


import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def      lowerCamelCase_    (       _a       ):




		"""simple docstring"""






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

		return orig_key






def      lowerCamelCase_    (       _a	,   _a       ):




		"""simple docstring"""






		for key in orig_state_dict.copy().keys():
				lowerCAmelCase__   :			List[str]         =					orig_state_dict.pop(UpperCamelCase__       )

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

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

		return orig_state_dict






def      lowerCamelCase_    (       _a	,   _a	,   _a       ):




		"""simple docstring"""






		lowerCAmelCase__   :			Dict         =					torch.load(UpperCamelCase__	,   map_location='''cpu'''       )['''model_state_dict''']
		lowerCAmelCase__   :			Tuple         =					YosoConfig.from_json_file(UpperCamelCase__       )
		lowerCAmelCase__   :			Any         =					YosoForMaskedLM(UpperCamelCase__       )

		lowerCAmelCase__   :			Union[str, Any]         =					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__":
		lowerCamelCase						=		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.'''
		)
		lowerCamelCase						=		parser.parse_args()
		convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)











def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				list[int]:







			'''simple docstring'''

			if length <= 0 or not isinstance(UpperCamelCase__			,			UpperCamelCase__ ):
						raise ValueError('''Length must be a positive integer.''' )
			return [n * (2 * n - 1) for n in range(UpperCamelCase__ )]


if __name__ == "__main__":
				print(hexagonal_numbers(length=5))
				print(hexagonal_numbers(length=10))



