code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import json
  import os
  from collections.abc import Mapping
  from typing import Dict, List, Optional, Union
  
  import numpy as np
  import torch
  
  from .imports import is_safetensors_available
  def _A		(		lowercase ,					lowercase ,					lowercase ,					lowercase=None	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=None
      # Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
      if str(weight.dtype	) == "torch.bfloat16":
          # Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
          a					=weight.view(torch.intaa	)
          a					='''bfloat16'''
      a					=weight.cpu().numpy()
      a					=os.path.join(lowercase ,					f'''{weight_name}.dat'''	)
      if index is not None:
          if dtype is None:
              a					=str(array.dtype	)
          a					={'''dtype''': dtype, '''shape''': list(array.shape	)}
      if array.ndim == 0:
          a					=array[None]
      a					=np.memmap(lowercase ,					dtype=array.dtype ,					mode='''w+''' ,					shape=array.shape	)
      a					=array[:]
      file_array.flush()
      return index
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=tuple(weight_info['''shape''']	)
      if shape == ():
          # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
          a					=(1,)
  
      a					=weight_info['''dtype''']
      if dtype == "bfloat16":
          # NumPy does not support bfloat16 so this was saved as a int16
          a					='''int16'''
  
      a					=np.memmap(lowercase ,					dtype=lowercase ,					shape=lowercase ,					mode='''r'''	)
  
      if len(weight_info['''shape''']	) == 0:
          a					=weight[0]
      a					=torch.tensor(lowercase	)
      if weight_info["dtype"] == "bfloat16":
          a					=weight.view(torch.bfloataa	)
  
      return weight
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      if index is None or len(lowercase	) == 0:
          # Nothing to save
          return
  
      a					=os.path.join(lowercase ,					'''index.json'''	)
      if os.path.isfile(lowercase	):
          with open(lowercase ,					'''r''' ,					encoding='''utf-8'''	) as f:
              a					=json.load(lowercase	)
      else:
          a					={}
      current_index.update(lowercase	)
  
      with open(lowercase ,					'''w''' ,					encoding='''utf-8'''	) as f:
          json.dump(lowercase ,					lowercase ,					indent=2	)
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      os.makedirs(lowercase ,					exist_ok=lowercase	)
      a					={}
      for name, parameter in state_dict.items():
          a					=offload_weight(lowercase ,					lowercase ,					lowercase ,					index=lowercase	)
  
      # Update index
      save_offload_index(lowercase ,					lowercase	)
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def __init__(	self				,					__A				,					__A		)      ->  Optional[Any]:
       a					=dataset
       a					=prefix
  
  
   def __getitem__(	self				,					__A		)      ->  Optional[Any]:
       return self.dataset[f'''{self.prefix}{key}''']
  
  
   def __iter__(	self		)      ->  int:
       return iter([key for key in self.dataset if key.startswith(self.prefix		)]		)
  
  
  
  
  
   def __len__(	self		)      ->  Any:
       return len(self.dataset		)
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def __init__(	self				,					__A = None				,					__A = None				,					__A = None				,					__A=None				,					)      ->  Any:
       if state_dict is None and save_folder is None:
           raise ValueError('''Need either a `state_dict` or a `save_folder` containing offloaded weights.'''		)
  
       a					={} if state_dict is None else state_dict
       a					=save_folder
       if index is None and save_folder is not None:
           with open(os.path.join(__A				,					'''index.json'''		)		) as f:
               a					=json.load(__A		)
       a					={} if index is None else index
       a					=list(self.state_dict.keys()		)
       self.all_keys.extend([key for key in self.index if key not in self.all_keys]		)
       a					=device
  
  
   def __getitem__(	self				,					__A		)      ->  int:
       # State dict gets priority
       if key in self.state_dict:
           return self.state_dict[key]
       a					=self.index[key]
       if weight_info.get('''safetensors_file'''		) is not None:
           if not is_safetensors_available():
               raise ImportError('''These offloaded weights require the use of safetensors: `pip install safetensors`.'''		)
  
           from safetensors import safe_open
  
           a					='''cpu''' if self.device is None else self.device
           with safe_open(weight_info['''safetensors_file''']				,					framework='''pt'''				,					device=__A		) as f:
               a					=f.get_tensor(weight_info.get('''weight_name'''				,					__A		)		)
  
           if "dtype" in weight_info:
               return tensor.to(getattr(__A				,					weight_info['''dtype''']		)		)
           else:
               return tensor
  
       a					=os.path.join(self.save_folder				,					f'''{key}.dat'''		)
       return load_offloaded_weight(__A				,					__A		)
  
  
   def __iter__(	self		)      ->  Optional[int]:
       return iter(self.all_keys		)
  
