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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


__UpperCAmelCase			:  Dict										=				"Update an existing config file with the latest defaults while maintaining the old configuration."







def      A__				(       SCREAMING_SNAKE_CASE__)  ->       Optional[Any]:
  __snake_case:		Optional[Any]					    =		args.config_file
  if config_file is None and Path(SCREAMING_SNAKE_CASE__).exists():
    __snake_case:		Optional[int]					    =		default_config_file
  elif not Path(SCREAMING_SNAKE_CASE__).exists():
    raise ValueError(F'''The passed config file located at {config_file} doesn\'t exist.''')
  __snake_case:		Union[str, Any]					    =		load_config_from_file(SCREAMING_SNAKE_CASE__)

  if config_file.endswith(""".json"""):
    config.to_json_file(SCREAMING_SNAKE_CASE__)
  else:
    config.to_yaml_file(SCREAMING_SNAKE_CASE__)
  return config_file







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       List[str]:
  __snake_case:		Union[str, Any]					    =		parser.add_parser("""update"""   ,   parents=SCREAMING_SNAKE_CASE__   ,   help=SCREAMING_SNAKE_CASE__   ,   formatter_class=SCREAMING_SNAKE_CASE__)
  parser.add_argument(
      """--config_file"""   ,   default=SCREAMING_SNAKE_CASE__   ,   help=(
          """The path to the config file to update. Will default to a file named default_config.yaml in the cache """
          """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
          """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
          """with 'huggingface'."""
      )   ,   )

  parser.set_defaults(func=SCREAMING_SNAKE_CASE__)
  return parser







def      A__				(       SCREAMING_SNAKE_CASE__)  ->       int:
  __snake_case:		List[Any]					    =		update_config(SCREAMING_SNAKE_CASE__)
  print(F'''Sucessfully updated the configuration file at {config_file}.''')













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				,							)






