






import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


lowerCamelCase     :							Tuple    ={
    '''cola''': 2,
    '''mnli''': 3,
    '''mrpc''': 2,
    '''sst-2''': 2,
    '''sts-b''': 1,
    '''qqp''': 2,
    '''qnli''': 2,
    '''rte''': 2,
    '''wnli''': 2,
}


logging.set_verbosity_info()







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase=None      )			->     Optional[int]:
     # Initialise PyTorch model
     UpperCamelCase__      :       List[str]	    =  XLNetConfig.from_json_file(__lowerCAmelCase      )

     UpperCamelCase__      :       List[str]	    =  finetuning_task.lower() if finetuning_task is not None else ""
     if finetuning_task in GLUE_TASKS_NUM_LABELS:
          print(f'Building PyTorch XLNetForSequenceClassification model from configuration: {config}'      )
          UpperCamelCase__      :       Tuple	    =  finetuning_task
          UpperCamelCase__      :       List[Any]	    =  GLUE_TASKS_NUM_LABELS[finetuning_task]
          UpperCamelCase__      :       Optional[Any]	    =  XLNetForSequenceClassification(__lowerCAmelCase      )
     elif "squad" in finetuning_task:
          UpperCamelCase__      :       Union[str, Any]	    =  finetuning_task
          UpperCamelCase__      :       int	    =  XLNetForQuestionAnswering(__lowerCAmelCase      )
     else:
          UpperCamelCase__      :       Any	    =  XLNetLMHeadModel(__lowerCAmelCase      )

     # Load weights from tf checkpoint
     load_tf_weights_in_xlnet(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )

     # Save pytorch-model
     UpperCamelCase__      :       int	    =  os.path.join(__lowerCAmelCase ,   __lowerCAmelCase      )
     UpperCamelCase__      :       str	    =  os.path.join(__lowerCAmelCase ,   __lowerCAmelCase      )
     print(f'Save PyTorch model to {os.path.abspath(__lowerCAmelCase      )}'      )
     torch.save(model.state_dict() ,   __lowerCAmelCase      )
     print(f'Save configuration file to {os.path.abspath(__lowerCAmelCase      )}'      )
     with open(__lowerCAmelCase ,   "w" ,   encoding="utf-8"      ) as f:
          f.write(config.to_json_string()      )


if __name__ == "__main__":
 lowerCamelCase     :							Dict    =argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--xlnet_config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained XLNet model. \n'''
         '''This specifies the model architecture.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''',
     default=None,
     type=str,
     required=True,
     help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
 )
 parser.add_argument(
     '''--finetuning_task''',
     default=None,
     type=str,
     help='''Name of a task on which the XLNet TensorFlow model was fine-tuned''',
 )
 lowerCamelCase     :							Union[str, Any]    =parser.parse_args()
 print(args)

 convert_xlnet_checkpoint_to_pytorch(
     args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
 )







from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

lowerCamelCase     :							Optional[Any]    ={
    '''edbeeching/decision-transformer-gym-hopper-medium''': (
        '''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}





class       __a  (   A__	):
 _lowerCAmelCase	:	str				=			'''decision_transformer'''
 _lowerCAmelCase	:	Optional[Any]				=			['''past_key_values''']
 _lowerCAmelCase	:	Optional[int]				=			{
     '''max_position_embeddings''': '''n_positions''',
     '''num_attention_heads''': '''n_head''',
     '''num_hidden_layers''': '''n_layer''',
 }







 def __init__(		self :       List[Any]							,       SCREAMING_SNAKE_CASE :       Optional[int]=17							,       SCREAMING_SNAKE_CASE :       Optional[int]=4							,       SCREAMING_SNAKE_CASE :       Optional[int]=1_28							,       SCREAMING_SNAKE_CASE :       Dict=40_96							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=True							,       SCREAMING_SNAKE_CASE :       Tuple=1							,       SCREAMING_SNAKE_CASE :       Optional[int]=10_24							,       SCREAMING_SNAKE_CASE :       Any=3							,       SCREAMING_SNAKE_CASE :       str=1							,       SCREAMING_SNAKE_CASE :       Optional[int]=None							,       SCREAMING_SNAKE_CASE :       Dict="relu"							,       SCREAMING_SNAKE_CASE :       Optional[int]=0.1							,       SCREAMING_SNAKE_CASE :       str=0.1							,       SCREAMING_SNAKE_CASE :       str=0.1							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=1e-5							,       SCREAMING_SNAKE_CASE :       Optional[int]=0.0_2							,       SCREAMING_SNAKE_CASE :       Optional[Any]=True							,       SCREAMING_SNAKE_CASE :       Tuple=True							,       SCREAMING_SNAKE_CASE :       int=5_02_56							,       SCREAMING_SNAKE_CASE :       List[Any]=5_02_56							,       SCREAMING_SNAKE_CASE :       Any=False							,       SCREAMING_SNAKE_CASE :       List[Any]=False							,       **SCREAMING_SNAKE_CASE :       Optional[int]							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       List[str]	    =  state_dim
      UpperCamelCase__      :       Optional[int]	    =  act_dim
      UpperCamelCase__      :       Dict	    =  hidden_size
      UpperCamelCase__      :       Any	    =  max_ep_len
      UpperCamelCase__      :       Optional[Any]	    =  action_tanh
      UpperCamelCase__      :       str	    =  vocab_size
      UpperCamelCase__      :       List[str]	    =  n_positions
      UpperCamelCase__      :       Tuple	    =  n_layer
      UpperCamelCase__      :       Union[str, Any]	    =  n_head
      UpperCamelCase__      :       Dict	    =  n_inner
      UpperCamelCase__      :       int	    =  activation_function
      UpperCamelCase__      :       List[str]	    =  resid_pdrop
      UpperCamelCase__      :       Optional[int]	    =  embd_pdrop
      UpperCamelCase__      :       Optional[Any]	    =  attn_pdrop
      UpperCamelCase__      :       Optional[Any]	    =  layer_norm_epsilon
      UpperCamelCase__      :       int	    =  initializer_range
      UpperCamelCase__      :       Dict	    =  scale_attn_weights
      UpperCamelCase__      :       Tuple	    =  use_cache
      UpperCamelCase__      :       List[str]	    =  scale_attn_by_inverse_layer_idx
      UpperCamelCase__      :       Union[str, Any]	    =  reorder_and_upcast_attn

      UpperCamelCase__      :       Optional[Any]	    =  bos_token_id
      UpperCamelCase__      :       Optional[int]	    =  eos_token_id

      super().__init__(bos_token_id=SCREAMING_SNAKE_CASE							,       eos_token_id=SCREAMING_SNAKE_CASE							,       **SCREAMING_SNAKE_CASE						)