


'''simple docstring'''





import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification


def 					a__    (				UpperCamelCase_	:			Optional[Any]	):
   UpperCAmelCase__  :Tuple            =			[
       "encoder.version",
       "decoder.version",
       "model.encoder.version",
       "model.decoder.version",
       "_float_tensor",
       "decoder.output_projection.weight",
   ]
   for k in ignore_keys:
      state_dict.pop(__lowerCAmelCase,      __lowerCAmelCase	)


def 					a__    (				UpperCamelCase_	:			List[str]	):
   UpperCAmelCase__  :List[Any]            =			emb.weight.shape
   UpperCAmelCase__  :List[str]            =			nn.Linear(__lowerCAmelCase,      __lowerCAmelCase,      bias=__lowerCAmelCase	)
   UpperCAmelCase__  :str            =			emb.weight.data
   return lin_layer


def 					a__    (				UpperCamelCase_	:			Optional[Any],      UpperCamelCase_	:			int="uclanlp/plbart-base",      UpperCamelCase_	:			Optional[Any]=False,      UpperCamelCase_	:			Dict=False	):
   UpperCAmelCase__  :Any            =			torch.load(__lowerCAmelCase,      map_location='''cpu'''	)["model"]
   remove_ignore_keys_(__lowerCAmelCase	)
   UpperCAmelCase__  :Union[str, Any]            =			state_dict["encoder.embed_tokens.weight"].shape[0]

   UpperCAmelCase__  :int            =			PLBartConfig.from_pretrained(__lowerCAmelCase,      vocab_size=__lowerCAmelCase	)

   UpperCAmelCase__  :Union[str, Any]            =			state_dict["decoder.embed_tokens.weight"]
   if not classification:
      UpperCAmelCase__  :Optional[int]            =			PLBartForConditionalGeneration(__lowerCAmelCase	)
      model.model.load_state_dict(__lowerCAmelCase	)
      if finetuned:
         UpperCAmelCase__  :List[Any]            =			make_linear_from_emb(model.model.shared	)

   else:
      UpperCAmelCase__  :List[Any]            =			{}
      for key, value in state_dict.copy().items():
         if key.startswith('''classification_heads.sentence_classification_head'''	):
            UpperCAmelCase__  :List[str]            =			value
            state_dict.pop(__lowerCAmelCase	)
      UpperCAmelCase__  :List[Any]            =			PLBartForSequenceClassification(__lowerCAmelCase	)
      model.model.load_state_dict(__lowerCAmelCase	)
      model.classification_head.load_state_dict(__lowerCAmelCase	)

   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'''





from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
    import torch

__lowerCamelCase       				=	logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
    from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):


    def 					a__    (				cls	:			Optional[int]	):
       return cls


def 					a__    (				UpperCamelCase_	:			Union[Tuple, List],      UpperCamelCase_	:			Optional[Union[List["torch.Generator"], "torch.Generator"]] = None,      UpperCamelCase_	:			Optional["torch.device"] = None,      UpperCamelCase_	:			Optional["torch.dtype"] = None,      UpperCamelCase_	:			Optional["torch.layout"] = None,      ):
   UpperCAmelCase__  :Union[str, Any]            =			device
   UpperCAmelCase__  :List[Any]            =			shape[0]

   UpperCAmelCase__  :Tuple            =			layout or torch.strided
   UpperCAmelCase__  :Union[str, Any]            =			device or torch.device('''cpu'''	)

   if generator is not None:
      UpperCAmelCase__  :str            =			generator.device.type if not isinstance(UpperCamelCase_,      UpperCamelCase_	) else generator[0].device.type
      if gen_device_type != device.type and gen_device_type == "cpu":
         UpperCAmelCase__  :Tuple            =			'''cpu'''
         if device != "mps":
            logger.info(
                F'''The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'''
                F''' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'''
                F''' slighly speed up this function by passing a generator that was created on the {device} device.'''	)
      elif gen_device_type != device.type and gen_device_type == "cuda":
         raise ValueError(F'''Cannot generate a {device} tensor from a generator of type {gen_device_type}.'''	)

   if isinstance(UpperCamelCase_,      UpperCamelCase_	):
      UpperCAmelCase__  :List[str]            =			(1,) + shape[1:]
      UpperCAmelCase__  :Optional[int]            =			[
          torch.randn(UpperCamelCase_,      generator=generator[i],      device=UpperCamelCase_,      dtype=UpperCamelCase_,      layout=UpperCamelCase_	)
          for i in range(UpperCamelCase_	)
      ]
      UpperCAmelCase__  :int            =			torch.cat(UpperCamelCase_,      dim=0	).to(UpperCamelCase_	)
   else:
      UpperCAmelCase__  :Optional[int]            =			torch.randn(UpperCamelCase_,      generator=UpperCamelCase_,      device=UpperCamelCase_,      dtype=UpperCamelCase_,      layout=UpperCamelCase_	).to(UpperCamelCase_	)

   return latents


def 					a__    (				UpperCamelCase_	:			Dict	):
   if is_torch_version('''<''',      '''2.0.0'''	) or not hasattr(UpperCamelCase_,      '''_dynamo'''	):
      return False
   return isinstance(UpperCamelCase_,      torch._dynamo.eval_frame.OptimizedModule	)



