import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification




def      lowerCAmelCase_				(  __A							)  ->       List[str]:







      '''simple docstring'''


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




def      lowerCAmelCase_				(  __A							)  ->       List[Any]:







      '''simple docstring'''


      UpperCAmelCase__	,   UpperCAmelCase__			=						emb.weight.shape
      UpperCAmelCase__			=						nn.Linear(__A,						__A,						bias=__A							)
      UpperCAmelCase__			=						emb.weight.data
      return lin_layer




def      lowerCAmelCase_				(  __A,						__A="uclanlp/plbart-base",						__A=False,						__A=False							)  ->       Any:







      '''simple docstring'''


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

      UpperCAmelCase__			=						PLBartConfig.from_pretrained(__A,						vocab_size=__A							)

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

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

      return model


if __name__ == "__main__":
      UpperCamelCase__       							=      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'
      )
      UpperCamelCase__       							=      parser.parse_args()
      UpperCamelCase__       							=      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)




from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__       							=      logging.get_logger(__name__)

UpperCamelCase__       							=      {
    'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json'
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}





class   A							(       UpperCAmelCase_  ):
   __UpperCAmelCase				:				int          =    'cpmant'


   def __init__(self  :		Optional[Any] ,						__UpperCAmelCase  :		int = 3_0_7_2_0 ,						__UpperCAmelCase  :		int = 4_0_9_6 ,						__UpperCAmelCase  :		int = 3_2 ,						__UpperCAmelCase  :		int = 1_2_8 ,						__UpperCAmelCase  :		int = 1_0_2_4_0 ,						__UpperCAmelCase  :		int = 4_8 ,						__UpperCAmelCase  :		int = 0.0 ,						__UpperCAmelCase  :		int = 5_1_2 ,						__UpperCAmelCase  :		int = 2_0_4_8 ,						__UpperCAmelCase  :		int = 1E-6 ,						__UpperCAmelCase  :		float = 1.0 ,						__UpperCAmelCase  :		int = 3_2 ,						__UpperCAmelCase  :		int = 3_2 ,						__UpperCAmelCase  :		int = 3_2 ,						__UpperCAmelCase  :		bool = True ,						**__UpperCAmelCase  :		Dict ,						)		->       int:
         """simple docstring"""



         super().__init__(**__UpperCAmelCase	)
         UpperCAmelCase__			=						prompt_types
         UpperCAmelCase__			=						prompt_length
         UpperCAmelCase__			=						segment_types
         UpperCAmelCase__			=						hidden_size
         UpperCAmelCase__			=						num_attention_heads
         UpperCAmelCase__			=						dim_head
         UpperCAmelCase__			=						dim_ff
         UpperCAmelCase__			=						num_hidden_layers
         UpperCAmelCase__			=						position_bias_num_buckets
         UpperCAmelCase__			=						position_bias_max_distance
         UpperCAmelCase__			=						dropout_p
         UpperCAmelCase__			=						eps
         UpperCAmelCase__			=						use_cache
         UpperCAmelCase__			=						vocab_size
         UpperCAmelCase__			=						init_std



