


"""simple docstring"""






from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
					import pyximport

					pyximport.install(setup_args={"""include_dirs""": np.get_include()})
					from . import algos_graphormer  # noqa E402





def 							A_					(   snake_case__					,				snake_case__ = 5_12		)				->     Tuple:
			_UpperCamelCase				:List[Any]				    =  x.shape[1] if len(x.shape		) > 1 else 1
			_UpperCamelCase				:Tuple				    =  1 + np.arange(0					,				feature_num * offset					,				snake_case__					,				dtype=np.intaa		)
			_UpperCamelCase				:List[Any]				    =  x + feature_offset
			return x





def 							A_					(   snake_case__					,				snake_case__=True		)				->     List[Any]:
			requires_backends(snake_case__					,				['''cython''']		)

			if keep_features and "edge_attr" in item.keys():  # edge_attr
						_UpperCamelCase				:Tuple				    =  np.asarray(item['''edge_attr''']					,				dtype=np.intaa		)
			else:
						_UpperCamelCase				:Any				    =  np.ones((len(item['''edge_index'''][0]		), 1)					,				dtype=np.intaa		)  # same embedding for all

			if keep_features and "node_feat" in item.keys():  # input_nodes
						_UpperCamelCase				:Any				    =  np.asarray(item['''node_feat''']					,				dtype=np.intaa		)
			else:
						_UpperCamelCase				:Optional[Any]				    =  np.ones((item['''num_nodes'''], 1)					,				dtype=np.intaa		)  # same embedding for all

			_UpperCamelCase				:List[Any]				    =  np.asarray(item['''edge_index''']					,				dtype=np.intaa		)

			_UpperCamelCase				:Optional[int]				    =  convert_to_single_emb(snake_case__		) + 1
			_UpperCamelCase				:Tuple				    =  item['''num_nodes''']

			if len(edge_attr.shape		) == 1:
						_UpperCamelCase				:Any				    =  edge_attr[:, None]
			_UpperCamelCase				:str				    =  np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]					,				dtype=np.intaa		)
			_UpperCamelCase				:str				    =  convert_to_single_emb(snake_case__		) + 1

			# node adj matrix [num_nodes, num_nodes] bool
			_UpperCamelCase				:Optional[Any]				    =  np.zeros([num_nodes, num_nodes]					,				dtype=snake_case__		)
			_UpperCamelCase				:Optional[int]				    =  True

			_UpperCamelCase    ,			_UpperCamelCase				:Optional[Any]				    =  algos_graphormer.floyd_warshall(snake_case__		)
			_UpperCamelCase				:Any				    =  np.amax(snake_case__		)

			_UpperCamelCase				:List[Any]				    =  algos_graphormer.gen_edge_input(snake_case__					,				snake_case__					,				snake_case__		)
			_UpperCamelCase				:Any				    =  np.zeros([num_nodes + 1, num_nodes + 1]					,				dtype=np.single		)  # with graph token

			# combine
			_UpperCamelCase				:str				    =  input_nodes + 1  # we shift all indices by one for padding
			_UpperCamelCase				:List[Any]				    =  attn_bias
			_UpperCamelCase				:str				    =  attn_edge_type
			_UpperCamelCase				:List[str]				    =  shortest_path_result.astype(np.intaa		) + 1  # we shift all indices by one for padding
			_UpperCamelCase				:List[str]				    =  np.sum(snake_case__					,				axis=1		).reshape(-1		) + 1  # we shift all indices by one for padding
			_UpperCamelCase				:Any				    =  item['''in_degree''']  # for undirected graph
			_UpperCamelCase				:Any				    =  input_edges + 1  # we shift all indices by one for padding
			if "labels" not in item:
						_UpperCamelCase				:Union[str, Any]				    =  item['''y''']

			return item






class     A:

		"""simple docstring"""





		def __init__(   self				,							SCREAMING_SNAKE_CASE__=20				,							SCREAMING_SNAKE_CASE__=False      )			->  int:

					"""simple docstring"""



					if not is_cython_available():
								raise ImportError('''Graphormer preprocessing needs Cython (pyximport)'''      )

					_UpperCamelCase				:Union[str, Any]				    =  spatial_pos_max
					_UpperCamelCase				:Optional[int]				    =  on_the_fly_processing





		def __call__(   self				,							SCREAMING_SNAKE_CASE__      )			->  Dict[str, Any]:

