





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand







def        __lowerCamelCase (							__a       :Namespace   )		->    Any:






		"""simple docstring"""

		return ConvertCommand(
		    args.model_type   ,					args.tf_checkpoint   ,					args.pytorch_dump_output   ,					args.config   ,					args.finetuning_task_name   )


A   : Any			=				'''
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
'''






class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			@staticmethod
			def        a_	(      __lowerCAmelCase       :   ArgumentParser       )     ->     Optional[Any]:







					"""simple docstring"""






					A__  				=       parser.add_parser(
					    """convert"""							,     help="""CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."""							,     )
					train_parser.add_argument("""--model_type"""							,     type=__lowerCAmelCase							,     required=__lowerCAmelCase							,     help="""Model's type."""       )
					train_parser.add_argument(
					    """--tf_checkpoint"""							,     type=__lowerCAmelCase							,     required=__lowerCAmelCase							,     help="""TensorFlow checkpoint path or folder."""       )
					train_parser.add_argument(
					    """--pytorch_dump_output"""							,     type=__lowerCAmelCase							,     required=__lowerCAmelCase							,     help="""Path to the PyTorch saved model output."""       )
					train_parser.add_argument("""--config"""							,     type=__lowerCAmelCase							,     default=""""""							,     help="""Configuration file path or folder."""       )
					train_parser.add_argument(
					    """--finetuning_task_name"""							,     type=__lowerCAmelCase							,     default=__lowerCAmelCase							,     help="""Optional fine-tuning task name if the TF model was a finetuned model."""							,     )
					train_parser.set_defaults(func=__lowerCAmelCase       )






			def __init__(      self       :   Union[str, Any]							,     __lowerCAmelCase       :   str							,     __lowerCAmelCase       :   str							,     __lowerCAmelCase       :   str							,     __lowerCAmelCase       :   str							,     __lowerCAmelCase       :   str							,     *__lowerCAmelCase       :   Any							,     )     ->     Optional[int]:







					"""simple docstring"""






					A__  				=       logging.get_logger("""transformers-cli/converting"""       )

					self._logger.info(f'Loading model {model_type}'       )
					A__  				=       model_type
					A__  				=       tf_checkpoint
					A__  				=       pytorch_dump_output
					A__  				=       config
					A__  				=       finetuning_task_name







			def        a_	(      self       :   Tuple       )     ->     Optional[Any]:







					"""simple docstring"""






					if self._model_type == "albert":
							try:
									from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
									    convert_tf_checkpoint_to_pytorch,
									)
							except ImportError:
									raise ImportError(__lowerCAmelCase       )

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							,     self._config							,     self._pytorch_dump_output       )
					elif self._model_type == "bert":
							try:
									from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
									    convert_tf_checkpoint_to_pytorch,
									)
							except ImportError:
									raise ImportError(__lowerCAmelCase       )

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							,     self._config							,     self._pytorch_dump_output       )
					elif self._model_type == "funnel":
							try:
									from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
									    convert_tf_checkpoint_to_pytorch,
									)
							except ImportError:
									raise ImportError(__lowerCAmelCase       )

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							,     self._config							,     self._pytorch_dump_output       )
					elif self._model_type == "t5":
							try:
									from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
							except ImportError:
									raise ImportError(__lowerCAmelCase       )

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							,     self._config							,     self._pytorch_dump_output       )
					elif self._model_type == "gpt":
							from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
							    convert_openai_checkpoint_to_pytorch,
							)

							convert_openai_checkpoint_to_pytorch(self._tf_checkpoint							,     self._config							,     self._pytorch_dump_output       )
					elif self._model_type == "transfo_xl":
							try:
									from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
									    convert_transfo_xl_checkpoint_to_pytorch,
									)
							except ImportError:
									raise ImportError(__lowerCAmelCase       )

							if "ckpt" in self._tf_checkpoint.lower():
									A__  				=       self._tf_checkpoint
									A__  				=       """"""
							else:
									A__  				=       self._tf_checkpoint
									A__  				=       """"""
							convert_transfo_xl_checkpoint_to_pytorch(
							    __lowerCAmelCase							,     self._config							,     self._pytorch_dump_output							,     __lowerCAmelCase       )
					elif self._model_type == "gpt2":
							try:
									from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
									    convert_gpta_checkpoint_to_pytorch,
									)
							except ImportError:
									raise ImportError(__lowerCAmelCase       )

							convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint							,     self._config							,     self._pytorch_dump_output       )
					elif self._model_type == "xlnet":
							try:
									from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
									    convert_xlnet_checkpoint_to_pytorch,
									)
							except ImportError:
									raise ImportError(__lowerCAmelCase       )

							convert_xlnet_checkpoint_to_pytorch(
							    self._tf_checkpoint							,     self._config							,     self._pytorch_dump_output							,     self._finetuning_task_name       )
					elif self._model_type == "xlm":
							from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
							    convert_xlm_checkpoint_to_pytorch,
							)

							convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint							,     self._pytorch_dump_output       )
					elif self._model_type == "lxmert":
							from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
							    convert_lxmert_checkpoint_to_pytorch,
							)

							convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint							,     self._pytorch_dump_output       )
					elif self._model_type == "rembert":
							from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
							    convert_rembert_tf_checkpoint_to_pytorch,
							)

							convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint							,     self._config							,     self._pytorch_dump_output       )
					else:
							raise ValueError(
							    """--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"""       )










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()



