

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


_SCREAMING_SNAKE_CASE					:  Any             =      {
    '''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     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A=None    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   XLNetConfig.from_json_file(_A    )

     SCREAMING_SNAKE_CASE__   =   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}'''    )
          SCREAMING_SNAKE_CASE__   =   finetuning_task
          SCREAMING_SNAKE_CASE__   =   GLUE_TASKS_NUM_LABELS[finetuning_task]
          SCREAMING_SNAKE_CASE__   =   XLNetForSequenceClassification(_A    )
     elif "squad" in finetuning_task:
          SCREAMING_SNAKE_CASE__   =   finetuning_task
          SCREAMING_SNAKE_CASE__   =   XLNetForQuestionAnswering(_A    )
     else:
          SCREAMING_SNAKE_CASE__   =   XLNetLMHeadModel(_A    )

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

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


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE					:  List[str]             =      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''',
       )
       _SCREAMING_SNAKE_CASE					:  str             =      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


_SCREAMING_SNAKE_CASE					:  Any             =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  int             =      {
    '''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 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "decision_transformer"
  a									= ["past_key_values"]
  a									= {
      "max_position_embeddings": "n_positions",
      "num_attention_heads": "n_head",
      "num_hidden_layers": "n_layer",
  }






  def __init__(							self						:		Tuple     ,       __lowerCamelCase						:		Any=17     ,       __lowerCamelCase						:		Any=4     ,       __lowerCamelCase						:		List[Any]=128     ,       __lowerCamelCase						:		Union[str, Any]=4096     ,       __lowerCamelCase						:		Optional[Any]=True     ,       __lowerCamelCase						:		Any=1     ,       __lowerCamelCase						:		List[Any]=1024     ,       __lowerCamelCase						:		Any=3     ,       __lowerCamelCase						:		Dict=1     ,       __lowerCamelCase						:		List[Any]=None     ,       __lowerCamelCase						:		str="relu"     ,       __lowerCamelCase						:		Dict=0.1     ,       __lowerCamelCase						:		int=0.1     ,       __lowerCamelCase						:		List[str]=0.1     ,       __lowerCamelCase						:		str=1e-5     ,       __lowerCamelCase						:		Optional[Any]=0.02     ,       __lowerCamelCase						:		int=True     ,       __lowerCamelCase						:		Tuple=True     ,       __lowerCamelCase						:		Optional[int]=5_0256     ,       __lowerCamelCase						:		Tuple=5_0256     ,       __lowerCamelCase						:		int=False     ,       __lowerCamelCase						:		Optional[int]=False     ,       **__lowerCamelCase						:		Tuple     ,       )		->							Optional[Any]:
       SCREAMING_SNAKE_CASE__   =   state_dim
       SCREAMING_SNAKE_CASE__   =   act_dim
       SCREAMING_SNAKE_CASE__   =   hidden_size
       SCREAMING_SNAKE_CASE__   =   max_ep_len
       SCREAMING_SNAKE_CASE__   =   action_tanh
       SCREAMING_SNAKE_CASE__   =   vocab_size
       SCREAMING_SNAKE_CASE__   =   n_positions
       SCREAMING_SNAKE_CASE__   =   n_layer
       SCREAMING_SNAKE_CASE__   =   n_head
       SCREAMING_SNAKE_CASE__   =   n_inner
       SCREAMING_SNAKE_CASE__   =   activation_function
       SCREAMING_SNAKE_CASE__   =   resid_pdrop
       SCREAMING_SNAKE_CASE__   =   embd_pdrop
       SCREAMING_SNAKE_CASE__   =   attn_pdrop
       SCREAMING_SNAKE_CASE__   =   layer_norm_epsilon
       SCREAMING_SNAKE_CASE__   =   initializer_range
       SCREAMING_SNAKE_CASE__   =   scale_attn_weights
       SCREAMING_SNAKE_CASE__   =   use_cache
       SCREAMING_SNAKE_CASE__   =   scale_attn_by_inverse_layer_idx
       SCREAMING_SNAKE_CASE__   =   reorder_and_upcast_attn

       SCREAMING_SNAKE_CASE__   =   bos_token_id
       SCREAMING_SNAKE_CASE__   =   eos_token_id

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


