





'''simple docstring'''







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


snake_case_       :    str          =					logging.get_logger(__name__)

snake_case_       :    Union[str, 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    __a (lowerCamelCase       ):
							__a   :				int       =    "graphormer"
							__a   :				Union[str, Any]       =    ["past_key_values"]

							def __init__(							self							:       Dict    ,       __magic_name__							:       int = 1    ,       __magic_name__							:       int = 5_12 * 9    ,       __magic_name__							:       int = 5_12 * 3    ,       __magic_name__							:       int = 5_12    ,       __magic_name__							:       int = 5_12    ,       __magic_name__							:       int = 5_12    ,       __magic_name__							:       int = 1_28    ,       __magic_name__							:       int = 5    ,       __magic_name__							:       int = 10_24    ,       __magic_name__							:       str = "multi_hop"    ,       __magic_name__							:       int = 5_12    ,       __magic_name__							:       bool = False    ,       __magic_name__							:       int = 12    ,       __magic_name__							:       int = 7_68    ,       __magic_name__							:       int = 7_68    ,       __magic_name__							:       int = 32    ,       __magic_name__							:       float = 0.1    ,       __magic_name__							:       float = 0.1    ,       __magic_name__							:       float = 0.0    ,       __magic_name__							:       bool = False    ,       __magic_name__							:       bool = False    ,       __magic_name__							:       bool = False    ,       __magic_name__							:       str = "gelu"    ,       __magic_name__							:       float = None    ,       __magic_name__							:       bool = False    ,       __magic_name__							:       int = 0    ,       __magic_name__							:       bool = False    ,       __magic_name__							:       float = 0.0    ,       __magic_name__							:       int = 8    ,       __magic_name__							:       int = None    ,       __magic_name__							:       int = None    ,       __magic_name__							:       bool = True    ,       __magic_name__							:       bool = True    ,       __magic_name__							:       Optional[int]=0    ,       __magic_name__							:       str=1    ,       __magic_name__							:       Tuple=2    ,       **__magic_name__							:       int    ,       )   ->    Dict:







													"""simple docstring"""
													UpperCAmelCase_     : Optional[Any]         =					num_classes
													UpperCAmelCase_     : Tuple         =					num_atoms
													UpperCAmelCase_     : Tuple         =					num_in_degree
													UpperCAmelCase_     : Optional[Any]         =					num_out_degree
													UpperCAmelCase_     : Optional[int]         =					num_edges
													UpperCAmelCase_     : Dict         =					num_spatial
													UpperCAmelCase_     : int         =					num_edge_dis
													UpperCAmelCase_     : List[Any]         =					edge_type
													UpperCAmelCase_     : List[str]         =					multi_hop_max_dist
													UpperCAmelCase_     : int         =					spatial_pos_max
													UpperCAmelCase_     : List[str]         =					max_nodes
													UpperCAmelCase_     : Any         =					num_hidden_layers
													UpperCAmelCase_     : int         =					embedding_dim
													UpperCAmelCase_     : str         =					embedding_dim
													UpperCAmelCase_     : Dict         =					ffn_embedding_dim
													UpperCAmelCase_     : Optional[int]         =					num_attention_heads
													UpperCAmelCase_     : Optional[int]         =					dropout
													UpperCAmelCase_     : List[Any]         =					attention_dropout
													UpperCAmelCase_     : Union[str, Any]         =					layerdrop
													UpperCAmelCase_     : Dict         =					encoder_normalize_before
													UpperCAmelCase_     : List[str]         =					pre_layernorm
													UpperCAmelCase_     : List[Any]         =					apply_graphormer_init
													UpperCAmelCase_     : int         =					activation_fn
													UpperCAmelCase_     : int         =					embed_scale
													UpperCAmelCase_     : Union[str, Any]         =					freeze_embeddings
													UpperCAmelCase_     : Any         =					num_trans_layers_to_freeze
													UpperCAmelCase_     : Dict         =					share_input_output_embed
													UpperCAmelCase_     : List[str]         =					traceable
													UpperCAmelCase_     : Optional[Any]         =					q_noise
													UpperCAmelCase_     : List[str]         =					qn_block_size

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

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












'''simple docstring'''







from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest


@require_tokenizers
class    __a (lowerCamelCase       ):
							__a   :				Any       =    SqueezeBertTokenizer
							__a   :				Dict       =    SqueezeBertTokenizerFast
							__a   :				Any       =    True

							def       UpperCAmelCase__		(							self							:       Union[str, Any]    ,       **__magic_name__							:       Dict				)   ->    int:







													"""simple docstring"""
													return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname    ,       **__magic_name__				)



							@slow
							def       UpperCAmelCase__		(							self							:       Union[str, Any]				)   ->    int:







													"""simple docstring"""
													UpperCAmelCase_     : List[str]         =					SqueezeBertTokenizer.from_pretrained('''squeezebert/squeezebert-mnli-headless'''				)

													UpperCAmelCase_     : Any         =					tokenizer.encode('''sequence builders'''    ,       add_special_tokens=__magic_name__				)
													UpperCAmelCase_     : Optional[Any]         =					tokenizer.encode('''multi-sequence build'''    ,       add_special_tokens=__magic_name__				)

													UpperCAmelCase_     : List[Any]         =					tokenizer.build_inputs_with_special_tokens(__magic_name__				)
													UpperCAmelCase_     : Any         =					tokenizer.build_inputs_with_special_tokens(__magic_name__    ,       __magic_name__				)

													assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
													assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
													    tokenizer.sep_token_id
													]





