, subs in zip(lowerCAmelCase_		,      lowerCAmelCase_			):
															xpath += f"/{tagname}"
															if subs != 0:
																					xpath += f"[{subs}]"
									return xpath



			def __call__(				self	:						Union[str, Any]		,      lowerCAmelCase_	:						str			)      ->		BatchFeature:





									'''simple docstring'''
									A__					:       List[str]     =False

									# Check that strings has a valid type
									if isinstance(lowerCAmelCase_		,      lowerCAmelCase_			):
															A__					:       int     =True
									elif isinstance(lowerCAmelCase_		,      (list, tuple)			):
															if len(lowerCAmelCase_			) == 0 or isinstance(html_strings[0]		,      lowerCAmelCase_			):
																					A__					:       str     =True

									if not valid_strings:
															raise ValueError(
															    """HTML strings must of type `str`, `List[str]` (batch of examples), """
															    f"but is of type {type(lowerCAmelCase_			)}."			)

									A__					:       Optional[Any]     =bool(isinstance(lowerCAmelCase_		,      (list, tuple)			) and (isinstance(html_strings[0]		,      lowerCAmelCase_			))			)

									if not is_batched:
															A__					:       List[str]     =[html_strings]

									# Get nodes + xpaths
									A__					:       int     =[]
									A__					:       Optional[int]     =[]
									for html_string in html_strings:
															A__   ,						A__   ,						A__					:       Optional[Any]     =self.get_three_from_single(lowerCAmelCase_			)
															nodes.append(lowerCAmelCase_			)
															A__					:       str     =[]
															for node, tag_list, sub_list in zip(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_			):
																					A__					:       Any     =self.construct_xpath(lowerCAmelCase_		,      lowerCAmelCase_			)
																					xpath_strings.append(lowerCAmelCase_			)
															xpaths.append(lowerCAmelCase_			)

									# return as Dict
									A__					:       List[Any]     ={"""nodes""": nodes, """xpaths""": xpaths}
									A__					:       Optional[int]     =BatchFeature(data=lowerCAmelCase_		,      tensor_type=lowerCAmelCase_			)

									return encoded_inputs




'''simple docstring'''



from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			def lowercase__						(				self	:						Any		,      lowerCAmelCase_	:						float			)      ->		float:





									'''simple docstring'''
									return 0.0


def 						__lowerCamelCase	(       __snake_case					:		np.ndarray,		__snake_case					:		int	)	->							tuple[int | float, int | float]:






						"""simple docstring"""





						A__					:       Tuple     =min([-20, np.min(fft_results[1 : samplerate // 2 - 1]	)]	)
						A__					:       str     =max([20, np.max(fft_results[1 : samplerate // 2 - 1]	)]	)
						return lowest, highest
def 						__lowerCamelCase	(       __snake_case					:		FilterType,		__snake_case					:		int	)	->							None:






						"""simple docstring"""





						A__					:       Any     =512
						A__					:       int     =[1] + [0] * (size - 1)
						A__					:       int     =[filter_type.process(__snake_case	) for item in inputs]

						A__					:       Union[str, Any]     =[0] * (samplerate - size)  # zero-padding
						outputs += filler
						A__					:       List[Any]     =np.abs(np.fft.fft(__snake_case	)	)
						A__					:       int     =20 * np.logaa(__snake_case	)

						# Frequencies on log scale from 24 to nyquist frequency
						plt.xlim(24,		samplerate / 2 - 1	)
						plt.xlabel("""Frequency (Hz)"""	)
						plt.xscale("""log"""	)

						# Display within reasonable bounds
						A__					:       Union[str, Any]     =get_bounds(__snake_case,		__snake_case	)
						plt.ylim(max([-80, bounds[0]]	),		min([80, bounds[1]]	)	)
						plt.ylabel("""Gain (dB)"""	)

						plt.plot(__snake_case	)
						plt.show()
def 						__lowerCamelCase	(       __snake_case					:		FilterType,		__snake_case					:		int	)	->							None:






						"""simple docstring"""





						A__					:       List[Any]     =512
						A__					:       List[Any]     =[1] + [0] * (size - 1)
						A__					:       Dict     =[filter_type.process(__snake_case	) for item in inputs]

						A__					:       Union[str, Any]     =[0] * (samplerate - size)  # zero-padding
						outputs += filler
						A__					:       str     =np.angle(np.fft.fft(__snake_case	)	)

						# Frequencies on log scale from 24 to nyquist frequency
						plt.xlim(24,		samplerate / 2 - 1	)
						plt.xlabel("""Frequency (Hz)"""	)
						plt.xscale("""log"""	)

						plt.ylim(-2 * pi,		2 * pi	)
						plt.ylabel("""Phase shift (Radians)"""	)
						plt.plot(np.unwrap(__snake_case,		-2 * pi	)	)
						plt.show()

