code: 
  
  
  
  
  
  
  import argparse
  
  from tax import checkpoints
  
  from transformers import FlaxTaForConditionalGeneration, TaConfig
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase    )				->			Dict:
       UpperCamelCase__    :     Optional[int]        =						TaConfig.from_pretrained(__a    )
       UpperCamelCase__    :     Dict        =						FlaxTaForConditionalGeneration(config=__a    )
       UpperCamelCase__    :     List[Any]        =						checkpoints.load_tax_checkpoint(__a    )
  
       UpperCamelCase__    :     List[str]        =						'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']
  
       # Encoder
       for layer_index in range(config.num_layers    ):
            UpperCamelCase__    :     str        =						f'layers_{str(__a    )}'
  
            # Self-Attention
            UpperCamelCase__    :     Dict        =						tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
            UpperCamelCase__    :     int        =						tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
            UpperCamelCase__    :     int        =						tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
            UpperCamelCase__    :     Tuple        =						tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']
  
            # Layer Normalization
            UpperCamelCase__    :     Optional[int]        =						tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']
  
            if split_mlp_wi:
                 UpperCamelCase__    :     Optional[int]        =						tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                 UpperCamelCase__    :     Tuple        =						tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
            else:
                 UpperCamelCase__    :     int        =						tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
  
            UpperCamelCase__    :     int        =						tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
  
            # Layer Normalization
            UpperCamelCase__    :     Optional[Any]        =						tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
  
            # Assigning
            UpperCamelCase__    :     Dict        =						tax_attention_key
            UpperCamelCase__    :     str        =						tax_attention_out
            UpperCamelCase__    :     Any        =						tax_attention_query
            UpperCamelCase__    :     str        =						tax_attention_value
  
            UpperCamelCase__    :     List[Any]        =						tax_attention_layer_norm
  
            if split_mlp_wi:
                 UpperCamelCase__    :     int        =						tax_mlp_wi_a
                 UpperCamelCase__    :     Dict        =						tax_mlp_wi_a
            else:
                 UpperCamelCase__    :     Tuple        =						tax_mlp_wi
  
            UpperCamelCase__    :     List[str]        =						tax_mlp_wo
            UpperCamelCase__    :     Union[str, Any]        =						tax_mlp_layer_norm
  
       # Only for layer 0:
       UpperCamelCase__    :     Optional[Any]        =						tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
       UpperCamelCase__    :     Union[str, Any]        =						tax_encoder_rel_embedding
  
       # Assigning
       UpperCamelCase__    :     List[str]        =						tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
       UpperCamelCase__    :     Union[str, Any]        =						tax_encoder_norm
  
       # Decoder
       for layer_index in range(config.num_decoder_layers    ):
            UpperCamelCase__    :     Optional[int]        =						f'layers_{str(__a    )}'
  
            # Self-Attention
            UpperCamelCase__    :     Optional[Any]        =						tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
            UpperCamelCase__    :     str        =						tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
            UpperCamelCase__    :     Dict        =						tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
            UpperCamelCase__    :     int        =						tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']
  
            # Layer Normalization
            UpperCamelCase__    :     Tuple        =						tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
                '''scale'''
            ]
  
            # Encoder-Decoder-Attention
            UpperCamelCase__    :     List[str]        =						tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
                '''kernel'''
            ]
            UpperCamelCase__    :     List[str]        =						tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
                '''kernel'''
            ]
            UpperCamelCase__    :     Optional[Any]        =						tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
                '''kernel'''
            ]
            UpperCamelCase__    :     List[Any]        =						tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
                '''kernel'''
            ]
  
            # Layer Normalization
            UpperCamelCase__    :     Any        =						tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']
  
            # MLP
            if split_mlp_wi:
                 UpperCamelCase__    :     List[Any]        =						tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                 UpperCamelCase__    :     Any        =						tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
            else:
                 UpperCamelCase__    :     Union[str, Any]        =						tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
  
            UpperCamelCase__    :     Dict        =						tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
  
            # Layer Normalization
            UpperCamelCase__    :     Optional[int]        =						tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
  
            # Assigning
            UpperCamelCase__    :     Optional[int]        =						tax_attention_key
            UpperCamelCase__    :     Tuple        =						tax_attention_out
            UpperCamelCase__    :     Optional[int]        =						tax_attention_query
            UpperCamelCase__    :     Dict        =						tax_attention_value
  
