code: 
  
  """simple docstring"""
  import json
  import os
  import shutil
  import tempfile
  import unittest
  
  from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
  from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
  from transformers.utils import FEATURE_EXTRACTOR_NAME
  
  from .test_feature_extraction_wavaveca import floats_list
  
  class 			lowerCamelCase  (					unittest.TestCase      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Any						=						"""<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z""".split(""" """  )
          UpperCAmelCase__ :       Optional[Any]						=						dict(zip(_lowerCamelCase       ,					range(len(_lowerCamelCase  )  )  )  )
  
          UpperCAmelCase__ :       Optional[Any]						=						{
              """pad_token""": """<pad>""",
              """unk_token""": """<unk>""",
              """bos_token""": """<s>""",
              """eos_token""": """</s>""",
          }
          UpperCAmelCase__ :       Union[str, Any]						=						{
              """feature_size""": 1,
              """padding_value""": 0.0,
              """sampling_rate""": 16000,
              """return_attention_mask""": False,
              """do_normalize""": True,
          }
  
          UpperCAmelCase__ :       Optional[Any]						=						tempfile.mkdtemp()
          UpperCAmelCase__ :       Optional[int]						=						os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES["""vocab_file"""]  )
          UpperCAmelCase__ :       Optional[Any]						=						os.path.join(self.tmpdirname       ,					_lowerCamelCase  )
          with open(self.vocab_file       ,					"""w"""       ,					encoding="""utf-8"""  ) as fp:
                fp.write(json.dumps(_lowerCamelCase  ) + """\n"""  )
  
          with open(self.feature_extraction_file       ,					"""w"""       ,					encoding="""utf-8"""  ) as fp:
                fp.write(json.dumps(_lowerCamelCase  ) + """\n"""  )
  
  
  
    def    _a			(self       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[Any]						=						self.add_kwargs_tokens_map.copy()
          kwargs.update(_lowerCamelCase  )
          return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname       ,					**_lowerCamelCase  )
  
  
  
    def    _a			(self       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname       ,					**_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          shutil.rmtree(self.tmpdirname  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[Any]						=						self.get_tokenizer()
          UpperCAmelCase__ :       str						=						self.get_feature_extractor()
  
          UpperCAmelCase__ :       List[Any]						=						WavaVecaProcessor(tokenizer=_lowerCamelCase       ,					feature_extractor=_lowerCamelCase  )
  
          processor.save_pretrained(self.tmpdirname  )
          UpperCAmelCase__ :       Tuple						=						WavaVecaProcessor.from_pretrained(self.tmpdirname  )
  
          self.assertEqual(processor.tokenizer.get_vocab()       ,					tokenizer.get_vocab()  )
          self.assertIsInstance(processor.tokenizer       ,					_lowerCamelCase  )
  
          self.assertEqual(processor.feature_extractor.to_json_string()       ,					feature_extractor.to_json_string()  )
          self.assertIsInstance(processor.feature_extractor       ,					_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[str]						=						WavaVecaProcessor(tokenizer=self.get_tokenizer()       ,					feature_extractor=self.get_feature_extractor()  )
          processor.save_pretrained(self.tmpdirname  )
  
          UpperCAmelCase__ :       Any						=						self.get_tokenizer(bos_token="""(BOS)"""       ,					eos_token="""(EOS)"""  )
          UpperCAmelCase__ :       Any						=						self.get_feature_extractor(do_normalize=_lowerCamelCase       ,					padding_value=1.0  )
  
          UpperCAmelCase__ :       Union[str, Any]						=						WavaVecaProcessor.from_pretrained(
              self.tmpdirname       ,					bos_token="""(BOS)"""       ,					eos_token="""(EOS)"""       ,					do_normalize=_lowerCamelCase       ,					padding_value=1.0  )
  
          self.assertEqual(processor.tokenizer.get_vocab()       ,					tokenizer_add_kwargs.get_vocab()  )
          self.assertIsInstance(processor.tokenizer       ,					_lowerCamelCase  )
  
          self.assertEqual(processor.feature_extractor.to_json_string()       ,					feature_extractor_add_kwargs.to_json_string()  )
          self.assertIsInstance(processor.feature_extractor       ,					_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						self.get_feature_extractor()
          UpperCAmelCase__ :       Tuple						=						self.get_tokenizer()
  
