


"""simple docstring"""






from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def      lowerCamelCase       (     _UpperCamelCase		:      Optional[Any]							)		->       Optional[int]:

							'''simple docstring'''



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


UpperCAmelCase    :     Union[str, Any]   						=		'\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      lowerCamelCase__   (     __A				):







					"""simple docstring"""


					@staticmethod
					def lowerCamelCase__							(       UpperCamelCase    : ArgumentParser  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Dict      							=					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    : Optional[int] ,       UpperCamelCase    : str ,       UpperCamelCase    : str ,       UpperCamelCase    : str ,       UpperCamelCase    : str ,       UpperCamelCase    : str ,       *UpperCamelCase    : Optional[int] ,       ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					logging.get_logger("""transformers-cli/converting"""  )

												self._logger.info(f'''Loading model {model_type}'''  )
												__UpperCAmelCase   :						Any      							=					model_type
												__UpperCAmelCase   :						Dict      							=					tf_checkpoint
												__UpperCAmelCase   :						Dict      							=					pytorch_dump_output
												__UpperCAmelCase   :						str      							=					config
												__UpperCAmelCase   :						Optional[int]      							=					finetuning_task_name






					def lowerCamelCase__							(       self    : Optional[int]  ):
												'''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():
																										__UpperCAmelCase   :						Dict      							=					self._tf_checkpoint
																										__UpperCAmelCase   :						Optional[int]      							=					""
																			else:
																										__UpperCAmelCase   :						str      							=					self._tf_checkpoint
																										__UpperCAmelCase   :						str      							=					""
																			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]"""  )








"""simple docstring"""
from __future__ import annotations







class 			UpperCamelCase_       :





	def __init__(      self					: Any	,	lowerCAmelCase_					: int						)						->			None:
				UpperCAmelCase_     : Any			     =					data
				UpperCAmelCase_     : Node | None			     =					None
				UpperCAmelCase_     : Node | None			     =					None





def 				snake_case      (   A__						):  # In Order traversal of the tree
			if tree:
						display(tree.left						)
						print(tree.data						)
						display(tree.right						)

def 				snake_case      (   A__						):
			return 1 + max(depth_of_tree(tree.left						)	,depth_of_tree(tree.right						)						) if tree else 0

def 				snake_case      (   A__						):
			if not tree:
						return True
			if tree.left and tree.right:
						return is_full_binary_tree(tree.left						) and is_full_binary_tree(tree.right						)
			else:
						return not tree.left and not tree.right

def 				snake_case      (   ):  # Main function for testing.
			UpperCAmelCase_     : List[str]			     =					Node(1						)
			UpperCAmelCase_     : Any			     =					Node(2						)
			UpperCAmelCase_     : Optional[Any]			     =					Node(3						)
			UpperCAmelCase_     : Union[str, Any]			     =					Node(4						)
			UpperCAmelCase_     : int			     =					Node(5						)
			UpperCAmelCase_     : Optional[int]			     =					Node(6						)
			UpperCAmelCase_     : Any			     =					Node(7						)
			UpperCAmelCase_     : List[str]			     =					Node(8						)
			UpperCAmelCase_     : List[Any]			     =					Node(9						)

			print(is_full_binary_tree(A__						)						)
			print(depth_of_tree(A__						)						)
			print("Tree is: "						)
			display(A__						)


if __name__ == "__main__":
			main()



