




import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification





def 		A_			(		lowercase_	)							->List[Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						[
     'encoder.version',
     'decoder.version',
     'model.encoder.version',
     'model.decoder.version',
     '_float_tensor',
     'decoder.output_projection.weight',
 ]
 for k in ignore_keys:
  state_dict.pop(lowercase_					,       lowercase_	)





def 		A_			(		lowercase_	)							->List[str]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						emb.weight.shape
 SCREAMING_SNAKE_CASE							  =						nn.Linear(lowercase_					,       lowercase_					,       bias=lowercase_	)
 SCREAMING_SNAKE_CASE							  =						emb.weight.data
 return lin_layer





def 		A_			(		lowercase_					,       lowercase_="uclanlp/plbart-base"					,       lowercase_=False					,       lowercase_=False	)							->Union[str, Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						torch.load(lowercase_					,       map_location='cpu'	)['model']
 remove_ignore_keys_(lowercase_	)
 SCREAMING_SNAKE_CASE							  =						state_dict['encoder.embed_tokens.weight'].shape[0]

 SCREAMING_SNAKE_CASE							  =						PLBartConfig.from_pretrained(lowercase_					,       vocab_size=lowercase_	)

 SCREAMING_SNAKE_CASE							  =						state_dict['decoder.embed_tokens.weight']
 if not classification:
  SCREAMING_SNAKE_CASE							  =						PLBartForConditionalGeneration(lowercase_	)
  model.model.load_state_dict(lowercase_	)
  if finetuned:
   SCREAMING_SNAKE_CASE							  =						make_linear_from_emb(model.model.shared	)

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

 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)












import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm





def 		A_			(		lowercase_					,       lowercase_ = False	)							->Union[str, Any]:







 """simple docstring"""

 if device_specific:
  seed += AcceleratorState().process_index
 random.seed(lowercase_	)
 np.random.seed(lowercase_	)
 torch.manual_seed(lowercase_	)
 if is_xpu_available():
  torch.xpu.manual_seed_all(lowercase_	)
 elif is_npu_available():
  torch.npu.manual_seed_all(lowercase_	)
 else:
  torch.cuda.manual_seed_all(lowercase_	)
 # ^^ safe to call this function even if cuda is not available
 if is_tpu_available():
  xm.set_rng_state(lowercase_	)





def 		A_			(		lowercase_ = None					,       lowercase_ = None	)							->Optional[Any]:







 """simple docstring"""

 if rng_type == RNGType.TORCH:
  SCREAMING_SNAKE_CASE							  =						torch.get_rng_state()
 elif rng_type == RNGType.CUDA:
  SCREAMING_SNAKE_CASE							  =						torch.cuda.get_rng_state()
 elif rng_type == RNGType.XLA:
  assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
  SCREAMING_SNAKE_CASE							  =						torch.tensor(xm.get_rng_state()	)
 elif rng_type == RNGType.NPU:
  assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
  SCREAMING_SNAKE_CASE							  =						torch.npu.get_rng_state()
 elif rng_type == RNGType.XPU:
  assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
  SCREAMING_SNAKE_CASE							  =						torch.xpu.get_rng_state()
 elif rng_type == RNGType.GENERATOR:
  assert generator is not None, "Need a generator to synchronize its seed."
  SCREAMING_SNAKE_CASE							  =						generator.get_state()

 # Broadcast the rng state from device 0 to other devices
 SCREAMING_SNAKE_CASE							  =						AcceleratorState()
 if state.distributed_type == DistributedType.TPU:
  SCREAMING_SNAKE_CASE							  =						rng_state.to(xm.xla_device()	)
  xm.collective_broadcast([rng_state]	)
  xm.mark_step()
  SCREAMING_SNAKE_CASE							  =						rng_state.cpu()
 elif (
     state.distributed_type in CUDA_DISTRIBUTED_TYPES
     or state.distributed_type == DistributedType.MULTI_NPU
     or state.distributed_type == DistributedType.MULTI_XPU
 ):
  SCREAMING_SNAKE_CASE							  =						rng_state.to(state.device	)
  torch.distributed.broadcast(lowercase_					,       0	)
  SCREAMING_SNAKE_CASE							  =						rng_state.cpu()
 elif state.distributed_type == DistributedType.MULTI_CPU:
  torch.distributed.broadcast(lowercase_					,       0	)

 # Set the broadcast rng state
 if rng_type == RNGType.TORCH:
  torch.set_rng_state(lowercase_	)
 elif rng_type == RNGType.CUDA:
  torch.cuda.set_rng_state(lowercase_	)
 elif rng_type == RNGType.NPU:
  torch.npu.set_rng_state(lowercase_	)
 elif rng_type == RNGType.XPU:
  torch.xpu.set_rng_state(lowercase_	)
 elif rng_type == RNGType.XLA:
  xm.set_rng_state(rng_state.item()	)
 elif rng_type == RNGType.GENERATOR:
  generator.set_state(lowercase_	)





def 		A_			(		lowercase_					,       lowercase_ = None	)							->str:







 """simple docstring"""

 for rng_type in rng_types:
  synchronize_rng_state(RNGType(lowercase_	)					,       generator=lowercase_	)