          UpperCAmelCase__ :       int						=						WavaVecaProcessor(tokenizer=_lowerCamelCase       ,					feature_extractor=_lowerCamelCase  )
  
          UpperCAmelCase__ :       int						=						floats_list((3, 1000)  )
  
          UpperCAmelCase__ :       Optional[int]						=						feature_extractor(_lowerCamelCase       ,					return_tensors="""np"""  )
          UpperCAmelCase__ :       List[str]						=						processor(_lowerCamelCase       ,					return_tensors="""np"""  )
  
          for key in input_feat_extract.keys():
                self.assertAlmostEqual(input_feat_extract[key].sum()       ,					input_processor[key].sum()       ,					delta=1e-2  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Any						=						self.get_feature_extractor()
          UpperCAmelCase__ :       Dict						=						self.get_tokenizer()
  
          UpperCAmelCase__ :       Dict						=						WavaVecaProcessor(tokenizer=_lowerCamelCase       ,					feature_extractor=_lowerCamelCase  )
  
          UpperCAmelCase__ :       Any						=						"""This is a test string"""
  
          UpperCAmelCase__ :       Optional[Any]						=						processor(text=_lowerCamelCase  )
  
          UpperCAmelCase__ :       int						=						tokenizer(_lowerCamelCase  )
  
          for key in encoded_tok.keys():
                self.assertListEqual(encoded_tok[key]       ,					encoded_processor[key]  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Union[str, Any]						=						self.get_feature_extractor()
          UpperCAmelCase__ :       int						=						self.get_tokenizer()
  
          UpperCAmelCase__ :       str						=						WavaVecaProcessor(tokenizer=_lowerCamelCase       ,					feature_extractor=_lowerCamelCase  )
  
          UpperCAmelCase__ :       Any						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
          UpperCAmelCase__ :       Dict						=						processor.batch_decode(_lowerCamelCase  )
          UpperCAmelCase__ :       List[Any]						=						tokenizer.batch_decode(_lowerCamelCase  )
  
          self.assertListEqual(_lowerCamelCase       ,					_lowerCamelCase  )
  
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Tuple						=						self.get_feature_extractor()
          UpperCAmelCase__ :       str						=						self.get_tokenizer()
  
          UpperCAmelCase__ :       Any						=						WavaVecaProcessor(tokenizer=_lowerCamelCase       ,					feature_extractor=_lowerCamelCase  )
  
          self.assertListEqual(
              processor.model_input_names       ,					feature_extractor.model_input_names       ,					msg="""`processor` and `feature_extractor` model input names do not match"""       ,					)
  
  
style_context: 
  
  """simple docstring"""
  import argparse
  
  from tax import checkpoints
  
  from transformers import FlaxTaForConditionalGeneration, TaConfig
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Tuple:
        UpperCAmelCase__ :       int						=						TaConfig.from_pretrained(lowerCAmelCase						)
        UpperCAmelCase__ :       Optional[int]						=						FlaxTaForConditionalGeneration(config=lowerCAmelCase						)
        UpperCAmelCase__ :       int						=						checkpoints.load_tax_checkpoint(lowerCAmelCase						)
  
        UpperCAmelCase__ :       Optional[Any]						=						"""wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]
  
        # Encoder
        for layer_index in range(config.num_layers						):
              UpperCAmelCase__ :       Union[str, Any]						=						F"""layers_{str(lowerCAmelCase						)}"""
  
              # Self-Attention
              UpperCAmelCase__ :       Dict						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
              UpperCAmelCase__ :       Optional[Any]						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
              UpperCAmelCase__ :       Dict						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
              UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]
  
              # Layer Normalization
              UpperCAmelCase__ :       str						=						tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]
  
              if split_mlp_wi:
                    UpperCAmelCase__ :       int						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
                    UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
              else:
                    UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]
  
              UpperCAmelCase__ :       Tuple						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
  
