

'''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 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Tuple  ,  SCREAMING_SNAKE_CASE_     :	List[Any] = 5_1_2    )							->   Union[str, Any]:
      """simple docstring"""

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


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	int  ,  SCREAMING_SNAKE_CASE_     :	Union[str, Any]=True    )							->   int:
      """simple docstring"""

      requires_backends(SCREAMING_SNAKE_CASE_  ,  ["cython"]    )

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

      SCREAMING_SNAKE_CASE_      :			Dict     =       np.asarray(item["edge_index"]  ,  dtype=np.intaa    )

      SCREAMING_SNAKE_CASE_      :			Optional[int]     =       convert_to_single_emb(SCREAMING_SNAKE_CASE_    ) + 1
      SCREAMING_SNAKE_CASE_      :			Any     =       item["num_nodes"]

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

      # node adj matrix [num_nodes, num_nodes] bool
      SCREAMING_SNAKE_CASE_      :			List[Any]     =       np.zeros([num_nodes, num_nodes]  ,  dtype=SCREAMING_SNAKE_CASE_    )
      SCREAMING_SNAKE_CASE_      :			List[str]     =       True

      SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_      :			Optional[int]     =       algos_graphormer.floyd_warshall(SCREAMING_SNAKE_CASE_    )
      SCREAMING_SNAKE_CASE_      :			str     =       np.amax(SCREAMING_SNAKE_CASE_    )

      SCREAMING_SNAKE_CASE_      :			str     =       algos_graphormer.gen_edge_input(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    )
      SCREAMING_SNAKE_CASE_      :			List[Any]     =       np.zeros([num_nodes + 1, num_nodes + 1]  ,  dtype=np.single    )  # with graph token

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

      return item





class 	SCREAMING_SNAKE_CASE__			:




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






          """simple docstring"""






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

          SCREAMING_SNAKE_CASE_      :			List[str]     =       spatial_pos_max
          SCREAMING_SNAKE_CASE_      :			Dict     =       on_the_fly_processing







    def __call__(					self				,     lowercase__			):






          """simple docstring"""






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

          if not isinstance(features[0]				,     lowerCamelCase_			):
                SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       [vars(lowerCamelCase_			) for f in features]
          SCREAMING_SNAKE_CASE_      :			List[str]     =       {}

          SCREAMING_SNAKE_CASE_      :			Tuple     =       max(len(i["input_nodes"]			) for i in features			)
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       len(features[0]["input_nodes"][0]			)
          SCREAMING_SNAKE_CASE_      :			Tuple     =       len(features[0]["attn_edge_type"][0][0]			)
          SCREAMING_SNAKE_CASE_      :			Any     =       max(len(i["input_edges"][0][0]			) for i in features			)
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       len(features[0]["input_edges"][0][0][0]			)
          SCREAMING_SNAKE_CASE_      :			str     =       len(lowerCamelCase_			)

          SCREAMING_SNAKE_CASE_      :			Any     =       torch.zeros(lowerCamelCase_				,     max_node_num + 1				,     max_node_num + 1				,     dtype=torch.float			)
          SCREAMING_SNAKE_CASE_      :			str     =       torch.zeros(lowerCamelCase_				,     lowerCamelCase_				,     lowerCamelCase_				,     lowerCamelCase_				,     dtype=torch.long			)
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       torch.zeros(lowerCamelCase_				,     lowerCamelCase_				,     lowerCamelCase_				,     dtype=torch.long			)
          SCREAMING_SNAKE_CASE_      :			Dict     =       torch.zeros(lowerCamelCase_				,     lowerCamelCase_				,     dtype=torch.long			)
          SCREAMING_SNAKE_CASE_      :			Any     =       torch.zeros(lowerCamelCase_				,     lowerCamelCase_				,     lowerCamelCase_				,     dtype=torch.long			)
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       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"]:
                      SCREAMING_SNAKE_CASE_      :			Tuple     =       torch.tensor(f[k]			)

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

                SCREAMING_SNAKE_CASE_      :			int     =       f["attn_bias"]
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       f[
                    "attn_edge_type"
                ]
                SCREAMING_SNAKE_CASE_      :			int     =       f["spatial_pos"]
                SCREAMING_SNAKE_CASE_      :			str     =       f["in_degree"]
                SCREAMING_SNAKE_CASE_      :			str     =       f["input_nodes"]
                SCREAMING_SNAKE_CASE_      :			Dict     =       f["input_edges"]

          SCREAMING_SNAKE_CASE_      :			int     =       batch["in_degree"]

