





import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
A   : Optional[int]			=				logging.get_logger(__name__)







def        __lowerCamelCase (							__a       :Dict   )		->    Optional[Any]:






		"""simple docstring"""

		A__  				=       torch.load(__a   ,					map_location="""cpu"""   )
		if "model" in sd.keys():
				A__  				=       torch.load(__a   ,					map_location="""cpu"""   )["""model"""]

		# pop unnecessary weights
		A__  				=       [
		    """decoder.version""",
		    """decoder.output_projection.weight""",
		]
		for key in keys_to_delete:
				if key in sd:
						sd.pop(__a   )

		A__  				=       {
		    """decoder.project_in_dim.weight""": """decoder.project_in.weight""",
		    """decoder.project_out_dim.weight""": """decoder.project_out.weight""",
		    """decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
		    """decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
		}
		for old_key, new_key in keys_to_rename.items():
				if old_key in sd:
						A__  				=       sd.pop(__a   )

		A__  				=       list(sd.keys()   )
		for key in keys:
				if ".qkv_proj." in key:
						A__  				=       sd[key]
						# We split QKV in separate Q,K,V

						A__  				=       key.replace(""".qkv_proj."""   ,					""".q_proj."""   )
						A__  				=       key.replace(""".qkv_proj."""   ,					""".k_proj."""   )
						A__  				=       key.replace(""".qkv_proj."""   ,					""".v_proj."""   )

						A__  				=       value.shape[0]
						assert depth % 3 == 0
						# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
						# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
						A__				,      A__				,      A__  				=       torch.split(__a   ,					depth // 3   ,					dim=0   )

						A__  				=       q
						A__  				=       k
						A__  				=       v
						del sd[key]

		return sd







@torch.no_grad()
def        __lowerCamelCase (							__a       :str   ,					__a       :Dict   ,					__a       :List[Any]=None   )		->    Dict:






		"""simple docstring"""

		A__  				=       load_checkpoint(__a   )

		if config is not None:
				A__  				=       OPTConfig.from_pretrained(__a   )
		else:
				A__  				=       OPTConfig()

		A__  				=       OPTModel(__a   ).half().eval()
		model.load_state_dict(__a   )

		# Check results
		Path(__a   ).mkdir(exist_ok=__a   )
		model.save_pretrained(__a   )


if __name__ == "__main__":
					A   : List[str]			=				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--fairseq_path''',
					    type=str,
					    help=(
					        '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
					        ''' https://huggingface.co/models?other=opt_metasq'''
					    ),
					)
					parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
					parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
					A   : Any			=				parser.parse_args()
					convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)










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()



