






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 						lowerCamelCase   (_snake_case    ):







		'''simple docstring'''



		_snake_case					:					Union[str, Any] 		=						'''graphormer'''
		_snake_case					:					int 		=						['''past_key_values''']



		def __init__(							self       ,	_UpperCamelCase = 1       ,	_UpperCamelCase = 5_1_2 * 9       ,	_UpperCamelCase = 5_1_2 * 3       ,	_UpperCamelCase = 5_1_2       ,	_UpperCamelCase = 5_1_2       ,	_UpperCamelCase = 5_1_2       ,	_UpperCamelCase = 1_2_8       ,	_UpperCamelCase = 5       ,	_UpperCamelCase = 1_0_2_4       ,	_UpperCamelCase = "multi_hop"       ,	_UpperCamelCase = 5_1_2       ,	_UpperCamelCase = False       ,	_UpperCamelCase = 1_2       ,	_UpperCamelCase = 7_6_8       ,	_UpperCamelCase = 7_6_8       ,	_UpperCamelCase = 3_2       ,	_UpperCamelCase = 0.1       ,	_UpperCamelCase = 0.1       ,	_UpperCamelCase = 0.0       ,	_UpperCamelCase = False       ,	_UpperCamelCase = False       ,	_UpperCamelCase = False       ,	_UpperCamelCase = "gelu"       ,	_UpperCamelCase = None       ,	_UpperCamelCase = False       ,	_UpperCamelCase = 0       ,	_UpperCamelCase = False       ,	_UpperCamelCase = 0.0       ,	_UpperCamelCase = 8       ,	_UpperCamelCase = None       ,	_UpperCamelCase = None       ,	_UpperCamelCase = True       ,	_UpperCamelCase = True       ,	_UpperCamelCase=0       ,	_UpperCamelCase=1       ,	_UpperCamelCase=2       ,	**_UpperCamelCase       ,	)			->							str:
							UpperCAmelCase_					:   Union[str, Any]   =		num_classes
							UpperCAmelCase_					:   str   =		num_atoms
							UpperCAmelCase_					:   Optional[int]   =		num_in_degree
							UpperCAmelCase_					:   Union[str, Any]   =		num_out_degree
							UpperCAmelCase_					:   int   =		num_edges
							UpperCAmelCase_					:   List[str]   =		num_spatial
							UpperCAmelCase_					:   Tuple   =		num_edge_dis
							UpperCAmelCase_					:   Union[str, Any]   =		edge_type
							UpperCAmelCase_					:   List[str]   =		multi_hop_max_dist
							UpperCAmelCase_					:   Union[str, Any]   =		spatial_pos_max
							UpperCAmelCase_					:   Dict   =		max_nodes
							UpperCAmelCase_					:   str   =		num_hidden_layers
							UpperCAmelCase_					:   Dict   =		embedding_dim
							UpperCAmelCase_					:   str   =		embedding_dim
							UpperCAmelCase_					:   Tuple   =		ffn_embedding_dim
							UpperCAmelCase_					:   List[str]   =		num_attention_heads
							UpperCAmelCase_					:   Optional[int]   =		dropout
							UpperCAmelCase_					:   int   =		attention_dropout
							UpperCAmelCase_					:   Optional[Any]   =		layerdrop
							UpperCAmelCase_					:   Tuple   =		encoder_normalize_before
							UpperCAmelCase_					:   List[Any]   =		pre_layernorm
							UpperCAmelCase_					:   Dict   =		apply_graphormer_init
							UpperCAmelCase_					:   Optional[Any]   =		activation_fn
							UpperCAmelCase_					:   Any   =		embed_scale
							UpperCAmelCase_					:   Tuple   =		freeze_embeddings
							UpperCAmelCase_					:   str   =		num_trans_layers_to_freeze
							UpperCAmelCase_					:   str   =		share_input_output_embed
							UpperCAmelCase_					:   List[str]   =		traceable
							UpperCAmelCase_					:   Union[str, Any]   =		q_noise
							UpperCAmelCase_					:   Optional[Any]   =		qn_block_size

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

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














import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 						lowerCamelCase   (_snake_case				,						unittest.TestCase    ):







		'''simple docstring'''



		_snake_case					:					Tuple 		=						BertweetTokenizer
		_snake_case					:					List[str] 		=						False



		def       __UpperCAmelCase    (							self     )			->							Optional[int]:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							UpperCAmelCase_					:   Optional[int]   =		['I', 'm', 'V@@', 'R@@', 'r', 'e@@']
							UpperCAmelCase_					:   Dict   =		dict(zip(_UpperCamelCase       ,	range(len(_UpperCamelCase     )     )     )     )
							UpperCAmelCase_					:   List[Any]   =		['#version: 0.2', 'a m</w>']
							UpperCAmelCase_					:   Union[str, Any]   =		{'unk_token': '<unk>'}

							UpperCAmelCase_					:   Optional[Any]   =		os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES['vocab_file']     )
							UpperCAmelCase_					:   List[str]   =		os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES['merges_file']     )
							with open(self.vocab_file       ,	'w'       ,	encoding='utf-8'     ) as fp:
												for token in vocab_tokens:
																	fp.write(f"{token} {vocab_tokens[token]}\n"     )
							with open(self.merges_file       ,	'w'       ,	encoding='utf-8'     ) as fp:
												fp.write('\n'.join(_UpperCamelCase     )     )



		def       __UpperCAmelCase    (							self       ,	**_UpperCamelCase     )			->							List[Any]:
							kwargs.update(self.special_tokens_map     )
							return BertweetTokenizer.from_pretrained(self.tmpdirname       ,	**_UpperCamelCase     )



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase     )			->							List[Any]:
							UpperCAmelCase_					:   Any   =		'I am VinAI Research'
							UpperCAmelCase_					:   Any   =		'I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
							return input_text, output_text



		def       __UpperCAmelCase    (							self     )			->							Tuple:
							UpperCAmelCase_					:   Union[str, Any]   =		BertweetTokenizer(self.vocab_file       ,	self.merges_file       ,	**self.special_tokens_map     )
							UpperCAmelCase_					:   Optional[Any]   =		'I am VinAI Research'
							UpperCAmelCase_					:   str   =		'I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
							UpperCAmelCase_					:   Any   =		tokenizer.tokenize(_UpperCamelCase     )
							self.assertListEqual(_UpperCamelCase       ,	_UpperCamelCase     )

							UpperCAmelCase_					:   Optional[int]   =		tokens + [tokenizer.unk_token]

							UpperCAmelCase_					:   List[str]   =		[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase     )       ,	_UpperCamelCase     )






