code: 
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       str:
  
  
  		'''simple docstring'''
  
  
  		if len(__lowerCAmelCase ) == 0 or not all(len(__lowerCAmelCase ) == 2 for pair in coordinates ):
  				raise ValueError('''The program cannot work out a fitting polynomial.''' )
  
  		if len({tuple(__lowerCAmelCase ) for pair in coordinates} ) != len(__lowerCAmelCase ):
  				raise ValueError('''The program cannot work out a fitting polynomial.''' )
  
  		snake_case     :					List[str]     =	{x for x, _ in coordinates}
  		if len(__lowerCAmelCase ) == 1:
  				return F'x={coordinates[0][0]}'
  
  		if len(__lowerCAmelCase ) != len(__lowerCAmelCase ):
  				raise ValueError('''The program cannot work out a fitting polynomial.''' )
  
  		snake_case     :					Any     =	len(__lowerCAmelCase )
  
  		snake_case     :					str     =	0
  		snake_case     :					list[list[float]]     =	[]
  		# put the x and x to the power values in a matrix
  		while count_of_line < x:
  				snake_case     :					int     =	0
  				snake_case     :					List[Any]     =	coordinates[count_of_line][0]
  				snake_case     :					list[float]     =	[]
  				while count_in_line < x:
  						count_line.append(a ** (x - (count_in_line + 1)) )
  						count_in_line += 1
  				matrix.append(__lowerCAmelCase )
  				count_of_line += 1
  
  		snake_case     :					List[Any]     =	0
  		# put the y values into a vector
  		snake_case     :					list[float]     =	[]
  		while count_of_line < x:
  				vector.append(coordinates[count_of_line][1] )
  				count_of_line += 1
  
  		snake_case     :					Tuple     =	0
  
  		while count < x:
  				snake_case     :					Dict     =	0
  				while zahlen < x:
  						if count == zahlen:
  								zahlen += 1
  						if zahlen == x:
  								break
  						snake_case     :					Optional[Any]     =	matrix[zahlen][count] / matrix[count][count]
  						for counting_columns, item in enumerate(matrix[count] ):
  								# manipulating all the values in the matrix
  								matrix[zahlen][counting_columns] -= item * bruch
  						# manipulating the values in the vector
  						vector[zahlen] -= vector[count] * bruch
  						zahlen += 1
  				count += 1
  
  		snake_case     :					Tuple     =	0
  		# make solutions
  		snake_case     :					list[str]     =	[]
  		while count < x:
  				solution.append(str(vector[count] / matrix[count][count] ) )
  				count += 1
  
  		snake_case     :					int     =	0
  		snake_case     :					List[str]     =	"f(x)="
  
  		while count < x:
  				snake_case     :					list[str]     =	solution[count].split('''E''' )
  				if len(__lowerCAmelCase ) > 1:
  						snake_case     :					Union[str, Any]     =	F'{remove_e[0]}*10^{remove_e[1]}'
  				solved += F'x^{x - (count + 1)}*{solution[count]}'
  				if count + 1 != x:
  						solved += "+"
  				count += 1
  
  		return solved
  
  
  if __name__ == "__main__":
  						print(points_to_polynomial([]))
  						print(points_to_polynomial([[]]))
  						print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
  						print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
  						print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
  						print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
  						print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
  						print(points_to_polynomial([[1, 3], [2, 6], [3, 1_1]]))
  						print(points_to_polynomial([[1, -3], [2, -6], [3, -1_1]]))
  						print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))
  
  
  
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)
  
  
  