





"""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 			_a						:






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




										'''simple docstring'''



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






				def     snake_case     (			self		:   str							)     ->				Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

										_UpperCamelCase      : Optional[int]         =   self.get_config()

										return config, pixel_values, labels






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




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   {
										    '''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     snake_case     (			self		:   List[Any],			lowerCAmelCase__		:   Optional[Any],			lowerCAmelCase__		:   Optional[Any],			lowerCAmelCase__		:   Any							)     ->				int:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   ViTHybridModel(config=lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()
										_UpperCamelCase      : List[str]         =   model(lowerCAmelCase__							)
										self.parent.assertEqual(result.last_hidden_state.shape,			(self.batch_size, self.seq_length, self.hidden_size)							)






				def     snake_case     (			self		:   Tuple,			lowerCAmelCase__		:   Tuple,			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   Union[str, Any]							)     ->				Optional[int]:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   self.type_sequence_label_size
										_UpperCamelCase      : str         =   ViTHybridForImageClassification(lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()
										_UpperCamelCase      : Dict         =   model(lowerCAmelCase__,			labels=lowerCAmelCase__							)
										self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.type_sequence_label_size)							)






				def     snake_case     (			self		:   Dict							)     ->				Union[str, Any]:




										'''simple docstring'''



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




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






				def     snake_case     (			self		:   Union[str, Any]							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : Union[str, Any]         =   ViTHybridModelTester(self							)
										_UpperCamelCase      : List[Any]         =   ConfigTester(self,			config_class=lowerCAmelCase__,			has_text_modality=lowerCAmelCase__,			hidden_size=3_7							)






				def     snake_case     (			self		:   Dict							)     ->				Optional[int]:




										'''simple docstring'''



										self.config_tester.run_common_tests()






				@unittest.skip(reason='''ViT does not use inputs_embeds'''							)
				def     snake_case     (			self		:   List[str]							)     ->				List[str]:




										'''simple docstring'''



										pass






				def     snake_case     (			self		:   List[Any]							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase       ,							_UpperCamelCase      : Any         =   self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																_UpperCamelCase      : List[str]         =   model_class(lowerCAmelCase__							)
																self.assertIsInstance(model.get_input_embeddings(),			(nn.Module)							)
																_UpperCamelCase      : Any         =   model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(lowerCAmelCase__,			nn.Linear							)							)






				def     snake_case     (			self		:   Tuple							)     ->				Optional[int]:




										'''simple docstring'''



										_UpperCamelCase       ,							_UpperCamelCase      : int         =   self.model_tester.prepare_config_and_inputs_for_common()

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

																_UpperCamelCase      : Union[str, Any]         =   ['''pixel_values''']
																self.assertListEqual(arg_names[:1],			lowerCAmelCase__							)






				def     snake_case     (			self		:   str							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__							)






				def     snake_case     (			self		:   Tuple							)     ->				List[str]:




										'''simple docstring'''



										_UpperCamelCase      : Union[str, Any]         =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__							)






				def     snake_case     (			self		:   Any							)     ->				str:




										'''simple docstring'''



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

										_UpperCamelCase      : Union[str, Any]         =   _config_zero_init(lowerCAmelCase__							)
										for model_class in self.all_model_classes:
																_UpperCamelCase      : int         =   model_class(config=lowerCAmelCase__							)
																# Skip the check for the backbone
																for name, module in model.named_modules():
																						if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																												_UpperCamelCase      : List[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     snake_case     (			self		:   Tuple							)     ->				List[str]:




										'''simple docstring'''



										for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_UpperCamelCase      : str         =   ViTHybridModel.from_pretrained(lowerCAmelCase__							)
																self.assertIsNotNone(lowerCAmelCase__							)





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






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






				@cached_property
				def     snake_case     (			self		:   List[str]							)     ->				Dict:




										'''simple docstring'''



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






				@slow
				def     snake_case     (			self		:   Optional[Any]							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(
										    lowerCAmelCase__							)

										_UpperCamelCase      : List[str]         =   self.default_image_processor
										_UpperCamelCase      : Optional[Any]         =   prepare_img()
										_UpperCamelCase      : List[Any]         =   image_processor(images=lowerCAmelCase__,			return_tensors='''pt'''							).to(lowerCAmelCase__							)

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

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

										_UpperCamelCase      : Any         =   torch.tensor([-1.9_090, -0.4_993, -0.2_389]							).to(lowerCAmelCase__							)

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






				@slow
				@require_accelerate
				def     snake_case     (			self		:   Any							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384'''							)
										_UpperCamelCase      : int         =   ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384''',			device_map='''auto'''							)

										_UpperCamelCase      : Dict         =   prepare_img()

										_UpperCamelCase      : Any         =   image_processor(images=lowerCAmelCase__,			return_tensors='''pt'''							)
										_UpperCamelCase      : List[str]         =   model(**lowerCAmelCase__							)
										_UpperCamelCase      : Union[str, Any]         =   outputs.logits
										# model predicts one of the 1000 ImageNet classes
										_UpperCamelCase      : List[str]         =   logits.argmax(-1							).item()

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










"""simple docstring"""




import logging

from transformers.configuration_utils import PretrainedConfig


UpperCamelCase_									=logging.getLogger(__name__)




class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   '''masked_bert'''






				def __init__(			self		:   Optional[Any],			lowerCAmelCase__		:   Dict=3_0_5_2_2,			lowerCAmelCase__		:   int=7_6_8,			lowerCAmelCase__		:   Tuple=1_2,			lowerCAmelCase__		:   Optional[Any]=1_2,			lowerCAmelCase__		:   Tuple=3_0_7_2,			lowerCAmelCase__		:   Optional[int]="gelu",			lowerCAmelCase__		:   Tuple=0.1,			lowerCAmelCase__		:   Tuple=0.1,			lowerCAmelCase__		:   Any=5_1_2,			lowerCAmelCase__		:   Optional[int]=2,			lowerCAmelCase__		:   Optional[int]=0.02,			lowerCAmelCase__		:   Union[str, Any]=1e-1_2,			lowerCAmelCase__		:   Union[str, Any]=0,			lowerCAmelCase__		:   Dict="topK",			lowerCAmelCase__		:   Union[str, Any]="constant",			lowerCAmelCase__		:   Union[str, Any]=0.0,			**lowerCAmelCase__		:   Any,			)     ->				List[Any]:




										'''simple docstring'''



										super().__init__(pad_token_id=lowerCAmelCase__,			**lowerCAmelCase__							)

										_UpperCamelCase      : Optional[Any]         =   vocab_size
										_UpperCamelCase      : int         =   hidden_size
										_UpperCamelCase      : List[Any]         =   num_hidden_layers
										_UpperCamelCase      : Any         =   num_attention_heads
										_UpperCamelCase      : List[str]         =   hidden_act
										_UpperCamelCase      : Tuple         =   intermediate_size
										_UpperCamelCase      : int         =   hidden_dropout_prob
										_UpperCamelCase      : str         =   attention_probs_dropout_prob
										_UpperCamelCase      : Optional[int]         =   max_position_embeddings
										_UpperCamelCase      : str         =   type_vocab_size
										_UpperCamelCase      : Optional[Any]         =   initializer_range
										_UpperCamelCase      : List[str]         =   layer_norm_eps
										_UpperCamelCase      : int         =   pruning_method
										_UpperCamelCase      : Union[str, Any]         =   mask_init
										_UpperCamelCase      : Any         =   mask_scale