          SCREAMING_SNAKE_CASE_      :			int     =       features[0]["labels"]
          if len(lowerCamelCase_			) == 1:  # one task
                if isinstance(sample[0]				,     lowerCamelCase_			):  # regression
                      SCREAMING_SNAKE_CASE_      :			Dict     =       torch.from_numpy(np.concatenate([i["labels"] for i in features]			)			)
                else:  # binary classification
                      SCREAMING_SNAKE_CASE_      :			Dict     =       torch.from_numpy(np.concatenate([i["labels"] for i in features]			)			)
          else:  # multi task classification, left to float to keep the NaNs
                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       torch.from_numpy(np.stack([i["labels"] for i in features]				,     axis=0			)			)

          return batch





'''simple docstring'''






import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
		from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import PoolFormerImageProcessor




class 		a__				(							a__  ):





							'''simple docstring'''






							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Union[str, Any]:
														lowerCAmelCase__          =	self.config_class(**self.inputs_dict   )
														self.parent.assertTrue(hasattr(lowerCamelCase_					,    '''hidden_sizes'''   )   )
														self.parent.assertTrue(hasattr(lowerCamelCase_					,    '''num_encoder_blocks'''   )   )




class 		a__				:





							'''simple docstring'''






							def __init__(		self					,    lowerCamelCase_					,    lowerCamelCase_=13					,    lowerCamelCase_=64					,    lowerCamelCase_=3					,    lowerCamelCase_=4					,    lowerCamelCase_=[2, 2, 2, 2]					,    lowerCamelCase_=[8, 4, 2, 1]					,    lowerCamelCase_=[16, 32, 64, 1_28]					,    lowerCamelCase_=[1, 4, 8, 16]					,    lowerCamelCase_=False					,    lowerCamelCase_=True					,    lowerCamelCase_="gelu"					,    lowerCamelCase_=0.1					,    lowerCamelCase_=0.02					,    lowerCamelCase_=3					,    lowerCamelCase_=None					,    )       ->							Any:
														lowerCAmelCase__          =	parent
														lowerCAmelCase__          =	batch_size
														lowerCAmelCase__          =	image_size
														lowerCAmelCase__          =	num_channels
														lowerCAmelCase__          =	num_encoder_blocks
														lowerCAmelCase__          =	sr_ratios
														lowerCAmelCase__          =	depths
														lowerCAmelCase__          =	hidden_sizes
														lowerCAmelCase__          =	downsampling_rates
														lowerCAmelCase__          =	is_training
														lowerCAmelCase__          =	use_labels
														lowerCAmelCase__          =	hidden_act
														lowerCAmelCase__          =	hidden_dropout_prob
														lowerCAmelCase__          =	initializer_range
														lowerCAmelCase__          =	num_labels
														lowerCAmelCase__          =	scope







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Optional[int]:
														lowerCAmelCase__          =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

														lowerCAmelCase__          =	None
														if self.use_labels:
																					lowerCAmelCase__          =	ids_tensor([self.batch_size, self.image_size, self.image_size]					,    self.num_labels   )

														lowerCAmelCase__          =	PoolFormerConfig(
														    image_size=self.image_size					,    num_channels=self.num_channels					,    num_encoder_blocks=self.num_encoder_blocks					,    depths=self.depths					,    hidden_sizes=self.hidden_sizes					,    hidden_act=self.hidden_act					,    hidden_dropout_prob=self.hidden_dropout_prob					,    initializer_range=self.initializer_range					,    )

														return config, pixel_values, labels







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Any:
														lowerCAmelCase__          =	PoolFormerModel(config=lowerCamelCase_   )
														model.to(lowerCamelCase_   )
														model.eval()
														lowerCAmelCase__          =	model(lowerCamelCase_   )
														lowerCAmelCase__          =	lowerCAmelCase__          =	self.image_size // 32.0
														self.parent.assertEqual(
														    result.last_hidden_state.shape					,    (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							str:
														lowerCAmelCase__          =	self.prepare_config_and_inputs()
														lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__          =	config_and_inputs
														lowerCAmelCase__          =	{'''pixel_values''': pixel_values}
														return config, inputs_dict




@require_torch
class 		a__				(							a__				,				a__				,				unittest.TestCase  ):