					"""simple docstring"""



					if self.on_the_fly_processing:
								_UpperCamelCase				:int				    =  [preprocess_item(SCREAMING_SNAKE_CASE__      ) for i in features]

					if not isinstance(features[0]				,							SCREAMING_SNAKE_CASE__      ):
								_UpperCamelCase				:str				    =  [vars(SCREAMING_SNAKE_CASE__      ) for f in features]
					_UpperCamelCase				:List[str]				    =  {}

					_UpperCamelCase				:List[Any]				    =  max(len(i['''input_nodes''']      ) for i in features      )
					_UpperCamelCase				:int				    =  len(features[0]['''input_nodes'''][0]      )
					_UpperCamelCase				:Any				    =  len(features[0]['''attn_edge_type'''][0][0]      )
					_UpperCamelCase				:Union[str, Any]				    =  max(len(i['''input_edges'''][0][0]      ) for i in features      )
					_UpperCamelCase				:List[Any]				    =  len(features[0]['''input_edges'''][0][0][0]      )
					_UpperCamelCase				:List[Any]				    =  len(SCREAMING_SNAKE_CASE__      )

					_UpperCamelCase				:Optional[Any]				    =  torch.zeros(SCREAMING_SNAKE_CASE__				,							max_node_num + 1				,							max_node_num + 1				,							dtype=torch.float      )
					_UpperCamelCase				:Union[str, Any]				    =  torch.zeros(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							dtype=torch.long      )
					_UpperCamelCase				:List[Any]				    =  torch.zeros(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							dtype=torch.long      )
					_UpperCamelCase				:Optional[int]				    =  torch.zeros(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							dtype=torch.long      )
					_UpperCamelCase				:List[str]				    =  torch.zeros(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							dtype=torch.long      )
					_UpperCamelCase				:int				    =  torch.zeros(
					    SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							dtype=torch.long      )

					for ix, f in enumerate(SCREAMING_SNAKE_CASE__      ):
								for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
											_UpperCamelCase				:str				    =  torch.tensor(f[k]      )

								if len(f['''attn_bias'''][1:, 1:][f['''spatial_pos'''] >= self.spatial_pos_max]      ) > 0:
											_UpperCamelCase				:List[Any]				    =  float('''-inf'''      )

								_UpperCamelCase				:Dict				    =  f['''attn_bias''']
								_UpperCamelCase				:List[str]				    =  f[
								    '''attn_edge_type'''
								]
								_UpperCamelCase				:Any				    =  f['''spatial_pos''']
								_UpperCamelCase				:Optional[int]				    =  f['''in_degree''']
								_UpperCamelCase				:List[Any]				    =  f['''input_nodes''']
								_UpperCamelCase				:Any				    =  f['''input_edges''']

					_UpperCamelCase				:Optional[Any]				    =  batch['''in_degree''']

					_UpperCamelCase				:Union[str, Any]				    =  features[0]['''labels''']
					if len(SCREAMING_SNAKE_CASE__      ) == 1:  # one task
								if isinstance(sample[0]				,							SCREAMING_SNAKE_CASE__      ):  # regression
											_UpperCamelCase				:Tuple				    =  torch.from_numpy(np.concatenate([i['''labels'''] for i in features]      )      )
								else:  # binary classification
											_UpperCamelCase				:Dict				    =  torch.from_numpy(np.concatenate([i['''labels'''] for i in features]      )      )
					else:  # multi task classification, left to float to keep the NaNs
								_UpperCamelCase				:Optional[int]				    =  torch.from_numpy(np.stack([i['''labels'''] for i in features]				,							axis=0      )      )

					return batch





"""simple docstring"""






import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__							:Optional[int]       =  get_tests_dir("""fixtures/spiece.model""")






@require_sentencepiece
@require_tokenizers
class     A(     lowerCamelCase__							,       unittest.TestCase		):

		"""simple docstring"""





		A         =       ErnieMTokenizer
		A         =       False
		A         =       True
		A         =       False
		A         =       False
		def      _UpperCamelCase(   self      )			->  Union[str, Any]:

					"""simple docstring"""



					super().setUp()

					# We have a SentencePiece fixture for testing
					_UpperCamelCase				:int				    =  ErnieMTokenizer(SCREAMING_SNAKE_CASE__				,							unk_token='''<unk>'''				,							pad_token='''<pad>'''      )
					tokenizer.save_pretrained(self.tmpdirname      )
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  List[Any]:

					"""simple docstring"""



