"""simple docstring"""


from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand







def 					__a   (		A      ):






					'''simple docstring'''



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


lowerCAmelCase_:					Tuple     =					"\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n"






class    a__    (   _a	):


			@staticmethod
			def 	snake_case__	(     _UpperCAmelCase  ):
								'''simple docstring'''



								lowercase__				 =							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=__UpperCamelCase,				required=__UpperCamelCase,				help="Model\'s type."  )
								train_parser.add_argument(
								    "--tf_checkpoint",				type=__UpperCamelCase,				required=__UpperCamelCase,				help="TensorFlow checkpoint path or folder."  )
								train_parser.add_argument(
								    "--pytorch_dump_output",				type=__UpperCamelCase,				required=__UpperCamelCase,				help="Path to the PyTorch saved model output."  )
								train_parser.add_argument("--config",				type=__UpperCamelCase,				default="",				help="Configuration file path or folder."  )
								train_parser.add_argument(
								    "--finetuning_task_name",				type=__UpperCamelCase,				default=__UpperCamelCase,				help="Optional fine-tuning task name if the TF model was a finetuned model.",				)
								train_parser.set_defaults(func=__UpperCamelCase  )


			def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				*_UpperCAmelCase,				):
								'''simple docstring'''



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

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


			def 	snake_case__	(     self  ):
								'''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(__UpperCamelCase  )

													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(__UpperCamelCase  )

													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(__UpperCamelCase  )

													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(__UpperCamelCase  )

													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(__UpperCamelCase  )

													if "ckpt" in self._tf_checkpoint.lower():
																		lowercase__				 =							self._tf_checkpoint
																		lowercase__				 =							""
													else:
																		lowercase__				 =							self._tf_checkpoint
																		lowercase__				 =							""
													convert_transfo_xl_checkpoint_to_pytorch(
													    __UpperCamelCase,				self._config,				self._pytorch_dump_output,				__UpperCamelCase  )
								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(__UpperCamelCase  )

													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(__UpperCamelCase  )

													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]"  )





"""simple docstring"""


from typing import Any

import numpy as np







def 					__a   (		A      ):






     '''simple docstring'''



     return np.array_equal(A    ,				matrix.conjugate().T      )







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							v.conjugate().T
     lowercase__				 =							v_star.dot(A      )
     assert isinstance(A    ,				np.ndarray      )
     return (v_star_dot.dot(A      )) / (v_star.dot(A      ))







def 					__a   (		):






     '''simple docstring'''



     lowercase__				 =							np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]      )
     lowercase__				 =							np.array([[1], [2], [3]]      )
     assert is_hermitian(A      ), f'''{a} is not hermitian.'''
     print(rayleigh_quotient(A    ,				A      )      )

     lowercase__				 =							np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]      )
     assert is_hermitian(A      ), f'''{a} is not hermitian.'''
     assert rayleigh_quotient(A    ,				A      ) == float(3      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 tests()




