"""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__    (	lowercase_     :	List[Any]      , lowercase_     :	int = 512						)  ->							List[Any]:
   __lowerCamelCase    		=					x.shape[1] if len(x.shape						) > 1 else 1
   __lowerCamelCase    		=					1 + np.arange(0      , feature_num * offset      , lowercase_      , dtype=np.intaa						)
   __lowerCamelCase    		=					x + feature_offset
   return x



def       A__    (	lowercase_     :	Optional[Any]      , lowercase_     :	Union[str, Any]=True						)  ->							str:
   requires_backends(lowercase_      , ['cython']						)

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

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

   __lowerCamelCase    		=					convert_to_single_emb(lowercase_						) + 1
   __lowerCamelCase    		=					item['num_nodes']

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

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

   __lowerCamelCase		,__lowerCamelCase    		=					algos_graphormer.floyd_warshall(lowercase_						)
   __lowerCamelCase    		=					np.amax(lowercase_						)

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

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

   return item






class      _SCREAMING_SNAKE_CASE							:



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE=20 ,				_SCREAMING_SNAKE_CASE=False	):


      '''simple docstring'''

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

      __lowerCamelCase    		=					spatial_pos_max
      __lowerCamelCase    		=					on_the_fly_processing







   def __call__(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      if self.on_the_fly_processing:
         __lowerCamelCase    		=					[preprocess_item(_SCREAMING_SNAKE_CASE	) for i in features]

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

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

      __lowerCamelCase    		=					torch.zeros(_SCREAMING_SNAKE_CASE ,				max_node_num + 1 ,				max_node_num + 1 ,				dtype=torch.float	)
      __lowerCamelCase    		=					torch.zeros(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				dtype=torch.long	)
      __lowerCamelCase    		=					torch.zeros(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				dtype=torch.long	)
      __lowerCamelCase    		=					torch.zeros(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				dtype=torch.long	)
      __lowerCamelCase    		=					torch.zeros(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				dtype=torch.long	)
      __lowerCamelCase    		=					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"]:
            __lowerCamelCase    		=					torch.tensor(f[k]	)

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

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

      __lowerCamelCase    		=					batch['in_degree']

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

      return batch









import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin


class      lowerCamelCase_ (			_lowerCAmelCase	,				unittest.TestCase):
 _lowerCAmelCase									=	SpeechaTextaTokenizer
 _lowerCAmelCase									=	False




 def     a       (			self							):
   super().setUp()

   __UpperCAmelCase	:  List[Any]		=     '<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'.split(' '							)
   __UpperCAmelCase	:  Dict		=     ['he re</w> 123', 'here a 1456']
   __UpperCAmelCase	:  Optional[int]		=     dict(zip(_UpperCAmelCase  ,  range(len(_UpperCAmelCase							)							)							)							)

   __UpperCAmelCase	:  List[Any]		=     {'pad_token': '<pad>', 'unk_token': '<unk>', 'bos_token': '<s>', 'eos_token': '</s>'}

   __UpperCAmelCase	:  Tuple		=     tempfile.mkdtemp()
   __UpperCAmelCase	:  str		=     os.path.join(self.tmpdirname  ,  VOCAB_FILES_NAMES['vocab_file']							)
   __UpperCAmelCase	:  List[Any]		=     os.path.join(self.tmpdirname  ,  VOCAB_FILES_NAMES['merges_file']							)

   with open(self.vocab_file  ,  'w'  ,  encoding='utf-8'							) as fp:
     fp.write(json.dumps(_UpperCAmelCase							) + '\n'							)

   with open(self.merges_file  ,  'w'							) as fp:
     fp.write('\n'.join(_UpperCAmelCase							)							)




 def     a       (			self							):
   __UpperCAmelCase	:  int		=     list(self.get_tokenizer().get_vocab().keys()							)

   self.assertEqual(vocab_keys[0]  ,  '<s>'							)
   self.assertEqual(vocab_keys[1]  ,  '<pad>'							)
   self.assertEqual(vocab_keys[-1]  ,  'vocab'							)
   self.assertEqual(len(_UpperCAmelCase							)  ,  14							)




 def     a       (			self							):
   self.assertEqual(self.get_tokenizer().vocab_size  ,  14							)




 def     a       (			self							):
   __UpperCAmelCase	:  int		=     SpeechaTextaTokenizer.from_pretrained(self.tmpdirname							)

   # make sure @@ is correctly concatenated
   __UpperCAmelCase	:  int		=     [4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
   __UpperCAmelCase	:  List[str]		=     tokenizer.decode(_UpperCAmelCase							)

   self.assertTrue(output_string == 'herecouple words ofthe'							)




 def     a       (			self							):
   __UpperCAmelCase	:  Union[str, Any]		=     SpeechaTextaTokenizer.from_pretrained(self.tmpdirname							)

   with tempfile.TemporaryDirectory() as tmp_dirname:
     tokenizer.save_pretrained(_UpperCAmelCase							)
     os.remove(os.path.join(_UpperCAmelCase  ,  'merges.txt'							)							)

     # load tokenizer without merges file should not throw an error
     __UpperCAmelCase	:  Optional[int]		=     SpeechaTextaTokenizer.from_pretrained(_UpperCAmelCase							)

   with tempfile.TemporaryDirectory() as tmp_dirname:
     # save tokenizer and load again
     tokenizer.save_pretrained(_UpperCAmelCase							)
     __UpperCAmelCase	:  Optional[int]		=     SpeechaTextaTokenizer.from_pretrained(_UpperCAmelCase							)

   self.assertIsNotNone(_UpperCAmelCase							)




 def     a       (			self							):
   if not self.test_slow_tokenizer:
     return

   __UpperCAmelCase	:  List[Any]		=     inspect.signature(self.tokenizer_class.__init__							)
   __UpperCAmelCase	:  List[Any]		=     self.get_tokenizer()

   for parameter_name, parameter in signature.parameters.items():
     if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
       self.assertIn(_UpperCAmelCase  ,  tokenizer.init_kwargs							)





