





import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


__UpperCAmelCase			:  Optional[Any]										=				get_logger(__name__)







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       List[str]:
  if text.startswith(SCREAMING_SNAKE_CASE__):
    return text[len(SCREAMING_SNAKE_CASE__) :]
  return text  # or whatever







def      A__				(       SCREAMING_SNAKE_CASE__)  ->       str:
  return {remove_prefix(SCREAMING_SNAKE_CASE__   ,   """model."""): v for k, v in sd.items()}







def      A__				(       SCREAMING_SNAKE_CASE__)  ->       str:
  __snake_case:		List[str]					    =		{}
  for k in state_dicts[0].keys():
    __snake_case:		Union[str, Any]					    =		[sd[k] for sd in state_dicts]
    __snake_case:		int					    =		sum(SCREAMING_SNAKE_CASE__) / len(SCREAMING_SNAKE_CASE__)
    assert isinstance(SCREAMING_SNAKE_CASE__   ,   torch.Tensor)
    __snake_case:		Dict					    =		new_t
  return new_sd







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       None:
  __snake_case:		str					    =		AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE__)
  if os.path.isfile(SCREAMING_SNAKE_CASE__):
    __snake_case:		List[Any]					    =		[pl_ckpt_path]
  else:
    assert os.path.isdir(SCREAMING_SNAKE_CASE__)
    __snake_case:		int					    =		list(Path(SCREAMING_SNAKE_CASE__).glob("""*.ckpt"""))
    assert ckpt_files, F'''could not find any ckpt files inside the {pl_ckpt_path} directory'''

  if len(SCREAMING_SNAKE_CASE__) > 1:
    logger.info(F'''averaging the weights of {ckpt_files}''')

  __snake_case:		int					    =		[sanitize(torch.load(SCREAMING_SNAKE_CASE__   ,   map_location="""cpu""")["""state_dict"""]) for x in ckpt_files]
  __snake_case:		Union[str, Any]					    =		average_state_dicts(SCREAMING_SNAKE_CASE__)

  __snake_case    ,  __snake_case:		str					    =		hf_model.load_state_dict(SCREAMING_SNAKE_CASE__   ,   strict=SCREAMING_SNAKE_CASE__)
  assert not missing, F'''missing keys: {missing}'''
  hf_model.save_pretrained(SCREAMING_SNAKE_CASE__)
  try:
    __snake_case:		Any					    =		AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__)
    tok.save_pretrained(SCREAMING_SNAKE_CASE__)
  except Exception:
    pass
    # dont copy tokenizer if cant


if __name__ == "__main__":
      fire.Fire(convert_pl_to_hf)













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


__UpperCAmelCase			:  List[str]										=				logging.get_logger(__name__)

__UpperCAmelCase			:  int										=				{
    # 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        __snake_case			(				__lowerCamelCase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	"""graphormer"""
       lowerCAmelCase__ 						=	["""past_key_values"""]




       def __init__(    self					:       List[Any]				,							A					:       int = 1				,							A					:       int = 512 * 9				,							A					:       int = 512 * 3				,							A					:       int = 512				,							A					:       int = 512				,							A					:       int = 512				,							A					:       int = 128				,							A					:       int = 5				,							A					:       int = 1_024				,							A					:       str = "multi_hop"				,							A					:       int = 512				,							A					:       bool = False				,							A					:       int = 12				,							A					:       int = 768				,							A					:       int = 768				,							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					:       Any=0				,							A					:       Optional[int]=1				,							A					:       Any=2				,							**A					:       Optional[Any]				,							):
         __snake_case:		List[str]					    =		num_classes
         __snake_case:		int					    =		num_atoms
         __snake_case:		List[str]					    =		num_in_degree
         __snake_case:		str					    =		num_out_degree
         __snake_case:		Tuple					    =		num_edges
         __snake_case:		Optional[int]					    =		num_spatial
         __snake_case:		int					    =		num_edge_dis
         __snake_case:		Tuple					    =		edge_type
         __snake_case:		str					    =		multi_hop_max_dist
         __snake_case:		List[str]					    =		spatial_pos_max
         __snake_case:		Optional[Any]					    =		max_nodes
         __snake_case:		Optional[int]					    =		num_hidden_layers
         __snake_case:		Union[str, Any]					    =		embedding_dim
         __snake_case:		Union[str, Any]					    =		embedding_dim
         __snake_case:		Optional[int]					    =		ffn_embedding_dim
         __snake_case:		List[str]					    =		num_attention_heads
         __snake_case:		Any					    =		dropout
         __snake_case:		Dict					    =		attention_dropout
         __snake_case:		Any					    =		layerdrop
         __snake_case:		Dict					    =		encoder_normalize_before
         __snake_case:		Any					    =		pre_layernorm
         __snake_case:		Optional[int]					    =		apply_graphormer_init
         __snake_case:		List[str]					    =		activation_fn
         __snake_case:		Optional[int]					    =		embed_scale
         __snake_case:		Dict					    =		freeze_embeddings
         __snake_case:		Any					    =		num_trans_layers_to_freeze
         __snake_case:		int					    =		share_input_output_embed
         __snake_case:		Dict					    =		traceable
         __snake_case:		str					    =		q_noise
         __snake_case:		Union[str, Any]					    =		qn_block_size

         # These parameters are here for future extensions
         # atm, the model only supports self attention
         __snake_case:		str					    =		kdim
         __snake_case:		Dict					    =		vdim
         __snake_case:		List[Any]					    =		self_attention
         __snake_case:		Tuple					    =		bias

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






