"""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  _lowerCamelCase			:







				def __init__( self    :       Any			,					lowerCamelCase_    :       Optional[Any]			,					lowerCamelCase_    :       int=1_3			,					lowerCamelCase_    :       Any=6_4			,					lowerCamelCase_    :       str=2			,					lowerCamelCase_    :       Dict=3			,					lowerCamelCase_    :       Any=True			,					lowerCamelCase_    :       Union[str, Any]=True			,					lowerCamelCase_    :       Dict=3_2			,					lowerCamelCase_    :       str=5			,					lowerCamelCase_    :       List[Any]=4			,					lowerCamelCase_    :       List[str]=3_7			,					lowerCamelCase_    :       List[str]="gelu"			,					lowerCamelCase_    :       int=0.1			,					lowerCamelCase_    :       int=0.1			,					lowerCamelCase_    :       Optional[int]=1_0			,					lowerCamelCase_    :       Dict=0.02			,					lowerCamelCase_    :       List[str]=[1, 1_6, 4, 4]			,					lowerCamelCase_    :       List[str]=None			,					):

											"""simple docstring"""






											_lowercase :    Optional[int]							=							parent
											_lowercase :    Optional[int]							=							batch_size
											_lowercase :    List[Any]							=							image_size
											_lowercase :    List[str]							=							patch_size
											_lowercase :    List[Any]							=							num_channels
											_lowercase :    Dict							=							is_training
											_lowercase :    Any							=							use_labels
											_lowercase :    List[str]							=							hidden_size
											_lowercase :    List[str]							=							num_hidden_layers
											_lowercase :    List[Any]							=							num_attention_heads
											_lowercase :    Optional[Any]							=							intermediate_size
											_lowercase :    Optional[int]							=							hidden_act
											_lowercase :    int							=							hidden_dropout_prob
											_lowercase :    List[Any]							=							attention_probs_dropout_prob
											_lowercase :    List[Any]							=							type_sequence_label_size
											_lowercase :    List[Any]							=							initializer_range
											_lowercase :    Optional[Any]							=							scope
											_lowercase :    str							=							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
											_lowercase :    Optional[Any]							=							(self.image_size // 3_2) ** 2
											_lowercase :    Dict							=							num_patches + 1







				def 							__UpperCAmelCase					( self    :       int						):

											"""simple docstring"""






											_lowercase :    Optional[int]							=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

											_lowercase :    Dict							=							None
											if self.use_labels:
																		_lowercase :    List[Any]							=							ids_tensor([self.batch_size]			,					self.type_sequence_label_size						)

											_lowercase :    str							=							self.get_config()

											return config, pixel_values, labels







				def 							__UpperCAmelCase					( self    :       int						):

											"""simple docstring"""






											_lowercase :    Dict							=							{
											    '''global_padding''': '''same''',
											    '''layer_type''': '''bottleneck''',
											    '''depths''': [3, 4, 9],
											    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
											    '''embedding_dynamic_padding''': True,
											    '''hidden_sizes''': [4, 8, 1_6, 3_2],
											    '''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=lowerCamelCase_			,					initializer_range=self.initializer_range			,					backbone_featmap_shape=self.backbone_featmap_shape			,					backbone_config=lowerCamelCase_			,					)







				def 							__UpperCAmelCase					( self    :       Tuple			,					lowerCamelCase_    :       str			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       Union[str, Any]						):

											"""simple docstring"""






											_lowercase :    str							=							ViTHybridModel(config=lowerCamelCase_						)
											model.to(lowerCamelCase_						)
											model.eval()
											_lowercase :    Any							=							model(lowerCamelCase_						)
											self.parent.assertEqual(result.last_hidden_state.shape			,					(self.batch_size, self.seq_length, self.hidden_size)						)







				def 							__UpperCAmelCase					( self    :       List[str]			,					lowerCamelCase_    :       Any			,					lowerCamelCase_    :       Dict			,					lowerCamelCase_    :       Any						):

											"""simple docstring"""






											_lowercase :    Optional[Any]							=							self.type_sequence_label_size
											_lowercase :    Any							=							ViTHybridForImageClassification(lowerCamelCase_						)
											model.to(lowerCamelCase_						)
											model.eval()
											_lowercase :    int							=							model(lowerCamelCase_			,					labels=lowerCamelCase_						)
											self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.type_sequence_label_size)						)







				def 							__UpperCAmelCase					( self    :       Dict						):

											"""simple docstring"""






											_lowercase :    int							=							self.prepare_config_and_inputs()
											_lowercase :    Union[str, Any]							=							config_and_inputs
											_lowercase :    Optional[Any]							=							{'''pixel_values''': pixel_values}
											return config, inputs_dict



@require_torch
class  _lowerCamelCase			(UpperCamelCase_						,							UpperCamelCase_						,							unittest.TestCase     ):
				_snake_case     =  (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
				_snake_case     =  (
				    {"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
				    if is_torch_available()
				    else {}
				)
				_snake_case     =  False
				_snake_case     =  False
				_snake_case     =  False







				def 							__UpperCAmelCase					( self    :       List[str]						):

