


'''simple docstring'''
import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	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(UpperCAmelCase    ,       UpperCAmelCase     )



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__       ,   snake_case__					:	Any          =					emb.weight.shape
       snake_case__					:	Optional[Any]          =					nn.Linear(UpperCAmelCase    ,       UpperCAmelCase    ,       bias=UpperCAmelCase     )
       snake_case__					:	List[Any]          =					emb.weight.data
       return lin_layer



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase="uclanlp/plbart-base"    ,       UpperCAmelCase=False    ,       UpperCAmelCase=False     ):






       """simple docstring"""





       snake_case__					:	List[Any]          =					torch.load(UpperCAmelCase    ,       map_location="""cpu"""     )["""model"""]
       remove_ignore_keys_(UpperCAmelCase     )
       snake_case__					:	Union[str, Any]          =					state_dict["""encoder.embed_tokens.weight"""].shape[0]

       snake_case__					:	Dict          =					PLBartConfig.from_pretrained(UpperCAmelCase    ,       vocab_size=UpperCAmelCase     )

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

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

       return model


if __name__ == "__main__":
     lowerCAmelCase__  =     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__  =     parser.parse_args()
     lowerCAmelCase__  =     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 math



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	Tuple          =					math.log(2    ,       10     ) * number
       snake_case__					:	int          =					round((log_number - int(UpperCAmelCase     ))    ,       15     )

       return difference



def 				lowerCAmelCase__	(       UpperCAmelCase = 67_8910     ):






       """simple docstring"""





       snake_case__					:	str          =					90
       snake_case__					:	Dict          =					0

       snake_case__					:	List[str]          =					math.log(1.2_3    ,       10     )
       snake_case__					:	Optional[Any]          =					math.log(1.2_4    ,       10     )
       snake_case__					:	Any          =					0

       while position < number:
              snake_case__					:	Tuple          =					log_difference(UpperCAmelCase     )

              if difference >= upper_limit:
                     power_iterator += 93

              elif difference < lower_limit:
                     power_iterator += 196

              else:
                     snake_case__					:	Union[str, Any]          =					power_iterator
                     power_iterator += 196
                     position += 1

       return previous_power


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     print(f"""{solution() = }""")




