




"""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      UpperCAmelCase_  :




    def __init__(   self						:	Union[str, Any]     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Optional[Any]=13     ,			snake_case_						:	Tuple=30     ,			snake_case_						:	Optional[int]=2     ,			snake_case_						:	List[str]=3     ,			snake_case_						:	Optional[Any]=True     ,			snake_case_						:	Union[str, Any]=True     ,			snake_case_						:	Optional[Any]=32     ,			snake_case_						:	Union[str, Any]=5     ,			snake_case_						:	List[Any]=4     ,			snake_case_						:	str=37     ,			snake_case_						:	List[Any]="gelu"     ,			snake_case_						:	str=0.1     ,			snake_case_						:	int=0.1     ,			snake_case_						:	Union[str, Any]=10     ,			snake_case_						:	int=0.02     ,			snake_case_						:	Union[str, Any]=None     ,			snake_case_						:	Union[str, Any]=2     ,			)				->							Optional[Any]:




     '''simple docstring'''

     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 							__magic_name__							(   self						:	str				)				->							Any:




     '''simple docstring'''

     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 							__magic_name__							(   self						:	Dict				)				->							Optional[int]:




     '''simple docstring'''

     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=snake_case_     ,			initializer_range=self.initializer_range     ,			encoder_stride=self.encoder_stride     ,			)




    def 							__magic_name__							(   self						:	int     ,			snake_case_						:	Any     ,			snake_case_						:	Tuple     ,			snake_case_						:	Optional[int]				)				->							List[Any]:




     '''simple docstring'''

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




    def 							__magic_name__							(   self						:	str     ,			snake_case_						:	List[str]     ,			snake_case_						:	int     ,			snake_case_						:	List[Any]				)				->							str:




     '''simple docstring'''

     A__	=    ViTForMaskedImageModeling(config=snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(snake_case_				)
     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(snake_case_				)
     model.to(snake_case_				)
     model.eval()

     A__	=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
     A__	=    model(snake_case_				)
     self.parent.assertEqual(result.reconstruction.shape     ,			(self.batch_size, 1, self.image_size, self.image_size)				)




    def 							__magic_name__							(   self						:	Any     ,			snake_case_						:	Any     ,			snake_case_						:	Any     ,			snake_case_						:	str				)				->							int:




     '''simple docstring'''

     A__	=    self.type_sequence_label_size
     A__	=    ViTForImageClassification(snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(snake_case_     ,			labels=snake_case_				)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.type_sequence_label_size)				)

     # test greyscale images
     A__	=    1
     A__	=    ViTForImageClassification(snake_case_				)
     model.to(snake_case_				)
     model.eval()

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




    def 							__magic_name__							(   self						:	int				)				->							List[str]:




     '''simple docstring'''

     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							):
    lowercase__						 =				(
        (
            ViTModel,
            ViTForImageClassification,
            ViTForMaskedImageModeling,
        )
        if is_torch_available()
        else ()
    )
    lowercase__						 =				(
        {'''feature-extraction''': ViTModel, '''image-classification''': ViTForImageClassification}
        if is_torch_available()
        else {}
    )
    lowercase__						 =				True

    lowercase__						 =				False
    lowercase__						 =				False
    lowercase__						 =				False




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




     '''simple docstring'''

     A__	=    ViTModelTester(self				)
     A__	=    ConfigTester(self     ,			config_class=snake_case_     ,			has_text_modality=snake_case_     ,			hidden_size=37				)




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




     '''simple docstring'''

     self.config_tester.run_common_tests()




    @unittest.skip(reason="ViT does not use inputs_embeds"				)
    def 							__magic_name__							(   self						:	List[Any]				)				->							Any:




     '''simple docstring'''

     pass




    def 							__magic_name__							(   self						:	Tuple				)				->							Any:




     '''simple docstring'''

     A__,				A__	=    self.model_tester.prepare_config_and_inputs_for_common()

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




    def 							__magic_name__							(   self						:	int				)				->							Any:




     '''simple docstring'''

     A__,				A__	=    self.model_tester.prepare_config_and_inputs_for_common()

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




    def 							__magic_name__							(   self						:	List[str]				)				->							List[Any]:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*snake_case_				)




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Tuple:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*snake_case_				)




    def 							__magic_name__							(   self						:	List[str]				)				->							List[Any]:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*snake_case_				)




