"""simple docstring"""





def    lowercase_   (   _lowerCamelCase:	list[int]						,   _lowerCamelCase:	list[int]      ) ->      tuple[float, float]:



							'''simple docstring'''

							if not len(_lowerCamelCase      ) == len(_lowerCamelCase      ) == 3:
														raise ValueError("Please enter a valid equation."      )
							if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
														raise ValueError("Both a & b of two equations can't be zero."      )

							# Extract the coefficients
							__lowerCamelCase   ,  __lowerCamelCase   ,  __lowerCamelCase					:		Union[str, Any]		   =     equationa
							__lowerCamelCase   ,  __lowerCamelCase   ,  __lowerCamelCase					:		str		   =     equationa

							# Calculate the determinants of the matrices
							__lowerCamelCase					:		Dict		   =     aa * ba - aa * ba
							__lowerCamelCase					:		Tuple		   =     ca * ba - ca * ba
							__lowerCamelCase					:		Tuple		   =     aa * ca - aa * ca

							# Check if the system of linear equations has a solution (using Cramer's rule)
							if determinant == 0:
														if determinant_x == determinant_y == 0:
																					raise ValueError("Infinite solutions. (Consistent system)"      )
														else:
																					raise ValueError("No solution. (Inconsistent system)"      )
							else:
														if determinant_x == determinant_y == 0:
																					# Trivial solution (Inconsistent system)
																					return (0.0, 0.0)
														else:
																					__lowerCamelCase					:		List[Any]		   =     determinant_x / determinant
																					__lowerCamelCase					:		Union[str, Any]		   =     determinant_y / determinant
																					# Non-Trivial Solution (Consistent system)
																					return (x, y)
"""simple docstring"""





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def    lowercase_   (   _lowerCamelCase:	Dict						,   _lowerCamelCase:	str						,   _lowerCamelCase:	Dict      ) ->      List[str]:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     TaConfig.from_json_file(_lowerCamelCase      )
							print(F"""Building PyTorch model from configuration: {config}"""      )
							__lowerCamelCase					:		Optional[int]		   =     TaForConditionalGeneration(_lowerCamelCase      )

							# Load weights from tf checkpoint
							load_tf_weights_in_ta(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase      )

							# Save pytorch-model
							print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
							model.save_pretrained(_lowerCamelCase      )


if __name__ == "__main__":
				__A  =				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 T5 model. \nThis specifies the model architecture.'''
				    ),
				)
				parser.add_argument(
				    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
				)
				__A  =				parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)