"""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		,__UpperCAmelCase		,__UpperCAmelCase=13		,__UpperCAmelCase=64		,__UpperCAmelCase=2		,__UpperCAmelCase=3		,__UpperCAmelCase=True		,__UpperCAmelCase=True		,__UpperCAmelCase=32		,__UpperCAmelCase=5		,__UpperCAmelCase=4		,__UpperCAmelCase=37		,__UpperCAmelCase="gelu"		,__UpperCAmelCase=0.1		,__UpperCAmelCase=0.1		,__UpperCAmelCase=10		,__UpperCAmelCase=0.0_2		,__UpperCAmelCase=[1, 16, 4, 4]		,__UpperCAmelCase=None		,)					->				List[Any]:
													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 // 32) ** 2
													A__	     =							num_patches + 1





						def 				snake_case__	(       self     )					->				Optional[Any]:
													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     )					->				int:
													A__	     =							{
													    '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=__UpperCAmelCase		,initializer_range=self.initializer_range		,backbone_featmap_shape=self.backbone_featmap_shape		,backbone_config=__UpperCAmelCase		,)





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				List[str]:
													A__	     =							ViTHybridModel(config=__UpperCAmelCase     )
													model.to(__UpperCAmelCase     )
													model.eval()
													A__	     =							model(__UpperCAmelCase     )
													self.parent.assertEqual(result.last_hidden_state.shape		,(self.batch_size, self.seq_length, self.hidden_size)     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Union[str, Any]:
													A__	     =							self.type_sequence_label_size
													A__	     =							ViTHybridForImageClassification(__UpperCAmelCase     )
													model.to(__UpperCAmelCase     )
													model.eval()
													A__	     =							model(__UpperCAmelCase		,labels=__UpperCAmelCase     )
													self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.type_sequence_label_size)     )





						def 				snake_case__	(       self     )					->				List[str]:
													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 ):
						lowerCAmelCase__  :						Optional[Any]													=		(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
						lowerCAmelCase__  :						List[str]													=		(
						    {'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification}
						    if is_torch_available()
						    else {}
						)
						lowerCAmelCase__  :						Union[str, Any]													=		False
						lowerCAmelCase__  :						Optional[int]													=		False
						lowerCAmelCase__  :						Dict													=		False





						def 				snake_case__	(       self     )					->				Optional[Any]:
													A__	     =							ViTHybridModelTester(self     )
													A__	     =							ConfigTester(self		,config_class=__UpperCAmelCase		,has_text_modality=__UpperCAmelCase		,hidden_size=37     )





						def 				snake_case__	(       self     )					->				List[Any]:
													self.config_tester.run_common_tests()





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





						def 				snake_case__	(       self     )					->				Union[str, Any]:
													A__     ,  A__	     =							self.model_tester.prepare_config_and_inputs_for_common()

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





						def 				snake_case__	(       self     )					->				Tuple:
													A__     ,  A__	     =							self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				A__	     =							model_class(__UpperCAmelCase     )
																				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]		,__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				Optional[int]:
													A__	     =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				Dict:
													A__	     =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				Dict:
													A__     ,  A__	     =							self.model_tester.prepare_config_and_inputs_for_common()

													A__	     =							_config_zero_init(__UpperCAmelCase     )
													for model_class in self.all_model_classes:
																				A__	     =							model_class(config=__UpperCAmelCase     )
																				# 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     )					->				List[Any]:
													for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				A__	     =							ViTHybridModel.from_pretrained(__UpperCAmelCase     )
																				self.assertIsNotNone(__UpperCAmelCase     )






def   UpperCAmelCase					(	):






							"""simple docstring"""





							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     )					->				List[str]:
													return (
													    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
													    if is_vision_available()
													    else None
													)





						@slow
						def 				snake_case__	(       self     )					->				List[Any]:
													A__	     =							ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(
													    __UpperCAmelCase     )

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

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

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

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

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





						@slow
						@require_accelerate
						def 				snake_case__	(       self     )					->				str:
													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=__UpperCAmelCase		,return_tensors='pt'     )
													A__	     =							model(**__UpperCAmelCase     )
													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'     )






"""simple docstring"""




import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Union[str, Any]													=		'M-CLIP'





						def __init__(       self		,__UpperCAmelCase=10_24		,__UpperCAmelCase=7_68		,**__UpperCAmelCase     )					->				List[str]:
													A__	     =							transformerDimSize
													A__	     =							imageDimSize
													super().__init__(**__UpperCAmelCase     )






class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Union[str, Any]													=		MCLIPConfig





						def __init__(       self		,__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				List[Any]:
													super().__init__(__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase     )
													A__	     =							XLMRobertaModel(__UpperCAmelCase     )
													A__	     =							torch.nn.Linear(
													    in_features=config.transformerDimensions		,out_features=config.numDims     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Tuple:
													A__	     =							self.transformer(input_ids=__UpperCAmelCase		,attention_mask=__UpperCAmelCase     )[0]
													A__	     =							(embs * attention_mask.unsqueeze(2     )).sum(dim=1     ) / attention_mask.sum(dim=1     )[:, None]
													return self.LinearTransformation(__UpperCAmelCase     ), embs





