






"""simple docstring"""







import argparse

import torch

from transformers import MraConfig, MraForMaskedLM





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_):
					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 "ff.0" in orig_key:
										a__ 							=	orig_key.replace('''ff.0'''			,  '''intermediate.dense''')
					if "ff.2" in orig_key:
										a__ 							=	orig_key.replace('''ff.2'''			,  '''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 "backbone.backbone.encoders" in orig_key:
										a__ 							=	orig_key.replace('''backbone.backbone.encoders'''			,  '''encoder.layer''')
					if "cls" not in orig_key:
										a__ 							=	'''mra.''' + orig_key

					return orig_key





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_):
					for key in orig_state_dict.copy().keys():
										a__ 							=	orig_state_dict.pop(lowerCamelCase_)

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

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

					return orig_state_dict





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_):
					a__ 							=	torch.load(lowerCamelCase_			,  map_location='''cpu''')['''model_state_dict''']
					a__ 							=	MraConfig.from_json_file(lowerCamelCase_)
					a__ 							=	MraForMaskedLM(lowerCamelCase_)

					a__ 							=	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__":
	__a							:						int   							=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--pytorch_model_path', default=None, type=str, required=True, help='Path to Mra pytorch checkpoint.'
	)
	parser.add_argument(
	    '--config_file',
	    default=None,
	    type=str,
	    required=True,
	    help='The json file for Mra model config.',
	)
	parser.add_argument(
	    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	__a							:						Union[str, Any]   							=			parser.parse_args()
	convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)










"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a							:						Union[str, Any]   							=			logging.get_logger(__name__)

__a							:						Optional[Any]   							=			{
    'tau/splinter-base': 'https://huggingface.co/tau/splinter-base/resolve/main/config.json',
    'tau/splinter-base-qass': 'https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json',
    'tau/splinter-large': 'https://huggingface.co/tau/splinter-large/resolve/main/config.json',
    'tau/splinter-large-qass': 'https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json',
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}







class 							_SCREAMING_SNAKE_CASE    (	__snake_case				):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE     ='splinter'




							def __init__( self:  Dict					,		__A:  List[Any]=30522					,		__A:  str=768					,		__A:  int=12					,		__A:  str=12					,		__A:  Tuple=3072					,		__A:  Dict="gelu"					,		__A:  Optional[int]=0.1					,		__A:  List[str]=0.1					,		__A:  str=512					,		__A:  List[Any]=2					,		__A:  Any=0.0_2					,		__A:  Any=1e-12					,		__A:  Tuple=True					,		__A:  List[Any]=0					,		__A:  List[str]=104					,		**__A:  Union[str, Any]					,		):

												'''simple docstring'''

												super().__init__(pad_token_id=__A					,		**__A     )

												a__ 							=	vocab_size
												a__ 							=	max_position_embeddings
												a__ 							=	hidden_size
												a__ 							=	num_hidden_layers
												a__ 							=	num_attention_heads
												a__ 							=	intermediate_size
												a__ 							=	hidden_act
												a__ 							=	hidden_dropout_prob
												a__ 							=	attention_probs_dropout_prob
												a__ 							=	initializer_range
												a__ 							=	type_vocab_size
												a__ 							=	layer_norm_eps
												a__ 							=	use_cache
												a__ 							=	question_token_id


