code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import math
  
  
  
  
  
  
  class    __A       :
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def __init__(	self				,					__A				,					__A				,					__A				,					__A		)      ->  int:
       a					=x
       a					=y
       a					=step_size
       a					=function_to_optimize
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  int:
       return self.function(self.x				,					self.y		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  str:
       a					=self.step_size
       return [
           SearchProblem(__A				,					__A				,					__A				,					self.function		)
           for x, y in (
               (self.x - step_size, self.y - step_size),
               (self.x - step_size, self.y),
               (self.x - step_size, self.y + step_size),
               (self.x, self.y - step_size),
               (self.x, self.y + step_size),
               (self.x + step_size, self.y - step_size),
               (self.x + step_size, self.y),
               (self.x + step_size, self.y + step_size),
           )
       ]
  
  
   def __hash__(	self		)      ->  List[Any]:
       return hash(str(self		)		)
  
  
   def __eq__(	self				,					__A		)      ->  Union[str, Any]:
       if isinstance(__A				,					__A		):
           return hash(str(self		)		) == hash(str(__A		)		)
       return False
  
  
  
  
  
   def __str__(	self		)      ->  int:
       return f'''x: {self.x} y: {self.y}'''
  
  def _A		(		lowercase ,					lowercase = True ,					lowercase = math.inf ,					lowercase = -math.inf ,					lowercase = math.inf ,					lowercase = -math.inf ,					lowercase = False ,					lowercase = 1_00_00 ,					):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=search_prob
      a					=[]  # list to store the current score at each iteration
      a					=0
      a					=False
      a					=set()
      while not solution_found and iterations < max_iter:
          visited.add(lowercase	)
          iterations += 1
          a					=current_state.score()
          scores.append(lowercase	)
          a					=current_state.get_neighbors()
          a					=-math.inf
          a					=math.inf
          a					=None  # to hold the next best neighbor
          for neighbor in neighbors:
              if neighbor in visited:
                  continue  # do not want to visit the same state again
              if (
                  neighbor.x > max_x
                  or neighbor.x < min_x
                  or neighbor.y > max_y
                  or neighbor.y < min_y
              ):
                  continue  # neighbor outside our bounds
              a					=neighbor.score() - current_score
              if find_max:  # finding max
                  # going to direction with greatest ascent
                  if change > max_change and change > 0:
                      a					=change
                      a					=neighbor
              else:  # finding min
                  # to direction with greatest descent
                  if change < min_change and change < 0:
                      a					=change
                      a					=neighbor
          if next_state is not None:
              # we found at least one neighbor which improved the current state
              a					=next_state
          else:
              # since we have no neighbor that improves the solution we stop the search
              a					=True
  
      if visualization:
          from matplotlib import pyplot as plt
  
          plt.plot(range(lowercase	) ,					lowercase	)
          plt.xlabel('''Iterations'''	)
          plt.ylabel('''Function values'''	)
          plt.show()
  
      return current_state
  
  
  if __name__ == "__main__":
         import doctest
  
         doctest.testmod()
         def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
             """simple docstring"""
             return (x**2) + (y**2)
  
         # starting the problem with initial coordinates (3, 4)
         lowerCamelCase_     :     List[str]    		=						SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
         lowerCamelCase_     :     Optional[Any]    		=						hill_climbing(prob, find_max=False)
         print(
             """The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: """
             F'{local_min.score()}'
         )
  
         # starting the problem with initial coordinates (12, 47)
         lowerCamelCase_     :     str    		=						SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
         lowerCamelCase_     :     Any    		=						hill_climbing(
             prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
         )
         print(
             """The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
             F'and 50 > y > - 5 found via hill climbing: {local_min.score()}'
         )
         def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
             """simple docstring"""
             return (3 * x**2) - (6 * y)
  
         lowerCamelCase_     :     Any    		=						SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
         lowerCamelCase_     :     Tuple    		=						hill_climbing(prob, find_max=True)
  
  
  
  
  
         print(
             """The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: """
             F'{local_min.score()}'
         )
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)