

'''simple docstring'''



import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def      a__ (						a__   ,					a__   ,					a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   TaConfig.from_pretrained(a__		)
     __SCREAMING_SNAKE_CASE						     =   FlaxTaForConditionalGeneration(config=a__		)
     __SCREAMING_SNAKE_CASE						     =   checkpoints.load_tax_checkpoint(a__		)

     __SCREAMING_SNAKE_CASE						     =   '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

     # Encoder
     for layer_index in range(config.num_layers		):
          __SCREAMING_SNAKE_CASE						     =   F'layers_{str(a__		)}'

          # Self-Attention
          __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
          __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
          __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
          __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

          # Layer Normalization
          __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

          if split_mlp_wi:
               __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
               __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
               __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

          __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

          # Layer Normalization
          __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

          # Assigning
          __SCREAMING_SNAKE_CASE						     =   tax_attention_key
          __SCREAMING_SNAKE_CASE						     =   tax_attention_out
          __SCREAMING_SNAKE_CASE						     =   tax_attention_query
          __SCREAMING_SNAKE_CASE						     =   tax_attention_value

          __SCREAMING_SNAKE_CASE						     =   tax_attention_layer_norm

          if split_mlp_wi:
               __SCREAMING_SNAKE_CASE						     =   tax_mlp_wi_a
               __SCREAMING_SNAKE_CASE						     =   tax_mlp_wi_a
          else:
               __SCREAMING_SNAKE_CASE						     =   tax_mlp_wi

          __SCREAMING_SNAKE_CASE						     =   tax_mlp_wo
          __SCREAMING_SNAKE_CASE						     =   tax_mlp_layer_norm

     # Only for layer 0:
     __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
     __SCREAMING_SNAKE_CASE						     =   tax_encoder_rel_embedding

     # Assigning
     __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
     __SCREAMING_SNAKE_CASE						     =   tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_decoder_layers		):
          __SCREAMING_SNAKE_CASE						     =   F'layers_{str(a__		)}'

          # Self-Attention
          __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
          __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
          __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
          __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

          # Layer Normalization
          __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
              '''scale'''
          ]

          # Encoder-Decoder-Attention
          __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
              '''kernel'''
          ]
          __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
              '''kernel'''
          ]
          __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
              '''kernel'''
          ]
          __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
              '''kernel'''
          ]

          # Layer Normalization
          __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

          # MLP
          if split_mlp_wi:
               __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
               __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
               __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

          __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

          # Layer Normalization
          __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

          # Assigning
          __SCREAMING_SNAKE_CASE						     =   tax_attention_key
          __SCREAMING_SNAKE_CASE						     =   tax_attention_out
          __SCREAMING_SNAKE_CASE						     =   tax_attention_query
          __SCREAMING_SNAKE_CASE						     =   tax_attention_value

          __SCREAMING_SNAKE_CASE						     =   tax_pre_attention_layer_norm

          __SCREAMING_SNAKE_CASE						     =   tax_enc_dec_attention_key
          __SCREAMING_SNAKE_CASE						     =   tax_enc_dec_attention_out
          __SCREAMING_SNAKE_CASE						     =   tax_enc_dec_attention_query
          __SCREAMING_SNAKE_CASE						     =   tax_enc_dec_attention_value

          __SCREAMING_SNAKE_CASE						     =   tax_cross_layer_norm

          if split_mlp_wi:
               __SCREAMING_SNAKE_CASE						     =   tax_mlp_wi_a
               __SCREAMING_SNAKE_CASE						     =   tax_mlp_wi_a
          else:
               __SCREAMING_SNAKE_CASE						     =   tax_mlp_wi

          __SCREAMING_SNAKE_CASE						     =   tax_mlp_wo

          __SCREAMING_SNAKE_CASE						     =   txa_mlp_layer_norm

     # Decoder Normalization
     __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
     __SCREAMING_SNAKE_CASE						     =   txa_decoder_norm

     # Only for layer 0:
     __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
     __SCREAMING_SNAKE_CASE						     =   tax_decoder_rel_embedding

     # Token Embeddings
     __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''token_embedder''']['''embedding''']
     __SCREAMING_SNAKE_CASE						     =   txa_token_embeddings

     # LM Head (only in v1.1 checkpoints)
     if "logits_dense" in tax_model["target"]["decoder"]:
          __SCREAMING_SNAKE_CASE						     =   tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

     flax_model.save_pretrained(a__		)
     print("""T5X Model was sucessfully converted!"""		)


if __name__ == "__main__":
  UpperCAmelCase				: Optional[int]         =  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the TX5 checkpoint.'
  )
  parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of T5 model.')
  parser.add_argument(
      '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
  )
  UpperCAmelCase				: Union[str, Any]         =  parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)








'''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      a__ (						a__   ,					a__		):




     """simple docstring"""


     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      ):



