
"""simple docstring"""





import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
				from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image






class     _UpperCAmelCase:



		def __init__(					self				,		__a				,		__a=13				,		__a=64				,		__a=2				,		__a=3				,		__a=True				,		__a=True				,		__a=32				,		__a=5				,		__a=4				,		__a=37				,		__a="gelu"				,		__a=0.1				,		__a=0.1				,		__a=10				,		__a=0.02				,		__a=[1, 16, 4, 4]				,		__a=None				,		)       ->       Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  parent
							_UpperCamelCase							 =  batch_size
							_UpperCamelCase							 =  image_size
							_UpperCamelCase							 =  patch_size
							_UpperCamelCase							 =  num_channels
							_UpperCamelCase							 =  is_training
							_UpperCamelCase							 =  use_labels
							_UpperCamelCase							 =  hidden_size
							_UpperCamelCase							 =  num_hidden_layers
							_UpperCamelCase							 =  num_attention_heads
							_UpperCamelCase							 =  intermediate_size
							_UpperCamelCase							 =  hidden_act
							_UpperCamelCase							 =  hidden_dropout_prob
							_UpperCamelCase							 =  attention_probs_dropout_prob
							_UpperCamelCase							 =  type_sequence_label_size
							_UpperCamelCase							 =  initializer_range
							_UpperCamelCase							 =  scope
							_UpperCamelCase							 =  backbone_featmap_shape

							# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							# the number of patches is based on the feature map of the backbone, which by default uses an output stride
							# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
							_UpperCamelCase							 =  (self.image_size // 32) ** 2
							_UpperCamelCase							 =  num_patches + 1



		def 						UpperCAmelCase							(					self)       ->       Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

							_UpperCamelCase							 =  None
							if self.use_labels:
												_UpperCamelCase							 =  ids_tensor([self.batch_size]				,		self.type_sequence_label_size)

							_UpperCamelCase							 =  self.get_config()

							return config, pixel_values, labels



		def 						UpperCAmelCase							(					self)       ->       Tuple:






							'''simple docstring'''
							_UpperCamelCase							 =  {
							    '''global_padding''': '''same''',
							    '''layer_type''': '''bottleneck''',
							    '''depths''': [3, 4, 9],
							    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
							    '''embedding_dynamic_padding''': True,
							    '''hidden_sizes''': [4, 8, 16, 32],
							    '''num_groups''': 2,
							}

							return ViTHybridConfig(
							    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				,		is_decoder=__a				,		initializer_range=self.initializer_range				,		backbone_featmap_shape=self.backbone_featmap_shape				,		backbone_config=__a				,		)



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a)       ->       Dict:






							'''simple docstring'''
							_UpperCamelCase							 =  ViTHybridModel(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase							 =  model(__a)
							self.parent.assertEqual(result.last_hidden_state.shape				,		(self.batch_size, self.seq_length, self.hidden_size))



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a)       ->       int:






							'''simple docstring'''
							_UpperCamelCase							 =  self.type_sequence_label_size
							_UpperCamelCase							 =  ViTHybridForImageClassification(__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase							 =  model(__a				,		labels=__a)
							self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.type_sequence_label_size))




		def 						UpperCAmelCase							(					self)       ->       Tuple:






							'''simple docstring'''
							_UpperCamelCase							 =  self.prepare_config_and_inputs()
							_UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase							 =  config_and_inputs
							_UpperCamelCase							 =  {'''pixel_values''': pixel_values}
							return config, inputs_dict






@require_torch
class     _UpperCAmelCase(    lowerCamelCase      ,     lowerCamelCase      ,     unittest.TestCase	):
		lowercase__						=      (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
		lowercase__						=      (
		    {'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification}
		    if is_torch_available()
		    else {}
		)
		lowercase__						=      False
		lowercase__						=      False
		lowercase__						=      False



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






							'''simple docstring'''
							_UpperCamelCase							 =  ViTHybridModelTester(self)
							_UpperCamelCase							 =  ConfigTester(self				,		config_class=__a				,		has_text_modality=__a				,		hidden_size=37)



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






							'''simple docstring'''
							self.config_tester.run_common_tests()



		@unittest.skip(reason='''ViT does not use inputs_embeds''')
		def 						UpperCAmelCase							(					self)       ->       Tuple:






							'''simple docstring'''
							pass



		def 						UpperCAmelCase							(					self)       ->       str:






							'''simple docstring'''
							_UpperCamelCase ,  _UpperCamelCase							 =  self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												_UpperCamelCase							 =  model_class(__a)
												self.assertIsInstance(model.get_input_embeddings()				,		(nn.Module))
												_UpperCamelCase							 =  model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(__a				,		nn.Linear))



		def 						UpperCAmelCase							(					self)       ->       Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase ,  _UpperCamelCase							 =  self.model_tester.prepare_config_and_inputs_for_common()

