"""simple docstring"""






import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):
   # Construct configuration depending on size
   lowercase__:     List[Any]      =   {'''small''': (5_1_2, 8, 2_4), '''medium''': (1_0_2_4, 8, 3_6), '''large''': (1_5_3_6, 1_6, 4_8)}
   lowercase__:     Any      =   MODELS[model_size]  # set model hyperparameters
   lowercase__:     str      =   ImageGPTConfig(n_embd=__UpperCAmelCase ,					n_layer=__UpperCAmelCase ,					n_head=__UpperCAmelCase				)
   lowercase__:     str      =   ImageGPTForCausalLM(__UpperCAmelCase				)

   # Load weights from numpy
   load_tf_weights_in_imagegpt(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				)

   # Save pytorch-model
   lowercase__:     Union[str, Any]      =   pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
   lowercase__:     Union[str, Any]      =   pytorch_dump_folder_path + '''/''' + CONFIG_NAME
   print(F"""Save PyTorch model to {pytorch_weights_dump_path}"""				)
   torch.save(model.state_dict() ,					__UpperCAmelCase				)
   print(F"""Save configuration file to {pytorch_config_dump_path}"""				)
   with open(__UpperCAmelCase ,					'''w''' ,					encoding='''utf-8'''				) as f:
      f.write(config.to_json_string()				)


if __name__ == "__main__":
       __A         =				argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--imagegpt_checkpoint_path",
           default=None,
           type=str,
           required=True,
           help="Path to the TensorFlow checkpoint path.",
       )
       parser.add_argument(
           "--model_size",
           default=None,
           type=str,
           required=True,
           help="Size of the model (can be either 'small', 'medium' or 'large').",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
       )
       __A         =				parser.parse_args()
       convert_imagegpt_checkpoint_to_pytorch(
           args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
       )

"""simple docstring"""






class 					UpperCAmelCase		:



    """simple docstring"""

    def __init__(     self					,    _UpperCAmelCase=None							):
       lowercase__:     Optional[int]      =   data
       lowercase__:     Dict      =   None






    def __repr__(     self							):
       lowercase__:     int      =   ''''''
       lowercase__:     Any      =   self
       while temp:
          string_rep += F"""<{temp.data}> ---> """
          lowercase__:     Any      =   temp.next
       string_rep += "<END>"
       return string_rep



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase				)						->						str:

   # if elements_list is empty
   if not elements_list:
      raise Exception('''The Elements List is empty'''				)

   # Set first element as Head
   lowercase__:     Union[str, Any]      =   Node(elements_list[0]				)
   lowercase__:     List[str]      =   head
   # Loop through elements from position 1
   for data in elements_list[1:]:
      lowercase__:     Tuple      =   Node(__UpperCAmelCase				)
      lowercase__:     Tuple      =   current.next
   return head


__A         =				[1, 3, 5, 3_2, 4_4, 1_2, 4_3]
print(f'''List: {list_data}''')
print("Creating Linked List from List.")
__A         =				make_linked_list(list_data)
print("Linked List:")




print(linked_list)
