


from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np






class     UpperCamelCase(  _a   ):


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		float						)			->	float:




											'''simple docstring'''

											return 0.0





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	tuple[int | float, int | float]:


						'''simple docstring'''






						__snake_case				=		min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
						__snake_case				=		max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
						return lowest, highest





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	None:


						'''simple docstring'''






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

						__snake_case				=		[0] * (samplerate - size)  # zero-padding
						outputs += filler
						__snake_case				=		np.abs(np.fft.fft(_lowerCAmelCase ) )
						__snake_case				=		20 * np.logaa(_lowerCAmelCase )

						# 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
						__snake_case				=		get_bounds(_lowerCAmelCase      ,	_lowerCAmelCase )
						plt.ylim(max([-80, bounds[0]] )      ,	min([80, bounds[1]] ) )
						plt.ylabel("Gain (dB)" )

						plt.plot(_lowerCAmelCase )
						plt.show()





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	None:


						'''simple docstring'''






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

						__snake_case				=		[0] * (samplerate - size)  # zero-padding
						outputs += filler
						__snake_case				=		np.angle(np.fft.fft(_lowerCAmelCase ) )

						# 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(_lowerCAmelCase      ,	-2 * pi ) )
						plt.show()









from ...configuration_utils import PretrainedConfig
from ...utils import logging


A							:					str     						=     logging.get_logger(__name__)

A							:					str     						=     {
    'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
    'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/config.json'
    # See all FNet models at https://huggingface.co/models?filter=fnet
}






class     UpperCamelCase(  _a   ):
					snake_case_		:				List[Any]										=	"""fnet"""


					def __init__(						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Dict=3_2_0_0_0     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=7_6_8     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=1_2     ,    SCREAMING_SNAKE_CASE	:		Any=3_0_7_2     ,    SCREAMING_SNAKE_CASE	:		Tuple="gelu_new"     ,    SCREAMING_SNAKE_CASE	:		Any=0.1     ,    SCREAMING_SNAKE_CASE	:		Dict=5_1_2     ,    SCREAMING_SNAKE_CASE	:		str=4     ,    SCREAMING_SNAKE_CASE	:		Tuple=0.02     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=1e-1_2     ,    SCREAMING_SNAKE_CASE	:		Dict=False     ,    SCREAMING_SNAKE_CASE	:		int=5_1_2     ,    SCREAMING_SNAKE_CASE	:		str=3     ,    SCREAMING_SNAKE_CASE	:		str=1     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=2     ,    **SCREAMING_SNAKE_CASE	:		List[Any]     ,    )			->	List[Any]:




											'''simple docstring'''

											super().__init__(pad_token_id=SCREAMING_SNAKE_CASE     ,    bos_token_id=SCREAMING_SNAKE_CASE     ,    eos_token_id=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)

											__snake_case				=		vocab_size
											__snake_case				=		max_position_embeddings
											__snake_case				=		hidden_size
											__snake_case				=		num_hidden_layers
											__snake_case				=		intermediate_size
											__snake_case				=		hidden_act
											__snake_case				=		hidden_dropout_prob
											__snake_case				=		initializer_range
											__snake_case				=		type_vocab_size
											__snake_case				=		layer_norm_eps
											__snake_case				=		use_tpu_fourier_optimizations
											__snake_case				=		tpu_short_seq_length





