






"""simple docstring"""
import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification


def        A	(							__snake_case:   List[str]     )						->		Any:

       """simple docstring"""





       __magic_name__										=						[
           "encoder.version",
           "decoder.version",
           "model.encoder.version",
           "model.decoder.version",
           "_float_tensor",
           "decoder.output_projection.weight",
       ]
       for k in ignore_keys:
              state_dict.pop(__snake_case   ,    __snake_case     )


def        A	(							__snake_case:   Optional[Any]     )						->		str:

       """simple docstring"""





       __magic_name__										=						emb.weight.shape
       __magic_name__										=						nn.Linear(__snake_case   ,    __snake_case   ,    bias=__snake_case     )
       __magic_name__										=						emb.weight.data
       return lin_layer


def        A	(							__snake_case:   Union[str, Any]   ,    __snake_case:   Tuple="uclanlp/plbart-base"   ,    __snake_case:   Tuple=False   ,    __snake_case:   Union[str, Any]=False     )						->		Optional[int]:

       """simple docstring"""





       __magic_name__										=						torch.load(__snake_case   ,    map_location='cpu'     )["model"]
       remove_ignore_keys_(__snake_case     )
       __magic_name__										=						state_dict["encoder.embed_tokens.weight"].shape[0]

       __magic_name__										=						PLBartConfig.from_pretrained(__snake_case   ,    vocab_size=__snake_case     )

       __magic_name__										=						state_dict["decoder.embed_tokens.weight"]
       if not classification:
              __magic_name__										=						PLBartForConditionalGeneration(__snake_case     )
              model.model.load_state_dict(__snake_case     )
              if finetuned:
                     __magic_name__										=						make_linear_from_emb(model.model.shared     )

       else:
              __magic_name__										=						{}
              for key, value in state_dict.copy().items():
                     if key.startswith('classification_heads.sentence_classification_head'     ):
                            __magic_name__										=						value
                            state_dict.pop(__snake_case     )
              __magic_name__										=						PLBartForSequenceClassification(__snake_case     )
              model.model.load_state_dict(__snake_case     )
              model.classification_head.load_state_dict(__snake_case     )

       return model


if __name__ == "__main__":
 snake_case   :   Dict    		=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument("""fairseq_path""", type=str, help="""model.pt on local filesystem.""")
 parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
 parser.add_argument(
     """--hf_config""",
     default="""uclanlp/plbart-base""",
     type=str,
     help="""Which huggingface architecture to use: plbart-base""",
 )
 parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
 parser.add_argument(
     """--classification""", action="""store_true""", help="""whether the model is a classification checkpoint"""
 )
 snake_case   :   Optional[Any]    		=		parser.parse_args()
 snake_case   :   Any    		=		convert_fairseq_plbart_checkpoint_from_disk(
     args.fairseq_path,
     hf_config_path=args.hf_config,
     finetuned=args.finetuned,
     classification=args.classification,
 )
 model.save_pretrained(args.pytorch_dump_folder_path)











"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case   :   int    		=		logging.get_logger(__name__)

snake_case   :   List[str]    		=		{
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}



class 					UpperCamelCase__   (							a_):







    """simple docstring"""
    __UpperCAmelCase											=   """nystromformer"""







    def __init__(		self   :  Dict							,						UpperCamelCase_   :  Optional[Any]=3_0_0_0_0							,						UpperCamelCase_   :  Dict=7_6_8							,						UpperCamelCase_   :  Tuple=1_2							,						UpperCamelCase_   :  Any=1_2							,						UpperCamelCase_   :  List[str]=3_0_7_2							,						UpperCamelCase_   :  Tuple="gelu_new"							,						UpperCamelCase_   :  Optional[Any]=0.1							,						UpperCamelCase_   :  str=0.1							,						UpperCamelCase_   :  Tuple=5_1_0							,						UpperCamelCase_   :  Optional[int]=2							,						UpperCamelCase_   :  Optional[int]=6_4							,						UpperCamelCase_   :  Tuple=6_4							,						UpperCamelCase_   :  Optional[int]=6_5							,						UpperCamelCase_   :  Optional[int]=False							,						UpperCamelCase_   :  List[str]=0.02							,						UpperCamelCase_   :  List[Any]=1e-5							,						UpperCamelCase_   :  Union[str, Any]=1							,						UpperCamelCase_   :  Tuple=0							,						UpperCamelCase_   :  List[Any]=2							,						**UpperCamelCase_   :  str							,						):





           '''simple docstring'''

           __magic_name__										=						vocab_size
           __magic_name__										=						max_position_embeddings
           __magic_name__										=						hidden_size
           __magic_name__										=						num_hidden_layers
           __magic_name__										=						num_attention_heads
           __magic_name__										=						intermediate_size
           __magic_name__										=						hidden_act
           __magic_name__										=						hidden_dropout_prob
           __magic_name__										=						attention_probs_dropout_prob
           __magic_name__										=						initializer_range
           __magic_name__										=						type_vocab_size
           __magic_name__										=						segment_means_seq_len
           __magic_name__										=						num_landmarks
           __magic_name__										=						conv_kernel_size
           __magic_name__										=						inv_coeff_init_option
           __magic_name__										=						layer_norm_eps
           super().__init__(pad_token_id=UpperCamelCase_							,						bos_token_id=UpperCamelCase_							,						eos_token_id=UpperCamelCase_							,						**UpperCamelCase_   )