
import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					if "model" in orig_key:
										lowercase							=						orig_key.replace('''model.''' ,			''''''		)
					if "norm1" in orig_key:
										lowercase							=						orig_key.replace('''norm1''' ,			'''attention.output.LayerNorm'''		)
					if "norm2" in orig_key:
										lowercase							=						orig_key.replace('''norm2''' ,			'''output.LayerNorm'''		)
					if "norm" in orig_key:
										lowercase							=						orig_key.replace('''norm''' ,			'''LayerNorm'''		)
					if "transformer" in orig_key:
										lowercase							=						orig_key.split('''.'''		)[0].split('''_'''		)[-1]
										lowercase							=						orig_key.replace(f'transformer_{layer_num}' ,			f'encoder.layer.{layer_num}'		)
					if "mha.attn" in orig_key:
										lowercase							=						orig_key.replace('''mha.attn''' ,			'''attention.self'''		)
					if "mha" in orig_key:
										lowercase							=						orig_key.replace('''mha''' ,			'''attention'''		)
					if "W_q" in orig_key:
										lowercase							=						orig_key.replace('''W_q''' ,			'''self.query'''		)
					if "W_k" in orig_key:
										lowercase							=						orig_key.replace('''W_k''' ,			'''self.key'''		)
					if "W_v" in orig_key:
										lowercase							=						orig_key.replace('''W_v''' ,			'''self.value'''		)
					if "ff1" in orig_key:
										lowercase							=						orig_key.replace('''ff1''' ,			'''intermediate.dense'''		)
					if "ff2" in orig_key:
										lowercase							=						orig_key.replace('''ff2''' ,			'''output.dense'''		)
					if "ff" in orig_key:
										lowercase							=						orig_key.replace('''ff''' ,			'''output.dense'''		)
					if "mlm_class" in orig_key:
										lowercase							=						orig_key.replace('''mlm.mlm_class''' ,			'''cls.predictions.decoder'''		)
					if "mlm" in orig_key:
										lowercase							=						orig_key.replace('''mlm''' ,			'''cls.predictions.transform'''		)
					if "cls" not in orig_key:
										lowercase							=						'''yoso.''' + orig_key

					return orig_key



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					for key in orig_state_dict.copy().keys():
										lowercase							=						orig_state_dict.pop(lowerCAmelCase__		)

										if ("pooler" in key) or ("sen_class" in key):
															continue
										else:
															lowercase							=						val

					lowercase							=						orig_state_dict['''cls.predictions.decoder.bias''']
					lowercase							=						torch.arange(lowerCAmelCase__		).expand((1, -1)		) + 2

					return orig_state_dict



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						torch.load(lowerCAmelCase__ ,			map_location='''cpu'''		)['''model_state_dict''']
					lowercase							=						YosoConfig.from_json_file(lowerCAmelCase__		)
					lowercase							=						YosoForMaskedLM(lowerCAmelCase__		)

					lowercase							=						convert_checkpoint_helper(config.max_position_embeddings ,			lowerCAmelCase__		)

					print(model.load_state_dict(lowerCAmelCase__		)		)
					model.eval()
					model.save_pretrained(lowerCAmelCase__		)

					print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'		)


if __name__ == "__main__":
							lowercase__		:Any   =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
							)
							parser.add_argument(
							    "--config_file",
							    default=None,
							    type=str,
							    required=True,
							    help="The json file for YOSO model config.",
							)
							parser.add_argument(
							    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
							)
							lowercase__		:Any   =    parser.parse_args()
							convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)









'''simple docstring'''






from __future__ import annotations

import typing
from collections import Counter




def     snake_case_					(     lowerCAmelCase_		)->		typing.Counter[int]:





       '''simple docstring'''


       _UpperCAmelCase  :						typing.Counter[int]			     = Counter()
       for base in range(1       ,   max_perimeter + 1		):
              for perpendicular in range(lowerCAmelCase_       ,   max_perimeter + 1		):
                     _UpperCAmelCase  :						List[str]			     = (base * base + perpendicular * perpendicular) ** 0.5
                     if hypotenuse == int(lowerCAmelCase_		):
                            _UpperCAmelCase  :						Optional[Any]			     = int(base + perpendicular + hypotenuse		)
                            if perimeter > max_perimeter:
                                   continue
                            triplets[perimeter] += 1
       return triplets




def     snake_case_					(     lowerCAmelCase_ = 1000		)->		int:





       '''simple docstring'''


       _UpperCAmelCase  :						int			     = pythagorean_triple(lowerCAmelCase_		)
       return triplets.most_common(1		)[0][0]


if __name__ == "__main__":
   print(f"""Perimeter {solution()} has maximum solutions""")



