



def    UpperCAmelCase					(  lowercase__						:					list[list[int]]	):



			'''simple docstring'''





			if len(lowercase__	) == 0 or not all(len(lowercase__	) == 2 for pair in coordinates	):
						raise ValueError("""The program cannot work out a fitting polynomial."""	)

			if len({tuple(lowercase__	) for pair in coordinates}	) != len(lowercase__	):
						raise ValueError("""The program cannot work out a fitting polynomial."""	)

			a__					=  {x for x, _ in coordinates}
			if len(lowercase__	) == 1:
						return f'x={coordinates[0][0]}'

			if len(lowercase__	) != len(lowercase__	):
						raise ValueError("""The program cannot work out a fitting polynomial."""	)

			a__					=  len(lowercase__	)

			a__					=  0
			a__					=  []
			# put the x and x to the power values in a matrix
			while count_of_line < x:
						a__					=  0
						a__					=  coordinates[count_of_line][0]
						a__					=  []
						while count_in_line < x:
									count_line.append(a ** (x - (count_in_line + 1))	)
									count_in_line += 1
						matrix.append(lowercase__	)
						count_of_line += 1

			a__					=  0
			# put the y values into a vector
			a__					=  []
			while count_of_line < x:
						vector.append(coordinates[count_of_line][1]	)
						count_of_line += 1

			a__					=  0

			while count < x:
						a__					=  0
						while zahlen < x:
									if count == zahlen:
												zahlen += 1
									if zahlen == x:
												break
									a__					=  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

			a__					=  0
			# make solutions
			a__					=  []
			while count < x:
						solution.append(str(vector[count] / matrix[count][count]	)	)
						count += 1

			a__					=  0
			a__					=  """f(x)="""

			while count < x:
						a__					=  solution[count].split("""E"""	)
						if len(lowercase__	) > 1:
									a__					=  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, 11]]))
	print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
	print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))






import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()




def    UpperCAmelCase					(  lowercase__						:					int				,					lowercase__						:					str				,					lowercase__						:					Union[str, Any]				,					lowercase__						:					Optional[int]				,					lowercase__						:					Optional[Any]	):



			'''simple docstring'''





			a__					=  BertConfig.from_pretrained(
			    """bert-large-cased"""				,					vocab_size=lowercase__				,					max_position_embeddings=512				,					is_decoder=lowercase__				,					add_cross_attention=lowercase__				,					)
			a__					=  bert_config.to_dict()
			del bert_config_dict["type_vocab_size"]
			a__					=  BertGenerationConfig(**lowercase__	)
			if is_encoder:
						a__					=  BertGenerationEncoder(lowercase__	)
			else:
						a__					=  BertGenerationDecoder(lowercase__	)
			print(f'Building PyTorch model from configuration: {config}'	)

			# Load weights from tf checkpoint
			load_tf_weights_in_bert_generation(
			    lowercase__				,					lowercase__				,					model_class="""bert"""				,					is_encoder_named_decoder=lowercase__				,					is_encoder=lowercase__				,					)

			# Save pytorch-model
			print(f'Save PyTorch model and config to {pytorch_dump_path}'	)
			model.save_pretrained(lowercase__	)


if __name__ == "__main__":
	_lowercase				:					str     					=argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--tf_hub_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
	)
	parser.add_argument(
	    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
	)
	parser.add_argument(
	    """--is_encoder_named_decoder""",
	    action="""store_true""",
	    help="""If decoder has to be renamed to encoder in PyTorch model.""",
	)
	parser.add_argument("""--is_encoder""", action="""store_true""", help="""If model is an encoder.""")
	parser.add_argument("""--vocab_size""", default=5_03_58, type=int, help="""Vocab size of model""")
	_lowercase				:					List[str]     					=parser.parse_args()
	convert_tf_checkpoint_to_pytorch(
	    args.tf_hub_path,
	    args.pytorch_dump_path,
	    args.is_encoder_named_decoder,
	    args.vocab_size,
	    is_encoder=args.is_encoder,
	)

