

from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand

def 						UpperCamelCase (lowercase_:			Namespace )					->				Optional[Any]:
	return ConvertCommand(
	    args.model_type   ,				args.tf_checkpoint   ,				args.pytorch_dump_output   ,				args.config   ,				args.finetuning_task_name )


A_	:			Optional[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    _a						(__magic_name__    ):


					'''simple docstring'''






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






					def __init__(			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	*A__   ,	):
						A__							:   Tuple					       =		logging.get_logger("""transformers-cli/converting"""      )

						self._logger.info(F"""Loading model {model_type}"""      )
						A__							:   int					       =		model_type
						A__							:   Optional[Any]					       =		tf_checkpoint
						A__							:   Optional[int]					       =		pytorch_dump_output
						A__							:   List[str]					       =		config
						A__							:   Any					       =		finetuning_task_name






					def   __A      (			self      ):
						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(A__      )

							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(A__      )

							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(A__      )

							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(A__      )

							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(A__      )

							if "ckpt" in self._tf_checkpoint.lower():
								A__							:   Any					       =		self._tf_checkpoint
								A__							:   Union[str, Any]					       =		""""""
							else:
								A__							:   Optional[Any]					       =		self._tf_checkpoint
								A__							:   List[str]					       =		""""""
							convert_transfo_xl_checkpoint_to_pytorch(
							    A__   ,	self._config   ,	self._pytorch_dump_output   ,	A__      )
						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(A__      )

							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(A__      )

							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]"""      )






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


A_	:			List[Any]  	=      {'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConfig']}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[Any]  	=      ['GPTNeoXTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[int]  	=      [
	    'GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GPTNeoXForCausalLM',
	    'GPTNeoXForQuestionAnswering',
	    'GPTNeoXForSequenceClassification',
	    'GPTNeoXForTokenClassification',
	    'GPTNeoXLayer',
	    'GPTNeoXModel',
	    'GPTNeoXPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	A_	:			Any  	=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