  
  
  
  
   def __len__(	self		)      ->  int:
       return len(self.all_keys		)
  
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					={}
      for module_name in submodule_names:
          # We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
          # submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
          result.update(
              {
                  key: param
                  for key, param in state_dict.items()
                  if key == module_name or key.startswith(module_name + '''.'''	)
              }	)
      return result
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import argparse
  
  from tax import checkpoints
  
  from transformers import FlaxTaForConditionalGeneration, TaConfig
  def _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=TaConfig.from_pretrained(lowercase	)
      a					=FlaxTaForConditionalGeneration(config=lowercase	)
      a					=checkpoints.load_tax_checkpoint(lowercase	)
  
      a					='''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']
  
      # Encoder
      for layer_index in range(config.num_layers	):
          a					=f'''layers_{str(lowercase	)}'''
  
          # Self-Attention
          a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
          a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
          a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
          a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']
  
          # Layer Normalization
          a					=tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']
  
          if split_mlp_wi:
              a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
              a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
              a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
  
          a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
  
          # Layer Normalization
          a					=tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
  
          # Assigning
          a					=tax_attention_key
          a					=tax_attention_out
          a					=tax_attention_query
          a					=tax_attention_value
  
          a					=tax_attention_layer_norm
  
          if split_mlp_wi:
              a					=tax_mlp_wi_a
              a					=tax_mlp_wi_a
          else:
              a					=tax_mlp_wi
  
          a					=tax_mlp_wo
          a					=tax_mlp_layer_norm
  
      # Only for layer 0:
      a					=tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
      a					=tax_encoder_rel_embedding
  
      # Assigning
      a					=tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
      a					=tax_encoder_norm
  
      # Decoder
      for layer_index in range(config.num_decoder_layers	):
          a					=f'''layers_{str(lowercase	)}'''
  
          # Self-Attention
          a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
          a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
          a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
          a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']
  
          # Layer Normalization
          a					=tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
              '''scale'''
          ]
  
          # Encoder-Decoder-Attention
          a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
              '''kernel'''
          ]
          a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
              '''kernel'''
          ]
          a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
              '''kernel'''
          ]
          a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
              '''kernel'''
          ]
  
          # Layer Normalization
          a					=tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']
  
          # MLP
          if split_mlp_wi:
              a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
              a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
              a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
  
          a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
  
          # Layer Normalization
          a					=tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
  
          # Assigning
          a					=tax_attention_key
          a					=tax_attention_out
          a					=tax_attention_query
          a					=tax_attention_value
  
          a					=tax_pre_attention_layer_norm
  
          a					=tax_enc_dec_attention_key
          a					=tax_enc_dec_attention_out
          a					=tax_enc_dec_attention_query
          a					=tax_enc_dec_attention_value
  
          a					=tax_cross_layer_norm
  
          if split_mlp_wi:
              a					=tax_mlp_wi_a
              a					=tax_mlp_wi_a
          else:
              a					=tax_mlp_wi
  
          a					=tax_mlp_wo
  
          a					=txa_mlp_layer_norm
  
      # Decoder Normalization
      a					=tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
      a					=txa_decoder_norm
  
      # Only for layer 0:
      a					=tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
      a					=tax_decoder_rel_embedding
  
      # Token Embeddings
      a					=tax_model['''target''']['''token_embedder''']['''embedding''']
      a					=txa_token_embeddings
  
      # LM Head (only in v1.1 checkpoints)
      if "logits_dense" in tax_model["target"]["decoder"]:
          a					=tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']
  
      flax_model.save_pretrained(lowercase	)
      print('''T5X Model was sucessfully converted!'''	)
  
  
  if __name__ == "__main__":
         lowerCamelCase_     :     Dict    		=						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."""
         )
         lowerCamelCase_     :     Optional[int]    		=						parser.parse_args()
         convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)