



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


UpperCAmelCase__  =			logging.get_logger(__name__)

UpperCAmelCase__  =			{
    # pcqm4mv1 now deprecated
    "graphormer-base": "https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json",
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}







class      __lowerCAmelCase       (     A		):
			UpperCamelCase     =	'''graphormer'''
			UpperCamelCase     =	['''past_key_values''']



			def __init__(		self  :    Tuple					,						A  :    int = 1					,						A  :    int = 5_12 * 9					,						A  :    int = 5_12 * 3					,						A  :    int = 5_12					,						A  :    int = 5_12					,						A  :    int = 5_12					,						A  :    int = 1_28					,						A  :    int = 5					,						A  :    int = 10_24					,						A  :    str = "multi_hop"					,						A  :    int = 5_12					,						A  :    bool = False					,						A  :    int = 12					,						A  :    int = 7_68					,						A  :    int = 7_68					,						A  :    int = 32					,						A  :    float = 0.1					,						A  :    float = 0.1					,						A  :    float = 0.0					,						A  :    bool = False					,						A  :    bool = False					,						A  :    bool = False					,						A  :    str = "gelu"					,						A  :    float = None					,						A  :    bool = False					,						A  :    int = 0					,						A  :    bool = False					,						A  :    float = 0.0					,						A  :    int = 8					,						A  :    int = None					,						A  :    int = None					,						A  :    bool = True					,						A  :    bool = True					,						A  :    Optional[Any]=0					,						A  :    Dict=1					,						A  :    List[Any]=2					,						**A  :    Optional[int]					,						)  ->       List[Any]:



						"""simple docstring"""






						_UpperCAmelCase									=  num_classes
						_UpperCAmelCase									=  num_atoms
						_UpperCAmelCase									=  num_in_degree
						_UpperCAmelCase									=  num_out_degree
						_UpperCAmelCase									=  num_edges
						_UpperCAmelCase									=  num_spatial
						_UpperCAmelCase									=  num_edge_dis
						_UpperCAmelCase									=  edge_type
						_UpperCAmelCase									=  multi_hop_max_dist
						_UpperCAmelCase									=  spatial_pos_max
						_UpperCAmelCase									=  max_nodes
						_UpperCAmelCase									=  num_hidden_layers
						_UpperCAmelCase									=  embedding_dim
						_UpperCAmelCase									=  embedding_dim
						_UpperCAmelCase									=  ffn_embedding_dim
						_UpperCAmelCase									=  num_attention_heads
						_UpperCAmelCase									=  dropout
						_UpperCAmelCase									=  attention_dropout
						_UpperCAmelCase									=  layerdrop
						_UpperCAmelCase									=  encoder_normalize_before
						_UpperCAmelCase									=  pre_layernorm
						_UpperCAmelCase									=  apply_graphormer_init
						_UpperCAmelCase									=  activation_fn
						_UpperCAmelCase									=  embed_scale
						_UpperCAmelCase									=  freeze_embeddings
						_UpperCAmelCase									=  num_trans_layers_to_freeze
						_UpperCAmelCase									=  share_input_output_embed
						_UpperCAmelCase									=  traceable
						_UpperCAmelCase									=  q_noise
						_UpperCAmelCase									=  qn_block_size

						# These parameters are here for future extensions
						# atm, the model only supports self attention
						_UpperCAmelCase									=  kdim
						_UpperCAmelCase									=  vdim
						_UpperCAmelCase									=  self_attention
						_UpperCAmelCase									=  bias

						super().__init__(
						    pad_token_id=A					,						bos_token_id=A					,						eos_token_id=A					,						**A					,						)





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 				A      (	_UpperCAmelCase							:  Tuple				,  _UpperCAmelCase							:  Any				,  _UpperCAmelCase							:  str       )							->					Union[str, Any]:







			'''simple docstring'''
			# Construct configuration depending on size
			_UpperCAmelCase									=  {'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase									=  MODELS[model_size]  # set model hyperparameters
			_UpperCAmelCase									=  ImageGPTConfig(n_embd=_UpperCAmelCase				,  n_layer=_UpperCAmelCase				,  n_head=_UpperCAmelCase       )
			_UpperCAmelCase									=  ImageGPTForCausalLM(_UpperCAmelCase       )

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

			# Save pytorch-model
			_UpperCAmelCase									=  pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			_UpperCAmelCase									=  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__":
							UpperCAmelCase__  =			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."
							)
							UpperCAmelCase__  =			parser.parse_args()
							convert_imagegpt_checkpoint_to_pytorch(
							    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
							)
