

import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 torch import nn

					from transformers import ViTMSNForImageClassification, ViTMSNModel
					from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import ViTImageProcessor






class        A_		:
							def __init__(		self		:			int   ,SCREAMING_SNAKE_CASE__		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=1_3   ,SCREAMING_SNAKE_CASE__		:			List[str]=3_0   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=2   ,SCREAMING_SNAKE_CASE__		:			int=3   ,SCREAMING_SNAKE_CASE__		:			List[Any]=True   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=True   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=3_2   ,SCREAMING_SNAKE_CASE__		:			Optional[int]=5   ,SCREAMING_SNAKE_CASE__		:			List[str]=4   ,SCREAMING_SNAKE_CASE__		:			str=3_7   ,SCREAMING_SNAKE_CASE__		:			int="gelu"   ,SCREAMING_SNAKE_CASE__		:			str=0.1   ,SCREAMING_SNAKE_CASE__		:			List[str]=0.1   ,SCREAMING_SNAKE_CASE__		:			List[str]=1_0   ,SCREAMING_SNAKE_CASE__		:			List[Any]=0.02   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=None   ,):
								__lowerCamelCase				:				str					       = parent
								__lowerCamelCase				:				Dict					       = batch_size
								__lowerCamelCase				:				str					       = image_size
								__lowerCamelCase				:				Optional[int]					       = patch_size
								__lowerCamelCase				:				Tuple					       = num_channels
								__lowerCamelCase				:				Dict					       = is_training
								__lowerCamelCase				:				int					       = use_labels
								__lowerCamelCase				:				List[str]					       = hidden_size
								__lowerCamelCase				:				List[str]					       = num_hidden_layers
								__lowerCamelCase				:				Optional[int]					       = num_attention_heads
								__lowerCamelCase				:				str					       = intermediate_size
								__lowerCamelCase				:				List[str]					       = hidden_act
								__lowerCamelCase				:				Tuple					       = hidden_dropout_prob
								__lowerCamelCase				:				Any					       = attention_probs_dropout_prob
								__lowerCamelCase				:				List[Any]					       = type_sequence_label_size
								__lowerCamelCase				:				List[Any]					       = initializer_range
								__lowerCamelCase				:				Optional[Any]					       = scope

								# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								__lowerCamelCase				:				Any					       = (image_size // patch_size) ** 2
								__lowerCamelCase				:				str					       = num_patches + 1
							def     lowerCAmelCase     (		self		:			List[Any]):
								__lowerCamelCase				:				List[str]					       = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

								__lowerCamelCase				:				Optional[Any]					       = None
								if self.use_labels:
									__lowerCamelCase				:				Optional[Any]					       = ids_tensor([self.batch_size]   ,self.type_sequence_label_size)

								__lowerCamelCase				:				Dict					       = self.get_config()

								return config, pixel_values, labels
							def     lowerCAmelCase     (		self		:			Optional[int]):
								return ViTMSNConfig(
								    image_size=self.image_size   ,patch_size=self.patch_size   ,num_channels=self.num_channels   ,hidden_size=self.hidden_size   ,num_hidden_layers=self.num_hidden_layers   ,num_attention_heads=self.num_attention_heads   ,intermediate_size=self.intermediate_size   ,hidden_act=self.hidden_act   ,hidden_dropout_prob=self.hidden_dropout_prob   ,attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,initializer_range=self.initializer_range   ,)
							def     lowerCAmelCase     (		self		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			str   ,SCREAMING_SNAKE_CASE__		:			Any):
								__lowerCamelCase				:				int					       = ViTMSNModel(config=SCREAMING_SNAKE_CASE__)
								model.to(SCREAMING_SNAKE_CASE__)
								model.eval()
								__lowerCamelCase				:				Any					       = model(SCREAMING_SNAKE_CASE__)
								self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size))
							def     lowerCAmelCase     (		self		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			str   ,SCREAMING_SNAKE_CASE__		:			str):
								__lowerCamelCase				:				List[Any]					       = self.type_sequence_label_size
								__lowerCamelCase				:				List[Any]					       = ViTMSNForImageClassification(SCREAMING_SNAKE_CASE__)
								model.to(SCREAMING_SNAKE_CASE__)
								model.eval()
								__lowerCamelCase				:				List[str]					       = model(SCREAMING_SNAKE_CASE__   ,labels=SCREAMING_SNAKE_CASE__)
								print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}')
								print('Labels: {labels}')
								self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.type_sequence_label_size))

								# test greyscale images
								__lowerCamelCase				:				int					       = 1
								__lowerCamelCase				:				Tuple					       = ViTMSNForImageClassification(SCREAMING_SNAKE_CASE__)
								model.to(SCREAMING_SNAKE_CASE__)
								model.eval()

								__lowerCamelCase				:				int					       = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
								__lowerCamelCase				:				Union[str, Any]					       = model(SCREAMING_SNAKE_CASE__)
								self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.type_sequence_label_size))


							def     lowerCAmelCase     (		self		:			Optional[Any]):
								__lowerCamelCase				:				Optional[int]					       = self.prepare_config_and_inputs()
								__lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase				:				Optional[Any]					       = config_and_inputs
								__lowerCamelCase				:				Tuple					       = {'pixel_values': pixel_values}
								return config, inputs_dict






@require_torch
class        A_		(       SCREAMING_SNAKE_CASE			,							SCREAMING_SNAKE_CASE			,							unittest.TestCase				):
							_UpperCAmelCase    :  Optional[int]						      =      (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
							_UpperCAmelCase    :  int						      =      (
							    {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
							    if is_torch_available()
							    else {}
							)

