
from importlib import import_module

from .logging import get_logger


lowerCamelCase__	=							get_logger(__name__)







class        lowerCAmelCase__      :

						def __init__(			self       ,							a       ,							a=None				)					->      Any:


								'''simple docstring'''

								_UpperCamelCase						    =	attrs or []
								if module is not None:
										for key in module.__dict__:
												if key in attrs or not key.startswith("""__"""				):
														setattr(self       ,							a       ,							getattr(a       ,							a				)				)
								_UpperCamelCase						    =	module._original_module if isinstance(a       ,							_PatchedModuleObj				) else module







class        lowerCAmelCase__      :
						UpperCamelCase_							: Dict	=		[]

						def __init__(			self       ,							a       ,							a       ,							a       ,							a=None				)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	obj
								_UpperCamelCase						    =	target
								_UpperCamelCase						    =	new
								_UpperCamelCase						    =	target.split("""."""				)[0]
								_UpperCamelCase						    =	{}
								_UpperCamelCase						    =	attrs or []

						def __enter__(			self				)					->      Union[str, Any]:


								'''simple docstring'''

								*_UpperCamelCase    ,	_UpperCamelCase						    =	self.target.split("""."""				)

								# Patch modules:
								# it's used to patch attributes of submodules like "os.path.join";
								# in this case we need to patch "os" and "os.path"

								for i in range(len(a				)				):
										try:
												_UpperCamelCase						    =	import_module(""".""".join(submodules[: i + 1]				)				)
										except ModuleNotFoundError:
												continue
										# We iterate over all the globals in self.obj in case we find "os" or "os.path"
										for attr in self.obj.__dir__():
												_UpperCamelCase						    =	getattr(self.obj       ,							a				)
												# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
												# This allows to patch renamed modules like "from os import path as ospath".
												if obj_attr is submodule or (
												    (isinstance(a       ,							_PatchedModuleObj				) and obj_attr._original_module is submodule)
												):
														_UpperCamelCase						    =	obj_attr
														# patch at top level
														setattr(self.obj       ,							a       ,							_PatchedModuleObj(a       ,							attrs=self.attrs				)				)
														_UpperCamelCase						    =	getattr(self.obj       ,							a				)
														# construct lower levels patches
														for key in submodules[i + 1 :]:
																setattr(a       ,							a       ,							_PatchedModuleObj(getattr(a       ,							a       ,							a				)       ,							attrs=self.attrs				)				)
																_UpperCamelCase						    =	getattr(a       ,							a				)
														# finally set the target attribute
														setattr(a       ,							a       ,							self.new				)

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

								if submodules:  # if it's an attribute of a submodule like "os.path.join"
										try:
												_UpperCamelCase						    =	getattr(import_module(""".""".join(a				)				)       ,							a				)
										except (AttributeError, ModuleNotFoundError):
												return
										# We iterate over all the globals in self.obj in case we find "os.path.join"
										for attr in self.obj.__dir__():
												# We don't check for the name of the global, but rather if its value *is* "os.path.join".
												# This allows to patch renamed attributes like "from os.path import join as pjoin".
												if getattr(self.obj       ,							a				) is attr_value:
														_UpperCamelCase						    =	getattr(self.obj       ,							a				)
														setattr(self.obj       ,							a       ,							self.new				)
								elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
										_UpperCamelCase						    =	globals()["""__builtins__"""][target_attr]
										setattr(self.obj       ,							a       ,							self.new				)
								else:
										raise RuntimeError(F'Tried to patch attribute {target_attr} instead of a submodule.'				)

						def __exit__(			self       ,							*a				)					->      Union[str, Any]:


								'''simple docstring'''

								for attr in list(self.original				):
										setattr(self.obj       ,							a       ,							self.original.pop(a				)				)

						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								self.__enter__()
								self._active_patches.append(self				)







						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								try:
										self._active_patches.remove(self				)
								except ValueError:
										# If the patch hasn't been started this will fail
										return None

								return self.__exit__()





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand


def 							__A(lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






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


lowerCamelCase__	=							"\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__      (   __lowercase	):

						@staticmethod
						def  A_				(			a				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	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       ,							)					->      Optional[Any]:


								'''simple docstring'''

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

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







						def  A_				(			self				)					->      Tuple:


								'''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(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():
												_UpperCamelCase						    =	self._tf_checkpoint
												_UpperCamelCase						    =	""""""
										else:
												_UpperCamelCase						    =	self._tf_checkpoint
												_UpperCamelCase						    =	""""""
										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]"""				)