              # Layer Normalization
              UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
  
              # Assigning
              UpperCAmelCase__ :       str						=						tax_attention_key
              UpperCAmelCase__ :       Union[str, Any]						=						tax_attention_out
              UpperCAmelCase__ :       Union[str, Any]						=						tax_attention_query
              UpperCAmelCase__ :       List[Any]						=						tax_attention_value
  
              UpperCAmelCase__ :       Any						=						tax_attention_layer_norm
  
              if split_mlp_wi:
                    UpperCAmelCase__ :       Any						=						tax_mlp_wi_a
                    UpperCAmelCase__ :       List[Any]						=						tax_mlp_wi_a
              else:
                    UpperCAmelCase__ :       Union[str, Any]						=						tax_mlp_wi
  
              UpperCAmelCase__ :       int						=						tax_mlp_wo
              UpperCAmelCase__ :       List[Any]						=						tax_mlp_layer_norm
  
        # Only for layer 0:
        UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
        UpperCAmelCase__ :       str						=						tax_encoder_rel_embedding
  
        # Assigning
        UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
        UpperCAmelCase__ :       Optional[Any]						=						tax_encoder_norm
  
        # Decoder
        for layer_index in range(config.num_decoder_layers						):
              UpperCAmelCase__ :       str						=						F"""layers_{str(lowerCAmelCase						)}"""
  
              # Self-Attention
              UpperCAmelCase__ :       List[Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
              UpperCAmelCase__ :       str						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
              UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
              UpperCAmelCase__ :       Union[str, Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]
  
              # Layer Normalization
              UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
                  """scale"""
              ]
  
              # Encoder-Decoder-Attention
              UpperCAmelCase__ :       str						=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
                  """kernel"""
              ]
              UpperCAmelCase__ :       Union[str, Any]						=						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__ :       Dict						=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
                  """kernel"""
              ]
  
              # Layer Normalization
              UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]
  
              # MLP
              if split_mlp_wi:
                    UpperCAmelCase__ :       Union[str, Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
                    UpperCAmelCase__ :       int						=						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__ :       Optional[int]						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
  
              # Layer Normalization
              UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
  
              # Assigning
              UpperCAmelCase__ :       Union[str, Any]						=						tax_attention_key
              UpperCAmelCase__ :       str						=						tax_attention_out
              UpperCAmelCase__ :       Tuple						=						tax_attention_query
              UpperCAmelCase__ :       int						=						tax_attention_value
  
              UpperCAmelCase__ :       List[Any]						=						tax_pre_attention_layer_norm
  
              UpperCAmelCase__ :       Union[str, Any]						=						tax_enc_dec_attention_key
              UpperCAmelCase__ :       Optional[int]						=						tax_enc_dec_attention_out
              UpperCAmelCase__ :       List[Any]						=						tax_enc_dec_attention_query
              UpperCAmelCase__ :       Any						=						tax_enc_dec_attention_value
  
              UpperCAmelCase__ :       List[Any]						=						tax_cross_layer_norm
  
              if split_mlp_wi:
                    UpperCAmelCase__ :       Tuple						=						tax_mlp_wi_a
                    UpperCAmelCase__ :       Dict						=						tax_mlp_wi_a
              else:
                    UpperCAmelCase__ :       str						=						tax_mlp_wi
  
              UpperCAmelCase__ :       Tuple						=						tax_mlp_wo
  
              UpperCAmelCase__ :       Any						=						txa_mlp_layer_norm
  
        # Decoder Normalization
        UpperCAmelCase__ :       int						=						tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
        UpperCAmelCase__ :       Union[str, Any]						=						txa_decoder_norm
  
        # Only for layer 0:
        UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
        UpperCAmelCase__ :       Optional[int]						=						tax_decoder_rel_embedding
  
        # Token Embeddings
        UpperCAmelCase__ :       Tuple						=						tax_model["""target"""]["""token_embedder"""]["""embedding"""]
        UpperCAmelCase__ :       int						=						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(lowerCAmelCase						)
        print("""T5X Model was sucessfully converted!"""						)
  
  
  if __name__ == "__main__":
      _A						=     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."""
      )
      _A						=     parser.parse_args()
      convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
  
  