


'''simple docstring'''



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






def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  Union[str, Any]		, _SCREAMING_SNAKE_CASE :  bool = True  ):
					__a     :			Union[str, Any]          =    (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

					__a     :			Optional[int]          =    is_compiled_module(UpperCamelCase_  )
					if is_compiled:
										__a     :			Tuple          =    model
										__a     :			Dict          =    model._orig_mod

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

					while isinstance(UpperCamelCase_		, UpperCamelCase_  ):
										__a     :			Tuple          =    model.module

					if not keep_fpaa_wrapper:
										__a     :			Union[str, Any]          =    getattr(UpperCamelCase_		, 'forward'  )
										__a     :			Optional[Any]          =    model.__dict__.pop('_original_forward'		, UpperCamelCase_  )
										if original_forward is not None:
															while hasattr(UpperCamelCase_		, '__wrapped__'  ):
																				__a     :			str          =    forward.__wrapped__
																				if forward == original_forward:
																									break
															__a     :			str          =    forward
										if getattr(UpperCamelCase_		, '_converted_to_transformer_engine'		, UpperCamelCase_  ):
															convert_model(UpperCamelCase_		, to_transformer_engine=UpperCamelCase_  )

					if is_compiled:
										__a     :			int          =    model
										__a     :			Dict          =    compiled_model

					return model






def     lowerCamelCase				():
					PartialState().wait_for_everyone()






def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  int		, _SCREAMING_SNAKE_CASE :  int  ):
					if PartialState().distributed_type == DistributedType.TPU:
										xm.save(UpperCamelCase_		, UpperCamelCase_  )
					elif PartialState().local_process_index == 0:
										torch.save(UpperCamelCase_		, UpperCamelCase_  )






@contextmanager
def     lowerCamelCase				(**_SCREAMING_SNAKE_CASE :  Optional[Any]  ):
					for key, value in kwargs.items():
										__a     :			List[Any]          =    str(UpperCamelCase_  )

					yield

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






def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  List[str]  ):
					if not hasattr(UpperCamelCase_		, '__qualname__'  ) and not hasattr(UpperCamelCase_		, '__name__'  ):
										__a     :			str          =    getattr(UpperCamelCase_		, '__class__'		, UpperCamelCase_  )
					if hasattr(UpperCamelCase_		, '__qualname__'  ):
										return obj.__qualname__
					if hasattr(UpperCamelCase_		, '__name__'  ):
										return obj.__name__
					return str(UpperCamelCase_  )






def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  Optional[Any]		, _SCREAMING_SNAKE_CASE :  str  ):
					for key, value in source.items():
										if isinstance(UpperCamelCase_		, UpperCamelCase_  ):
															__a     :			int          =    destination.setdefault(UpperCamelCase_		, {}  )
															merge_dicts(UpperCamelCase_		, UpperCamelCase_  )
										else:
															__a     :			Union[str, Any]          =    value

					return destination






def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  int = None  ):
					if port is None:
										__a     :			List[Any]          =    29_500
					with socket.socket(socket.AF_INET		, socket.SOCK_STREAM  ) as s:
										return s.connect_ex(('localhost', port)  ) == 0







'''simple docstring'''





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def        a__     (					UpperCamelCase_						:      Tuple						):
   UpperCAmelCase__	:Dict      =      [
       '''encoder.version''',
       '''decoder.version''',
       '''model.encoder.version''',
       '''model.decoder.version''',
       '''decoder.output_projection.weight''',
       '''_float_tensor''',
       '''encoder.embed_positions._float_tensor''',
       '''decoder.embed_positions._float_tensor''',
   ]
   for k in ignore_keys:
      state_dict.pop(UpperCamelCase_,   UpperCamelCase_						)


def        a__     (					UpperCamelCase_						:      Union[str, Any]						):
   UpperCAmelCase__   ,						UpperCAmelCase__	:Optional[Any]      =      emb.weight.shape
   UpperCAmelCase__	:int      =      nn.Linear(UpperCamelCase_,   UpperCamelCase_,   bias=UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      emb.weight.data
   return lin_layer


def        a__     (					UpperCamelCase_						:      str						):
   UpperCAmelCase__	:List[str]      =      torch.load(UpperCamelCase_,   map_location='''cpu'''						)
   UpperCAmelCase__	:List[Any]      =      mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
   UpperCAmelCase__	:List[Any]      =      mam_aaa['''model''']
   remove_ignore_keys_(UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      state_dict['''encoder.embed_tokens.weight'''].shape[0]

   UpperCAmelCase__	:Union[str, Any]      =      MaMaaaConfig(
       vocab_size=UpperCamelCase_,   max_position_embeddings=1_024,   encoder_layers=args.encoder_layers,   decoder_layers=args.decoder_layers,   encoder_attention_heads=args.encoder_attention_heads,   decoder_attention_heads=args.decoder_attention_heads,   encoder_ffn_dim=args.encoder_ffn_embed_dim,   decoder_ffn_dim=args.decoder_ffn_embed_dim,   d_model=args.encoder_embed_dim,   encoder_layerdrop=args.encoder_layerdrop,   decoder_layerdrop=args.decoder_layerdrop,   dropout=args.dropout,   attention_dropout=args.attention_dropout,   activation_dropout=args.activation_dropout,   activation_function='''relu''',   )

   UpperCAmelCase__	:str      =      state_dict['''decoder.embed_tokens.weight''']
   UpperCAmelCase__	:Tuple      =      MaMaaaForConditionalGeneration(UpperCamelCase_						)
   model.model.load_state_dict(UpperCamelCase_,   strict=UpperCamelCase_						)
   UpperCAmelCase__	:Optional[int]      =      make_linear_from_emb(model.model.shared						)

   return model


if __name__ == "__main__":
    __lowerCamelCase												=						argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('''fairseq_path''', type=str, help='''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.''')
    __lowerCamelCase												=						parser.parse_args()
    __lowerCamelCase												=						convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)



