"""simple docstring"""




import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


__lowerCamelCase											=  ["small", "medium", "large"]

__lowerCamelCase											=  "lm_head.decoder.weight"
__lowerCamelCase											=  "lm_head.weight"






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							torch.load(UpperCamelCase__				)
							A__	     =							d.pop(UpperCamelCase__				)
							os.makedirs(UpperCamelCase__       ,    exist_ok=UpperCamelCase__				)
							torch.save(UpperCamelCase__       ,    os.path.join(UpperCamelCase__       ,    UpperCamelCase__				)				)


if __name__ == "__main__":
				__lowerCamelCase											=  argparse.ArgumentParser()
				parser.add_argument("--dialogpt_path", default=".", type=str)
				__lowerCamelCase											=  parser.parse_args()
				for MODEL in DIALOGPT_MODELS:
								__lowerCamelCase											=  os.path.join(args.dialogpt_path, F'''{MODEL}_ft.pkl''')
								__lowerCamelCase											=  F'''./DialoGPT-{MODEL}'''
								convert_dialogpt_checkpoint(
								    checkpoint_path,
								    pytorch_dump_folder_path,
								)






"""simple docstring"""




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 				snake_case__	(       self		,__UpperCAmelCase     )					->				float:
													return 0.0






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





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






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





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

							A__	     =							[0] * (samplerate - size)  # zero-padding
							outputs += filler
							A__	     =							np.abs(np.fft.fft(UpperCamelCase__				)				)
							A__	     =							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
							A__	     =							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					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





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

							A__	     =							[0] * (samplerate - size)  # zero-padding
							outputs += filler
							A__	     =							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()





