




'''simple docstring'''



from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Namespace  )			->			Dict:
   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 		lowercase_   (			A       ):


   """simple docstring"""

   @staticmethod
   def 							lowerCAmelCase_      (   __lowerCamelCase :     ArgumentParser   ):

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



   def __init__(   self :     Optional[int]  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     str  ,				*__lowerCamelCase :     List[Any]  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				logging.get_logger("transformers-cli/converting"   )

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



   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """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(_snake_case   )

         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(_snake_case   )

         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(_snake_case   )

         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(_snake_case   )

         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(_snake_case   )

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

         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(_snake_case   )

         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_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase_			=						{
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						['LayoutLMv2TokenizerFast']

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						['LayoutLMv2FeatureExtractor']
 lowerCamelCase_			=						['LayoutLMv2ImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						[
     'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
     'LayoutLMv2ForQuestionAnswering',
     'LayoutLMv2ForSequenceClassification',
     'LayoutLMv2ForTokenClassification',
     'LayoutLMv2Layer',
     'LayoutLMv2Model',
     'LayoutLMv2PreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
 from .processing_layoutlmva import LayoutLMvaProcessor
 from .tokenization_layoutlmva import LayoutLMvaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_layoutlmva import (
      LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      LayoutLMvaForQuestionAnswering,
      LayoutLMvaForSequenceClassification,
      LayoutLMvaForTokenClassification,
      LayoutLMvaLayer,
      LayoutLMvaModel,
      LayoutLMvaPreTrainedModel,
  )
else:
 import sys

 lowerCamelCase_			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

