





import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM







def        __lowerCamelCase (							__a       :Any   )		->    str:






		"""simple docstring"""

		if "model" in orig_key:
				A__  				=       orig_key.replace("""model."""   ,					""""""   )
		if "norm1" in orig_key:
				A__  				=       orig_key.replace("""norm1"""   ,					"""attention.output.LayerNorm"""   )
		if "norm2" in orig_key:
				A__  				=       orig_key.replace("""norm2"""   ,					"""output.LayerNorm"""   )
		if "norm" in orig_key:
				A__  				=       orig_key.replace("""norm"""   ,					"""LayerNorm"""   )
		if "transformer" in orig_key:
				A__  				=       orig_key.split("""."""   )[0].split("""_"""   )[-1]
				A__  				=       orig_key.replace(F'transformer_{layer_num}'   ,					F'encoder.layer.{layer_num}'   )
		if "mha.attn" in orig_key:
				A__  				=       orig_key.replace("""mha.attn"""   ,					"""attention.self"""   )
		if "mha" in orig_key:
				A__  				=       orig_key.replace("""mha"""   ,					"""attention"""   )
		if "W_q" in orig_key:
				A__  				=       orig_key.replace("""W_q"""   ,					"""self.query"""   )
		if "W_k" in orig_key:
				A__  				=       orig_key.replace("""W_k"""   ,					"""self.key"""   )
		if "W_v" in orig_key:
				A__  				=       orig_key.replace("""W_v"""   ,					"""self.value"""   )
		if "ff1" in orig_key:
				A__  				=       orig_key.replace("""ff1"""   ,					"""intermediate.dense"""   )
		if "ff2" in orig_key:
				A__  				=       orig_key.replace("""ff2"""   ,					"""output.dense"""   )
		if "ff" in orig_key:
				A__  				=       orig_key.replace("""ff"""   ,					"""output.dense"""   )
		if "mlm_class" in orig_key:
				A__  				=       orig_key.replace("""mlm.mlm_class"""   ,					"""cls.predictions.decoder"""   )
		if "mlm" in orig_key:
				A__  				=       orig_key.replace("""mlm"""   ,					"""cls.predictions.transform"""   )
		if "cls" not in orig_key:
				A__  				=       """yoso.""" + orig_key

		return orig_key







def        __lowerCamelCase (							__a       :Union[str, Any]   ,					__a       :Union[str, Any]   )		->    str:






		"""simple docstring"""

		for key in orig_state_dict.copy().keys():
				A__  				=       orig_state_dict.pop(__a   )

				if ("pooler" in key) or ("sen_class" in key):
						continue
				else:
						A__  				=       val

		A__  				=       orig_state_dict["""cls.predictions.decoder.bias"""]
		A__  				=       torch.arange(__a   ).expand((1, -1)   ) + 2

		return orig_state_dict







def        __lowerCamelCase (							__a       :Optional[int]   ,					__a       :int   ,					__a       :List[Any]   )		->    Any:






		"""simple docstring"""

		A__  				=       torch.load(__a   ,					map_location="""cpu"""   )["""model_state_dict"""]
		A__  				=       YosoConfig.from_json_file(__a   )
		A__  				=       YosoForMaskedLM(__a   )

		A__  				=       convert_checkpoint_helper(config.max_position_embeddings   ,					__a   )

		print(model.load_state_dict(__a   )   )
		model.eval()
		model.save_pretrained(__a   )

		print(F'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'   )


if __name__ == "__main__":
					A   : Tuple			=				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
					)
					parser.add_argument(
					    '''--config_file''',
					    default=None,
					    type=str,
					    required=True,
					    help='''The json file for YOSO model config.''',
					)
					parser.add_argument(
					    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
					)
					A   : Any			=				parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)










import unittest

import numpy as np







def        __lowerCamelCase (							__a       :np.ndarray   ,					__a       :np.ndarray   ,					__a       :np.ndarray   ,					__a       :np.ndarray | None = None   ,					)		->    np.ndarray:






		"""simple docstring"""

		A__  				=       np.shape(__a   )
		A__  				=       np.shape(__a   )
		A__  				=       np.shape(__a   )

		if shape_a[0] != shape_b[0]:
				A__  				=       (
				    """Expected the same number of rows for A and B. """
				    F'Instead found A of size {shape_a} and B of size {shape_b}'
				)
				raise ValueError(__a   )

		if shape_b[1] != shape_c[1]:
				A__  				=       (
				    """Expected the same number of columns for B and C. """
				    F'Instead found B of size {shape_b} and C of size {shape_c}'
				)
				raise ValueError(__a   )

		A__  				=       pseudo_inv
		if a_inv is None:
				try:
						A__  				=       np.linalg.inv(__a   )
				except np.linalg.LinAlgError:
						raise ValueError(
						    """Input matrix A is not invertible. Cannot compute Schur complement."""   )

		return mat_c - mat_b.T @ a_inv @ mat_b


class   A      (unittest.TestCase   ):
			'''simple docstring'''




			def        a_	(      self       :   Union[str, Any]       )     ->     None:







					"""simple docstring"""






					A__  				=       np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
					A__  				=       np.array([[0, 3], [3, 0], [2, 3]]       )
					A__  				=       np.array([[2, 1], [6, 3]]       )

					A__  				=       schur_complement(__lowerCAmelCase							,     __lowerCAmelCase							,     __lowerCAmelCase       )

					A__  				=       np.block([[a, b], [b.T, c]]       )

					A__  				=       np.linalg.det(__lowerCAmelCase       )
					A__  				=       np.linalg.det(__lowerCAmelCase       )
					A__  				=       np.linalg.det(__lowerCAmelCase       )

					self.assertAlmostEqual(__lowerCAmelCase							,     det_a * det_s       )






			def        a_	(      self       :   str       )     ->     None:







					"""simple docstring"""






					A__  				=       np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
					A__  				=       np.array([[0, 3], [3, 0], [2, 3]]       )
					A__  				=       np.array([[2, 1], [6, 3]]       )

					with self.assertRaises(__lowerCAmelCase       ):
							schur_complement(__lowerCAmelCase							,     __lowerCAmelCase							,     __lowerCAmelCase       )







			def        a_	(      self       :   List[str]       )     ->     None:







					"""simple docstring"""






					A__  				=       np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
					A__  				=       np.array([[0, 3], [3, 0], [2, 3]]       )
					A__  				=       np.array([[2, 1, 3], [6, 3, 5]]       )

					with self.assertRaises(__lowerCAmelCase       ):
							schur_complement(__lowerCAmelCase							,     __lowerCAmelCase							,     __lowerCAmelCase       )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					unittest.main()



