


'''simple docstring'''


import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	str							=				TaConfig.from_json_file(SCREAMING_SNAKE_CASE__					)
				print(f"""Building PyTorch model from configuration: {config}"""					)
				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				TaForConditionalGeneration(SCREAMING_SNAKE_CASE__					)

				# Load weights from tf checkpoint
				load_tf_weights_in_ta(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)

				# Save pytorch-model
				print(f"""Save PyTorch model to {pytorch_dump_path}"""					)
				model.save_pretrained(SCREAMING_SNAKE_CASE__					)


if __name__ == "__main__":
				UpperCAmelCase_		:       Any							       =		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(
				    '--config_file',
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
				    ),
				)
				parser.add_argument(
				    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
				)
				UpperCAmelCase_		:       Any							       =		parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)








'''simple docstring'''


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


UpperCAmelCase_		:       Tuple							       =		logging.get_logger(__name__)

UpperCAmelCase_		:       List[Any]							       =		{
    # 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    lowercase__  (						_snake_case ):





							'''simple docstring'''


							A_							:				Union[str, Any]				     =  """graphormer"""
							A_							:				Tuple				     =  ["""past_key_values"""]






							def __init__(   self   , __snake_case = 1   , __snake_case = 512 * 9   , __snake_case = 512 * 3   , __snake_case = 512   , __snake_case = 512   , __snake_case = 512   , __snake_case = 128   , __snake_case = 5   , __snake_case = 1024   , __snake_case = "multi_hop"   , __snake_case = 512   , __snake_case = False   , __snake_case = 12   , __snake_case = 768   , __snake_case = 768   , __snake_case = 32   , __snake_case = 0.1   , __snake_case = 0.1   , __snake_case = 0.0   , __snake_case = False   , __snake_case = False   , __snake_case = False   , __snake_case = "gelu"   , __snake_case = None   , __snake_case = False   , __snake_case = 0   , __snake_case = False   , __snake_case = 0.0   , __snake_case = 8   , __snake_case = None   , __snake_case = None   , __snake_case = True   , __snake_case = True   , __snake_case=0   , __snake_case=1   , __snake_case=2   , **__snake_case   , ):
											_SCREAMING_SNAKE_CASE			:	Dict							=				num_classes
											_SCREAMING_SNAKE_CASE			:	Dict							=				num_atoms
											_SCREAMING_SNAKE_CASE			:	Dict							=				num_in_degree
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				num_out_degree
											_SCREAMING_SNAKE_CASE			:	Any							=				num_edges
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				num_spatial
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				num_edge_dis
											_SCREAMING_SNAKE_CASE			:	int							=				edge_type
											_SCREAMING_SNAKE_CASE			:	Tuple							=				multi_hop_max_dist
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				spatial_pos_max
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				max_nodes
											_SCREAMING_SNAKE_CASE			:	str							=				num_hidden_layers
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				embedding_dim
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				embedding_dim
											_SCREAMING_SNAKE_CASE			:	int							=				ffn_embedding_dim
											_SCREAMING_SNAKE_CASE			:	Dict							=				num_attention_heads
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				dropout
											_SCREAMING_SNAKE_CASE			:	List[str]							=				attention_dropout
											_SCREAMING_SNAKE_CASE			:	int							=				layerdrop
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				encoder_normalize_before
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				pre_layernorm
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				apply_graphormer_init
											_SCREAMING_SNAKE_CASE			:	List[str]							=				activation_fn
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				embed_scale
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				freeze_embeddings
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				num_trans_layers_to_freeze
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				share_input_output_embed
											_SCREAMING_SNAKE_CASE			:	Tuple							=				traceable
											_SCREAMING_SNAKE_CASE			:	List[str]							=				q_noise
											_SCREAMING_SNAKE_CASE			:	str							=				qn_block_size

											# These parameters are here for future extensions
											# atm, the model only supports self attention
											_SCREAMING_SNAKE_CASE			:	Any							=				kdim
											_SCREAMING_SNAKE_CASE			:	int							=				vdim
											_SCREAMING_SNAKE_CASE			:	Any							=				self_attention
											_SCREAMING_SNAKE_CASE			:	Tuple							=				bias

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




