


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	(					SCREAMING_SNAKE_CASE						:					Any     ,			SCREAMING_SNAKE_CASE						:					int = 512  ):







 """simple docstring"""



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

def       _a	(					SCREAMING_SNAKE_CASE						:					Any     ,			SCREAMING_SNAKE_CASE						:					List[str]=True  ):







 """simple docstring"""



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

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

 UpperCamelCase__       :     Dict 							= np.asarray(item['''edge_index''']     ,			dtype=np.intaa  )

 UpperCamelCase__       :     List[str] 							= convert_to_single_emb(_a  ) + 1
 UpperCamelCase__       :     int 							= item['''num_nodes''']

 if len(edge_attr.shape  ) == 1:
  UpperCamelCase__       :     Any 							= edge_attr[:, None]
 UpperCamelCase__       :     List[Any] 							= np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]     ,			dtype=np.intaa  )
 UpperCamelCase__       :     Optional[Any] 							= convert_to_single_emb(_a  ) + 1

 # node adj matrix [num_nodes, num_nodes] bool
 UpperCamelCase__       :     Tuple 							= np.zeros([num_nodes, num_nodes]     ,			dtype=_a  )
 UpperCamelCase__       :     Any 							= True

 UpperCamelCase__							,					UpperCamelCase__       :     int 							= algos_graphormer.floyd_warshall(_a  )
 UpperCamelCase__       :     str 							= np.amax(_a  )

 UpperCamelCase__       :     Tuple 							= algos_graphormer.gen_edge_input(_a     ,			_a     ,			_a  )
 UpperCamelCase__       :     Union[str, Any] 							= np.zeros([num_nodes + 1, num_nodes + 1]     ,			dtype=np.single  )  # with graph token

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

 return item






class    __magic_name__						:


     def __init__(   self				:  int				,						lowerCamelCase__				:  Optional[int]=20				,						lowerCamelCase__				:  Any=False      )	->   Tuple:





      '''simple docstring'''






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

      UpperCamelCase__       :     Any 							= spatial_pos_max
      UpperCamelCase__       :     Dict 							= on_the_fly_processing


     def __call__(   self				:  Union[str, Any]				,						lowerCamelCase__				:  List[dict]      )	->   Dict[str, Any]:





      '''simple docstring'''






      if self.on_the_fly_processing:
       UpperCamelCase__       :     Optional[int] 							= [preprocess_item(lowerCAmelCase_      ) for i in features]

      if not isinstance(features[0]				,						lowerCAmelCase_      ):
       UpperCamelCase__       :     List[str] 							= [vars(lowerCAmelCase_      ) for f in features]
      UpperCamelCase__       :     Optional[Any] 							= {}

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

      UpperCamelCase__       :     str 							= torch.zeros(lowerCAmelCase_				,						max_node_num + 1				,						max_node_num + 1				,						dtype=torch.float      )
      UpperCamelCase__       :     List[Any] 							= torch.zeros(lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						dtype=torch.long      )
      UpperCamelCase__       :     Union[str, Any] 							= torch.zeros(lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						dtype=torch.long      )
      UpperCamelCase__       :     List[str] 							= torch.zeros(lowerCAmelCase_				,						lowerCAmelCase_				,						dtype=torch.long      )
      UpperCamelCase__       :     Any 							= torch.zeros(lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						dtype=torch.long      )
      UpperCamelCase__       :     List[str] 							= torch.zeros(
          lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						dtype=torch.long      )

      for ix, f in enumerate(lowerCAmelCase_      ):
       for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
        UpperCamelCase__       :     Dict 							= 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__       :     List[str] 							= f['''attn_bias''']
       UpperCamelCase__       :     Optional[Any] 							= f[
           '''attn_edge_type'''
       ]
       UpperCamelCase__       :     Optional[int] 							= f['''spatial_pos''']
       UpperCamelCase__       :     Tuple 							= f['''in_degree''']
       UpperCamelCase__       :     Any 							= f['''input_nodes''']
       UpperCamelCase__       :     str 							= f['''input_edges''']

      UpperCamelCase__       :     Any 							= batch['''in_degree''']

      UpperCamelCase__       :     List[str] 							= features[0]['''labels''']
      if len(lowerCAmelCase_      ) == 1:  # one task
       if isinstance(sample[0]				,						lowerCAmelCase_      ):  # regression
        UpperCamelCase__       :     Tuple 							= torch.from_numpy(np.concatenate([i['''labels'''] for i in features]      )      )
       else:  # binary classification
        UpperCamelCase__       :     List[Any] 							= 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 shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
     from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
     A							     = False

if is_vision_available():
     from PIL import Image

     from transformers import (
         AutoProcessor,
         PixaStructImageProcessor,
         PixaStructProcessor,
         PreTrainedTokenizerFast,
         TaTokenizer,
     )



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11       ,				reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'       ,				)
@require_vision
@require_torch
class 			lowerCAmelCase__ (							unittest.TestCase							):



