




'''simple docstring'''


import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM
def 		snake_case_   (UpperCamelCase	:  Dict				):





							'''simple docstring'''


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

							return orig_key
def 		snake_case_   (UpperCamelCase	:  Optional[int]			,						UpperCamelCase	:  Tuple				):





							'''simple docstring'''


							for key in orig_state_dict.copy().keys():
														_a     					=    orig_state_dict.pop(UpperCamelCase				)

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

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

							return orig_state_dict
def 		snake_case_   (UpperCamelCase	:  Any			,						UpperCamelCase	:  List[Any]			,						UpperCamelCase	:  List[Any]				):





							'''simple docstring'''


							_a     					=    torch.load(UpperCamelCase			,						map_location='''cpu'''				)['''model_state_dict''']
							_a     					=    YosoConfig.from_json_file(UpperCamelCase				)
							_a     					=    YosoForMaskedLM(UpperCamelCase				)

							_a     					=    convert_checkpoint_helper(config.max_position_embeddings			,						UpperCamelCase				)

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

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


if __name__ == "__main__":
				_snake_case				:				Dict       							=		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.'
				)
				_snake_case				:				List[Any]       							=		parser.parse_args()
				convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)







'''simple docstring'''


from __future__ import annotations
def 		snake_case_   (UpperCamelCase	:  list[int]				):





							'''simple docstring'''


							if not nums:
														return 0
							_a     					=    nums[0]
							_a     					=    0
							for num in nums[1:]:
														_a					,							_a     					=    (
														    max_excluding + num,
														    max(UpperCamelCase			,						UpperCamelCase				),
														)
							return max(UpperCamelCase			,						UpperCamelCase				)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