    @slow
    def 							__magic_name__							(   self						:	Union[str, Any]				)				->							Tuple:




     '''simple docstring'''

     for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      A__	=    ViTModel.from_pretrained(snake_case_				)
      self.assertIsNotNone(snake_case_				)







def   _SCREAMING_SNAKE_CASE       (							)						->		Any:
 A__	=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
 return image






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




    @cached_property
    def 							__magic_name__							(   self						:	Any				)				->							Dict:




     '''simple docstring'''

     return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224"				) if is_vision_available() else None




    @slow
    def 							__magic_name__							(   self						:	str				)				->							int:




     '''simple docstring'''

     A__	=    ViTForImageClassification.from_pretrained("google/vit-base-patch16-224"				).to(snake_case_				)

     A__	=    self.default_image_processor
     A__	=    prepare_img()
     A__	=    image_processor(images=snake_case_     ,			return_tensors="pt"				).to(snake_case_				)

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

     # verify the logits
     A__	=    torch.Size((1, 1_000)				)
     self.assertEqual(outputs.logits.shape     ,			snake_case_				)

     A__	=    torch.tensor([-0.2744, 0.8215, -0.0836]				).to(snake_case_				)

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




    @slow
    def 							__magic_name__							(   self						:	Dict				)				->							Any:




     '''simple docstring'''

     A__	=    ViTModel.from_pretrained("facebook/dino-vits8"				).to(snake_case_				)

     A__	=    ViTImageProcessor.from_pretrained("facebook/dino-vits8"     ,			size=480				)
     A__	=    prepare_img()
     A__	=    image_processor(images=snake_case_     ,			return_tensors="pt"				)
     A__	=    inputs.pixel_values.to(snake_case_				)

     # forward pass
     with torch.no_grad():
      A__	=    model(snake_case_     ,			interpolate_pos_encoding=snake_case_				)

     # verify the logits
     A__	=    torch.Size((1, 3_601, 384)				)
     self.assertEqual(outputs.last_hidden_state.shape     ,			snake_case_				)

     A__	=    torch.tensor(
         [[4.2340, 4.3906, -6.6692], [4.5463, 1.8928, -6.7257], [4.4429, 0.8496, -5.8585]]				).to(snake_case_				)

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




    @slow
    @require_accelerate
    @require_torch_gpu
    def 							__magic_name__							(   self						:	str				)				->							Any:




     '''simple docstring'''

     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=snake_case_     ,			return_tensors="pt"				)
     A__	=    inputs.pixel_values.to(snake_case_				)

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











"""simple docstring"""



from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				['''image_processor''', '''tokenizer''']
    lowercase__						 =				'''AutoImageProcessor'''
    lowercase__						 =				'''AutoTokenizer'''




    def __init__(   self						:	str     ,			snake_case_						:	Dict     ,			snake_case_						:	List[str]				)				->							str:




     '''simple docstring'''

     super().__init__(snake_case_     ,			snake_case_				)
     A__	=    self.image_processor




    def __call__(   self						:	int     ,			snake_case_						:	Any=None     ,			snake_case_						:	Any=None     ,			snake_case_						:	Union[str, Any]=None     ,			**snake_case_						:	Optional[int]				)				->							Optional[int]:




     '''simple docstring'''

     if text is None and images is None:
      raise ValueError("You have to specify either text or images. Both cannot be none."				)

     if text is not None:
      A__	=    self.tokenizer(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if images is not None:
      A__	=    self.image_processor(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if text is not None and images is not None:
      A__	=    image_features.pixel_values
      return encoding
     elif text is not None:
      return encoding
     else:
      return BatchEncoding(data=dict(**snake_case_				)     ,			tensor_type=snake_case_				)




    def 							__magic_name__							(   self						:	Optional[int]     ,			*snake_case_						:	Union[str, Any]     ,			**snake_case_						:	List[Any]				)				->							int:




     '''simple docstring'''

     return self.tokenizer.batch_decode(*snake_case_     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	List[str]     ,			*snake_case_						:	List[str]     ,			**snake_case_						:	Optional[int]				)				->							Tuple:




     '''simple docstring'''

     return self.tokenizer.decode(*snake_case_     ,			**snake_case_				)




    @property
    def 							__magic_name__							(   self						:	List[Any]				)				->							List[Any]:




     '''simple docstring'''

     return ["input_ids", "attention_mask", "pixel_values"]





