



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


__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE           =  {
    """s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						a__   =  "open-llama"







						def __init__(	self      :			Optional[int]	,	__lowerCamelCase      :			Optional[Any]=10_00_00	,	__lowerCamelCase      :			List[str]=40_96	,	__lowerCamelCase      :			Any=1_10_08	,	__lowerCamelCase      :			str=32	,	__lowerCamelCase      :			List[str]=32	,	__lowerCamelCase      :			Dict="silu"	,	__lowerCamelCase      :			List[Any]=20_48	,	__lowerCamelCase      :			str=0.02	,	__lowerCamelCase      :			List[str]=1e-6	,	__lowerCamelCase      :			Dict=True	,	__lowerCamelCase      :			str=0	,	__lowerCamelCase      :			Optional[Any]=1	,	__lowerCamelCase      :			List[Any]=2	,	__lowerCamelCase      :			Any=False	,	__lowerCamelCase      :			Optional[int]=True	,	__lowerCamelCase      :			str=0.1	,	__lowerCamelCase      :			Any=0.1	,	__lowerCamelCase      :			Tuple=True	,	__lowerCamelCase      :			str=True	,	__lowerCamelCase      :			Optional[int]=None	,	**__lowerCamelCase      :			Optional[Any]	,	)      ->				Optional[Any]:
									A							:					Optional[int]              =						vocab_size
									A							:					Optional[Any]              =						max_position_embeddings
									A							:					Tuple              =						hidden_size
									A							:					List[Any]              =						intermediate_size
									A							:					Tuple              =						num_hidden_layers
									A							:					Any              =						num_attention_heads
									A							:					Optional[int]              =						hidden_act
									A							:					List[str]              =						initializer_range
									A							:					int              =						rms_norm_eps
									A							:					List[str]              =						use_cache
									A							:					Any              =						kwargs.pop(
									    "use_memorry_efficient_attention"	,	__lowerCamelCase )
									A							:					str              =						hidden_dropout_prob
									A							:					Any              =						attention_dropout_prob
									A							:					Tuple              =						use_stable_embedding
									A							:					Optional[int]              =						shared_input_output_embedding
									A							:					Any              =						rope_scaling
									self._rope_scaling_validation()

									super().__init__(
									    pad_token_id=__lowerCamelCase	,	bos_token_id=__lowerCamelCase	,	eos_token_id=__lowerCamelCase	,	tie_word_embeddings=__lowerCamelCase	,	**__lowerCamelCase	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Dict:
									if self.rope_scaling is None:
												return

									if not isinstance(self.rope_scaling	,	__lowerCamelCase ) or len(self.rope_scaling ) != 2:
												raise ValueError(
												    "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
												    F"""got {self.rope_scaling}""" )
									A							:					Any              =						self.rope_scaling.get("type"	,	__lowerCamelCase )
									A							:					Union[str, Any]              =						self.rope_scaling.get("factor"	,	__lowerCamelCase )
									if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
												raise ValueError(
												    F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}""" )
									if rope_scaling_factor is None or not isinstance(__lowerCamelCase	,	__lowerCamelCase ) or rope_scaling_factor <= 1.0:
												raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}""" )




import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Optional[Any]              =						RobertaPreLayerNormConfig.from_pretrained(
			    _lowerCamelCase ,    architectures=["RobertaPreLayerNormForMaskedLM"]    )

			# convert state_dict
			A							:					List[Any]              =						torch.load(hf_hub_download(repo_id=_lowerCamelCase ,    filename="pytorch_model.bin"    )    )
			A							:					Union[str, Any]              =						{}
			for tensor_key, tensor_value in original_state_dict.items():
						# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
						if tensor_key.startswith("roberta."    ):
									A							:					int              =						"roberta_prelayernorm." + tensor_key[len("roberta."    ) :]

						# The original implementation contains weights which are not used, remove them from the state_dict
						if tensor_key.endswith(".self.LayerNorm.weight"    ) or tensor_key.endswith(".self.LayerNorm.bias"    ):
									continue

						A							:					Any              =						tensor_value

			A							:					Optional[int]              =						RobertaPreLayerNormForMaskedLM.from_pretrained(
			    pretrained_model_name_or_path=_lowerCamelCase ,    config=_lowerCamelCase ,    state_dict=_lowerCamelCase    )
			model.save_pretrained(_lowerCamelCase    )

			# convert tokenizer
			A							:					Optional[Any]              =						AutoTokenizer.from_pretrained(_lowerCamelCase    )
			tokenizer.save_pretrained(_lowerCamelCase    )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE           =  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--checkpoint-repo""",
							    default=None,
							    type=str,
							    required=True,
							    help="""Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							__SCREAMING_SNAKE_CASE           =  parser.parse_args()
							convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)