							_UpperCAmelCase    :  Tuple						      =      False
							_UpperCAmelCase    :  Dict						      =      False
							_UpperCAmelCase    :  Optional[int]						      =      False
							_UpperCAmelCase    :  Any						      =      False
							def     lowerCAmelCase     (		self		:			Tuple):
								__lowerCamelCase				:				Union[str, Any]					       = ViTMSNModelTester(self)
								__lowerCamelCase				:				int					       = ConfigTester(self   ,config_class=SCREAMING_SNAKE_CASE__   ,has_text_modality=SCREAMING_SNAKE_CASE__   ,hidden_size=3_7)
							def     lowerCAmelCase     (		self		:			Tuple):
								self.config_tester.run_common_tests()
							@unittest.skip(reason='ViTMSN does not use inputs_embeds')
							def     lowerCAmelCase     (		self		:			List[str]):
								pass
							def     lowerCAmelCase     (		self		:			Dict):
								__lowerCamelCase       , __lowerCamelCase				:				Any					       = self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									__lowerCamelCase				:				Optional[int]					       = model_class(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(model.get_input_embeddings()   ,(nn.Module))
									__lowerCamelCase				:				str					       = model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__   ,nn.Linear))
							def     lowerCAmelCase     (		self		:			int):
								__lowerCamelCase       , __lowerCamelCase				:				Union[str, Any]					       = self.model_tester.prepare_config_and_inputs_for_common()

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

									__lowerCamelCase				:				Optional[Any]					       = ['pixel_values']
									self.assertListEqual(arg_names[:1]   ,SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			str):
								__lowerCamelCase				:				Optional[int]					       = self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			Optional[int]):
								__lowerCamelCase				:				Optional[int]					       = self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__)


							@slow
							def     lowerCAmelCase     (		self		:			List[Any]):
								for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__lowerCamelCase				:				str					       = ViTMSNModel.from_pretrained(SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)






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






@require_torch
@require_vision
class        A_		(       unittest.TestCase				):
							@cached_property
							def     lowerCAmelCase     (		self		:			Optional[Any]):
								return ViTImageProcessor.from_pretrained('facebook/vit-msn-small') if is_vision_available() else None


							@slow
							def     lowerCAmelCase     (		self		:			Optional[Any]):
								torch.manual_seed(2)
								__lowerCamelCase				:				Tuple					       = ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small').to(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				List[Any]					       = self.default_image_processor
								__lowerCamelCase				:				int					       = prepare_img()
								__lowerCamelCase				:				Optional[int]					       = image_processor(images=SCREAMING_SNAKE_CASE__   ,return_tensors='pt').to(SCREAMING_SNAKE_CASE__)

								# forward pass
								with torch.no_grad():
									__lowerCamelCase				:				List[str]					       = model(**SCREAMING_SNAKE_CASE__)

								# verify the logits
								__lowerCamelCase				:				Dict					       = torch.Size((1, 1_0_0_0))
								self.assertEqual(outputs.logits.shape   ,SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				Dict					       = torch.tensor([-0.0803, -0.4454, -0.2375]).to(SCREAMING_SNAKE_CASE__)

								self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,SCREAMING_SNAKE_CASE__   ,atol=1E-4))








import heapq as hq
import math
from collections.abc import Iterator







class       lowerCamelCase__   :





     def __init__(self		,     UpperCAmelCase       )     ->   Any:
      _lowercase							      =str(id_       )
      _lowercase							      =None
      _lowercase							      =None
      _lowercase							      =[]
      _lowercase							      ={}  # {vertex:distance}





     def __lt__(self		,     UpperCAmelCase       )     ->   List[str]:
      return self.key < other.key





     def __repr__(self       )     ->   str:
      return self.id





     def      __A					(self		,     UpperCAmelCase       )     ->   Dict:
      self.neighbors.append(UpperCAmelCase       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Optional[Any]:
      _lowercase							      =weight



def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case						)     ->			List[str]:







 """simple docstring"""


 graph[a - 1].add_neighbor(graph[b - 1]						)
 graph[b - 1].add_neighbor(graph[a - 1]						)
 # add the edges:
 graph[a - 1].add_edge(graph[b - 1]     ,						__snake_case						)
 graph[b - 1].add_edge(graph[a - 1]     ,						__snake_case						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			list:







 """simple docstring"""


 _lowercase							      =[]
 for u in graph:
  _lowercase							      =math.inf
  _lowercase							      =None
 _lowercase							      =0
 _lowercase							      =graph[:]
 while q:
  _lowercase							      =min(__snake_case						)
  q.remove(__snake_case						)
  for v in u.neighbors:
   if (v in q) and (u.edges[v.id] < v.key):
    _lowercase							      =u
    _lowercase							      =u.edges[v.id]
 for i in range(1     ,						len(__snake_case						)						):
  a.append((int(graph[i].id						) + 1, int(graph[i].pi.id						) + 1)						)
 return a
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			Iterator[tuple]:







 """simple docstring"""


 for u in graph:
  _lowercase							      =math.inf
  _lowercase							      =None
 _lowercase							      =0

 _lowercase							      =list(__snake_case						)
 hq.heapify(__snake_case						)

 while h:
  _lowercase							      =hq.heappop(__snake_case						)
  for v in u.neighbors:
   if (v in h) and (u.edges[v.id] < v.key):
    _lowercase							      =u
    _lowercase							      =u.edges[v.id]
    hq.heapify(__snake_case						)

 for i in range(1     ,						len(__snake_case						)						):
  yield (int(graph[i].id						) + 1, int(graph[i].pi.id						) + 1)
def 				UpperCAmelCase_					(							)     ->			None:







 """simple docstring"""




if __name__ == "__main__":
  import doctest

  doctest.testmod()