					_UpperCamelCase				:List[Any]				    =  '''this is a test'''
					_UpperCamelCase				:Optional[Any]				    =  '''this is a test'''
					return input_text, output_text
		def      _UpperCamelCase(   self      )			->  Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase				:Dict				    =  '''<pad>'''
					_UpperCamelCase				:Optional[Any]				    =  0

					self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE__      )				,							SCREAMING_SNAKE_CASE__      )
					self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE__      )				,							SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  int:

					"""simple docstring"""



					_UpperCamelCase				:Dict				    =  list(self.get_tokenizer().get_vocab().keys()      )

					self.assertEqual(vocab_keys[0]				,							'''<pad>'''      )
					self.assertEqual(vocab_keys[1]				,							'''<unk>'''      )
					self.assertEqual(vocab_keys[-1]				,							'''▁eloquent'''      )
					self.assertEqual(len(SCREAMING_SNAKE_CASE__      )				,							3_00_00      )
		def      _UpperCamelCase(   self      )			->  str:

					"""simple docstring"""



					self.assertEqual(self.get_tokenizer().vocab_size				,							3_00_00      )
		def      _UpperCamelCase(   self      )			->  Dict:

					"""simple docstring"""



					if not self.test_rust_tokenizer:
								return

					_UpperCamelCase				:Tuple				    =  self.get_tokenizer()
					_UpperCamelCase				:str				    =  self.get_rust_tokenizer()

					_UpperCamelCase				:List[Any]				    =  '''I was born in 92000, and this is falsé.'''

					_UpperCamelCase				:Any				    =  tokenizer.tokenize(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:List[Any]				    =  rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )

					_UpperCamelCase				:List[str]				    =  tokenizer.encode(SCREAMING_SNAKE_CASE__				,							add_special_tokens=SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:List[str]				    =  rust_tokenizer.encode(SCREAMING_SNAKE_CASE__				,							add_special_tokens=SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )

					_UpperCamelCase				:Any				    =  self.get_rust_tokenizer()
					_UpperCamelCase				:List[Any]				    =  tokenizer.encode(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:int				    =  rust_tokenizer.encode(SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  Dict:

					"""simple docstring"""



					_UpperCamelCase				:Any				    =  ErnieMTokenizer(SCREAMING_SNAKE_CASE__				,							do_lower_case=SCREAMING_SNAKE_CASE__				,							unk_token='''<unk>'''				,							pad_token='''<pad>'''      )

					_UpperCamelCase				:List[str]				    =  tokenizer.tokenize('''This is a test'''      )
					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							['''▁this''', '''▁is''', '''▁a''', '''▁test''']      )

					self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__      )				,							[48, 25, 21, 12_89]      )

					_UpperCamelCase				:str				    =  tokenizer.tokenize('''I was born in 92000, and this is falsé.'''      )
					# ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
					self.assertListEqual(
					    SCREAMING_SNAKE_CASE__				,							['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''']      )
					_UpperCamelCase				:Optional[int]				    =  tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							[31, 23, 3_86, 19, 5_18, 30_50, 15, 17, 48, 25, 82_56, 18, 1, 9]      )

					_UpperCamelCase				:Dict				    =  tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(
					    SCREAMING_SNAKE_CASE__				,							['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''']				,							)
		def      _UpperCamelCase(   self      )			->  List[str]:

					"""simple docstring"""



					_UpperCamelCase				:Dict				    =  ErnieMTokenizer(SCREAMING_SNAKE_CASE__				,							unk_token='''<unk>'''				,							pad_token='''<pad>'''      )

					_UpperCamelCase				:str				    =  tokenizer.encode('''sequence builders'''      )
					_UpperCamelCase				:Optional[Any]				    =  tokenizer.encode('''multi-sequence build'''      )

					_UpperCamelCase				:int				    =  tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:Union[str, Any]				    =  tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )

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





		@slow
		def      _UpperCamelCase(   self      )			->  int:

					"""simple docstring"""



					_UpperCamelCase				:List[str]				    =  {'''input_ids''': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 9, 3_04, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 5, 5, 5, 16, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 64_60, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 35_59, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
					# fmt: on

					self.tokenizer_integration_test_util(
					    expected_encoding=SCREAMING_SNAKE_CASE__				,							model_name='''susnato/ernie-m-base_pytorch'''				,							sequences=[
					        '''Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '''
					        '''general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '''
					        '''Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '''
					        '''models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.''',
					        '''BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '''
					        '''conditioning on both left and right context in all layers.''',
					        '''The quick brown fox jumps over the lazy dog.''',
					    ]				,							)