            UpperCamelCase__    :     List[Any]        =						tax_pre_attention_layer_norm
  
            UpperCamelCase__    :     Optional[int]        =						tax_enc_dec_attention_key
            UpperCamelCase__    :     Optional[int]        =						tax_enc_dec_attention_out
            UpperCamelCase__    :     Optional[Any]        =						tax_enc_dec_attention_query
            UpperCamelCase__    :     List[Any]        =						tax_enc_dec_attention_value
  
            UpperCamelCase__    :     List[Any]        =						tax_cross_layer_norm
  
            if split_mlp_wi:
                 UpperCamelCase__    :     Tuple        =						tax_mlp_wi_a
                 UpperCamelCase__    :     Tuple        =						tax_mlp_wi_a
            else:
                 UpperCamelCase__    :     Any        =						tax_mlp_wi
  
            UpperCamelCase__    :     int        =						tax_mlp_wo
  
            UpperCamelCase__    :     Dict        =						txa_mlp_layer_norm
  
       # Decoder Normalization
       UpperCamelCase__    :     int        =						tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
       UpperCamelCase__    :     Tuple        =						txa_decoder_norm
  
       # Only for layer 0:
       UpperCamelCase__    :     List[str]        =						tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
       UpperCamelCase__    :     Tuple        =						tax_decoder_rel_embedding
  
       # Token Embeddings
       UpperCamelCase__    :     List[str]        =						tax_model['''target''']['''token_embedder''']['''embedding''']
       UpperCamelCase__    :     Union[str, Any]        =						txa_token_embeddings
  
       # LM Head (only in v1.1 checkpoints)
       if "logits_dense" in tax_model["target"]["decoder"]:
            UpperCamelCase__    :     str        =						tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']
  
       flax_model.save_pretrained(__a    )
       print("T5X Model was sucessfully converted!"    )
  
  
  if __name__ == "__main__":
   lowerCamelCase			:							Union[str, Any]       =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			:							List[Any]       =parser.parse_args()
   convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
  
style_context: 
  
  
  
  
  
  
  import os
  import unittest
  
  from transformers import MPNetTokenizerFast
  from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
  from transformers.testing_utils import require_tokenizers, slow
  
  from ...test_tokenization_common import TokenizerTesterMixin
  
  
  
  
  
  @require_tokenizers
  class     __a						(					A__ ,     unittest.TestCase ):
   _lowerCAmelCase : str 				=				MPNetTokenizer
   _lowerCAmelCase : List[str] 				=				MPNetTokenizerFast
   _lowerCAmelCase : List[Any] 				=				True
   _lowerCAmelCase : Union[str, Any] 				=				True
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     List[Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        super().setUp()
  
        UpperCamelCase__    :     List[Any]        =						[
            "[UNK]",
            "[CLS]",
            "[SEP]",
            "[PAD]",
            "[MASK]",
            "want",
            "##want",
            "##ed",
            "wa",
            "un",
            "runn",
            "##ing",
            ",",
            "low",
            "lowest",
        ]
        UpperCamelCase__    :     int        =						os.path.join(self.tmpdirname							,     VOCAB_FILES_NAMES["vocab_file"]		)
        with open(self.vocab_file							,     "w"							,     encoding="utf-8"		) as vocab_writer:
             vocab_writer.write("".join([x + "\n" for x in vocab_tokens]		)		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Tuple							,     SCREAMING_SNAKE_CASE      :     List[str]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     List[str]        =						"UNwant\u00E9d,running"
        UpperCamelCase__    :     List[str]        =						"unwanted, running"
        return input_text, output_text
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Any		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     str        =						self.tokenizer_class(self.vocab_file		)
  
        UpperCamelCase__    :     Any        =						tokenizer.tokenize("UNwant\u00E9d,running"		)
        self.assertListEqual(SCREAMING_SNAKE_CASE							,     ["un", "##want", "##ed", ",", "runn", "##ing"]		)
        self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE		)							,     [9, 6, 7, 12, 10, 11]		)
  
  
  
  
  
  
  
   @slow
   def       __lowercase      (	self      :     str		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Union[str, Any]        =						self.tokenizer_class.from_pretrained("microsoft/mpnet-base"		)
  
        UpperCamelCase__    :     Optional[int]        =						tokenizer.encode("sequence builders"							,     add_special_tokens=SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     Optional[int]        =						tokenizer.encode("multi-sequence build"							,     add_special_tokens=SCREAMING_SNAKE_CASE		)
  
        UpperCamelCase__    :     Tuple        =						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     Optional[int]        =						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)
  
        assert encoded_sentence == [0] + text + [2]
        assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]