

'''simple docstring'''

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


UpperCAmelCase					 =	logging.get_logger(__name__)

UpperCAmelCase					 =	{
    """studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""",
    """studio-ousia/luke-large""": """https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json""",
}


class 	lowerCAmelCase  (   a_ ):
       lowerCAmelCase_												=			"luke"







       def __init__(				self      :			str    ,   __lowercase      :			Optional[Any]=50267    ,   __lowercase      :			Dict=500000    ,   __lowercase      :			Union[str, Any]=768    ,   __lowercase      :			Dict=256    ,   __lowercase      :			List[str]=12    ,   __lowercase      :			str=12    ,   __lowercase      :			Tuple=3072    ,   __lowercase      :			Optional[int]="gelu"    ,   __lowercase      :			Any=0.1    ,   __lowercase      :			str=0.1    ,   __lowercase      :			Optional[Any]=512    ,   __lowercase      :			Any=2    ,   __lowercase      :			Optional[int]=0.0_2    ,   __lowercase      :			Optional[int]=1E-12    ,   __lowercase      :			Union[str, Any]=True    ,   __lowercase      :			str=None    ,   __lowercase      :			Union[str, Any]=1    ,   __lowercase      :			int=0    ,   __lowercase      :			Tuple=2    ,   **__lowercase      :			Tuple    ,   ):







            """simple docstring"""







            super().__init__(pad_token_id=_a    ,   bos_token_id=_a    ,   eos_token_id=_a    ,   **_a   )

            __lowercase      		=vocab_size
            __lowercase      		=entity_vocab_size
            __lowercase      		=hidden_size
            __lowercase      		=entity_emb_size
            __lowercase      		=num_hidden_layers
            __lowercase      		=num_attention_heads
            __lowercase      		=hidden_act
            __lowercase      		=intermediate_size
            __lowercase      		=hidden_dropout_prob
            __lowercase      		=attention_probs_dropout_prob
            __lowercase      		=max_position_embeddings
            __lowercase      		=type_vocab_size
            __lowercase      		=initializer_range
            __lowercase      		=layer_norm_eps
            __lowercase      		=use_entity_aware_attention
            __lowercase      		=classifier_dropout




"""simple docstring"""




import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def     __magic_name__      ( __snake_case							:			List[Any]    )					->							Dict:
       lowercase       :							Optional[int]									=				[
           "decoder.version",
           "decoder.output_projection.weight",
           "_float_tensor",
           "decoder.embed_positions._float_tensor",
       ]
       for k in ignore_keys:
              state_dict.pop(__snake_case	,					__snake_case    )


def     __magic_name__      ( __snake_case							:			str    )					->							Optional[Any]:
       lowercase						,       lowercase       :							Tuple									=				emb.weight.shape
       lowercase       :							str									=				nn.Linear(__snake_case	,					__snake_case	,					bias=__snake_case    )
       lowercase       :							Optional[Any]									=				emb.weight.data
       return lin_layer


def     __magic_name__      ( __snake_case							:			Any    )					->							str:
       lowercase       :							int									=				torch.load(__snake_case	,					map_location="cpu"    )
       lowercase       :							Optional[int]									=				Namespace(**checkpoint["cfg"]["model"]    )
       lowercase       :							Union[str, Any]									=				checkpoint["model"]
       remove_ignore_keys_(__snake_case    )
       lowercase       :							Optional[int]									=				state_dict["decoder.embed_tokens.weight"].shape[0]

       lowercase       :							int									=				{key.replace("decoder"	,					"model"    ): val for key, val in state_dict.items()}

       lowercase       :							List[Any]									=				XGLMConfig(
           vocab_size=__snake_case	,					max_position_embeddings=args.max_target_positions	,					num_layers=args.decoder_layers	,					attention_heads=args.decoder_attention_heads	,					ffn_dim=args.decoder_ffn_embed_dim	,					d_model=args.decoder_embed_dim	,					layerdrop=args.decoder_layerdrop	,					dropout=args.dropout	,					attention_dropout=args.attention_dropout	,					activation_dropout=args.activation_dropout	,					activation_function="gelu"	,					scale_embedding=not args.no_scale_embedding	,					tie_word_embeddings=args.share_decoder_input_output_embed	,					)

       lowercase       :							int									=				XGLMForCausalLM(__snake_case    )
       lowercase       :							Any									=				model.load_state_dict(__snake_case	,					strict=__snake_case    )
       print(__snake_case    )
       lowercase       :							Tuple									=				make_linear_from_emb(model.model.embed_tokens    )

       return model


if __name__ == "__main__":
   _A     :			List[Any]  						=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
   parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
   _A     :			Tuple  						=			parser.parse_args()
   _A     :			List[str]  						=			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
   model.save_pretrained(args.pytorch_dump_folder_path)