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

												_UpperCamelCase							 =  ['''pixel_values''']
												self.assertListEqual(arg_names[:1]				,		__a)



		def 						UpperCAmelCase							(					self)       ->       int:






							'''simple docstring'''
							_UpperCamelCase							 =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__a)



		def 						UpperCAmelCase							(					self)       ->       Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__a)



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






							'''simple docstring'''
							_UpperCamelCase ,  _UpperCamelCase							 =  self.model_tester.prepare_config_and_inputs_for_common()

							_UpperCamelCase							 =  _config_zero_init(__a)
							for model_class in self.all_model_classes:
												_UpperCamelCase							 =  model_class(config=__a)
												# Skip the check for the backbone
												for name, module in model.named_modules():
																	if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																						_UpperCamelCase							 =  [F'''{name}.{key}''' for key in module.state_dict().keys()]
																						break

												for name, param in model.named_parameters():
																	if param.requires_grad:
																						if name in backbone_params:
																											continue
																						self.assertIn(
																						    ((param.data.mean() * 1e9).round() / 1e9).item()				,		[0.0, 1.0]				,		msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''				,		)




		@slow
		def 						UpperCAmelCase							(					self)       ->       Optional[int]:






							'''simple docstring'''
							for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_UpperCamelCase							 =  ViTHybridModel.from_pretrained(__a)
												self.assertIsNotNone(__a)





def       lowerCamelCase__		(			)							->		str:


					"""simple docstring"""





					_UpperCamelCase							 =  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
					return image






@require_torch
@require_vision
class     _UpperCAmelCase(    unittest.TestCase	):



		@cached_property
		def 						UpperCAmelCase							(					self)       ->       str:






							'''simple docstring'''
							return (
							    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0])
							    if is_vision_available()
							    else None
							)



		@slow
		def 						UpperCAmelCase							(					self)       ->       List[Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
							    __a)

							_UpperCamelCase							 =  self.default_image_processor
							_UpperCamelCase							 =  prepare_img()
							_UpperCamelCase							 =  image_processor(images=__a				,		return_tensors='''pt''').to(__a)

							# forward pass
							with torch.no_grad():
												_UpperCamelCase							 =  model(**__a)

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

							_UpperCamelCase							 =  torch.tensor([-1.9090, -0.4993, -0.2389]).to(__a)

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




		@slow
		@require_accelerate
		def 						UpperCAmelCase							(					self)       ->       Optional[int]:






							'''simple docstring'''
							_UpperCamelCase							 =  ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''')
							_UpperCamelCase							 =  ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384'''				,		device_map='''auto''')

							_UpperCamelCase							 =  prepare_img()

							_UpperCamelCase							 =  image_processor(images=__a				,		return_tensors='''pt''')
							_UpperCamelCase							 =  model(**__a)
							_UpperCamelCase							 =  outputs.logits
							# model predicts one of the 1000 ImageNet classes
							_UpperCamelCase							 =  logits.argmax(-1).item()

							self.assertTrue(model.config.idalabel[predicted_class_idx]				,		'''tabby, tabby cat''')








"""simple docstring"""





class     _UpperCAmelCase:



		def __init__(					self)       ->       Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  {}



		def 						UpperCAmelCase							(					self)       ->       None:






							'''simple docstring'''
							print(self.vertex)
							for i in self.vertex:
												print(__a				,		''' -> '''				,		''' -> '''.join([str(__a) for j in self.vertex[i]]))



		def 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       None:






							'''simple docstring'''
							# check if vertex is already present,
							if from_vertex in self.vertex:
												self.vertex[from_vertex].append(__a)
							else:
												# else make a new vertex
												_UpperCamelCase							 =  [to_vertex]



		def 						UpperCAmelCase							(					self)       ->       None:






							'''simple docstring'''
							# visited array for storing already visited nodes
							_UpperCamelCase							 =  [False] * len(self.vertex)

							# call the recursive helper function
							for i in range(len(self.vertex)):
												if not visited[i]:
																	self.dfs_recursive(__a				,		__a)




		def 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       None:






							'''simple docstring'''
							# mark start vertex as visited
							_UpperCamelCase							 =  True

							print(__a				,		end=''' ''')

							# Recur for all the vertices that are adjacent to this node
							for i in self.vertex:
												if not visited[i]:
																	self.dfs_recursive(__a				,		__a)


if __name__ == "__main__":
				_a														=		Graph()
				g.add_edge(0, 1)
				g.add_edge(0, 2)
				g.add_edge(1, 2)
				g.add_edge(2, 0)
				g.add_edge(2, 3)
				g.add_edge(3, 3)

				g.print_graph()
				print("""DFS:""")
				g.dfs()

				# OUTPUT:
				# 0  ->  1 -> 2
				# 1  ->  2
				# 2  ->  0 -> 3
				# 3  ->  3
				# DFS:
				#  0 1 2 3






