





"""simple docstring"""
import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


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




def 			__snake_case						(      __A ,__A    )		->				str:
   if isinstance(__A ,(list, tuple)    ):
      return honor_type(__A ,(move_to_device(__A ,__A    ) for t in state)    )
   elif isinstance(__A ,__A    ):
      return type(__A    )({k: move_to_device(__A ,__A    ) for k, v in state.items()}    )
   elif isinstance(__A ,torch.Tensor    ):
      return state.to(__A    )
   return state







class lowerCamelCase__  (    torch.optim.Optimizer				):



   def __init__(       self      ,   snake_case      ,   snake_case=True      ,   snake_case=None	) ->    Dict:







      """simple docstring"""
      lowercase    :			List[str]  =       optimizer
      lowercase    :			List[Any]  =       scaler
      lowercase    :			Dict  =       AcceleratorState()
      lowercase    :			Union[str, Any]  =       GradientState()
      lowercase    :			str  =       device_placement
      lowercase    :			str  =       False
      lowercase    :			Optional[Any]  =       None

      # Handle device placement
      if device_placement:
         lowercase    :			Dict  =       self.optimizer.state_dict()
         if self.accelerator_state.distributed_type == DistributedType.TPU:
            xm.send_cpu_data_to_device(snake_case      ,   self.accelerator_state.device	)
         else:
            lowercase    :			List[Any]  =       move_to_device(snake_case      ,   self.accelerator_state.device	)
         self.optimizer.load_state_dict(snake_case	)



   @property
   def    _UpperCAmelCase	(       self	) ->    str:







      """simple docstring"""
      return self.optimizer.state



   @state.setter
   def    _UpperCAmelCase	(       self      ,   snake_case	) ->    Optional[Any]:







      """simple docstring"""
      lowercase    :			int  =       state



   @property
   def    _UpperCAmelCase	(       self	) ->    str:







      """simple docstring"""
      return self.optimizer.param_groups



   @param_groups.setter
   def    _UpperCAmelCase	(       self      ,   snake_case	) ->    Any:







      """simple docstring"""
      lowercase    :			Dict  =       param_groups



   @property
   def    _UpperCAmelCase	(       self	) ->    Optional[int]:







      """simple docstring"""
      return self.optimizer.defaults



   @defaults.setter
   def    _UpperCAmelCase	(       self      ,   snake_case	) ->    Any:







      """simple docstring"""
      lowercase    :			int  =       defaults



   def    _UpperCAmelCase	(       self      ,   snake_case	) ->    int:







      """simple docstring"""
      self.optimizer.add_param_group(snake_case	)



   def    _UpperCAmelCase	(       self      ,   snake_case	) ->    str:







      """simple docstring"""
      if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
         xm.send_cpu_data_to_device(snake_case      ,   self.accelerator_state.device	)
      self.optimizer.load_state_dict(snake_case	)



   def    _UpperCAmelCase	(       self	) ->    Dict:







      """simple docstring"""
      return self.optimizer.state_dict()



   def    _UpperCAmelCase	(       self      ,   snake_case=None	) ->    int:







      """simple docstring"""
      if self.gradient_state.sync_gradients:
         lowercase    :			Dict  =       """set_to_none""" in inspect.signature(self.optimizer.zero_grad	).parameters
         if accept_arg:
            if set_to_none is None:
               lowercase    :			str  =       False
            self.optimizer.zero_grad(set_to_none=snake_case	)
         else:
            if set_to_none is not None:
               raise ValueError("""`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer."""	)
            self.optimizer.zero_grad()



   def    _UpperCAmelCase	(       self      ,   snake_case=None	) ->    Optional[Any]:







      """simple docstring"""
      if self.gradient_state.sync_gradients:
         if self.accelerator_state.distributed_type == DistributedType.TPU:
            lowercase    :			List[Any]  =       {"""closure""": closure} if closure is not None else {}
            xm.optimizer_step(self.optimizer      ,   optimizer_args=snake_case	)
         elif self.scaler is not None:
            lowercase    :			Tuple  =       False
            if self._last_scale is None:
               # `get_scale` is an async operation requiring full synchronization
               # on CPU and GPUs before finishing. As a result, we store away
               # the prior one to reduce the call overhead
               lowercase    :			int  =       self.scaler.get_scale()
               lowercase    :			Optional[Any]  =       True
            self.scaler.step(self.optimizer      ,   snake_case	)
            self.scaler.update()
            lowercase    :			Optional[int]  =       self.scaler.get_scale()
            if not new_scale:
               # If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
               lowercase    :			Dict  =       scale_after < self._last_scale
            lowercase    :			Optional[int]  =       scale_after

         else:
            self.optimizer.step(snake_case	)



   def    _UpperCAmelCase	(       self      ,   snake_case	) ->    Dict:







      """simple docstring"""
      for param_group in self.optimizer.param_groups:
         lowercase    :			str  =       [parameters_map.get(snake_case      ,   snake_case	) for p in param_group["""params"""]]



   @property
   def    _UpperCAmelCase	(       self	) ->    Optional[Any]:







      """simple docstring"""
      warnings.warn(
          """The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use """
          """`optimizer.step_was_skipped` instead."""      ,   snake_case      ,   )
      return self._is_overflow



   @property
   def    _UpperCAmelCase	(       self	) ->    Optional[int]:







      """simple docstring"""
      return self._is_overflow



   def __getstate__(       self	) ->    Any:







      """simple docstring"""
      return self.__dict__.copy()





   def __setstate__(       self      ,   snake_case	) ->    List[Any]:







      """simple docstring"""
      self.__dict__.update(snake_case	)









"""simple docstring"""
import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()




def 			__snake_case						(      __A ,__A ,__A ,__A ,__A    )		->				Optional[int]:
   # Initialise PyTorch model
   lowercase    :			Optional[Any]  =       BertConfig.from_pretrained(
       """bert-large-cased""" ,vocab_size=__A ,max_position_embeddings=512 ,is_decoder=__A ,add_cross_attention=__A ,)
   lowercase    :			List[str]  =       bert_config.to_dict()
   del bert_config_dict["type_vocab_size"]
   lowercase    :			Tuple  =       BertGenerationConfig(**__A    )
   if is_encoder:
      lowercase    :			Dict  =       BertGenerationEncoder(__A    )
   else:
      lowercase    :			Dict  =       BertGenerationDecoder(__A    )
   print(F'''Building PyTorch model from configuration: {config}'''    )

   # Load weights from tf checkpoint
   load_tf_weights_in_bert_generation(
       __A ,__A ,model_class="""bert""" ,is_encoder_named_decoder=__A ,is_encoder=__A ,)

   # Save pytorch-model
   print(F'''Save PyTorch model and config to {pytorch_dump_path}'''    )
   model.save_pretrained(__A    )


if __name__ == "__main__":
   lowerCAmelCase:	Dict							      =argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
   )
   parser.add_argument(
       "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   parser.add_argument(
       "--is_encoder_named_decoder",
       action="store_true",
       help="If decoder has to be renamed to encoder in PyTorch model.",
   )
   parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
   parser.add_argument("--vocab_size", default=50358, type=int, help="Vocab size of model")
   lowerCAmelCase:	Optional[int]							      =parser.parse_args()
   convert_tf_checkpoint_to_pytorch(
       args.tf_hub_path,
       args.pytorch_dump_path,
       args.is_encoder_named_decoder,
       args.vocab_size,
       is_encoder=args.is_encoder,
   )


