


from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand




def    a   (							A__							)	-> int:




      '''simple docstring'''

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


a_ :Optional[int]  							=			'\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  (						_UpperCAmelCase			):







     @staticmethod
     def 							lowercase__       (						_lowercase :						ArgumentParser      ):
           SCREAMING_SNAKE_CASE__       :  Optional[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=_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 :						Any   ,   _lowercase :						str   ,   _lowercase :						str   ,   _lowercase :						str   ,   _lowercase :						str   ,   _lowercase :						str   ,   *_lowercase :						Optional[Any]   ,   ):
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		logging.get_logger('''transformers-cli/converting'''      )

           self._logger.info(f"""Loading model {model_type}"""      )
           SCREAMING_SNAKE_CASE__       :  Dict						     =		model_type
           SCREAMING_SNAKE_CASE__       :  Any						     =		tf_checkpoint
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		pytorch_dump_output
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		config
           SCREAMING_SNAKE_CASE__       :  int						     =		finetuning_task_name







     def 							lowercase__       (						self :						Any      ):
           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():
                       SCREAMING_SNAKE_CASE__       :  int						     =		self._tf_checkpoint
                       SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		''''''
                 else:
                       SCREAMING_SNAKE_CASE__       :  int						     =		self._tf_checkpoint
                       SCREAMING_SNAKE_CASE__       :  List[Any]						     =		''''''
                 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 collections import OrderedDict
from typing import Mapping

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


_snake_case : Optional[int]  				=						logging.get_logger(__name__)

_snake_case : Any  				=						{
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}

class      A       (						_a       ):
							lowercase_							     =							'roformer'







							def __init__(    self			:    str				,     lowerCAmelCase_			:    int=5_00_00				,     lowerCAmelCase_			:    Any=None				,     lowerCAmelCase_			:    int=7_68				,     lowerCAmelCase_			:    Tuple=12				,     lowerCAmelCase_			:    Any=12				,     lowerCAmelCase_			:    List[str]=30_72				,     lowerCAmelCase_			:    Dict="gelu"				,     lowerCAmelCase_			:    Optional[int]=0.1				,     lowerCAmelCase_			:    List[Any]=0.1				,     lowerCAmelCase_			:    int=15_36				,     lowerCAmelCase_			:    Optional[Any]=2				,     lowerCAmelCase_			:    int=0.0_2				,     lowerCAmelCase_			:    Dict=1e-12				,     lowerCAmelCase_			:    Any=0				,     lowerCAmelCase_			:    Optional[Any]=False				,     lowerCAmelCase_			:    Tuple=True				,     **lowerCAmelCase_			:    Optional[int]				,     )     ->				str:


														"""simple docstring"""


														super().__init__(pad_token_id=lowerCAmelCase_				,     **lowerCAmelCase_   )

														_a      	=							vocab_size
														_a      	=							hidden_size if embedding_size is None else embedding_size
														_a      	=							hidden_size
														_a      	=							num_hidden_layers
														_a      	=							num_attention_heads
														_a      	=							hidden_act
														_a      	=							intermediate_size
														_a      	=							hidden_dropout_prob
														_a      	=							attention_probs_dropout_prob
														_a      	=							max_position_embeddings
														_a      	=							type_vocab_size
														_a      	=							initializer_range
														_a      	=							layer_norm_eps
														_a      	=							rotary_value
														_a      	=							use_cache



class      A       (						_a       ):







							@property
							def   __lowerCAmelCase     (    self			:    Any   )     ->				Mapping[str, Mapping[int, str]]:


														"""simple docstring"""


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