							'''simple docstring'''






							lowercase__			:   Dict							=      (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
							lowercase__			:   Any							=      (
							    {"feature-extraction": PoolFormerModel, "image-classification": PoolFormerForImageClassification}
							    if is_torch_available()
							    else {}
							)

							lowercase__			:   Optional[int]							=      False
							lowercase__			:   Optional[int]							=      False
							lowercase__			:   Union[str, Any]							=      False
							lowercase__			:   List[Any]							=      False
							lowercase__			:   Tuple							=      False







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Union[str, Any]:
														lowerCAmelCase__          =	PoolFormerModelTester(self   )
														lowerCAmelCase__          =	PoolFormerConfigTester(self					,    config_class=lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[Any]:
														self.config_tester.run_common_tests()







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[Any]:
														lowerCAmelCase__          =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCamelCase_   )







							@unittest.skip('''PoolFormer does not use inputs_embeds'''   )
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							str:
														pass







							@unittest.skip('''PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'''   )
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Tuple:
														pass







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							int:
														def check_hidden_states_output(lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   ):
																					lowerCAmelCase__          =	model_class(lowerCamelCase_   )
																					model.to(lowerCamelCase_   )
																					model.eval()

																					with torch.no_grad():
																												lowerCAmelCase__          =	model(**self._prepare_for_class(lowerCamelCase_					,    lowerCamelCase_   )   )

																					lowerCAmelCase__          =	outputs.hidden_states

																					lowerCAmelCase__          =	self.model_tester.num_encoder_blocks
																					self.assertEqual(len(lowerCamelCase_   )					,    lowerCamelCase_   )

																					# verify the first hidden states (first block)
																					self.assertListEqual(
																					    list(hidden_states[0].shape[-3:]   )					,    [
																					        self.model_tester.hidden_sizes[0],
																					        self.model_tester.image_size // 4,
																					        self.model_tester.image_size // 4,
																					    ]					,    )

														lowerCAmelCase__   ,					lowerCAmelCase__          =	self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCAmelCase__          =	True
																					check_hidden_states_output(lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   )

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					lowerCAmelCase__          =	True

																					check_hidden_states_output(lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							int:
														if not self.model_tester.is_training:
																					return

														lowerCAmelCase__   ,					lowerCAmelCase__          =	self.model_tester.prepare_config_and_inputs_for_common()
														lowerCAmelCase__          =	True

														for model_class in self.all_model_classes:
																					if model_class in get_values(lowerCamelCase_   ):
																												continue
																					lowerCAmelCase__          =	model_class(lowerCamelCase_   )
																					model.to(lowerCamelCase_   )
																					model.train()
																					lowerCAmelCase__          =	self._prepare_for_class(lowerCamelCase_					,    lowerCamelCase_					,    return_labels=lowerCamelCase_   )
																					lowerCAmelCase__          =	model(**lowerCamelCase_   ).loss
																					loss.backward()







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							int:
														lowerCAmelCase__   ,					lowerCAmelCase__          =	self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCAmelCase__          =	model_class(lowerCamelCase_   )
																					lowerCAmelCase__          =	inspect.signature(model.forward   )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					lowerCAmelCase__          =	[*signature.parameters.keys()]

																					lowerCAmelCase__          =	['''pixel_values''']
																					self.assertListEqual(arg_names[:1]					,    lowerCamelCase_   )







							@slow
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							int:
														for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCAmelCase__          =	PoolFormerModel.from_pretrained(lowerCamelCase_   )
																					self.assertIsNotNone(lowerCamelCase_   )


def 					_snake_case   (     )						->    Optional[int]:
							lowerCAmelCase__          =	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
							return image






@require_torch
class 		a__				(							unittest.TestCase  ):





							'''simple docstring'''






							@slow
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Dict:
														lowerCAmelCase__          =	PoolFormerImageProcessor()
														lowerCAmelCase__          =	PoolFormerForImageClassification.from_pretrained('''sail/poolformer_s12'''   ).to(lowerCamelCase_   )

														lowerCAmelCase__          =	image_processor(images=prepare_img()					,    return_tensors='''pt'''   ).to(lowerCamelCase_   )

														# forward pass
														with torch.no_grad():
																					lowerCAmelCase__          =	model(**lowerCamelCase_   )

														# verify the logits
														lowerCAmelCase__          =	torch.Size((1, 10_00)   )
														self.assertEqual(outputs.logits.shape					,    lowerCamelCase_   )

														lowerCAmelCase__          =	torch.tensor([-0.6_113, 0.1_685, -0.0_492]   ).to(lowerCamelCase_   )
														self.assertTrue(torch.allclose(outputs.logits[0, :3]					,    lowerCamelCase_					,    atol=1e-4   )   )