
"""simple docstring"""





from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt



def  lowerCamelCase__						(     __snake_case      )				->						bool:


					"""simple docstring"""





					_UpperCamelCase									=    int(number**0.5      )
					return number == sq * sq



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						tuple[int, int]:


					"""simple docstring"""





					_UpperCamelCase									=    x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
					_UpperCamelCase									=    x_den * y_den * z_den
					_UpperCamelCase									=    gcd(__snake_case,	__snake_case      )
					top //= hcf
					bottom //= hcf
					return top, bottom



def  lowerCamelCase__						(     __snake_case = 35      )				->						int:


					"""simple docstring"""





					_UpperCamelCase									=    set()
					_UpperCamelCase									=    42
					_UpperCamelCase									=    Fraction(0      )
					_UpperCamelCase									=    42

					for x_num in range(1,	order + 1      ):
										for x_den in range(x_num + 1,	order + 1      ):
															for y_num in range(1,	order + 1      ):
																				for y_den in range(y_num + 1,	order + 1      ):
																									# n=1
																									_UpperCamelCase									=    x_num * y_den + x_den * y_num
																									_UpperCamelCase									=    x_den * y_den
																									_UpperCamelCase									=    gcd(__snake_case,	__snake_case      )
																									z_num //= hcf
																									z_den //= hcf
																									if 0 < z_num < z_den <= order:
																														_UpperCamelCase									=    add_three(
																														    __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )
																														unique_s.add(__snake_case      )

																									# n=2
																									_UpperCamelCase									=    (
																									    x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
																									)
																									_UpperCamelCase									=    x_den * x_den * y_den * y_den
																									if is_sq(__snake_case      ) and is_sq(__snake_case      ):
																														_UpperCamelCase									=    int(sqrt(__snake_case      )      )
																														_UpperCamelCase									=    int(sqrt(__snake_case      )      )
																														_UpperCamelCase									=    gcd(__snake_case,	__snake_case      )
																														z_num //= hcf
																														z_den //= hcf
																														if 0 < z_num < z_den <= order:
																																			_UpperCamelCase									=    add_three(
																																			    __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )
																																			unique_s.add(__snake_case      )

                    # n=-1
																									_UpperCamelCase									=    x_num * y_num
																									_UpperCamelCase									=    x_den * y_num + x_num * y_den
																									_UpperCamelCase									=    gcd(__snake_case,	__snake_case      )
																									z_num //= hcf
																									z_den //= hcf
																									if 0 < z_num < z_den <= order:
																														_UpperCamelCase									=    add_three(
																														    __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )
																														unique_s.add(__snake_case      )

																									# n=2
																									_UpperCamelCase									=    x_num * x_num * y_num * y_num
																									_UpperCamelCase									=    (
																									    x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
																									)
																									if is_sq(__snake_case      ) and is_sq(__snake_case      ):
																														_UpperCamelCase									=    int(sqrt(__snake_case      )      )
																														_UpperCamelCase									=    int(sqrt(__snake_case      )      )
																														_UpperCamelCase									=    gcd(__snake_case,	__snake_case      )
																														z_num //= hcf
																														z_den //= hcf
																														if 0 < z_num < z_den <= order:
																																			_UpperCamelCase									=    add_three(
																																			    __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )
																																			unique_s.add(__snake_case      )

					for num, den in unique_s:
										total += Fraction(__snake_case,	__snake_case      )

					return total.denominator + total.numerator


if __name__ == "__main__":
				print(F"""{solution() = }""")












import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()


def 		lowerCamelCase__       (						_A	,      _A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	MobileBertConfig.from_json_file(_A				)
	print(f"Building PyTorch model from configuration: {config}"				)
	snake_case_						       =	MobileBertForPreTraining(_A				)
	# Load weights from tf checkpoint
	snake_case_						       =	load_tf_weights_in_mobilebert(_A	,      _A	,      _A				)
	# Save pytorch-model
	print(f"Save PyTorch model to {pytorch_dump_path}"				)
	torch.save(model.state_dict()	,      _A				)


if __name__ == "__main__":
		lowercase__							:	Dict											=		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(
		    "--mobilebert_config_file",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        "The config json file corresponding to the pre-trained MobileBERT model. \n"
		        "This specifies the model architecture."
		    ),
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		lowercase__							:	Tuple											=		parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
