





import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Union[str, Any]:
  lowercase__           =  [
      '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       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Dict:
  lowercase__      ,   lowercase__           =  emb.weight.shape
  lowercase__           =  nn.Linear(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						bias=_SCREAMING_SNAKE_CASE		)
  lowercase__           =  emb.weight.data
  return lin_layer




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE="facebook/mbart-large-en-ro"		,						_SCREAMING_SNAKE_CASE=False		,						_SCREAMING_SNAKE_CASE=False		)    ->       str:
  lowercase__           =  torch.load(_SCREAMING_SNAKE_CASE		,						map_location='cpu'		)['model']
  remove_ignore_keys_(_SCREAMING_SNAKE_CASE		)
  lowercase__           =  state_dict['encoder.embed_tokens.weight'].shape[0]

  lowercase__           =  MBartConfig.from_pretrained(_SCREAMING_SNAKE_CASE		,						vocab_size=_SCREAMING_SNAKE_CASE		)
  if mbart_aa and finetuned:
    lowercase__           =  'relu'

  lowercase__           =  state_dict['decoder.embed_tokens.weight']
  lowercase__           =  MBartForConditionalGeneration(_SCREAMING_SNAKE_CASE		)
  model.model.load_state_dict(_SCREAMING_SNAKE_CASE		)

  if finetuned:
    lowercase__           =  make_linear_from_emb(model.model.shared		)

  return model


if __name__ == "__main__":
      lowercase_          =		argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a path to a 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="""facebook/mbart-large-cc25""",
          type=str,
          help="""Which huggingface architecture to use: mbart-large""",
      )
      parser.add_argument("""--mbart_50""", action="""store_true""", help="""whether the model is mMART-50 checkpoint""")
      parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
      lowercase_          =		parser.parse_args()
      lowercase_          =		convert_fairseq_mbart_checkpoint_from_disk(
          args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
      )
      model.save_pretrained(args.pytorch_dump_folder_path)









import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
      from deepspeed import DeepSpeedEngine

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




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       List[str]:
  if is_torch_version('<'		,						'2.0.0'		) or not hasattr(_SCREAMING_SNAKE_CASE		,						'_dynamo'		):
    return False
  return isinstance(_SCREAMING_SNAKE_CASE		,						torch._dynamo.eval_frame.OptimizedModule		)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE = True		)    ->       Dict:
  lowercase__           =  (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

  lowercase__           =  is_compiled_module(_SCREAMING_SNAKE_CASE		)
  if is_compiled:
    lowercase__           =  model
    lowercase__           =  model._orig_mod

  if is_deepspeed_available():
    options += (DeepSpeedEngine,)

  while isinstance(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		):
    lowercase__           =  model.module

  if not keep_fpaa_wrapper:
    lowercase__           =  getattr(_SCREAMING_SNAKE_CASE		,						'forward'		)
    lowercase__           =  model.__dict__.pop('_original_forward'		,						_SCREAMING_SNAKE_CASE		)
    if original_forward is not None:
      while hasattr(_SCREAMING_SNAKE_CASE		,						'__wrapped__'		):
        lowercase__           =  forward.__wrapped__
        if forward == original_forward:
          break
      lowercase__           =  forward
    if getattr(_SCREAMING_SNAKE_CASE		,						'_converted_to_transformer_engine'		,						_SCREAMING_SNAKE_CASE		):
      convert_model(_SCREAMING_SNAKE_CASE		,						to_transformer_engine=_SCREAMING_SNAKE_CASE		)

  if is_compiled:
    lowercase__           =  model
    lowercase__           =  compiled_model

  return model




def       __UpperCamelCase  ()    ->       Tuple:
  PartialState().wait_for_everyone()




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       List[Any]:
  if PartialState().distributed_type == DistributedType.TPU:
    xm.save(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)
  elif PartialState().local_process_index == 0:
    torch.save(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)




@contextmanager
def       __UpperCamelCase  (**_SCREAMING_SNAKE_CASE		)    ->       Optional[Any]:
  for key, value in kwargs.items():
    lowercase__           =  str(_SCREAMING_SNAKE_CASE		)

  yield

  for key in kwargs:
    if key.upper() in os.environ:
      del os.environ[key.upper()]




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       List[str]:
  if not hasattr(_SCREAMING_SNAKE_CASE		,						'__qualname__'		) and not hasattr(_SCREAMING_SNAKE_CASE		,						'__name__'		):
    lowercase__           =  getattr(_SCREAMING_SNAKE_CASE		,						'__class__'		,						_SCREAMING_SNAKE_CASE		)
  if hasattr(_SCREAMING_SNAKE_CASE		,						'__qualname__'		):
    return obj.__qualname__
  if hasattr(_SCREAMING_SNAKE_CASE		,						'__name__'		):
    return obj.__name__
  return str(_SCREAMING_SNAKE_CASE		)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Dict:
  for key, value in source.items():
    if isinstance(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		):
      lowercase__           =  destination.setdefault(_SCREAMING_SNAKE_CASE		,						{}		)
      merge_dicts(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)
    else:
      lowercase__           =  value

  return destination




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE = None		)    ->       bool:
  if port is None:
    lowercase__           =  29500
  with socket.socket(socket.AF_INET		,						socket.SOCK_STREAM		) as s:
    return s.connect_ex(('localhost', port)		) == 0


