


'''simple docstring'''






import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
 from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor




class 	a       :
 def __init__(	self ,			__magic_name__ ,			__magic_name__=13 ,			__magic_name__=30 ,			__magic_name__=2 ,			__magic_name__=3 ,			__magic_name__=True ,			__magic_name__=True ,			__magic_name__=32 ,			__magic_name__=5 ,			__magic_name__=4 ,			__magic_name__=37 ,			__magic_name__="gelu" ,			__magic_name__=0.1 ,			__magic_name__=0.1 ,			__magic_name__=10 ,			__magic_name__=0.0_2 ,			__magic_name__=None ,			__magic_name__=2 ,			)   ->							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      =							encoder_stride

      # in ViT, 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 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      _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 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      return ViTConfig(
          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=__magic_name__ ,			initializer_range=self.initializer_range ,			encoder_stride=self.encoder_stride ,			)
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							str:
      _a      =							ViTModel(config=__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__  )
      self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.seq_length, self.hidden_size)  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Tuple:
      _a      =							ViTForMaskedImageModeling(config=__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__  )
      self.parent.assertEqual(
          result.reconstruction.shape ,			(self.batch_size, self.num_channels, self.image_size, self.image_size)  )

      # test greyscale images
      _a      =							1
      _a      =							ViTForMaskedImageModeling(__magic_name__  )
      model.to(__magic_name__  )
      model.eval()

      _a      =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
      _a      =							model(__magic_name__  )
      self.parent.assertEqual(result.reconstruction.shape ,			(self.batch_size, 1, self.image_size, self.image_size)  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							List[str]:
      _a      =							self.type_sequence_label_size
      _a      =							ViTForImageClassification(__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__ ,			labels=__magic_name__  )
      self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.type_sequence_label_size)  )

      # test greyscale images
      _a      =							1
      _a      =							ViTForImageClassification(__magic_name__  )
      model.to(__magic_name__  )
      model.eval()

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




 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      _a      =							self.prepare_config_and_inputs()
      (
          (
          _a
      ) ,				(
          _a
      ) ,				(
          _a
      ) ,				
      )      =							config_and_inputs
      _a      =							{'pixel_values': pixel_values}
      return config, inputs_dict





@require_torch
class 	a       (       _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			unittest.TestCase    ):
 _lowerCAmelCase	   =		(
     (
         ViTModel,
         ViTForImageClassification,
         ViTForMaskedImageModeling,
     )
     if is_torch_available()
     else ()
 )
 _lowerCAmelCase	   =		(
     {"""feature-extraction""": ViTModel, """image-classification""": ViTForImageClassification}
     if is_torch_available()
     else {}
 )
 _lowerCAmelCase	   =		True

 _lowerCAmelCase	   =		False
 _lowerCAmelCase	   =		False
 _lowerCAmelCase	   =		False
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      _a      =							ViTModelTester(self  )
      _a      =							ConfigTester(self ,			config_class=__magic_name__ ,			has_text_modality=__magic_name__ ,			hidden_size=37  )
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      self.config_tester.run_common_tests()
 @unittest.skip(reason='ViT does not use inputs_embeds'  )
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      pass
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      _a ,				_a      =							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _a      =							model_class(__magic_name__  )
           self.assertIsInstance(model.get_input_embeddings() ,			(nn.Module)  )
           _a      =							model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(__magic_name__ ,			nn.Linear  )  )
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      _a ,				_a      =							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _a      =							model_class(__magic_name__  )
           _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] ,			__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__magic_name__  )




 @slow
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a      =							ViTModel.from_pretrained(__magic_name__  )
           self.assertIsNotNone(__magic_name__  )






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




     '''simple docstring'''



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






@require_torch
@require_vision
class 	a       (       unittest.TestCase    ):
 @cached_property
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'  ) if is_vision_available() else None
 @slow
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      _a      =							ViTForImageClassification.from_pretrained('google/vit-base-patch16-224'  ).to(__magic_name__  )

      _a      =							self.default_image_processor
      _a      =							prepare_img()
      _a      =							image_processor(images=__magic_name__ ,			return_tensors='pt'  ).to(__magic_name__  )

      # forward pass
      with torch.no_grad():
           _a      =							model(**__magic_name__  )

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

      _a      =							torch.tensor([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6]  ).to(__magic_name__  )

      self.assertTrue(torch.allclose(outputs.logits[0, :3] ,			__magic_name__ ,			atol=1e-4  )  )
 @slow
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      # ViT models have an `interpolate_pos_encoding` argument in their forward method,
      # allowing to interpolate the pre-trained position embeddings in order to use
      # the model on higher resolutions. The DINO model by Facebook AI leverages this
      # to visualize self-attention on higher resolution images.
      _a      =							ViTModel.from_pretrained('facebook/dino-vits8'  ).to(__magic_name__  )

      _a      =							ViTImageProcessor.from_pretrained('facebook/dino-vits8' ,			size=4_80  )
      _a      =							prepare_img()
      _a      =							image_processor(images=__magic_name__ ,			return_tensors='pt'  )
      _a      =							inputs.pixel_values.to(__magic_name__  )

      # forward pass
      with torch.no_grad():
           _a      =							model(__magic_name__ ,			interpolate_pos_encoding=__magic_name__  )

      # verify the logits
      _a      =							torch.Size((1, 36_01, 3_84)  )
      self.assertEqual(outputs.last_hidden_state.shape ,			__magic_name__  )

      _a      =							torch.tensor(
          [[4.2_3_4_0, 4.3_9_0_6, -6.6_6_9_2], [4.5_4_6_3, 1.8_9_2_8, -6.7_2_5_7], [4.4_4_2_9, 0.8_4_9_6, -5.8_5_8_5]]  ).to(__magic_name__  )

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




 @slow
 @require_accelerate
 @require_torch_gpu
 def 							__UpperCAmelCase						(	self  )   ->							int:
      _a      =							ViTModel.from_pretrained('facebook/dino-vits8' ,			torch_dtype=torch.floataa ,			device_map='auto'  )
      _a      =							self.default_image_processor

      _a      =							prepare_img()
      _a      =							image_processor(images=__magic_name__ ,			return_tensors='pt'  )
      _a      =							inputs.pixel_values.to(__magic_name__  )

      # forward pass to make sure inference works in fp16
      with torch.no_grad():
           _a      =							model(__magic_name__  )





'''simple docstring'''






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     assert (
         isinstance(lowerCAmelCase__			,  lowerCAmelCase__						) and number_of_steps > 0
     ), f'number_of_steps needs to be positive integer, your input {number_of_steps}'
     if number_of_steps == 1:
          return 1
     _a ,				_a      =							1, 1
     for _ in range(number_of_steps - 1						):
          _a ,				_a      =							current + previous, current
     return current


if __name__ == "__main__":
 import doctest

 doctest.testmod()