 """simple docstring"""



 def __init__(	self      : Tuple      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]      ,							__SCREAMING_SNAKE_CASE      : Dict=True      ,							__SCREAMING_SNAKE_CASE      : List[Any]=None    )						->				str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   optimizer
      __SCREAMING_SNAKE_CASE						     =   scaler
      __SCREAMING_SNAKE_CASE						     =   AcceleratorState()
      __SCREAMING_SNAKE_CASE						     =   GradientState()
      __SCREAMING_SNAKE_CASE						     =   device_placement
      __SCREAMING_SNAKE_CASE						     =   False
      __SCREAMING_SNAKE_CASE						     =   None

      # Handle device placement
      if device_placement:
           __SCREAMING_SNAKE_CASE						     =   self.optimizer.state_dict()
           if self.accelerator_state.distributed_type == DistributedType.TPU:
                xm.send_cpu_data_to_device(__SCREAMING_SNAKE_CASE      ,							self.accelerator_state.device    )
           else:
                __SCREAMING_SNAKE_CASE						     =   move_to_device(__SCREAMING_SNAKE_CASE      ,							self.accelerator_state.device    )
           self.optimizer.load_state_dict(__SCREAMING_SNAKE_CASE    )
 @property
 def    UpperCAmelCase__	(	self      : str    )						->				Union[str, Any]:


      """simple docstring"""




      return self.optimizer.state
 @state.setter
 def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]    )						->				List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   state
 @property
 def    UpperCAmelCase__	(	self      : Optional[int]    )						->				str:


      """simple docstring"""




      return self.optimizer.param_groups
 @param_groups.setter
 def    UpperCAmelCase__	(	self      : Dict      ,							__SCREAMING_SNAKE_CASE      : str    )						->				List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   param_groups
 @property
 def    UpperCAmelCase__	(	self      : List[Any]    )						->				Any:


      """simple docstring"""




      return self.optimizer.defaults
 @defaults.setter
 def    UpperCAmelCase__	(	self      : Any      ,							__SCREAMING_SNAKE_CASE      : Dict    )						->				Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   defaults
 def    UpperCAmelCase__	(	self      : Any      ,							__SCREAMING_SNAKE_CASE      : Any    )						->				List[str]:


      """simple docstring"""




      self.optimizer.add_param_group(__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : List[str]    )						->				Dict:


      """simple docstring"""




      if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
           xm.send_cpu_data_to_device(__SCREAMING_SNAKE_CASE      ,							self.accelerator_state.device    )
      self.optimizer.load_state_dict(__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : Union[str, Any]    )						->				Union[str, Any]:


      """simple docstring"""




      return self.optimizer.state_dict()
 def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : List[str]=None    )						->				Any:


      """simple docstring"""




      if self.gradient_state.sync_gradients:
           __SCREAMING_SNAKE_CASE						     =   """set_to_none""" in inspect.signature(self.optimizer.zero_grad    ).parameters
           if accept_arg:
                if set_to_none is None:
                     __SCREAMING_SNAKE_CASE						     =   False
                self.optimizer.zero_grad(set_to_none=__SCREAMING_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      : List[str]      ,							__SCREAMING_SNAKE_CASE      : List[Any]=None    )						->				Dict:


      """simple docstring"""




      if self.gradient_state.sync_gradients:
           if self.accelerator_state.distributed_type == DistributedType.TPU:
                __SCREAMING_SNAKE_CASE						     =   {"""closure""": closure} if closure is not None else {}
                xm.optimizer_step(self.optimizer      ,							optimizer_args=__SCREAMING_SNAKE_CASE    )
           elif self.scaler is not None:
                __SCREAMING_SNAKE_CASE						     =   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
                     __SCREAMING_SNAKE_CASE						     =   self.scaler.get_scale()
                     __SCREAMING_SNAKE_CASE						     =   True
                self.scaler.step(self.optimizer      ,							__SCREAMING_SNAKE_CASE    )
                self.scaler.update()
                __SCREAMING_SNAKE_CASE						     =   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.
                     __SCREAMING_SNAKE_CASE						     =   scale_after < self._last_scale
                __SCREAMING_SNAKE_CASE						     =   scale_after

           else:
                self.optimizer.step(__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : Union[str, Any]      ,							__SCREAMING_SNAKE_CASE      : Any    )						->				Any:


      """simple docstring"""




      for param_group in self.optimizer.param_groups:
           __SCREAMING_SNAKE_CASE						     =   [parameters_map.get(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    ) for p in param_group["""params"""]]
 @property
 def    UpperCAmelCase__	(	self      : int    )						->				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."""      ,							__SCREAMING_SNAKE_CASE      ,							)
      return self._is_overflow
 @property
 def    UpperCAmelCase__	(	self      : Union[str, Any]    )						->				Optional[int]:


      """simple docstring"""




      return self._is_overflow
 def __getstate__(	self      : Tuple    )						->				str:


      """simple docstring"""




      return self.__dict__.copy()


 def __setstate__(	self      : str      ,							__SCREAMING_SNAKE_CASE      : Optional[int]    )						->				Any:


      """simple docstring"""




      self.__dict__.update(__SCREAMING_SNAKE_CASE    )






