code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import dataclasses
  import enum
  import functools
  import math
  import re
  
  # The following script is adapted from the script of TaPas.
  # Original: https://github.com/google-research/tapas/master/wikisql_utils.py
  from typing import Any, List, Text
  
  
  lowerCamelCase_     :     int    		=						"""none"""
  lowerCamelCase_     :     Any    		=						"""none"""
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=value.split(lowercase	)
      return len(lowercase	) > 1 and any((len(lowercase	) == 3 for x in split)	)
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      if isinstance(lowercase ,					lowercase	):
          return value
      if isinstance(lowercase ,					lowercase	):
          return float(lowercase	)
      if not isinstance(lowercase ,					lowercase	):
          raise ValueError('''Argument value is not a string. Can\'t parse it as float'''	)
      a					=value
  
      try:
          # Example: 1,000.7
          if "." in sanitized and "," in sanitized:
              return float(sanitized.replace(''',''' ,					''''''	)	)
          # 1,000
          if "," in sanitized and _split_thousands(''',''' ,					lowercase	):
              return float(sanitized.replace(''',''' ,					''''''	)	)
          # 5,5556
          if "," in sanitized and sanitized.count(''','''	) == 1 and not _split_thousands(''',''' ,					lowercase	):
              return float(sanitized.replace(''',''' ,					'''.'''	)	)
          # 0.0.0.1
          if sanitized.count('''.'''	) > 1:
              return float(sanitized.replace('''.''' ,					''''''	)	)
          # 0,0,0,1
          if sanitized.count(''','''	) > 1:
              return float(sanitized.replace(''',''' ,					''''''	)	)
          return float(lowercase	)
      except ValueError:
          # Avoid adding the sanitized value in the error message.
          raise ValueError('''Unable to convert value to float'''	)
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      if answer is None:
          return None
      try:
          a					=convert_to_float(lowercase	)
          if isinstance(lowercase ,					lowercase	) and math.isnan(lowercase	):
              return None
          return value
      except ValueError:
          return answer.lower()
  
  
  lowerCamelCase_     :     Dict    		=						{
      """text""": lambda x: x,
      """real""": convert_to_float,
  }
  
  
  
  
  
  
  class    __A       ( enum.Enum    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						0
   __lowerCAmelCase				      =						1
   __lowerCAmelCase				      =						2
   __lowerCAmelCase				      =						3
   __lowerCAmelCase				      =						4
   __lowerCAmelCase				      =						5
  
  
  
  
  
  
  class    __A       ( enum.Enum    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						0
   __lowerCAmelCase				      =						1
   __lowerCAmelCase				      =						2
  
  
  
  
  
  
  @dataclasses.dataclass
  class    __A       :
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						42
   __lowerCAmelCase				      =						42
   __lowerCAmelCase				      =						42
  
  lowerCamelCase_     :     Dict    		=						re.compile(R"""\w+|[^\w\s]+""", re.UNICODE | re.MULTILINE | re.DOTALL)
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      return list(_TOKENIZER.findall(x.lower()	)	)
  def _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      if operator == _Operator.EQUALS:
          return src == tgt
      elif operator == _Operator.GREATER:
          return src > tgt
      elif operator == _Operator.LESSER:
          return src < tgt
      raise ValueError(f'''Unknown operator: {operator}'''	)
  def _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=table['''types''']
      return _TYPE_CONVERTER[types[column]](lowercase	)
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      return isinstance(lowercase ,					lowercase	)
  def _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      for cond in conditions:
          a					=row[cond.column]
  
          a					=_parse_value(lowercase ,					cond.column ,					cond.cmp_value	)
  
          if _is_string(lowercase	) and _is_string(lowercase	):
              a					=_normalize_for_match(lowercase	)
              a					=_normalize_for_match(lowercase	)
  
          if not isinstance(lowercase ,					type(lowercase	)	):
              raise ValueError('''Type difference {} != {}'''.format(type(lowercase	) ,					type(lowercase	)	)	)
  
          if not _compare(cond.operator ,					lowercase ,					lowercase	):
              return False
      return True
  def _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      if not answer_coordinates:
          if aggregation_op == _Aggregation.COUNT:
              return 0.0
          else:
              return EMPTY_ANSWER_AGG
  
      # Count can support non numeric answers.
      if aggregation_op == _Aggregation.COUNT:
          return float(len(lowercase	)	)
  
      # If we have just one answer, if float returns it or try a conversion.
      a					=[table['''rows'''][i][j] for (i, j) in answer_coordinates]
      if len(lowercase	) == 1:
          try:
              return convert_to_float(values[0]	)
          except ValueError as e:
              if aggregation_op != _Aggregation.NONE:
                  raise e
  
      if aggregation_op == _Aggregation.NONE:
          return None
  
      # Other aggregation only support numeric values. Bail out if we have strings.
      if not all((isinstance(lowercase ,					(int, float)	) for v in values)	):
          return None
  
      if aggregation_op == _Aggregation.SUM:
          return float(sum(lowercase	)	)
      elif aggregation_op == _Aggregation.AVERAGE:
          return sum(lowercase	) / len(lowercase	)
      else:
          raise ValueError(f'''Unknown aggregation: {aggregation_op}'''	)
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=sql_query['''agg''']
      if aggregation_op_index >= 3:
          a					=_Aggregation(lowercase	)
      else:
          a					=_Aggregation.NONE
  
      a					=sql_query['''sel''']
      a					=[
          _Condition(lowercase ,					_Operator(lowercase	) ,					lowercase	)
          for column, operator, cmp_value in zip(
              sql_query['''conds''']['''column_index'''] ,					sql_query['''conds''']['''operator_index'''] ,					sql_query['''conds''']['''condition''']	)
      ]
  
      a					=[]
      for row in range(len(table['''rows''']	)	):
          if _respect_conditions(lowercase ,					table['''rows'''][row] ,					lowercase	):
              indices.append((row, target_column)	)
  
      if not indices:
          return [], aggregation_op
  
      if len(lowercase	) == 1:
          return indices, aggregation_op
  
      # Parsing of MIN/MAX.
      if aggregation_op_index in (1, 2):
          a					={2: min, 1: max}
          a					=[(table['''rows'''][i][j], index) for index, (i, j) in enumerate(lowercase	)]
          a					=functools.reduce(operators[sql_query['''agg''']] ,					lowercase	)
  
          a					=[indices[reduced[1]]]
          return ret, _Aggregation.NONE
  
      return indices, aggregation_op
  def _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      if float_answer is not None:
          return [str(lowercase	)]
      return [str(table['''real_rows'''][r][c]	) for r, c in answer_coordinates]
  
  
  
  
  
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a	,   a					=_get_answer_coordinates(lowercase ,					lowercase	)
      a					=_get_float_answer(lowercase ,					lowercase ,					lowercase	)
      a					=_get_answer_text(lowercase ,					lowercase ,					lowercase	)
      # keep the original data the same with TaPas
      if len(lowercase	) == 0:
          a					=[EMPTY_ANSWER]
      return answer_text
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)