





from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput



@dataclass
class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      None
 lowercase_		=      None
 lowercase_		=      None
 lowercase_		=      None



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''

 def __init__(self    :       Dict	,			UpperCAmelCase_    :       Optional[int]=1	,			UpperCAmelCase_    :       int=0	,			UpperCAmelCase_    :       List[Any]=2	,			UpperCAmelCase_    :       List[Any]=512	,			UpperCAmelCase_    :       Tuple="cls"	,			UpperCAmelCase_    :       Tuple=False	,			UpperCAmelCase_    :       Optional[int]=True	,			**UpperCAmelCase_    :       Optional[int]	,			)	->Optional[int]:



   '''simple docstring'''



   super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE	,			bos_token_id=_SCREAMING_SNAKE_CASE	,			eos_token_id=_SCREAMING_SNAKE_CASE	,			**_SCREAMING_SNAKE_CASE)
   lowerCamelCase__:				int        =project_dim
   lowerCamelCase__:				Optional[Any]        =pooler_fn
   lowerCamelCase__:				str        =learn_encoder
   lowerCamelCase__:				List[Any]        =use_attention_mask



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      [R"pooler", R"logit_scale"]
 lowercase_		=      [R"position_ids", R"predictions.decoder.bias"]
 lowercase_		=      "roberta"
 lowercase_		=      RobertaSeriesConfig

 def __init__(self    :       List[Any]	,			UpperCAmelCase_    :       str)	->List[Any]:



   '''simple docstring'''



   super().__init__(_SCREAMING_SNAKE_CASE)
   lowerCamelCase__:				int        =XLMRobertaModel(_SCREAMING_SNAKE_CASE)
   lowerCamelCase__:				Tuple        =nn.Linear(config.hidden_size	,			config.project_dim)
   lowerCamelCase__:				Optional[Any]        =getattr(_SCREAMING_SNAKE_CASE	,			"has_pre_transformation"	,			_SCREAMING_SNAKE_CASE)
   if self.has_pre_transformation:
     lowerCamelCase__:				Optional[Any]        =nn.Linear(config.hidden_size	,			config.project_dim)
     lowerCamelCase__:				List[Any]        =nn.LayerNorm(config.hidden_size	,			eps=config.layer_norm_eps)
   self.post_init()







 def 							SCREAMING_SNAKE_CASE_ (self    :       int	,			UpperCAmelCase_    :       Dict = None	,			UpperCAmelCase_    :       int = None	,			UpperCAmelCase_    :       Optional[int] = None	,			UpperCAmelCase_    :       Any = None	,			UpperCAmelCase_    :       List[Any] = None	,			UpperCAmelCase_    :       Optional[int] = None	,			UpperCAmelCase_    :       Dict = None	,			UpperCAmelCase_    :       Dict = None	,			UpperCAmelCase_    :       List[Any] = None	,			UpperCAmelCase_    :       Dict = None	,			UpperCAmelCase_    :       List[str] = None	,			)	->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =return_dict if return_dict is not None else self.config.use_return_dict

   lowerCamelCase__:				Tuple        =self.base_model(
       input_ids=_SCREAMING_SNAKE_CASE	,			attention_mask=_SCREAMING_SNAKE_CASE	,			token_type_ids=_SCREAMING_SNAKE_CASE	,			position_ids=_SCREAMING_SNAKE_CASE	,			head_mask=_SCREAMING_SNAKE_CASE	,			inputs_embeds=_SCREAMING_SNAKE_CASE	,			encoder_hidden_states=_SCREAMING_SNAKE_CASE	,			encoder_attention_mask=_SCREAMING_SNAKE_CASE	,			output_attentions=_SCREAMING_SNAKE_CASE	,			output_hidden_states=True if self.has_pre_transformation else output_hidden_states	,			return_dict=_SCREAMING_SNAKE_CASE	,			)

   if self.has_pre_transformation:
     lowerCamelCase__:				List[str]        =outputs['''hidden_states'''][-2]
     lowerCamelCase__:				Tuple        =self.pre_LN(_SCREAMING_SNAKE_CASE)
     lowerCamelCase__:				List[Any]        =self.transformation_pre(_SCREAMING_SNAKE_CASE)

     return TransformationModelOutput(
         projection_state=_SCREAMING_SNAKE_CASE	,			last_hidden_state=outputs.last_hidden_state	,			hidden_states=outputs.hidden_states	,			attentions=outputs.attentions	,			)
   else:
     lowerCamelCase__:				Union[str, Any]        =self.transformation(outputs.last_hidden_state)
     return TransformationModelOutput(
         projection_state=_SCREAMING_SNAKE_CASE	,			last_hidden_state=outputs.last_hidden_state	,			hidden_states=outputs.hidden_states	,			attentions=outputs.attentions	,			)








from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand





def 					lowerCAmelCase_			(	__a		)					->		int:







  """simple docstring"""





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


__A     =    "\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        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''

 @staticmethod
 def 							SCREAMING_SNAKE_CASE_ (UpperCAmelCase_    :       ArgumentParser)	->str:



   '''simple docstring'''



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

 def __init__(self    :       List[Any]	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       str	,			*UpperCAmelCase_    :       Optional[int]	,			)	->List[str]:



   '''simple docstring'''



   lowerCamelCase__:				Dict        =logging.get_logger("transformers-cli/converting")

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







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

     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(UpperCAmelCase_)

     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(UpperCAmelCase_)

     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(UpperCAmelCase_)

     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(UpperCAmelCase_)

     if "ckpt" in self._tf_checkpoint.lower():
       lowerCamelCase__:				Tuple        =self._tf_checkpoint
       lowerCamelCase__:				List[str]        =""
     else:
       lowerCamelCase__:				Any        =self._tf_checkpoint
       lowerCamelCase__:				Dict        =""
     convert_transfo_xl_checkpoint_to_pytorch(
         UpperCAmelCase_	,			self._config	,			self._pytorch_dump_output	,			UpperCAmelCase_)
   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(UpperCAmelCase_)

     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(UpperCAmelCase_)

     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]")

