


"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

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


__SCREAMING_SNAKE_CASE				:     Tuple		=     logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE				:     List[str]		=     {
    "squeezebert/squeezebert-uncased": (
        "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"
    ),
    "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json",
    "squeezebert/squeezebert-mnli-headless": (
        "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"
    ),
}





class   __A  (__lowerCamelCase):





							'''simple docstring'''

							__lowercase:		Union[str, Any]				   =     SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
							__lowercase:		Optional[int]				   =     """squeezebert"""



							def __init__(					self				:   Union[str, Any]		,							UpperCAmelCase_				:   List[Any]=30_522		,							UpperCAmelCase_				:   str=768		,							UpperCAmelCase_				:   Optional[Any]=12		,							UpperCAmelCase_				:   List[Any]=12		,							UpperCAmelCase_				:   Optional[int]=3_072		,							UpperCAmelCase_				:   List[str]="gelu"		,							UpperCAmelCase_				:   str=0.1		,							UpperCAmelCase_				:   int=0.1		,							UpperCAmelCase_				:   Tuple=512		,							UpperCAmelCase_				:   int=2		,							UpperCAmelCase_				:   Union[str, Any]=0.02		,							UpperCAmelCase_				:   Tuple=1E-12		,							UpperCAmelCase_				:   Any=0		,							UpperCAmelCase_				:   Optional[Any]=768		,							UpperCAmelCase_				:   List[str]=4		,							UpperCAmelCase_				:   Dict=4		,							UpperCAmelCase_				:   List[Any]=4		,							UpperCAmelCase_				:   Optional[Any]=1		,							UpperCAmelCase_				:   int=4		,							UpperCAmelCase_				:   int=4		,							**UpperCAmelCase_				:   List[Any]		,							)       ->Any:



														"""simple docstring"""



														super().__init__(pad_token_id=UpperCAmelCase_		,							**UpperCAmelCase_							)

														snake_case_     							=		vocab_size
														snake_case_     							=		hidden_size
														snake_case_     							=		num_hidden_layers
														snake_case_     							=		num_attention_heads
														snake_case_     							=		hidden_act
														snake_case_     							=		intermediate_size
														snake_case_     							=		hidden_dropout_prob
														snake_case_     							=		attention_probs_dropout_prob
														snake_case_     							=		max_position_embeddings
														snake_case_     							=		type_vocab_size
														snake_case_     							=		initializer_range
														snake_case_     							=		layer_norm_eps
														snake_case_     							=		embedding_size
														snake_case_     							=		q_groups
														snake_case_     							=		k_groups
														snake_case_     							=		v_groups
														snake_case_     							=		post_attention_groups
														snake_case_     							=		intermediate_groups
														snake_case_     							=		output_groups





class   __A  (__lowerCamelCase):





							'''simple docstring'''

							@property
							def 		lowerCAmelCase							(					self				:   Optional[int]							)       ->Tuple:



														"""simple docstring"""



														if self.task == "multiple-choice":
																					snake_case_     							=		{0: """batch""", 1: """choice""", 2: """sequence"""}
														else:
																					snake_case_     							=		{0: """batch""", 1: """sequence"""}
														return OrderedDict(
														    [
														        ("""input_ids""", dynamic_axis),
														        ("""attention_mask""", dynamic_axis),
														        ("""token_type_ids""", dynamic_axis),
														    ]							)










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				,							)






