from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


UpperCAmelCase							   =      logging.get_logger(__name__)

UpperCAmelCase							   =      {
    """uclanlp/plbart-base""": """https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json""",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}






class 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   UpperCamelCase_				:						List[str] 			=   'plbart'
   UpperCamelCase_				:						int 			=   ['past_key_values']
   UpperCamelCase_				:						Dict 			=   {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
   def __init__(    self							,     lowerCAmelCase_=5_00_05							,     lowerCAmelCase_=10_24							,     lowerCAmelCase_=6							,     lowerCAmelCase_=30_72							,     lowerCAmelCase_=12							,     lowerCAmelCase_=6							,     lowerCAmelCase_=30_72							,     lowerCAmelCase_=12							,     lowerCAmelCase_=0.0							,     lowerCAmelCase_=0.0							,     lowerCAmelCase_=True							,     lowerCAmelCase_=True							,     lowerCAmelCase_="gelu"							,     lowerCAmelCase_=7_68							,     lowerCAmelCase_=0.1							,     lowerCAmelCase_=0.1							,     lowerCAmelCase_=0.0							,     lowerCAmelCase_=0.02							,     lowerCAmelCase_=0.0							,     lowerCAmelCase_=True							,     lowerCAmelCase_=1							,     lowerCAmelCase_=0							,     lowerCAmelCase_=2							,     lowerCAmelCase_=2							,     **lowerCAmelCase_							,     )	->Dict:
        a  :    Optional[int]				=						vocab_size
        a  :    Union[str, Any]				=						max_position_embeddings
        a  :    Optional[Any]				=						d_model
        a  :    Optional[int]				=						encoder_ffn_dim
        a  :    Any				=						encoder_layers
        a  :    Any				=						encoder_attention_heads
        a  :    Tuple				=						decoder_ffn_dim
        a  :    Optional[Any]				=						decoder_layers
        a  :    int				=						decoder_attention_heads
        a  :    Optional[Any]				=						dropout
        a  :    int				=						attention_dropout
        a  :    Union[str, Any]				=						activation_dropout
        a  :    Tuple				=						activation_function
        a  :    Union[str, Any]				=						init_std
        a  :    List[str]				=						encoder_layerdrop
        a  :    Optional[Any]				=						decoder_layerdrop
        a  :    int				=						classifier_dropout
        a  :    Tuple				=						use_cache
        a  :    Optional[Any]				=						encoder_layers
        a  :    Tuple				=						scale_embedding  # scale factor will be sqrt(d_model) if True
        super().__init__(
            pad_token_id=lowerCAmelCase_							,     bos_token_id=lowerCAmelCase_							,     eos_token_id=lowerCAmelCase_							,     is_encoder_decoder=lowerCAmelCase_							,     forced_eos_token_id=lowerCAmelCase_							,     **lowerCAmelCase_							,     )






class 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   @property
   def _lowercase(    self    )	->Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
                ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
            ]    )


   @property
   def _lowercase(    self    )	->Mapping[str, Mapping[int, str]]:
        if self.use_past:
             return OrderedDict(
                 [
                     ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
                     ('''past_keys''', {0: '''batch''', 2: '''sequence'''}),
                     ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
                 ]    )
        else:
             return OrderedDict(
                 [
                     ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
                     ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
                 ]    )
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 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   UpperCamelCase_				:						int 			=   'graphormer'
   UpperCamelCase_				:						Tuple 			=   ['past_key_values']
   def __init__(    self							,     lowerCAmelCase_ = 1							,     lowerCAmelCase_ = 5_12 * 9							,     lowerCAmelCase_ = 5_12 * 3							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = 1_28							,     lowerCAmelCase_ = 5							,     lowerCAmelCase_ = 10_24							,     lowerCAmelCase_ = "multi_hop"							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 12							,     lowerCAmelCase_ = 7_68							,     lowerCAmelCase_ = 7_68							,     lowerCAmelCase_ = 32							,     lowerCAmelCase_ = 0.1							,     lowerCAmelCase_ = 0.1							,     lowerCAmelCase_ = 0.0							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = "gelu"							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 0							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 0.0							,     lowerCAmelCase_ = 8							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = True							,     lowerCAmelCase_ = True							,     lowerCAmelCase_=0							,     lowerCAmelCase_=1							,     lowerCAmelCase_=2							,     **lowerCAmelCase_							,     )	->Optional[int]:
        a  :    List[Any]				=						num_classes
        a  :    Any				=						num_atoms
        a  :    Dict				=						num_in_degree
        a  :    int				=						num_out_degree
        a  :    Dict				=						num_edges
        a  :    Tuple				=						num_spatial
        a  :    Dict				=						num_edge_dis
        a  :    Optional[int]				=						edge_type
        a  :    List[Any]				=						multi_hop_max_dist
        a  :    Optional[int]				=						spatial_pos_max
        a  :    str				=						max_nodes
        a  :    Optional[Any]				=						num_hidden_layers
        a  :    Any				=						embedding_dim
        a  :    Dict				=						embedding_dim
        a  :    Dict				=						ffn_embedding_dim
        a  :    List[Any]				=						num_attention_heads
        a  :    List[str]				=						dropout
        a  :    int				=						attention_dropout
        a  :    Optional[Any]				=						layerdrop
        a  :    str				=						encoder_normalize_before
        a  :    Optional[int]				=						pre_layernorm
        a  :    Tuple				=						apply_graphormer_init
        a  :    int				=						activation_fn
        a  :    Any				=						embed_scale
        a  :    Optional[int]				=						freeze_embeddings
        a  :    Tuple				=						num_trans_layers_to_freeze
        a  :    List[str]				=						share_input_output_embed
        a  :    List[Any]				=						traceable
        a  :    Optional[Any]				=						q_noise
        a  :    Dict				=						qn_block_size

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

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