
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							:				Optional[int]	, snake_case							:				int = 512	) ->      str:



						'''simple docstring'''

						__UpperCamelCase       					=     x.shape[1] if len(x.shape	) > 1 else 1
						__UpperCamelCase       					=     1 + np.arange(0	, feature_num * offset	, snake_case	, dtype=np.intaa	)
						__UpperCamelCase       					=     x + feature_offset
						return x




def 		A_	(							snake_case							:				Any	, snake_case							:				int=True	) ->      Union[str, Any]:



						'''simple docstring'''

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

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

						__UpperCamelCase       					=     np.asarray(item['''edge_index''']	, dtype=np.intaa	)

						__UpperCamelCase       					=     convert_to_single_emb(snake_case	) + 1
						__UpperCamelCase       					=     item['''num_nodes''']

						if len(edge_attr.shape	) == 1:
												__UpperCamelCase       					=     edge_attr[:, None]
						__UpperCamelCase       					=     np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]	, dtype=np.intaa	)
						__UpperCamelCase       					=     convert_to_single_emb(snake_case	) + 1

						# node adj matrix [num_nodes, num_nodes] bool
						__UpperCamelCase       					=     np.zeros([num_nodes, num_nodes]	, dtype=snake_case	)
						__UpperCamelCase       					=     True

						__UpperCamelCase					,							__UpperCamelCase       					=     algos_graphormer.floyd_warshall(snake_case	)
						__UpperCamelCase       					=     np.amax(snake_case	)

						__UpperCamelCase       					=     algos_graphormer.gen_edge_input(snake_case	, snake_case	, snake_case	)
						__UpperCamelCase       					=     np.zeros([num_nodes + 1, num_nodes + 1]	, dtype=np.single	)  # with graph token

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

						return item






class     SCREAMING_SNAKE_CASE__      :

							"""simple docstring"""






							def __init__(					self       ,		SCREAMING_SNAKE_CASE_=20       ,		SCREAMING_SNAKE_CASE_=False     )->				Any:





													'''simple docstring'''






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

													__UpperCamelCase       					=     spatial_pos_max
													__UpperCamelCase       					=     on_the_fly_processing



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





													'''simple docstring'''






													if self.on_the_fly_processing:
																			__UpperCamelCase       					=     [preprocess_item(SCREAMING_SNAKE_CASE_     ) for i in features]

													if not isinstance(features[0]       ,		SCREAMING_SNAKE_CASE_     ):
																			__UpperCamelCase       					=     [vars(SCREAMING_SNAKE_CASE_     ) for f in features]
													__UpperCamelCase       					=     {}

													__UpperCamelCase       					=     max(len(i['''input_nodes''']     ) for i in features     )
													__UpperCamelCase       					=     len(features[0]['''input_nodes'''][0]     )
													__UpperCamelCase       					=     len(features[0]['''attn_edge_type'''][0][0]     )
													__UpperCamelCase       					=     max(len(i['''input_edges'''][0][0]     ) for i in features     )
													__UpperCamelCase       					=     len(features[0]['''input_edges'''][0][0][0]     )
													__UpperCamelCase       					=     len(SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     torch.zeros(SCREAMING_SNAKE_CASE_       ,		max_node_num + 1       ,		max_node_num + 1       ,		dtype=torch.float     )
													__UpperCamelCase       					=     torch.zeros(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		dtype=torch.long     )
													__UpperCamelCase       					=     torch.zeros(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		dtype=torch.long     )
													__UpperCamelCase       					=     torch.zeros(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		dtype=torch.long     )
													__UpperCamelCase       					=     torch.zeros(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		dtype=torch.long     )
													__UpperCamelCase       					=     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       					=     torch.tensor(f[k]     )

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

																			__UpperCamelCase       					=     f['''attn_bias''']
																			__UpperCamelCase       					=     f[
																			    '''attn_edge_type'''
																			]
																			__UpperCamelCase       					=     f['''spatial_pos''']
																			__UpperCamelCase       					=     f['''in_degree''']
																			__UpperCamelCase       					=     f['''input_nodes''']
																			__UpperCamelCase       					=     f['''input_edges''']

