



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		( _UpperCAmelCase				:   Optional[int]					,  _UpperCAmelCase				:   int = 5_12		):







		'''simple docstring'''





		__UpperCAmelCase       :	Optional[Any]						=      x.shape[1] if len(x.shape		) > 1 else 1
		__UpperCAmelCase       :	List[str]						=      1 + np.arange(0					,  feature_num * offset					,  _UpperCAmelCase					,  dtype=np.intaa		)
		__UpperCAmelCase       :	List[str]						=      x + feature_offset
		return x

def 			a		( _UpperCAmelCase				:   Union[str, Any]					,  _UpperCAmelCase				:   Tuple=True		):







		'''simple docstring'''





		requires_backends(_UpperCAmelCase					,  ['''cython''']		)

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

		__UpperCAmelCase       :	int						=      np.asarray(item['''edge_index''']					,  dtype=np.intaa		)

		__UpperCAmelCase       :	Dict						=      convert_to_single_emb(_UpperCAmelCase		) + 1
		__UpperCAmelCase       :	Any						=      item['''num_nodes''']

		if len(edge_attr.shape		) == 1:
				__UpperCAmelCase       :	Optional[int]						=      edge_attr[:, None]
		__UpperCAmelCase       :	Union[str, Any]						=      np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]					,  dtype=np.intaa		)
		__UpperCAmelCase       :	Tuple						=      convert_to_single_emb(_UpperCAmelCase		) + 1

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

		__UpperCAmelCase				,   __UpperCAmelCase       :	Tuple						=      algos_graphormer.floyd_warshall(_UpperCAmelCase		)
		__UpperCAmelCase       :	Tuple						=      np.amax(_UpperCAmelCase		)

		__UpperCAmelCase       :	Any						=      algos_graphormer.gen_edge_input(_UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase		)
		__UpperCAmelCase       :	Optional[int]						=      np.zeros([num_nodes + 1, num_nodes + 1]					,  dtype=np.single		)  # with graph token

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

		return item

class 						UpperCAmelCase__     :




			'''simple docstring'''






			def __init__(  self				:					Optional[int]			,			a_				:					Optional[int]=20			,			a_				:					Optional[Any]=False					):
					'''simple docstring'''





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

					__UpperCAmelCase       :	int						=      spatial_pos_max
					__UpperCAmelCase       :	Any						=      on_the_fly_processing




			def __call__(  self				:					Optional[int]			,			a_				:					List[dict]					):
					'''simple docstring'''





					if self.on_the_fly_processing:
							__UpperCAmelCase       :	str						=      [preprocess_item(a_					) for i in features]

					if not isinstance(features[0]			,			a_					):
							__UpperCAmelCase       :	Tuple						=      [vars(a_					) for f in features]
					__UpperCAmelCase       :	Tuple						=      {}

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

					__UpperCAmelCase       :	Tuple						=      torch.zeros(a_			,			max_node_num + 1			,			max_node_num + 1			,			dtype=torch.float					)
					__UpperCAmelCase       :	int						=      torch.zeros(a_			,			a_			,			a_			,			a_			,			dtype=torch.long					)
					__UpperCAmelCase       :	List[Any]						=      torch.zeros(a_			,			a_			,			a_			,			dtype=torch.long					)
					__UpperCAmelCase       :	int						=      torch.zeros(a_			,			a_			,			dtype=torch.long					)
					__UpperCAmelCase       :	str						=      torch.zeros(a_			,			a_			,			a_			,			dtype=torch.long					)
					__UpperCAmelCase       :	int						=      torch.zeros(
					    a_			,			a_			,			a_			,			a_			,			a_			,			dtype=torch.long					)

					for ix, f in enumerate(a_					):
							for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
									__UpperCAmelCase       :	List[Any]						=      torch.tensor(f[k]					)

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

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

					__UpperCAmelCase       :	Dict						=      batch['''in_degree''']

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

					return batch










import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
				from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

				from .test_feature_extraction_speechta import floats_list


__A       =get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")

@require_torch
class 						UpperCAmelCase__     (     unittest.TestCase  ):




			'''simple docstring'''






			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





					__UpperCAmelCase       :	str						=      tempfile.mkdtemp()

					__UpperCAmelCase       :	Optional[int]						=      SpeechTaTokenizer(a_					)
					tokenizer.save_pretrained(self.tmpdirname					)

					__UpperCAmelCase       :	Union[str, Any]						=      {
					    '''feature_size''': 1,
					    '''padding_value''': 0.0,
					    '''sampling_rate''': 1_60_00,
					    '''do_normalize''': False,
					    '''num_mel_bins''': 80,
					    '''hop_length''': 16,
					    '''win_length''': 64,
					    '''win_function''': '''hann_window''',
					    '''fmin''': 80,
					    '''fmax''': 76_00,
					    '''mel_floor''': 1e-10,
					    '''reduction_factor''': 2,
					    '''return_attention_mask''': True,
					}

