


import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__ =						logging.get_logger(__name__)






def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      Any:
						__lowercase					=				RobertaPreLayerNormConfig.from_pretrained(
						    lowercase__						,       architectures=["""RobertaPreLayerNormForMaskedLM"""]   )

						# convert state_dict
						__lowercase					=				torch.load(hf_hub_download(repo_id=lowercase__						,       filename="""pytorch_model.bin"""   )   )
						__lowercase					=				{}
						for tensor_key, tensor_value in original_state_dict.items():
												# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
												if tensor_key.startswith("""roberta."""   ):
																		__lowercase					=				"""roberta_prelayernorm.""" + tensor_key[len("""roberta."""   ) :]

												# The original implementation contains weights which are not used, remove them from the state_dict
												if tensor_key.endswith(""".self.LayerNorm.weight"""   ) or tensor_key.endswith(""".self.LayerNorm.bias"""   ):
																		continue

												__lowercase					=				tensor_value

						__lowercase					=				RobertaPreLayerNormForMaskedLM.from_pretrained(
						    pretrained_model_name_or_path=lowercase__						,       config=lowercase__						,       state_dict=lowercase__   )
						model.save_pretrained(lowercase__   )

						# convert tokenizer
						__lowercase					=				AutoTokenizer.from_pretrained(lowercase__   )
						tokenizer.save_pretrained(lowercase__   )


if __name__ == "__main__":
			UpperCamelCase__ =						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--checkpoint-repo",
			    default=None,
			    type=str,
			    required=True,
			    help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			UpperCamelCase__ =						parser.parse_args()
			convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)










import unittest

import numpy as np






def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__						,       lowercase__ = None						,       ) ->      np.ndarray:
						__lowercase					=				np.shape(lowercase__   )
						__lowercase					=				np.shape(lowercase__   )
						__lowercase					=				np.shape(lowercase__   )

						if shape_a[0] != shape_b[0]:
												__lowercase					=				(
												    """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(lowercase__   )

						if shape_b[1] != shape_c[1]:
												__lowercase					=				(
												    """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(lowercase__   )

						__lowercase					=				pseudo_inv
						if a_inv is None:
												try:
																		__lowercase					=				np.linalg.inv(lowercase__   )
												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 			_lowerCAmelCase   ( unittest.TestCase			):






			"""simple docstring"""





			def 		snake_case__			(				self	:       Dict   )						->					None:
									"""simple docstring"""







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

									__lowercase					=				schur_complement(lowercase			,     lowercase			,     lowercase   )

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

									__lowercase					=				np.linalg.det(lowercase   )
									__lowercase					=				np.linalg.det(lowercase   )
									__lowercase					=				np.linalg.det(lowercase   )

									self.assertAlmostEqual(lowercase			,     det_a * det_s   )





			def 		snake_case__			(				self	:       Tuple   )						->					None:
									"""simple docstring"""







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

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







			def 		snake_case__			(				self	:       Tuple   )						->					None:
									"""simple docstring"""







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

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


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			unittest.main()





