'''simple docstring'''

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


_A      :				Optional[Any] 				=    logging.get_logger(__name__)

_A      :				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					(    UpperCAmelCase__      ):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE					:	Optional[int]				     = """graphormer"""
 _SCREAMING_SNAKE_CASE					:	str				     = ["""past_key_values"""]
 def __init__(				self       :     List[str]				,	SCREAMING_SNAKE_CASE__       :     int = 1				,	SCREAMING_SNAKE_CASE__       :     int = 5_12 * 9				,	SCREAMING_SNAKE_CASE__       :     int = 5_12 * 3				,	SCREAMING_SNAKE_CASE__       :     int = 5_12				,	SCREAMING_SNAKE_CASE__       :     int = 5_12				,	SCREAMING_SNAKE_CASE__       :     int = 5_12				,	SCREAMING_SNAKE_CASE__       :     int = 1_28				,	SCREAMING_SNAKE_CASE__       :     int = 5				,	SCREAMING_SNAKE_CASE__       :     int = 10_24				,	SCREAMING_SNAKE_CASE__       :     str = "multi_hop"				,	SCREAMING_SNAKE_CASE__       :     int = 5_12				,	SCREAMING_SNAKE_CASE__       :     bool = False				,	SCREAMING_SNAKE_CASE__       :     int = 12				,	SCREAMING_SNAKE_CASE__       :     int = 7_68				,	SCREAMING_SNAKE_CASE__       :     int = 7_68				,	SCREAMING_SNAKE_CASE__       :     int = 32				,	SCREAMING_SNAKE_CASE__       :     float = 0.1				,	SCREAMING_SNAKE_CASE__       :     float = 0.1				,	SCREAMING_SNAKE_CASE__       :     float = 0.0				,	SCREAMING_SNAKE_CASE__       :     bool = False				,	SCREAMING_SNAKE_CASE__       :     bool = False				,	SCREAMING_SNAKE_CASE__       :     bool = False				,	SCREAMING_SNAKE_CASE__       :     str = "gelu"				,	SCREAMING_SNAKE_CASE__       :     float = None				,	SCREAMING_SNAKE_CASE__       :     bool = False				,	SCREAMING_SNAKE_CASE__       :     int = 0				,	SCREAMING_SNAKE_CASE__       :     bool = False				,	SCREAMING_SNAKE_CASE__       :     float = 0.0				,	SCREAMING_SNAKE_CASE__       :     int = 8				,	SCREAMING_SNAKE_CASE__       :     int = None				,	SCREAMING_SNAKE_CASE__       :     int = None				,	SCREAMING_SNAKE_CASE__       :     bool = True				,	SCREAMING_SNAKE_CASE__       :     bool = True				,	SCREAMING_SNAKE_CASE__       :     Any=0				,	SCREAMING_SNAKE_CASE__       :     List[str]=1				,	SCREAMING_SNAKE_CASE__       :     str=2				,	**SCREAMING_SNAKE_CASE__       :     Union[str, Any]				,	)  ->   List[Any]:
  __lowerCAmelCase    							=						num_classes
  __lowerCAmelCase    							=						num_atoms
  __lowerCAmelCase    							=						num_in_degree
  __lowerCAmelCase    							=						num_out_degree
  __lowerCAmelCase    							=						num_edges
  __lowerCAmelCase    							=						num_spatial
  __lowerCAmelCase    							=						num_edge_dis
  __lowerCAmelCase    							=						edge_type
  __lowerCAmelCase    							=						multi_hop_max_dist
  __lowerCAmelCase    							=						spatial_pos_max
  __lowerCAmelCase    							=						max_nodes
  __lowerCAmelCase    							=						num_hidden_layers
  __lowerCAmelCase    							=						embedding_dim
  __lowerCAmelCase    							=						embedding_dim
  __lowerCAmelCase    							=						ffn_embedding_dim
  __lowerCAmelCase    							=						num_attention_heads
  __lowerCAmelCase    							=						dropout
  __lowerCAmelCase    							=						attention_dropout
  __lowerCAmelCase    							=						layerdrop
  __lowerCAmelCase    							=						encoder_normalize_before
  __lowerCAmelCase    							=						pre_layernorm
  __lowerCAmelCase    							=						apply_graphormer_init
  __lowerCAmelCase    							=						activation_fn
  __lowerCAmelCase    							=						embed_scale
  __lowerCAmelCase    							=						freeze_embeddings
  __lowerCAmelCase    							=						num_trans_layers_to_freeze
  __lowerCAmelCase    							=						share_input_output_embed
  __lowerCAmelCase    							=						traceable
  __lowerCAmelCase    							=						q_noise
  __lowerCAmelCase    							=						qn_block_size

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

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







'''simple docstring'''

import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()







def 					UpperCamelCase_						( snake_case_	:	List[str]						,		snake_case_	:	Optional[Any]				) ->					Tuple:






 '''simple docstring'''

 __lowerCAmelCase    							=						CanineConfig()
 __lowerCAmelCase    							=						CanineModel(snake_case_				)
 model.eval()

 print(f"""Building PyTorch model from configuration: {config}"""				)

 # Load weights from tf checkpoint
 load_tf_weights_in_canine(snake_case_						,		snake_case_						,		snake_case_				)

 # Save pytorch-model (weights and configuration)
 print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
 model.save_pretrained(snake_case_				)

 # Save tokenizer files
 __lowerCAmelCase    							=						CanineTokenizer()
 print(f"""Save tokenizer files to {pytorch_dump_path}"""				)
 tokenizer.save_pretrained(snake_case_				)


if __name__ == "__main__":
 _A      :				int 				=    argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''',
     default=None,
     type=str,
     required=True,
     help='''Path to the TensorFlow checkpoint. Should end with model.ckpt''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''',
     default=None,
     type=str,
     required=True,
     help='''Path to a folder where the PyTorch model will be placed.''',
 )
 _A      :				Dict 				=    parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)






