






import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def 							A     (       a_							,a_							,a_					)     ->						Tuple:
					# Construct model
					if openai_config_file == "":
										__UpperCamelCase      :					int       		=OpenAIGPTConfig()
					else:
										__UpperCamelCase      :					Any       		=OpenAIGPTConfig.from_json_file(a_					)
					__UpperCamelCase      :					Any       		=OpenAIGPTModel(a_					)

					# Load weights from numpy
					load_tf_weights_in_openai_gpt(a_							,a_							,a_					)

					# Save pytorch-model
					__UpperCamelCase      :					Optional[Any]       		=pytorch_dump_folder_path + '/' + WEIGHTS_NAME
					__UpperCamelCase      :					Optional[Any]       		=pytorch_dump_folder_path + '/' + CONFIG_NAME
					print(F'Save PyTorch model to {pytorch_weights_dump_path}'					)
					torch.save(model.state_dict()							,a_					)
					print(F'Save configuration file to {pytorch_config_dump_path}'					)
					with open(a_							,'w'							,encoding='utf-8'					) as f:
										f.write(config.to_json_string()					)


if __name__ == "__main__":
							A_       :int       =						argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--openai_checkpoint_folder_path''',
							    default=None,
							    type=str,
							    required=True,
							    help='''Path to the TensorFlow checkpoint path.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
							)
							parser.add_argument(
							    '''--openai_config_file''',
							    default='''''',
							    type=str,
							    help=(
							        '''An optional config json file corresponding to the pre-trained OpenAI model. \n'''
							        '''This specifies the model architecture.'''
							    ),
							)
							A_       :Optional[int]       =						parser.parse_args()
							convert_openai_checkpoint_to_pytorch(
							    args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
							)





from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    def _a					(			self					,   A_    )  ->       float:
         return 0.0

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					np.ndarray			,     SCREAMING_SNAKE_CASE__			:					int				):
     __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 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					FilterType			,     SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=5_12
     __UpperCamelCase    							=[1] + [0] * (size - 1)
     __UpperCamelCase    							=[filter_type.process(SCREAMING_SNAKE_CASE__				) for item in inputs]

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

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

     plt.plot(SCREAMING_SNAKE_CASE__				)
     plt.show()





def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					FilterType			,     SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=5_12
     __UpperCamelCase    							=[1] + [0] * (size - 1)
     __UpperCamelCase    							=[filter_type.process(SCREAMING_SNAKE_CASE__				) for item in inputs]

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

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





