



import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory



def 		lowerCAmelCase__    (					)->  Any:
    raise RuntimeError('''CUDA out of memory.'''	)



class     _UpperCAmelCase			(       nn.Module     ):




     def __init__(				self):
         super().__init__()
         A__		 =					nn.Linear(3 ,						4)
         A__		 =					nn.BatchNormad(4)
         A__		 =					nn.Linear(4 ,						5)




     def 		snake_case_		(				self ,						a__):
         return self.lineara(self.batchnorm(self.lineara(UpperCamelCase__)))







class     _UpperCAmelCase			(       unittest.TestCase     ):




     def 		snake_case_		(				self):
         A__		 =					[]

         @find_executable_batch_size(starting_batch_size=1_2_8)
         def mock_training_loop_function(a__):
             nonlocal batch_sizes
             batch_sizes.append(UpperCamelCase__)
             if batch_size != 8:
                 raise_fake_out_of_memory()

         mock_training_loop_function()
         self.assertListEqual(UpperCamelCase__ ,						[1_2_8, 6_4, 3_2, 1_6, 8])




     def 		snake_case_		(				self):
         A__		 =					[]

         @find_executable_batch_size(starting_batch_size=1_2_8)
         def mock_training_loop_function(a__ ,						a__):
             nonlocal batch_sizes
             batch_sizes.append(UpperCamelCase__)
             if batch_size != 8:
                 raise_fake_out_of_memory()
             return batch_size, arga

         A__		 =					mock_training_loop_function('''hello''')
         self.assertListEqual(UpperCamelCase__ ,						[1_2_8, 6_4, 3_2, 1_6, 8])
         self.assertListEqual([bs, arga] ,						[8, '''hello'''])




     def 		snake_case_		(				self):





         @find_executable_batch_size(starting_batch_size=0)
         def mock_training_loop_function(a__):
             pass

         with self.assertRaises(UpperCamelCase__) as cm:
             mock_training_loop_function()
             self.assertIn('''No executable batch size found, reached zero.''' ,						cm.exception.args[0])




     def 		snake_case_		(				self):





         @find_executable_batch_size(starting_batch_size=1_6)
         def mock_training_loop_function(a__):
             if batch_size > 0:
                 raise_fake_out_of_memory()
             pass

         with self.assertRaises(UpperCamelCase__) as cm:
             mock_training_loop_function()
             self.assertIn('''No executable batch size found, reached zero.''' ,						cm.exception.args[0])




     def 		snake_case_		(				self):





         @find_executable_batch_size(starting_batch_size=1_2_8)
         def mock_training_loop_function(a__ ,						a__ ,						a__):
             if batch_size != 8:
                 raise raise_fake_out_of_memory()

         with self.assertRaises(UpperCamelCase__) as cm:
             mock_training_loop_function(1_2_8 ,						'''hello''' ,						'''world''')
             self.assertIn('''Batch size was passed into `f`''' ,						cm.exception.args[0])
             self.assertIn('''`f(arg1=\'hello\', arg2=\'world\')''' ,						cm.exception.args[0])




     def 		snake_case_		(				self):





         @find_executable_batch_size(starting_batch_size=1_6)
         def mock_training_loop_function(a__):
             raise ValueError('''Oops, we had an error!''')

         with self.assertRaises(UpperCamelCase__) as cm:
             mock_training_loop_function()
             self.assertIn('''Oops, we had an error!''' ,						cm.exception.args[0])




     @require_cuda
     def 		snake_case_		(				self):
         A__		 =					torch.cuda.memory_allocated()
         A__		 =					ModelForTest()
         model.cuda()
         self.assertGreater(torch.cuda.memory_allocated() ,						UpperCamelCase__)
         A__		 =					release_memory(UpperCamelCase__)
         self.assertEqual(torch.cuda.memory_allocated() ,						UpperCamelCase__)





