from ...processing_utils import ProcessorMixin







class 	__snake_case      (   lowercase_      ):
 __lowerCamelCase			:      Union[str, Any]           =					["""image_processor""", """feature_extractor"""]
 __lowerCamelCase			:      Tuple           =					"""TvltImageProcessor"""
 __lowerCamelCase			:      Optional[Any]           =					"""TvltFeatureExtractor"""
 def __init__(     self						,      snake_case__						,      snake_case__  )		->							Optional[int]:
       '''simple docstring'''





       super().__init__(image_processor=__UpperCamelCase						,      feature_extractor=__UpperCamelCase  )

       UpperCAmelCase			:       Dict			     =image_processor
       UpperCAmelCase			:       Union[str, Any]			     =feature_extractor
 def __call__(     self						,      snake_case__=None						,      snake_case__=None						,      snake_case__=None						,      snake_case__=None						,      snake_case__=False						,      snake_case__=False						,      *snake_case__						,      **snake_case__						,      )		->							Optional[Any]:
       '''simple docstring'''





       if images is None and audio is None:
             raise ValueError('''You need to specify either an `images` or `audio` input to process.'''  )

       UpperCAmelCase			:       str			     =None
       if images is not None:
             UpperCAmelCase			:       Any			     =self.image_processor(__UpperCamelCase						,      mask_pixel=__UpperCamelCase						,      *__UpperCamelCase						,      **__UpperCamelCase  )
       if images_mixed is not None:
             UpperCAmelCase			:       Dict			     =self.image_processor(__UpperCamelCase						,      is_mixed=__UpperCamelCase						,      *__UpperCamelCase						,      **__UpperCamelCase  )
       if audio is not None:
             UpperCAmelCase			:       Dict			     =self.feature_extractor(
                 __UpperCamelCase						,      *__UpperCamelCase						,      sampling_rate=__UpperCamelCase						,      mask_audio=__UpperCamelCase						,      **__UpperCamelCase  )

       UpperCAmelCase			:       Optional[Any]			     ={}
       if audio is not None:
             output_dict.update(__UpperCamelCase  )
       if images is not None:
             output_dict.update(__UpperCamelCase  )
       if images_mixed_dict is not None:
             output_dict.update(__UpperCamelCase  )
       return output_dict






 @property
 def 		UpperCAmelCase__				(     self  )		->							List[Any]:
       '''simple docstring'''





       UpperCAmelCase			:       List[Any]			     =self.image_processor.model_input_names
       UpperCAmelCase			:       Union[str, Any]			     =self.feature_extractor.model_input_names
       return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names  )  )







"""simple docstring"""
import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import ViTMSNForImageClassification, ViTMSNModel
		from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor






class 					_UpperCAmelCase   :


					def __init__(					self			:List[Any]     ,       __UpperCamelCase			:Tuple     ,       __UpperCamelCase			:List[str]=13     ,       __UpperCamelCase			:Any=30     ,       __UpperCamelCase			:int=2     ,       __UpperCamelCase			:Union[str, Any]=3     ,       __UpperCamelCase			:Union[str, Any]=True     ,       __UpperCamelCase			:Optional[int]=True     ,       __UpperCamelCase			:List[str]=32     ,       __UpperCamelCase			:List[Any]=5     ,       __UpperCamelCase			:Dict=4     ,       __UpperCamelCase			:List[str]=37     ,       __UpperCamelCase			:str="gelu"     ,       __UpperCamelCase			:Union[str, Any]=0.1     ,       __UpperCamelCase			:List[Any]=0.1     ,       __UpperCamelCase			:Tuple=10     ,       __UpperCamelCase			:Tuple=0.02     ,       __UpperCamelCase			:int=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

												# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
												A		=  (image_size // patch_size) ** 2
												A		=  num_patches + 1


					def 	lowerCamelCase       (					self			: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 	lowerCamelCase       (					self			:Union[str, Any]		):
												return ViTMSNConfig(
												    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     ,       initializer_range=self.initializer_range     ,       )


					def 	lowerCamelCase       (					self			:Dict     ,       __UpperCamelCase			:Dict     ,       __UpperCamelCase			:Any     ,       __UpperCamelCase			:Any		):
												A		=  ViTMSNModel(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 	lowerCamelCase       (					self			:Optional[int]     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:Union[str, Any]     ,       __UpperCamelCase			:Optional[Any]		):
												A		=  self.type_sequence_label_size
												A		=  ViTMSNForImageClassification(__UpperCamelCase		)
												model.to(__UpperCamelCase		)
												model.eval()
												A		=  model(__UpperCamelCase     ,       labels=__UpperCamelCase		)
												print("Pixel and labels shape: {pixel_values.shape}, {labels.shape}"		)
												print("Labels: {labels}"		)
												self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.type_sequence_label_size)		)

												# test greyscale images
												A		=  1
												A		=  ViTMSNForImageClassification(__UpperCamelCase		)
												model.to(__UpperCamelCase		)
												model.eval()

												A		=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
												A		=  model(__UpperCamelCase		)
												self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.type_sequence_label_size)		)


					def 	lowerCamelCase       (					self			:Optional[Any]		):
												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   (	lowercase_     ,							lowercase_     ,							unittest.TestCase ):
					UpperCamelCase      =							(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
					UpperCamelCase      =							(
					    {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
					    if is_torch_available()
					    else {}
					)

					UpperCamelCase      =							False
					UpperCamelCase      =							False
					UpperCamelCase      =							False
					UpperCamelCase      =							False


					def 	lowerCamelCase       (					self			:Optional[int]		):
												A		=  ViTMSNModelTester(self		)
												A		=  ConfigTester(self     ,       config_class=__UpperCamelCase     ,       has_text_modality=__UpperCamelCase     ,       hidden_size=37		)


					def 	lowerCamelCase       (					self			:Any		):
												self.config_tester.run_common_tests()


					@unittest.skip(reason="ViTMSN does not use inputs_embeds"		)
					def 	lowerCamelCase       (					self			:Union[str, Any]		):
												pass


					def 	lowerCamelCase       (					self			:int		):
												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 	lowerCamelCase       (					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 	lowerCamelCase       (					self			:List[str]		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Dict		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase		)


					@slow
					def 	lowerCamelCase       (					self			:List[Any]		):
												for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			A		=  ViTMSNModel.from_pretrained(__UpperCamelCase		)
																			self.assertIsNotNone(__UpperCamelCase		)







def 					A__				(			):
							A		=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
							return image






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


					@cached_property
					def 	lowerCamelCase       (					self			:Union[str, Any]		):
												return ViTImageProcessor.from_pretrained("facebook/vit-msn-small"		) if is_vision_available() else None


					@slow
					def 	lowerCamelCase       (					self			:Any		):
												torch.manual_seed(2		)
												A		=  ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"		).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([-0.0_803, -0.4_454, -0.2_375]		).to(__UpperCamelCase		)

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