													__UpperCamelCase       					=     batch['''in_degree''']

													__UpperCamelCase       					=     features[0]['''labels''']
													if len(SCREAMING_SNAKE_CASE_     ) == 1:  # one task
																			if isinstance(sample[0]       ,		SCREAMING_SNAKE_CASE_     ):  # regression
																									__UpperCamelCase       					=     torch.from_numpy(np.concatenate([i['''labels'''] for i in features]     )     )
																			else:  # binary classification
																									__UpperCamelCase       					=     torch.from_numpy(np.concatenate([i['''labels'''] for i in features]     )     )
													else:  # multi task classification, left to float to keep the NaNs
																			__UpperCamelCase       					=     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


lowercase__							:							List[Any]													=     get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")






@require_torch
class     SCREAMING_SNAKE_CASE__      (							unittest.TestCase      ):

							"""simple docstring"""






							def       A__						(					self     )->				List[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     tempfile.mkdtemp()

													__UpperCamelCase       					=     SpeechTaTokenizer(SCREAMING_SNAKE_CASE_     )
													tokenizer.save_pretrained(self.tmpdirname     )

													__UpperCamelCase       					=     {
													    '''feature_size''': 1,
													    '''padding_value''': 0.0,
													    '''sampling_rate''': 16000,
													    '''do_normalize''': False,
													    '''num_mel_bins''': 80,
													    '''hop_length''': 16,
													    '''win_length''': 64,
													    '''win_function''': '''hann_window''',
													    '''fmin''': 80,
													    '''fmax''': 7600,
													    '''mel_floor''': 1E-10,
													    '''reduction_factor''': 2,
													    '''return_attention_mask''': True,
													}

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



							def       A__						(					self       ,		**SCREAMING_SNAKE_CASE_     )->				Union[str, Any]:





													'''simple docstring'''






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



							def       A__						(					self       ,		**SCREAMING_SNAKE_CASE_     )->				Union[str, Any]:





													'''simple docstring'''






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



							def       A__						(					self     )->				str:





													'''simple docstring'''






													shutil.rmtree(self.tmpdirname     )



							def       A__						(					self     )->				Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_tokenizer()
													__UpperCamelCase       					=     self.get_feature_extractor()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													processor.save_pretrained(self.tmpdirname     )
													__UpperCamelCase       					=     SpeechTaProcessor.from_pretrained(self.tmpdirname     )

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

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



							def       A__						(					self     )->				List[str]:





													'''simple docstring'''






													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=self.get_tokenizer()       ,		feature_extractor=self.get_feature_extractor()     )
													processor.save_pretrained(self.tmpdirname     )

													__UpperCamelCase       					=     self.get_tokenizer(bos_token='''(BOS)'''       ,		eos_token='''(EOS)'''     )
													__UpperCamelCase       					=     self.get_feature_extractor(do_normalize=SCREAMING_SNAKE_CASE_       ,		padding_value=1.0     )

													__UpperCamelCase       					=     SpeechTaProcessor.from_pretrained(
													    self.tmpdirname       ,		bos_token='''(BOS)'''       ,		eos_token='''(EOS)'''       ,		do_normalize=SCREAMING_SNAKE_CASE_       ,		padding_value=1.0     )

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

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



							def       A__						(					self     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     floats_list((3, 1000)     )

													__UpperCamelCase       					=     feature_extractor(audio=SCREAMING_SNAKE_CASE_       ,		return_tensors='''np'''     )
													__UpperCamelCase       					=     processor(audio=SCREAMING_SNAKE_CASE_       ,		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       A__						(					self     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     floats_list((3, 1000)     )

													__UpperCamelCase       					=     feature_extractor(audio_target=SCREAMING_SNAKE_CASE_       ,		return_tensors='''np'''     )
													__UpperCamelCase       					=     processor(audio_target=SCREAMING_SNAKE_CASE_       ,		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       A__						(					self     )->				int:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     '''This is a test string'''

													__UpperCamelCase       					=     processor(text=SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     tokenizer(SCREAMING_SNAKE_CASE_     )

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



							def       A__						(					self     )->				int:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     '''This is a test string'''

													__UpperCamelCase       					=     processor(text_target=SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     tokenizer(SCREAMING_SNAKE_CASE_     )

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



							def       A__						(					self     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

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

													__UpperCamelCase       					=     processor.batch_decode(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     tokenizer.batch_decode(SCREAMING_SNAKE_CASE_     )

													self.assertListEqual(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )



							def       A__						(					self     )->				List[str]:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

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




