


'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE				:							Optional[int]		       =    logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE				:							str		       =    {
    # 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      (      lowerCAmelCase__	):

						"""simple docstring"""






						_UpperCAmelCase		:					Tuple      	="graphormer"
						_UpperCAmelCase		:					Tuple      	=["past_key_values"]






						def __init__(     self       :			List[str]   ,     lowerCAmelCase       :			int = 1   ,     lowerCAmelCase       :			Optional[int] = 5_12 * 9   ,     lowerCAmelCase       :			Union[str, Any] = 5_12 * 3   ,     lowerCAmelCase       :			List[Any] = 5_12   ,     lowerCAmelCase       :			List[str] = 5_12   ,     lowerCAmelCase       :			int = 5_12   ,     lowerCAmelCase       :			List[Any] = 1_28   ,     lowerCAmelCase       :			Tuple = 5   ,     lowerCAmelCase       :			int = 10_24   ,     lowerCAmelCase       :			List[str] = "multi_hop"   ,     lowerCAmelCase       :			Tuple = 5_12   ,     lowerCAmelCase       :			Tuple = False   ,     lowerCAmelCase       :			Any = 12   ,     lowerCAmelCase       :			Any = 7_68   ,     lowerCAmelCase       :			Tuple = 7_68   ,     lowerCAmelCase       :			Optional[int] = 32   ,     lowerCAmelCase       :			Union[str, Any] = 0.1   ,     lowerCAmelCase       :			List[str] = 0.1   ,     lowerCAmelCase       :			Tuple = 0.0   ,     lowerCAmelCase       :			Union[str, Any] = False   ,     lowerCAmelCase       :			int = False   ,     lowerCAmelCase       :			Any = False   ,     lowerCAmelCase       :			int = "gelu"   ,     lowerCAmelCase       :			Tuple = None   ,     lowerCAmelCase       :			Optional[Any] = False   ,     lowerCAmelCase       :			str = 0   ,     lowerCAmelCase       :			Union[str, Any] = False   ,     lowerCAmelCase       :			Optional[Any] = 0.0   ,     lowerCAmelCase       :			int = 8   ,     lowerCAmelCase       :			List[Any] = None   ,     lowerCAmelCase       :			List[Any] = None   ,     lowerCAmelCase       :			Dict = True   ,     lowerCAmelCase       :			List[Any] = True   ,     lowerCAmelCase       :			Optional[int]=0   ,     lowerCAmelCase       :			str=1   ,     lowerCAmelCase       :			Any=2   ,     **lowerCAmelCase       :			Optional[Any]   ,     ):
													A_ 						=		num_classes
													A_ 						=		num_atoms
													A_ 						=		num_in_degree
													A_ 						=		num_out_degree
													A_ 						=		num_edges
													A_ 						=		num_spatial
													A_ 						=		num_edge_dis
													A_ 						=		edge_type
													A_ 						=		multi_hop_max_dist
													A_ 						=		spatial_pos_max
													A_ 						=		max_nodes
													A_ 						=		num_hidden_layers
													A_ 						=		embedding_dim
													A_ 						=		embedding_dim
													A_ 						=		ffn_embedding_dim
													A_ 						=		num_attention_heads
													A_ 						=		dropout
													A_ 						=		attention_dropout
													A_ 						=		layerdrop
													A_ 						=		encoder_normalize_before
													A_ 						=		pre_layernorm
													A_ 						=		apply_graphormer_init
													A_ 						=		activation_fn
													A_ 						=		embed_scale
													A_ 						=		freeze_embeddings
													A_ 						=		num_trans_layers_to_freeze
													A_ 						=		share_input_output_embed
													A_ 						=		traceable
													A_ 						=		q_noise
													A_ 						=		qn_block_size

													# These parameters are here for future extensions
													# atm, the model only supports self attention
													A_ 						=		kdim
													A_ 						=		vdim
													A_ 						=		self_attention
													A_ 						=		bias

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









'''simple docstring'''
import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
							from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


__SCREAMING_SNAKE_CASE				:							int		       =		get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_torch
@require_torchaudio
@require_sentencepiece
class 				__lowerCAmelCase      (      unittest.TestCase	):

						"""simple docstring"""






						def 							_UpperCAmelCase					(     self       :			List[str]  ):
													A_ 						=		tempfile.mkdtemp()

													A_ 						=		["<s>", "<pad>", "</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est"]
													A_ 						=		dict(zip(lowerCAmelCase   ,     range(len(lowerCAmelCase  )  )  )  )
													A_ 						=		Path(self.tmpdirname  )
													save_json(lowerCAmelCase   ,     save_dir / VOCAB_FILES_NAMES["vocab_file"]  )
													if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
																				copyfile(lowerCAmelCase   ,     save_dir / VOCAB_FILES_NAMES["spm_file"]  )

													A_ 						=		SpeechaTextTokenizer.from_pretrained(self.tmpdirname  )
													tokenizer.save_pretrained(self.tmpdirname  )

