


from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					def A   (					self			,							snake_case_    ) ->      float:







								'''simple docstring'''

								return 0.0




def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			__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 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = 5_12
			__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 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = 5_12
			__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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a			:			Optional[Any]												=					{
    '''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LlamaConfig'''],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Optional[int]												=					['''LlamaTokenizer''']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Optional[int]												=					['''LlamaTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Any												=					[
	    '''LlamaForCausalLM''',
	    '''LlamaModel''',
	    '''LlamaPreTrainedModel''',
	    '''LlamaForSequenceClassification''',
	]


if TYPE_CHECKING:
	from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama_fast import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
	import sys

	a			:			Dict												=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


