code: 
  '''simple docstring'''
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					)     ->       tuple[list[list[int]], list[list[str]]]:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Optional[int]     =	list(SCREAMING_SNAKE_CASE__ )
  		snake_case     :					List[str]     =	list(SCREAMING_SNAKE_CASE__ )
  		snake_case     :					Optional[int]     =	len(SCREAMING_SNAKE_CASE__ )
  		snake_case     :					Optional[int]     =	len(SCREAMING_SNAKE_CASE__ )
  
  		snake_case     :					Tuple     =	[
  		    [0 for _ in range(len_destination_seq + 1 )] for _ in range(len_source_seq + 1 )
  		]
  		snake_case     :					Optional[Any]     =	[
  		    ['''0''' for _ in range(len_destination_seq + 1 )] for _ in range(len_source_seq + 1 )
  		]
  
  		for i in range(1   ,					len_source_seq + 1 ):
  				snake_case     :					Optional[Any]     =	i * delete_cost
  				snake_case     :					Union[str, Any]     =	F'D{source_seq[i - 1]:c}'
  
  		for i in range(1   ,					len_destination_seq + 1 ):
  				snake_case     :					Any     =	i * insert_cost
  				snake_case     :					List[Any]     =	F'I{destination_seq[i - 1]:c}'
  
  		for i in range(1   ,					len_source_seq + 1 ):
  				for j in range(1   ,					len_destination_seq + 1 ):
  						if source_seq[i - 1] == destination_seq[j - 1]:
  								snake_case     :					Union[str, Any]     =	costs[i - 1][j - 1] + copy_cost
  								snake_case     :					Optional[Any]     =	F'C{source_seq[i - 1]:c}'
  						else:
  								snake_case     :					Dict     =	costs[i - 1][j - 1] + replace_cost
  								snake_case     :					str     =	F'R{source_seq[i - 1]:c}' + str(destination_seq[j - 1] )
  
  						if costs[i - 1][j] + delete_cost < costs[i][j]:
  								snake_case     :					Any     =	costs[i - 1][j] + delete_cost
  								snake_case     :					str     =	F'D{source_seq[i - 1]:c}'
  
  						if costs[i][j - 1] + insert_cost < costs[i][j]:
  								snake_case     :					List[Any]     =	costs[i][j - 1] + insert_cost
  								snake_case     :					Tuple     =	F'I{destination_seq[j - 1]:c}'
  
  		return costs, ops
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       list[str]:
  
  
  		'''simple docstring'''
  
  
  		if i == 0 and j == 0:
  				return []
  		else:
  				if ops[i][j][0] == "C" or ops[i][j][0] == "R":
  						snake_case     :					Dict     =	assemble_transformation(SCREAMING_SNAKE_CASE__   ,					i - 1   ,					j - 1 )
  						seq.append(ops[i][j] )
  						return seq
  				elif ops[i][j][0] == "D":
  						snake_case     :					Optional[int]     =	assemble_transformation(SCREAMING_SNAKE_CASE__   ,					i - 1   ,					SCREAMING_SNAKE_CASE__ )
  						seq.append(ops[i][j] )
  						return seq
  				else:
  						snake_case     :					Tuple     =	assemble_transformation(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					j - 1 )
  						seq.append(ops[i][j] )
  						return seq
  
  
  if __name__ == "__main__":
  						lowercase__					,     lowercase__						=			compute_transform_tables("Python", "Algorithms", -1, 1, 2, 2)
  
  						lowercase__						=			len(operations)
  						lowercase__						=			len(operations[0])
  						lowercase__						=			assemble_transformation(operations, m - 1, n - 1)
  
  						lowercase__						=			list("Python")
  						lowercase__						=			0
  						lowercase__						=			0
  
  						with open("min_cost.txt", "w") as file:
  												for op in sequence:
  																		print("".join(string))
  
  																		if op[0] == "C":
  																								file.write("%-16s" % "Copy %c" % op[1])
  																								file.write("\t\t\t" + "".join(string))
  																								file.write("\r\n")
  
  																								cost -= 1
  																		elif op[0] == "R":
  																								lowercase__						=			op[2]
  
  																								file.write("%-16s" % ("Replace %c" % op[1] + " with " + str(op[2])))
  																								file.write("\t\t" + "".join(string))
  																								file.write("\r\n")
  
  																								cost += 1
  																		elif op[0] == "D":
  																								string.pop(i)
  
  																								file.write("%-16s" % "Delete %c" % op[1])
  																								file.write("\t\t\t" + "".join(string))
  																								file.write("\r\n")
  
  																								cost += 2
  																		else:
  																								string.insert(i, op[1])
  
  																								file.write("%-16s" % "Insert %c" % op[1])
  																								file.write("\t\t\t" + "".join(string))
  																								file.write("\r\n")
  
  																								cost += 2
  
  																		i += 1
  
  												print("".join(string))
  												print("Cost: ", cost)
  
  												file.write("\r\nMinimum cost: " + str(cost))
  
  
  
style_context: 
  '''simple docstring'''
  import argparse
  import json
  
  from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Dict:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Tuple     =	json.load(open(SCREAMING_SNAKE_CASE__   ,					'''r''' ) )
  		snake_case     :					Any     =	GPTNeoConfig(
  		    hidden_size=config_json['''n_embd''']   ,					num_layers=config_json['''n_layer''']   ,					num_heads=config_json['''n_head''']   ,					attention_types=config_json['''attention_types''']   ,					max_position_embeddings=config_json['''n_positions''']   ,					resid_dropout=config_json['''res_dropout''']   ,					embed_dropout=config_json['''embed_dropout''']   ,					attention_dropout=config_json['''attn_dropout''']   ,					)
  		print(F'Building PyTorch model from configuration: {config}' )
  		snake_case     :					Union[str, Any]     =	GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__ )
  
  		# Load weights from tf checkpoint
  		load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
  
  		# Save pytorch-model
  		print(F'Save PyTorch model to {pytorch_dump_path}' )
  		model.save_pretrained(SCREAMING_SNAKE_CASE__ )
  
  
  if __name__ == "__main__":
  						lowercase__						=			argparse.ArgumentParser()
  						# Required parameters
  						parser.add_argument(
  						    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
  						)
  						parser.add_argument(
  						    "--config_file",
  						    default=None,
  						    type=str,
  						    required=True,
  						    help=(
  						        "The config json file corresponding to the pre-trained mesh-tf model. \n"
  						        "This specifies the model architecture."
  						    ),
  						)
  						parser.add_argument(
  						    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  						)
  						lowercase__						=			parser.parse_args()
  						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
  