



"""simple docstring"""




import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		int       )						->     Optional[Any]:





     '''simple docstring'''

     _UpperCAmelCase    :			List[Any]              =					[
         "encoder.version",
         "decoder.version",
         "model.encoder.version",
         "model.decoder.version",
         "_float_tensor",
         "decoder.output_projection.weight",
     ]
     for k in ignore_keys:
          state_dict.pop(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[Any]       )						->     Dict:





     '''simple docstring'''

     _UpperCAmelCase       , _UpperCAmelCase    :			Optional[int]              =					emb.weight.shape
     _UpperCAmelCase    :			Union[str, Any]              =					nn.Linear(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						bias=SCREAMING_SNAKE_CASE__       )
     _UpperCAmelCase    :			Union[str, Any]              =					emb.weight.data
     return lin_layer

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[Any]		,						SCREAMING_SNAKE_CASE__			:		str="uclanlp/plbart-base"		,						SCREAMING_SNAKE_CASE__			:		Dict=False		,						SCREAMING_SNAKE_CASE__			:		List[Any]=False       )						->     List[str]:





     '''simple docstring'''

     _UpperCAmelCase    :			Tuple              =					torch.load(SCREAMING_SNAKE_CASE__		,						map_location="cpu"       )["model"]
     remove_ignore_keys_(SCREAMING_SNAKE_CASE__       )
     _UpperCAmelCase    :			Optional[Any]              =					state_dict["encoder.embed_tokens.weight"].shape[0]

     _UpperCAmelCase    :			Any              =					PLBartConfig.from_pretrained(SCREAMING_SNAKE_CASE__		,						vocab_size=SCREAMING_SNAKE_CASE__       )

     _UpperCAmelCase    :			Optional[int]              =					state_dict["decoder.embed_tokens.weight"]
     if not classification:
          _UpperCAmelCase    :			Optional[int]              =					PLBartForConditionalGeneration(SCREAMING_SNAKE_CASE__       )
          model.model.load_state_dict(SCREAMING_SNAKE_CASE__       )
          if finetuned:
               _UpperCAmelCase    :			Tuple              =					make_linear_from_emb(model.model.shared       )

     else:
          _UpperCAmelCase    :			Optional[int]              =					{}
          for key, value in state_dict.copy().items():
               if key.startswith("classification_heads.sentence_classification_head"       ):
                    _UpperCAmelCase    :			Union[str, Any]              =					value
                    state_dict.pop(SCREAMING_SNAKE_CASE__       )
          _UpperCAmelCase    :			Any              =					PLBartForSequenceClassification(SCREAMING_SNAKE_CASE__       )
          model.model.load_state_dict(SCREAMING_SNAKE_CASE__       )
          model.classification_head.load_state_dict(SCREAMING_SNAKE_CASE__       )

     return model


if __name__ == "__main__":
 _lowerCAmelCase       :      Any							=							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       :      List[str]							=							parser.parse_args()
 _lowerCAmelCase       :      Union[str, 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"""




_lowerCAmelCase       :      str							=							{
    "0": 1,
    "1": 1,
    "2": 2,
    "3": 6,
    "4": 24,
    "5": 1_20,
    "6": 7_20,
    "7": 50_40,
    "8": 4_03_20,
    "9": 36_28_80,
}

_lowerCAmelCase       :      Optional[int]							=							{1_45: 1_45}

_lowerCAmelCase       :      int							=							{
    1_45: 0,
    1_69: 3,
    3_63_01: 3,
    14_54: 3,
    8_71: 2,
    4_53_61: 2,
    8_72: 2,
}

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		int       )						->     int:





     '''simple docstring'''

     if n in CACHE_SUM_DIGIT_FACTORIALS:
          return CACHE_SUM_DIGIT_FACTORIALS[n]
     _UpperCAmelCase    :			Dict              =					sum(DIGIT_FACTORIALS[let] for let in str(SCREAMING_SNAKE_CASE__       )       )
     _UpperCAmelCase    :			Any              =					ret
     return ret

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		set | None = None       )						->     int:





     '''simple docstring'''

     _UpperCAmelCase    :			Dict              =					previous or set()
     if n in CHAIN_LENGTH_CACHE:
          return CHAIN_LENGTH_CACHE[n]
     _UpperCAmelCase    :			Dict              =					sum_digit_factorials(SCREAMING_SNAKE_CASE__       )
     if next_number in previous:
          _UpperCAmelCase    :			List[str]              =					0
          return 0
     else:
          previous.add(SCREAMING_SNAKE_CASE__       )
          _UpperCAmelCase    :			str              =					1 + chain_length(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
          _UpperCAmelCase    :			str              =					ret
          return ret

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		int = 60		,						SCREAMING_SNAKE_CASE__			:		int = 1_000_000       )						->     int:





     '''simple docstring'''

     return sum(1 for i in range(1		,						SCREAMING_SNAKE_CASE__       ) if chain_length(SCREAMING_SNAKE_CASE__       ) == num_terms       )


if __name__ == "__main__":
 print(F"{solution() = }")
