





"""simple docstring"""



import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification







def 	__lowercase  (     snake_case_						:						int		)     ->int:



    '''simple docstring'''







    __A					:		Dict						=  [
        '''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 	__lowercase  (     snake_case_						:						int		)     ->Any:



    '''simple docstring'''







    __A				, __A					:		Tuple						=  emb.weight.shape
    __A					:		Dict						=  nn.Linear(snake_case_       ,snake_case_       ,bias=snake_case_		)
    __A					:		Optional[Any]						=  emb.weight.data
    return lin_layer







def 	__lowercase  (     snake_case_						:						List[Any]       ,snake_case_						:						str="uclanlp/plbart-base"       ,snake_case_						:						Union[str, Any]=False       ,snake_case_						:						str=False		)     ->List[Any]:



    '''simple docstring'''







    __A					:		Union[str, Any]						=  torch.load(snake_case_       ,map_location='''cpu'''		)['''model''']
    remove_ignore_keys_(snake_case_		)
    __A					:		Dict						=  state_dict['''encoder.embed_tokens.weight'''].shape[0]

    __A					:		List[str]						=  PLBartConfig.from_pretrained(snake_case_       ,vocab_size=snake_case_		)

    __A					:		Optional[Any]						=  state_dict['''decoder.embed_tokens.weight''']
    if not classification:
        __A					:		Any						=  PLBartForConditionalGeneration(snake_case_		)
        model.model.load_state_dict(snake_case_		)
        if finetuned:
            __A					:		Optional[int]						=  make_linear_from_emb(model.model.shared		)

    else:
        __A					:		int						=  {}
        for key, value in state_dict.copy().items():
            if key.startswith('''classification_heads.sentence_classification_head'''		):
                __A					:		Tuple						=  value
                state_dict.pop(snake_case_		)
        __A					:		Union[str, Any]						=  PLBartForSequenceClassification(snake_case_		)
        model.model.load_state_dict(snake_case_		)
        model.classification_head.load_state_dict(snake_case_		)

    return model


if __name__ == "__main__":
       a_				=    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"""
       )
       a_				=    parser.parse_args()
       a_				=    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"""



def 	__lowercase  (     snake_case_						:						Union[str, Any]		)     ->Optional[int]:



    '''simple docstring'''







    __A					:		Dict						=  [701, 301, 132, 57, 23, 10, 4, 1]
    for gap in gaps:
        for i in range(snake_case_       ,len(snake_case_		)		):
            __A					:		Tuple						=  collection[i]
            __A					:		Union[str, Any]						=  i
            while j >= gap and collection[j - gap] > insert_value:
                __A					:		Optional[int]						=  collection[j - gap]
                j -= gap
            if j != i:
                __A					:		Optional[int]						=  insert_value
    return collection


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       a_				=    input("""Enter numbers separated by a comma:\n""").strip()
       a_				=    [int(item) for item in user_input.split(""",""")]
       print(shell_sort(unsorted))