											"""simple docstring"""






											_lowercase :    List[Any]							=							ViTHybridModelTester(self						)
											_lowercase :    Tuple							=							ConfigTester(self			,					config_class=lowerCamelCase_			,					has_text_modality=lowerCamelCase_			,					hidden_size=3_7						)







				def 							__UpperCAmelCase					( self    :       str						):

											"""simple docstring"""






											self.config_tester.run_common_tests()







				@unittest.skip(reason='ViT does not use inputs_embeds'						)
				def 							__UpperCAmelCase					( self    :       Dict						):

											"""simple docstring"""






											pass







				def 							__UpperCAmelCase					( self    :       Any						):

											"""simple docstring"""






											_lowercase :    List[Any]							=							self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		_lowercase :    int							=							model_class(lowerCamelCase_						)
																		self.assertIsInstance(model.get_input_embeddings()			,					(nn.Module)						)
																		_lowercase :    Optional[Any]							=							model.get_output_embeddings()
																		self.assertTrue(x is None or isinstance(lowerCamelCase_			,					nn.Linear						)						)







				def 							__UpperCAmelCase					( self    :       Optional[int]						):

											"""simple docstring"""






											_lowercase :    Dict							=							self.model_tester.prepare_config_and_inputs_for_common()

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

																		_lowercase :    List[Any]							=							['''pixel_values''']
																		self.assertListEqual(arg_names[:1]			,					lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       Tuple						):

											"""simple docstring"""






											_lowercase :    Optional[Any]							=							self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       Dict						):

											"""simple docstring"""






											_lowercase :    Dict							=							self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       int						):

											"""simple docstring"""






											_lowercase :    List[str]							=							self.model_tester.prepare_config_and_inputs_for_common()

											_lowercase :    Optional[int]							=							_config_zero_init(lowerCamelCase_						)
											for model_class in self.all_model_classes:
																		_lowercase :    Dict							=							model_class(config=lowerCamelCase_						)
																		# Skip the check for the backbone
																		for name, module in model.named_modules():
																									if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																																_lowercase :    Any							=							[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    :       Dict						):

											"""simple docstring"""






											for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		_lowercase :    List[str]							=							ViTHybridModel.from_pretrained(lowerCamelCase_						)
																		self.assertIsNotNone(lowerCamelCase_						)



def      __lowerCAmelCase(		):






							"""simple docstring"""





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







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







				@cached_property
				def 							__UpperCAmelCase					( self    :       List[str]						):

											"""simple docstring"""






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







				@slow
				def 							__UpperCAmelCase					( self    :       int						):

											"""simple docstring"""






											_lowercase :    str							=							ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(
											    lowerCamelCase_						)

											_lowercase :    int							=							self.default_image_processor
											_lowercase :    Union[str, Any]							=							prepare_img()
											_lowercase :    Tuple							=							image_processor(images=lowerCamelCase_			,					return_tensors='pt'						).to(lowerCamelCase_						)

											# forward pass
											with torch.no_grad():
																		_lowercase :    Optional[int]							=							model(**lowerCamelCase_						)

											# verify the logits
											_lowercase :    int							=							torch.Size((1, 1_0_0_0)						)
											self.assertEqual(outputs.logits.shape			,					lowerCamelCase_						)

											_lowercase :    List[str]							=							torch.tensor([-1.9090, -0.4993, -0.2389]						).to(lowerCamelCase_						)

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







				@slow
				@require_accelerate
				def 							__UpperCAmelCase					( self    :       Optional[Any]						):

											"""simple docstring"""






											_lowercase :    Tuple							=							ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384'						)
											_lowercase :    Optional[int]							=							ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384'			,					device_map='auto'						)

											_lowercase :    Any							=							prepare_img()

											_lowercase :    Dict							=							image_processor(images=lowerCamelCase_			,					return_tensors='pt'						)
											_lowercase :    Dict							=							model(**lowerCamelCase_						)
											_lowercase :    Optional[int]							=							outputs.logits
											# model predicts one of the 1000 ImageNet classes
											_lowercase :    Any							=							logits.argmax(-1						).item()

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




"""simple docstring"""







def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	return not any(
	    neighbour == 1 and colored_vertices[i] == color
	    for i, neighbour in enumerate(__UpperCAmelCase			)			)


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	if index == len(__UpperCAmelCase			):
		return True

	# Recursive Step
	for i in range(__UpperCAmelCase			):
		if valid_coloring(graph[index]     ,__UpperCAmelCase     ,__UpperCAmelCase			):
			# Color current vertex
			_lowercase :    str							=							i
			# Validate coloring
			if util_color(__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,index + 1			):
				return True
			# Backtrack
			_lowercase :    List[str]							=							-1
	return False







def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Any							=							[-1] * len(__UpperCAmelCase			)

	if util_color(__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,0			):
		return colored_vertices

	return []