													A_ 						=		{
													    "feature_size": 24,
													    "num_mel_bins": 24,
													    "padding_value": 0.0,
													    "sampling_rate": 1_60_00,
													    "return_attention_mask": False,
													    "do_normalize": True,
													}
													save_json(lowerCAmelCase   ,     save_dir / FEATURE_EXTRACTOR_NAME  )






						def 							_UpperCAmelCase					(     self       :			Union[str, Any]   ,     **lowerCAmelCase       :			str  ):
													return SpeechaTextTokenizer.from_pretrained(self.tmpdirname   ,     **lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			List[Any]   ,     **lowerCAmelCase       :			Optional[Any]  ):
													return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname   ,     **lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			Any  ):
													shutil.rmtree(self.tmpdirname  )






						def 							_UpperCAmelCase					(     self       :			Union[str, Any]  ):
													A_ 						=		self.get_tokenizer()
													A_ 						=		self.get_feature_extractor()

													A_ 						=		SpeechaTextProcessor(tokenizer=lowerCAmelCase   ,     feature_extractor=lowerCAmelCase  )

													processor.save_pretrained(self.tmpdirname  )
													A_ 						=		SpeechaTextProcessor.from_pretrained(self.tmpdirname  )

													self.assertEqual(processor.tokenizer.get_vocab()   ,     tokenizer.get_vocab()  )
													self.assertIsInstance(processor.tokenizer   ,     lowerCAmelCase  )

													self.assertEqual(processor.feature_extractor.to_json_string()   ,     feature_extractor.to_json_string()  )
													self.assertIsInstance(processor.feature_extractor   ,     lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			Dict  ):
													A_ 						=		SpeechaTextProcessor(
													    tokenizer=self.get_tokenizer()   ,     feature_extractor=self.get_feature_extractor()  )
													processor.save_pretrained(self.tmpdirname  )

													A_ 						=		self.get_tokenizer(bos_token="(BOS)"   ,     eos_token="(EOS)"  )
													A_ 						=		self.get_feature_extractor(do_normalize=lowerCAmelCase   ,     padding_value=1.0  )

													A_ 						=		SpeechaTextProcessor.from_pretrained(
													    self.tmpdirname   ,     bos_token="(BOS)"   ,     eos_token="(EOS)"   ,     do_normalize=lowerCAmelCase   ,     padding_value=1.0  )

													self.assertEqual(processor.tokenizer.get_vocab()   ,     tokenizer_add_kwargs.get_vocab()  )
													self.assertIsInstance(processor.tokenizer   ,     lowerCAmelCase  )

													self.assertEqual(processor.feature_extractor.to_json_string()   ,     feature_extractor_add_kwargs.to_json_string()  )
													self.assertIsInstance(processor.feature_extractor   ,     lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			str  ):
													A_ 						=		self.get_feature_extractor()
													A_ 						=		self.get_tokenizer()

													A_ 						=		SpeechaTextProcessor(tokenizer=lowerCAmelCase   ,     feature_extractor=lowerCAmelCase  )

													A_ 						=		floats_list((3, 10_00)  )

													A_ 						=		feature_extractor(lowerCAmelCase   ,     return_tensors="np"  )
													A_ 						=		processor(lowerCAmelCase   ,     return_tensors="np"  )

													for key in input_feat_extract.keys():
																				self.assertAlmostEqual(input_feat_extract[key].sum()   ,     input_processor[key].sum()   ,     delta=1e-2  )






						def 							_UpperCAmelCase					(     self       :			Optional[int]  ):
													A_ 						=		self.get_feature_extractor()
													A_ 						=		self.get_tokenizer()

													A_ 						=		SpeechaTextProcessor(tokenizer=lowerCAmelCase   ,     feature_extractor=lowerCAmelCase  )

													A_ 						=		"This is a test string"

													A_ 						=		processor(text=lowerCAmelCase  )

													A_ 						=		tokenizer(lowerCAmelCase  )

													for key in encoded_tok.keys():
																				self.assertListEqual(encoded_tok[key]   ,     encoded_processor[key]  )






						def 							_UpperCAmelCase					(     self       :			int  ):
													A_ 						=		self.get_feature_extractor()
													A_ 						=		self.get_tokenizer()

													A_ 						=		SpeechaTextProcessor(tokenizer=lowerCAmelCase   ,     feature_extractor=lowerCAmelCase  )

													A_ 						=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													A_ 						=		processor.batch_decode(lowerCAmelCase  )
													A_ 						=		tokenizer.batch_decode(lowerCAmelCase  )

													self.assertListEqual(lowerCAmelCase   ,     lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			Tuple  ):
													A_ 						=		self.get_feature_extractor()
													A_ 						=		self.get_tokenizer()

													A_ 						=		SpeechaTextProcessor(tokenizer=lowerCAmelCase   ,     feature_extractor=lowerCAmelCase  )

													self.assertListEqual(
													    processor.model_input_names   ,     feature_extractor.model_input_names   ,     msg="`processor` and `feature_extractor` model input names do not match"   ,     )





