





"""simple docstring"""



from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand







def 		__lowercase     (   snake_case_  :  Namespace						)   ->Optional[Any]:



    '''simple docstring'''







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


a_ =			"""
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 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 @staticmethod
 def 					UpperCamelCase__(    __lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Union[str, Any]						 =     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					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						*__lowerCamelCase					,						):



     '''simple docstring'''




     __A    :						Tuple						 =     logging.get_logger('''transformers-cli/converting'''  )

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







 def 					UpperCamelCase__(    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(__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():
             __A    :						Tuple						 =     self._tf_checkpoint
             __A    :						str						 =     ''''''
         else:
             __A    :						Any						 =     self._tf_checkpoint
             __A    :						Optional[Any]						 =     ''''''
         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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_ =			logging.get_logger(__name__)

a_ =			{
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     """roberta"""

 def __init__(    self					,						__lowerCamelCase=5_0265					,						__lowerCamelCase=768					,						__lowerCamelCase=12					,						__lowerCamelCase=12					,						__lowerCamelCase=3072					,						__lowerCamelCase="gelu"					,						__lowerCamelCase=0.1					,						__lowerCamelCase=0.1					,						__lowerCamelCase=512					,						__lowerCamelCase=2					,						__lowerCamelCase=0.0_2					,						__lowerCamelCase=1e-1_2					,						__lowerCamelCase=1					,						__lowerCamelCase=0					,						__lowerCamelCase=2					,						__lowerCamelCase="absolute"					,						__lowerCamelCase=True					,						__lowerCamelCase=None					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     super().__init__(pad_token_id=__lowerCamelCase					,						bos_token_id=__lowerCamelCase					,						eos_token_id=__lowerCamelCase					,						**__lowerCamelCase  )

     __A    :						str						 =     vocab_size
     __A    :						Any						 =     hidden_size
     __A    :						Any						 =     num_hidden_layers
     __A    :						int						 =     num_attention_heads
     __A    :						List[Any]						 =     hidden_act
     __A    :						Any						 =     intermediate_size
     __A    :						List[str]						 =     hidden_dropout_prob
     __A    :						List[Any]						 =     attention_probs_dropout_prob
     __A    :						Optional[Any]						 =     max_position_embeddings
     __A    :						str						 =     type_vocab_size
     __A    :						int						 =     initializer_range
     __A    :						Tuple						 =     layer_norm_eps
     __A    :						str						 =     position_embedding_type
     __A    :						int						 =     use_cache
     __A    :						Optional[int]						 =     classifier_dropout






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     if self.task == "multiple-choice":
         __A    :						List[Any]						 =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
         __A    :						Optional[Any]						 =     {0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
         ]  )