					__UpperCAmelCase       :	Tuple						=      os.path.join(self.tmpdirname			,			a_					)
					with open(self.feature_extraction_file			,			'''w'''			,			encoding='''utf-8'''					) as fp:
							fp.write(json.dumps(a_					) + '''\n'''					)




			def 					snake_case__	(  self				:					Any			,			**a_				:					Tuple					):
					'''simple docstring'''





					return SpeechTaTokenizer.from_pretrained(self.tmpdirname			,			**a_					)




			def 					snake_case__	(  self				:					Dict			,			**a_				:					str					):
					'''simple docstring'''





					return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname			,			**a_					)




			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





					shutil.rmtree(self.tmpdirname					)




			def 					snake_case__	(  self				:					Dict					):
					'''simple docstring'''





					__UpperCAmelCase       :	int						=      self.get_tokenizer()
					__UpperCAmelCase       :	str						=      self.get_feature_extractor()

					__UpperCAmelCase       :	List[Any]						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

					processor.save_pretrained(self.tmpdirname					)
					__UpperCAmelCase       :	List[str]						=      SpeechTaProcessor.from_pretrained(self.tmpdirname					)

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

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




			def 					snake_case__	(  self				:					int					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[int]						=      SpeechTaProcessor(tokenizer=self.get_tokenizer()			,			feature_extractor=self.get_feature_extractor()					)
					processor.save_pretrained(self.tmpdirname					)

					__UpperCAmelCase       :	Optional[Any]						=      self.get_tokenizer(bos_token='''(BOS)'''			,			eos_token='''(EOS)'''					)
					__UpperCAmelCase       :	List[Any]						=      self.get_feature_extractor(do_normalize=a_			,			padding_value=1.0					)

					__UpperCAmelCase       :	List[str]						=      SpeechTaProcessor.from_pretrained(
					    self.tmpdirname			,			bos_token='''(BOS)'''			,			eos_token='''(EOS)'''			,			do_normalize=a_			,			padding_value=1.0					)

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

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




			def 					snake_case__	(  self				:					Union[str, Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[Any]						=      self.get_feature_extractor()
					__UpperCAmelCase       :	Union[str, Any]						=      self.get_tokenizer()

					__UpperCAmelCase       :	List[Any]						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

					__UpperCAmelCase       :	List[Any]						=      floats_list((3, 10_00)					)

					__UpperCAmelCase       :	Tuple						=      feature_extractor(audio=a_			,			return_tensors='''np'''					)
					__UpperCAmelCase       :	List[str]						=      processor(audio=a_			,			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 					snake_case__	(  self				:					Optional[Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	List[Any]						=      self.get_feature_extractor()
					__UpperCAmelCase       :	Dict						=      self.get_tokenizer()

					__UpperCAmelCase       :	Optional[Any]						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

					__UpperCAmelCase       :	Union[str, Any]						=      floats_list((3, 10_00)					)

					__UpperCAmelCase       :	Dict						=      feature_extractor(audio_target=a_			,			return_tensors='''np'''					)
					__UpperCAmelCase       :	str						=      processor(audio_target=a_			,			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 					snake_case__	(  self				:					Union[str, Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Union[str, Any]						=      self.get_feature_extractor()
					__UpperCAmelCase       :	Optional[int]						=      self.get_tokenizer()

					__UpperCAmelCase       :	Optional[Any]						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

					__UpperCAmelCase       :	List[str]						=      '''This is a test string'''

					__UpperCAmelCase       :	List[Any]						=      processor(text=a_					)
					__UpperCAmelCase       :	Optional[int]						=      tokenizer(a_					)

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




			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[Any]						=      self.get_feature_extractor()
					__UpperCAmelCase       :	Optional[Any]						=      self.get_tokenizer()

					__UpperCAmelCase       :	List[Any]						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

					__UpperCAmelCase       :	List[str]						=      '''This is a test string'''

					__UpperCAmelCase       :	Optional[Any]						=      processor(text_target=a_					)
					__UpperCAmelCase       :	Optional[int]						=      tokenizer(a_					)

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




			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





					__UpperCAmelCase       :	int						=      self.get_feature_extractor()
					__UpperCAmelCase       :	Union[str, Any]						=      self.get_tokenizer()

					__UpperCAmelCase       :	int						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

					__UpperCAmelCase       :	Optional[Any]						=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					__UpperCAmelCase       :	Optional[Any]						=      processor.batch_decode(a_					)
					__UpperCAmelCase       :	Any						=      tokenizer.batch_decode(a_					)

					self.assertListEqual(a_			,			a_					)




			def 					snake_case__	(  self				:					str					):
					'''simple docstring'''





					__UpperCAmelCase       :	str						=      self.get_feature_extractor()
					__UpperCAmelCase       :	str						=      self.get_tokenizer()

					__UpperCAmelCase       :	Optional[Any]						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

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





