
from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np







class 			A_  (     lowercase_  ):




	'''simple docstring'''


	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		return 0.0






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






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       512
	lowercase	    =       [1] + [0] * (size - 1)
	lowercase	    =       [filter_type.process(UpperCamelCase__				) for item in inputs]

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

	# 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
	lowercase	    =       get_bounds(UpperCamelCase__					,				UpperCamelCase__				)
	plt.ylim(max([-80, bounds[0]]				)					,				min([80, bounds[1]]				)				)
	plt.ylabel('Gain (dB)'				)

	plt.plot(UpperCamelCase__				)
	plt.show()






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       512
	lowercase	    =       [1] + [0] * (size - 1)
	lowercase	    =       [filter_type.process(UpperCamelCase__				) for item in inputs]

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

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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase						:  int									=			{
    """configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase						:  str									=			[
					    """LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """LiltForQuestionAnswering""",
					    """LiltForSequenceClassification""",
					    """LiltForTokenClassification""",
					    """LiltModel""",
					    """LiltPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_lilt import (
										    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LiltForQuestionAnswering,
										    LiltForSequenceClassification,
										    LiltForTokenClassification,
										    LiltModel,
										    LiltPreTrainedModel,
										)

else:
					import sys

					_UpperCAmelCase						:  Dict									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




