code: 
  
  
  import argparse
  
  from tax import checkpoints
  
  from transformers import FlaxTaForConditionalGeneration, TaConfig
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)						->							Dict:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				int           =			TaConfig.from_pretrained(UpperCAmelCase_			)
         _lowercase					:				Optional[int]           =			FlaxTaForConditionalGeneration(config=UpperCAmelCase_			)
         _lowercase					:				Optional[int]           =			checkpoints.load_tax_checkpoint(UpperCAmelCase_			)
  
         _lowercase					:				List[Any]           =			'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']
  
         # Encoder
         for layer_index in range(config.num_layers			):
                _lowercase					:				Dict           =			F'layers_{str(UpperCAmelCase_			)}'
  
                # Self-Attention
                _lowercase					:				Optional[Any]           =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
                _lowercase					:				List[Any]           =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
                _lowercase					:				Optional[int]           =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
                _lowercase					:				Optional[Any]           =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']
  
                # Layer Normalization
                _lowercase					:				Any           =			tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']
  
                if split_mlp_wi:
                       _lowercase					:				Tuple           =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                       _lowercase					:				Union[str, Any]           =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
                else:
                       _lowercase					:				List[Any]           =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
  
                _lowercase					:				List[str]           =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
  
                # Layer Normalization
                _lowercase					:				List[str]           =			tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
  
                # Assigning
                _lowercase					:				Dict           =			tax_attention_key
                _lowercase					:				Union[str, Any]           =			tax_attention_out
                _lowercase					:				Optional[Any]           =			tax_attention_query
                _lowercase					:				Optional[int]           =			tax_attention_value
  
                _lowercase					:				str           =			tax_attention_layer_norm
  
                if split_mlp_wi:
                       _lowercase					:				List[str]           =			tax_mlp_wi_a
                       _lowercase					:				Dict           =			tax_mlp_wi_a
                else:
                       _lowercase					:				Dict           =			tax_mlp_wi
  
                _lowercase					:				Any           =			tax_mlp_wo
                _lowercase					:				str           =			tax_mlp_layer_norm
  
         # Only for layer 0:
         _lowercase					:				List[str]           =			tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
         _lowercase					:				Optional[Any]           =			tax_encoder_rel_embedding
  
         # Assigning
         _lowercase					:				Union[str, Any]           =			tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
         _lowercase					:				Optional[int]           =			tax_encoder_norm
  
         # Decoder
         for layer_index in range(config.num_decoder_layers			):
                _lowercase					:				int           =			F'layers_{str(UpperCAmelCase_			)}'
  
                # Self-Attention
                _lowercase					:				Tuple           =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
                _lowercase					:				Tuple           =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
                _lowercase					:				Tuple           =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
                _lowercase					:				str           =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']
  
                # Layer Normalization
                _lowercase					:				int           =			tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
                    '''scale'''
                ]
  
                # Encoder-Decoder-Attention
                _lowercase					:				List[str]           =			tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
                    '''kernel'''
                ]
                _lowercase					:				Optional[int]           =			tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
                    '''kernel'''
                ]
                _lowercase					:				Tuple           =			tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
                    '''kernel'''
                ]
                _lowercase					:				Optional[Any]           =			tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
                    '''kernel'''
                ]
  
                # Layer Normalization
                _lowercase					:				Optional[Any]           =			tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']
  
                # MLP
                if split_mlp_wi:
                       _lowercase					:				List[str]           =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                       _lowercase					:				Optional[int]           =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
                else:
                       _lowercase					:				int           =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
  
                _lowercase					:				List[str]           =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
  
                # Layer Normalization
                _lowercase					:				List[Any]           =			tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
  
                # Assigning
                _lowercase					:				Dict           =			tax_attention_key
                _lowercase					:				str           =			tax_attention_out
                _lowercase					:				List[str]           =			tax_attention_query
                _lowercase					:				Union[str, Any]           =			tax_attention_value
  
                _lowercase					:				Dict           =			tax_pre_attention_layer_norm
  
                _lowercase					:				List[str]           =			tax_enc_dec_attention_key
                _lowercase					:				Tuple           =			tax_enc_dec_attention_out
                _lowercase					:				Dict           =			tax_enc_dec_attention_query
                _lowercase					:				Optional[int]           =			tax_enc_dec_attention_value
  
