


"""simple docstring"""







import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification
def _A		(		lowercase						):







    """simple docstring"""
    a											=[
        '''encoder.version''',
        '''decoder.version''',
        '''model.encoder.version''',
        '''model.decoder.version''',
        '''_float_tensor''',
        '''decoder.output_projection.weight''',
    ]
    for k in ignore_keys:
        state_dict.pop(lowercase			,       lowercase						)
def _A		(		lowercase						):







    """simple docstring"""
    a						, a											=emb.weight.shape
    a											=nn.Linear(lowercase			,       lowercase			,       bias=lowercase						)
    a											=emb.weight.data
    return lin_layer
def _A		(		lowercase			,       lowercase="uclanlp/plbart-base"			,       lowercase=False			,       lowercase=False						):







    """simple docstring"""
    a											=torch.load(lowercase			,       map_location='''cpu'''						)['''model''']
    remove_ignore_keys_(lowercase						)
    a											=state_dict['''encoder.embed_tokens.weight'''].shape[0]

    a											=PLBartConfig.from_pretrained(lowercase			,       vocab_size=lowercase						)

    a											=state_dict['''decoder.embed_tokens.weight''']
    if not classification:
        a											=PLBartForConditionalGeneration(lowercase						)
        model.model.load_state_dict(lowercase						)
        if finetuned:
            a											=make_linear_from_emb(model.model.shared						)

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

    return model


if __name__ == "__main__":
       lowerCamelCase_  :  Tuple     		=    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"""
       )
       lowerCamelCase_  :  Union[str, Any]     		=    parser.parse_args()
       lowerCamelCase_  :  Optional[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"""







import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class  __A							(			_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 @register_to_config
 def __init__(						self					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A = False					,       )   ->	Optional[Any]:
     super().__init__()

     a											=nn.Linear(__A					,       __A					,       bias=__A		)

     a											=nn.Embedding(__A					,       __A		)
     a											=False

     a											=nn.Dropout(p=__A		)

     a											=TaConfig(
         d_model=__A					,       num_heads=__A					,       d_kv=__A					,       d_ff=__A					,       feed_forward_proj=__A					,       dropout_rate=__A					,       is_decoder=__A					,       is_encoder_decoder=__A					,       )
     a											=nn.ModuleList()
     for lyr_num in range(__A		):
         a											=TaBlock(__A		)
         self.encoders.append(__A		)

     a											=TaLayerNorm(__A		)
     a											=nn.Dropout(p=__A		)





 def  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A		)   ->	str:
     a											=self.input_proj(__A		)

     # terminal relative positional encodings
     a											=encoder_inputs.shape[1]
     a											=torch.arange(__A					,       device=encoder_inputs.device		)

     a											=encoder_inputs_mask.sum(-1		)
     a											=torch.roll(input_positions.unsqueeze(0		)					,       tuple(seq_lens.tolist()		)					,       dims=0		)
     x += self.position_encoding(__A		)

     a											=self.dropout_pre(__A		)

     # inverted the attention mask
     a											=encoder_inputs.size()
     a											=self.get_extended_attention_mask(__A					,       __A		)

     for lyr in self.encoders:
         a											=lyr(__A					,       __A		)[0]
     a											=self.layer_norm(__A		)

     return self.dropout_post(__A		), encoder_inputs_mask