




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

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

							lowerCAmelCase =		{x for x, _ in coordinates}
							if len(_snake_case				) == 1:
														return F"""x={coordinates[0][0]}"""

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

							lowerCAmelCase =		len(_snake_case				)

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

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

							lowerCAmelCase =		0

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

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

							lowerCAmelCase =		0
							lowerCAmelCase =		'''f(x)='''

							while count < x:
														lowerCAmelCase =		solution[count].split('''E'''				)
														if len(_snake_case				) > 1:
																					lowerCAmelCase =		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 transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()



def    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							# Initialize PyTorch model
							lowerCAmelCase =		CanineConfig()
							lowerCAmelCase =		CanineModel(_snake_case				)
							model.eval()

							print(F"""Building PyTorch model from configuration: {config}"""				)

							# Load weights from tf checkpoint
							load_tf_weights_in_canine(_snake_case    ,			_snake_case    ,			_snake_case				)

							# Save pytorch-model (weights and configuration)
							print(F"""Save PyTorch model to {pytorch_dump_path}"""				)
							model.save_pretrained(_snake_case				)

							# Save tokenizer files
							lowerCAmelCase =		CanineTokenizer()
							print(F"""Save tokenizer files to {pytorch_dump_path}"""				)
							tokenizer.save_pretrained(_snake_case				)


if __name__ == "__main__":
		UpperCAmelCase_    =argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--tf_checkpoint_path""",
		    default=None,
		    type=str,
		    required=True,
		    help="""Path to the TensorFlow checkpoint. Should end with model.ckpt""",
		)
		parser.add_argument(
		    """--pytorch_dump_path""",
		    default=None,
		    type=str,
		    required=True,
		    help="""Path to a folder where the PyTorch model will be placed.""",
		)
		UpperCAmelCase_    =parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)



