
'''simple docstring'''







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Optional[int]   ,   __SCREAMING_SNAKE_CASE      :				Optional[Any]   ,   __SCREAMING_SNAKE_CASE      :				List[str]							):



						"""simple docstring"""





						lowercase_			:  str									= RoFormerConfig.from_json_file(__SCREAMING_SNAKE_CASE							)
						print(F'''Building PyTorch model from configuration: {config}'''							)
						lowercase_			:  Any									= RoFormerForMaskedLM(__SCREAMING_SNAKE_CASE							)

						# Load weights from tf checkpoint
						load_tf_weights_in_roformer(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE							)

						# Save pytorch-model
						print(F'''Save PyTorch model to {pytorch_dump_path}'''							)
						torch.save(model.state_dict()   ,   __SCREAMING_SNAKE_CASE   ,   _use_new_zipfile_serialization=__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
							_lowercase				:						List[Any]												= 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(
							    "--bert_config_file",
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        "The config json file corresponding to the pre-trained BERT 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				:						List[Any]												= parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





'''simple docstring'''







from itertools import combinations, product
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int   ,   __SCREAMING_SNAKE_CASE      :				int   ,   __SCREAMING_SNAKE_CASE      :				int   ,   __SCREAMING_SNAKE_CASE      :				int							):



						"""simple docstring"""





						if xa == ya == 0 or xa == ya == 0:
												return False
						lowercase_			:  Optional[Any]									= xa * xa + ya * ya
						lowercase_			:  List[str]									= xa * xa + ya * ya
						lowercase_			:  Any									= (xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
						return (
						    a_square + b_square == c_square
						    or a_square + c_square == b_square
						    or b_square + c_square == a_square
						)
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int = 50							):



						"""simple docstring"""





						return sum(
						    1
						    for pta, pta in combinations(product(range(limit + 1							)   ,   repeat=2							)   ,   2							)
						    if is_right(*__SCREAMING_SNAKE_CASE   ,   *__SCREAMING_SNAKE_CASE							)							)


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



