
'''simple docstring'''



from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand


def A				(__lowerCamelCase				:Namespace		):
						return ConvertCommand(
						    args.model_type			,     args.tf_checkpoint			,     args.pytorch_dump_output			,     args.config			,     args.finetuning_task_name		)


_lowercase    =		"""
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   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						@staticmethod
						def  _lowercase     ( _lowercase				):

												"""simple docstring"""



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




						def __init__( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       *_lowercase   ,       ):

												"""simple docstring"""



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

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




						def  _lowercase     ( 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(_lowercase				)

																		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(_lowercase				)

																		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(_lowercase				)

																		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(_lowercase				)

																		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(_lowercase				)

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

																		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(_lowercase				)

																		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 TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase    =		{"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase    =		[
						    """VAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """VanForImageClassification""",
						    """VanModel""",
						    """VanPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_van import (
												    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    VanForImageClassification,
												    VanModel,
												    VanPreTrainedModel,
												)

else:
						import sys

						_lowercase    =		_LazyModule(__name__, globals()["""__file__"""], _import_structure)