                _lowercase					:				Dict           =			tax_cross_layer_norm
  
                if split_mlp_wi:
                       _lowercase					:				Union[str, Any]           =			tax_mlp_wi_a
                       _lowercase					:				Tuple           =			tax_mlp_wi_a
                else:
                       _lowercase					:				Dict           =			tax_mlp_wi
  
                _lowercase					:				Tuple           =			tax_mlp_wo
  
                _lowercase					:				Union[str, Any]           =			txa_mlp_layer_norm
  
         # Decoder Normalization
         _lowercase					:				str           =			tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
         _lowercase					:				Tuple           =			txa_decoder_norm
  
         # Only for layer 0:
         _lowercase					:				Tuple           =			tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
         _lowercase					:				Optional[int]           =			tax_decoder_rel_embedding
  
         # Token Embeddings
         _lowercase					:				str           =			tax_model['''target''']['''token_embedder''']['''embedding''']
         _lowercase					:				Optional[Any]           =			txa_token_embeddings
  
         # LM Head (only in v1.1 checkpoints)
         if "logits_dense" in tax_model["target"]["decoder"]:
                _lowercase					:				Dict           =			tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']
  
         flax_model.save_pretrained(UpperCAmelCase_			)
         print('''T5X Model was sucessfully converted!'''			)
  
  
  if __name__ == "__main__":
     UpperCamelCase__				       =			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__				       =			parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
  
  
  
style_context: 
  
  
  import torch
  import torch.nn as nn
  from transformers.modeling_utils import ModuleUtilsMixin
  from transformers.models.ta.modeling_ta import (
      TaBlock,
      TaConfig,
      TaLayerNorm,
  )
  
  from ...configuration_utils import ConfigMixin, register_to_config
  from ...models import ModelMixin
  
  class 						UpperCAmelCase__   (      A_      ,						A_      ,						A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       @register_to_config
       def __init__( self       :       Dict	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       float	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       str	,      UpperCamelCase       :       bool = False	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__()
  
              _lowercase					:				Any           =			nn.Linear(UpperCamelCase	,      UpperCamelCase	,      bias=UpperCamelCase		)
  
              _lowercase					:				List[Any]           =			nn.Embedding(UpperCamelCase	,      UpperCamelCase		)
              _lowercase					:				Any           =			False
  
              _lowercase					:				Tuple           =			nn.Dropout(p=UpperCamelCase		)
  
              _lowercase					:				Any           =			TaConfig(
                  d_model=UpperCamelCase	,      num_heads=UpperCamelCase	,      d_kv=UpperCamelCase	,      d_ff=UpperCamelCase	,      feed_forward_proj=UpperCamelCase	,      dropout_rate=UpperCamelCase	,      is_decoder=UpperCamelCase	,      is_encoder_decoder=UpperCamelCase	,      )
              _lowercase					:				Any           =			nn.ModuleList()
              for lyr_num in range(UpperCamelCase		):
                     _lowercase					:				Tuple           =			TaBlock(UpperCamelCase		)
                     self.encoders.append(UpperCamelCase		)
  
              _lowercase					:				List[Any]           =			TaLayerNorm(UpperCamelCase		)
              _lowercase					:				Optional[Any]           =			nn.Dropout(p=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       int	,      UpperCamelCase       :       Optional[int]	,      UpperCamelCase       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[int]           =			self.input_proj(UpperCamelCase		)
  
              # terminal relative positional encodings
              _lowercase					:				Optional[int]           =			encoder_inputs.shape[1]
              _lowercase					:				Optional[int]           =			torch.arange(UpperCamelCase	,      device=encoder_inputs.device		)
  
              _lowercase					:				Any           =			encoder_inputs_mask.sum(-1		)
              _lowercase					:				int           =			torch.roll(input_positions.unsqueeze(0		)	,      tuple(seq_lens.tolist()		)	,      dims=0		)
              x += self.position_encoding(UpperCamelCase		)
  
              _lowercase					:				Optional[int]           =			self.dropout_pre(UpperCamelCase		)
  
              # inverted the attention mask
              _lowercase					:				Optional[int]           =			encoder_inputs.size()
              _lowercase					:				List[Any]           =			self.get_extended_attention_mask(UpperCamelCase	,      UpperCamelCase		)
  
              for lyr in self.encoders:
                     _lowercase					:				Optional[int]           =			lyr(UpperCamelCase	,      UpperCamelCase		)[0]
              _lowercase					:				List[str]           =			self.layer_norm(UpperCamelCase		)
  
              return self.dropout_post(UpperCamelCase		), encoder_inputs_mask