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__=1_3 ,						a__=6_4 ,						a__=2 ,						a__=3 ,						a__=True ,						a__=True ,						a__=3_2 ,						a__=5 ,						a__=4 ,						a__=3_7 ,						a__="gelu" ,						a__=0.1 ,						a__=0.1 ,						a__=1_0 ,						a__=0.0_2 ,						a__=[1, 1_6, 4, 4] ,						a__=None ,						):
         A__		 =					parent
         A__		 =					batch_size
         A__		 =					image_size
         A__		 =					patch_size
         A__		 =					num_channels
         A__		 =					is_training
         A__		 =					use_labels
         A__		 =					hidden_size
         A__		 =					num_hidden_layers
         A__		 =					num_attention_heads
         A__		 =					intermediate_size
         A__		 =					hidden_act
         A__		 =					hidden_dropout_prob
         A__		 =					attention_probs_dropout_prob
         A__		 =					type_sequence_label_size
         A__		 =					initializer_range
         A__		 =					scope
         A__		 =					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
         A__		 =					(self.image_size // 3_2) ** 2
         A__		 =					num_patches + 1




     def 		snake_case_		(				self):
         A__		 =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

         A__		 =					self.get_config()

         return config, pixel_values, labels




     def 		snake_case_		(				self):
         A__		 =					{
             '''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=a__ ,						initializer_range=self.initializer_range ,						backbone_featmap_shape=self.backbone_featmap_shape ,						backbone_config=a__ ,						)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__):
         A__		 =					ViTHybridModel(config=a__)
         model.to(a__)
         model.eval()
         A__		 =					model(a__)
         self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.hidden_size))




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__):
         A__		 =					self.type_sequence_label_size
         A__		 =					ViTHybridForImageClassification(a__)
         model.to(a__)
         model.eval()
         A__		 =					model(a__ ,						labels=a__)
         self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.type_sequence_label_size))




     def 		snake_case_		(				self):
         A__		 =					self.prepare_config_and_inputs()
         A__ ,	A__ ,	A__		 =					config_and_inputs
         A__		 =					{'''pixel_values''': pixel_values}
         return config, inputs_dict







@require_torch
class     _UpperCAmelCase			(       A__			,      A__			,      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):
         A__		 =					ViTHybridModelTester(self)
         A__		 =					ConfigTester(self ,						config_class=a__ ,						has_text_modality=a__ ,						hidden_size=3_7)




     def 		snake_case_		(				self):
         self.config_tester.run_common_tests()




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




     def 		snake_case_		(				self):
         A__ ,	A__		 =					self.model_tester.prepare_config_and_inputs_for_common()

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




     def 		snake_case_		(				self):
         A__ ,	A__		 =					self.model_tester.prepare_config_and_inputs_for_common()

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

             A__		 =					['''pixel_values''']
             self.assertListEqual(arg_names[:1] ,						a__)




     def 		snake_case_		(				self):
         A__		 =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a__)




     def 		snake_case_		(				self):
         A__		 =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*a__)




     def 		snake_case_		(				self):
         A__ ,	A__		 =					self.model_tester.prepare_config_and_inputs_for_common()

         A__		 =					_config_zero_init(a__)
         for model_class in self.all_model_classes:
             A__		 =					model_class(config=a__)
             # Skip the check for the backbone
             for name, module in model.named_modules():
                 if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                     A__		 =					[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):
         for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             A__		 =					ViTHybridModel.from_pretrained(a__)
             self.assertIsNotNone(a__)







def 		lowerCAmelCase__    (					)->  str:
    A__		 =					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
    return image



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




     @cached_property
     def 		snake_case_		(				self):
         return (
             ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0])
             if is_vision_available()
             else None
         )




     @slow
     def 		snake_case_		(				self):
         A__		 =					ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
             a__)

         A__		 =					self.default_image_processor
         A__		 =					prepare_img()
         A__		 =					image_processor(images=a__ ,						return_tensors='''pt''').to(a__)

         # forward pass
         with torch.no_grad():
             A__		 =					model(**a__)

         # verify the logits
         A__		 =					torch.Size((1, 1_0_0_0))
         self.assertEqual(outputs.logits.shape ,						a__)

         A__		 =					torch.tensor([-1.9_0_9_0, -0.4_9_9_3, -0.2_3_8_9]).to(a__)

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




     @slow
     @require_accelerate
     def 		snake_case_		(				self):
         A__		 =					ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''')
         A__		 =					ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384''' ,						device_map='''auto''')

         A__		 =					prepare_img()

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

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