



from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np







class     __lowerCAmelCase					(    A		):



			def 					_lowerCamelCase (		self     :						int				,	A     :						float)     ->					float:



						"""simple docstring"""






						return 0.0





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







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





def 							A			(  _UpperCAmelCase       :     FilterType							,     _UpperCAmelCase       :     int					)       ->				None:







			'''simple docstring'''
			_UpperCAmelCase	 =     512
			_UpperCAmelCase	 =     [1] + [0] * (size - 1)
			_UpperCAmelCase	 =     [filter_type.process(_UpperCAmelCase					) for item in inputs]

			_UpperCAmelCase	 =     [0] * (samplerate - size)  # zero-padding
			outputs += filler
			_UpperCAmelCase	 =     np.abs(np.fft.fft(_UpperCAmelCase					)					)
			_UpperCAmelCase	 =     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
			_UpperCAmelCase	 =     get_bounds(_UpperCAmelCase							,     _UpperCAmelCase					)
			plt.ylim(max([-80, bounds[0]]					)							,     min([80, bounds[1]]					)					)
			plt.ylabel('Gain (dB)'					)

			plt.plot(_UpperCAmelCase					)
			plt.show()





def 							A			(  _UpperCAmelCase       :     FilterType							,     _UpperCAmelCase       :     int					)       ->				None:







			'''simple docstring'''
			_UpperCAmelCase	 =     512
			_UpperCAmelCase	 =     [1] + [0] * (size - 1)
			_UpperCAmelCase	 =     [filter_type.process(_UpperCAmelCase					) for item in inputs]

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





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()





def 							A			(  _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     Optional[int]					)       ->				int:







			'''simple docstring'''
			# Initialise PyTorch model
			_UpperCAmelCase	 =     TaConfig.from_json_file(_UpperCAmelCase					)
			print(F"Building PyTorch model from configuration: {config}"					)
			_UpperCAmelCase	 =     TaForConditionalGeneration(_UpperCAmelCase					)

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase					)

			# Save pytorch-model
			print(F"Save PyTorch model to {pytorch_dump_path}"					)
			model.save_pretrained(_UpperCAmelCase					)


if __name__ == "__main__":
							UpperCAmelCase__     = argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
							)
							parser.add_argument(
							    "--config_file",
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        "The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."
							    ),
							)
							parser.add_argument(
							    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
							)
							UpperCAmelCase__     = parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