  '''simple docstring'''






  def 		_lowerCamelCase						(self	:			Tuple     ) ->							Tuple:
        snake_case_    							=				tempfile.mkdtemp()

        snake_case_    							=				PixaStructImageProcessor()
        snake_case_    							=				TaTokenizer.from_pretrained("t5-small"     )

        snake_case_    							=				PixaStructProcessor(lowerCAmelCase_						,			lowerCAmelCase_     )

        processor.save_pretrained(self.tmpdirname     )

  def 		_lowerCamelCase						(self	:			List[str]						,			**lowerCAmelCase_	:			int     ) ->							Optional[Any]:
        return AutoProcessor.from_pretrained(self.tmpdirname						,			**lowerCAmelCase_     ).tokenizer

  def 		_lowerCamelCase						(self	:			Union[str, Any]						,			**lowerCAmelCase_	:			List[str]     ) ->							List[Any]:
        return AutoProcessor.from_pretrained(self.tmpdirname						,			**lowerCAmelCase_     ).image_processor

  def 		_lowerCamelCase						(self	:			int     ) ->							List[str]:
        shutil.rmtree(self.tmpdirname     )

  def 		_lowerCamelCase						(self	:			Any     ) ->							Optional[Any]:
        snake_case_    							=				[np.random.randint(2_55						,			size=(3, 30, 4_00)						,			dtype=np.uinta     )]

        snake_case_    							=				[Image.fromarray(np.moveaxis(lowerCAmelCase_						,			0						,			-1     )     ) for x in image_inputs]

        return image_inputs

  def 		_lowerCamelCase						(self	:			Union[str, Any]     ) ->							Dict:
        snake_case_    							=				PixaStructProcessor(tokenizer=self.get_tokenizer()						,			image_processor=self.get_image_processor()     )
        processor.save_pretrained(self.tmpdirname     )

        snake_case_    							=				self.get_tokenizer(bos_token="(BOS)"						,			eos_token="(EOS)"     )
        snake_case_    							=				self.get_image_processor(do_normalize=lowerCAmelCase_						,			padding_value=1.0     )

        snake_case_    							=				PixaStructProcessor.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.image_processor.to_json_string()						,			image_processor_add_kwargs.to_json_string()     )
        self.assertIsInstance(processor.image_processor						,			lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Dict     ) ->							Optional[int]:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				self.prepare_image_inputs()

        snake_case_    							=				image_processor(lowerCAmelCase_						,			return_tensors="np"     )
        snake_case_    							=				processor(images=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 		_lowerCamelCase						(self	:			Tuple     ) ->							List[str]:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				"lower newer"

        snake_case_    							=				processor(text=lowerCAmelCase_     )

        snake_case_    							=				tokenizer(lowerCAmelCase_						,			return_token_type_ids=lowerCAmelCase_						,			add_special_tokens=lowerCAmelCase_     )

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

  def 		_lowerCamelCase						(self	:			str     ) ->							List[str]:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				"lower newer"
        snake_case_    							=				self.prepare_image_inputs()

        snake_case_    							=				processor(text=lowerCAmelCase_						,			images=lowerCAmelCase_     )

        self.assertListEqual(
            list(inputs.keys()     )						,			["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]     )

        # test if it raises when no input is passed
        with pytest.raises(lowerCAmelCase_     ):
              processor()

  def 		_lowerCamelCase						(self	:			Optional[Any]     ) ->							Any:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				"lower newer"
        snake_case_    							=				self.prepare_image_inputs()

        snake_case_    							=				processor(text=lowerCAmelCase_						,			images=lowerCAmelCase_     )

        snake_case_    							=				[5_12, 10_24, 20_48, 40_96]
        snake_case_    							=				[7_70, 7_70, 7_70, 7_70]
        # with text
        for i, max_patch in enumerate(lowerCAmelCase_     ):
              snake_case_    							=				processor(text=lowerCAmelCase_						,			images=lowerCAmelCase_						,			max_patches=lowerCAmelCase_     )
              self.assertEqual(inputs["flattened_patches"][0].shape[0]						,			lowerCAmelCase_     )
              self.assertEqual(inputs["flattened_patches"][0].shape[1]						,			expected_hidden_size[i]     )

        # without text input
        for i, max_patch in enumerate(lowerCAmelCase_     ):
              snake_case_    							=				processor(images=lowerCAmelCase_						,			max_patches=lowerCAmelCase_     )
              self.assertEqual(inputs["flattened_patches"][0].shape[0]						,			lowerCAmelCase_     )
              self.assertEqual(inputs["flattened_patches"][0].shape[1]						,			expected_hidden_size[i]     )

  def 		_lowerCamelCase						(self	:			int     ) ->							Any:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

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

        snake_case_    							=				processor.batch_decode(lowerCAmelCase_     )
        snake_case_    							=				tokenizer.batch_decode(lowerCAmelCase_     )

        self.assertListEqual(lowerCAmelCase_						,			lowerCAmelCase_     )


  def 		_lowerCamelCase						(self	:			Optional[int]     ) ->							str:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				"lower newer"
        snake_case_    							=				self.prepare_image_inputs()

        snake_case_    							=				processor(text=lowerCAmelCase_						,			images=lowerCAmelCase_     )

        # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
        self.assertListEqual(
            list(inputs.keys()     )						,			["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]     )

        snake_case_    							=				processor(text=lowerCAmelCase_     )

        # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
        self.assertListEqual(list(inputs.keys()     )						,			["input_ids", "attention_mask"]     )

