






"""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  lowerCamelCase__ :






    def __init__(			self     ,A     ,A=13     ,A=30     ,A=2     ,A=3     ,A=True     ,A=True     ,A=32     ,A=5     ,A=4     ,A=37     ,A="gelu"     ,A=0.1     ,A=0.1     ,A=10     ,A=0.02     ,A=None     ,A=2     ,):
          UpperCAmelCase          =	parent
          UpperCAmelCase          =	batch_size
          UpperCAmelCase          =	image_size
          UpperCAmelCase          =	patch_size
          UpperCAmelCase          =	num_channels
          UpperCAmelCase          =	is_training
          UpperCAmelCase          =	use_labels
          UpperCAmelCase          =	hidden_size
          UpperCAmelCase          =	num_hidden_layers
          UpperCAmelCase          =	num_attention_heads
          UpperCAmelCase          =	intermediate_size
          UpperCAmelCase          =	hidden_act
          UpperCAmelCase          =	hidden_dropout_prob
          UpperCAmelCase          =	attention_probs_dropout_prob
          UpperCAmelCase          =	type_sequence_label_size
          UpperCAmelCase          =	initializer_range
          UpperCAmelCase          =	scope
          UpperCAmelCase          =	encoder_stride

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






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

          UpperCAmelCase          =	self.get_config()

          return config, pixel_values, labels






    def     _UpperCamelCase							(			self ):
          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=A     ,initializer_range=self.initializer_range     ,encoder_stride=self.encoder_stride     ,)






    def     _UpperCamelCase							(			self     ,A     ,A     ,A ):
          UpperCAmelCase          =	ViTModel(config=A )
          model.to(A )
          model.eval()
          UpperCAmelCase          =	model(A )
          self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size) )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A ):
          UpperCAmelCase          =	ViTForMaskedImageModeling(config=A )
          model.to(A )
          model.eval()
          UpperCAmelCase          =	model(A )
          self.parent.assertEqual(
              result.reconstruction.shape     ,(self.batch_size, self.num_channels, self.image_size, self.image_size) )

          # test greyscale images
          UpperCAmelCase          =	1
          UpperCAmelCase          =	ViTForMaskedImageModeling(A )
          model.to(A )
          model.eval()

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






    def     _UpperCamelCase							(			self     ,A     ,A     ,A ):
          UpperCAmelCase          =	self.type_sequence_label_size
          UpperCAmelCase          =	ViTForImageClassification(A )
          model.to(A )
          model.eval()
          UpperCAmelCase          =	model(A     ,labels=A )
          self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size) )

          # test greyscale images
          UpperCAmelCase          =	1
          UpperCAmelCase          =	ViTForImageClassification(A )
          model.to(A )
          model.eval()

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






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.prepare_config_and_inputs()
          (
              (
              UpperCAmelCase
          )      , (
              UpperCAmelCase
          )      , (
              UpperCAmelCase
          )      , 
          )          =	config_and_inputs
          UpperCAmelCase          =	{"""pixel_values""": pixel_values}
          return config, inputs_dict







@require_torch
class  lowerCamelCase__ (	snake_case   ,					snake_case   ,					unittest.TestCase			):
    SCREAMING_SNAKE_CASE		=      (
        (
            ViTModel,
            ViTForImageClassification,
            ViTForMaskedImageModeling,
        )
        if is_torch_available()
        else ()
    )
    SCREAMING_SNAKE_CASE		=      (
        {'''feature-extraction''': ViTModel, '''image-classification''': ViTForImageClassification}
        if is_torch_available()
        else {}
    )
    SCREAMING_SNAKE_CASE		=      True

    SCREAMING_SNAKE_CASE		=      False
    SCREAMING_SNAKE_CASE		=      False
    SCREAMING_SNAKE_CASE		=      False






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	ViTModelTester(self )
          UpperCAmelCase          =	ConfigTester(self     ,config_class=A     ,has_text_modality=A     ,hidden_size=37 )






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






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






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase      , UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs_for_common()

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






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase      , UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs_for_common()

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

                UpperCAmelCase          =	["""pixel_values"""]
                self.assertListEqual(arg_names[:1]     ,A )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*A )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*A )






    @slow
    def     _UpperCamelCase							(			self ):
          for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase          =	ViTModel.from_pretrained(A )
                self.assertIsNotNone(A )







def _a			(		):







      """simple docstring"""






      UpperCAmelCase          =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
      return image







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






    @cached_property
    def     _UpperCamelCase							(			self ):
          return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224""" ) if is_vision_available() else None






    @slow
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	ViTForImageClassification.from_pretrained("""google/vit-base-patch16-224""" ).to(A )

          UpperCAmelCase          =	self.default_image_processor
          UpperCAmelCase          =	prepare_img()
          UpperCAmelCase          =	image_processor(images=A     ,return_tensors="""pt""" ).to(A )

          # forward pass
          with torch.no_grad():
                UpperCAmelCase          =	model(**A )

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

          UpperCAmelCase          =	torch.tensor([-0.2744, 0.8215, -0.0836] ).to(A )

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






    @slow
    def     _UpperCamelCase							(			self ):
          # 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.
          UpperCAmelCase          =	ViTModel.from_pretrained("""facebook/dino-vits8""" ).to(A )

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

          # forward pass
          with torch.no_grad():
                UpperCAmelCase          =	model(A     ,interpolate_pos_encoding=A )

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

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

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






    @slow
    @require_accelerate
    @require_torch_gpu
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	ViTModel.from_pretrained("""facebook/dino-vits8"""     ,torch_dtype=torch.floataa     ,device_map="""auto""" )
          UpperCAmelCase          =	self.default_image_processor

          UpperCAmelCase          =	prepare_img()
          UpperCAmelCase          =	image_processor(images=A     ,return_tensors="""pt""" )
          UpperCAmelCase          =	inputs.pixel_values.to(A )

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











"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase					      =  {
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  ["""RoFormerTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """RoFormerForCausalLM""",
      """RoFormerForMaskedLM""",
      """RoFormerForMultipleChoice""",
      """RoFormerForQuestionAnswering""",
      """RoFormerForSequenceClassification""",
      """RoFormerForTokenClassification""",
      """RoFormerLayer""",
      """RoFormerModel""",
      """RoFormerPreTrainedModel""",
      """load_tf_weights_in_roformer""",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFRoFormerForCausalLM""",
      """TFRoFormerForMaskedLM""",
      """TFRoFormerForMultipleChoice""",
      """TFRoFormerForQuestionAnswering""",
      """TFRoFormerForSequenceClassification""",
      """TFRoFormerForTokenClassification""",
      """TFRoFormerLayer""",
      """TFRoFormerModel""",
      """TFRoFormerPreTrainedModel""",
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """FlaxRoFormerForMaskedLM""",
      """FlaxRoFormerForMultipleChoice""",
      """FlaxRoFormerForQuestionAnswering""",
      """FlaxRoFormerForSequenceClassification""",
      """FlaxRoFormerForTokenClassification""",
      """FlaxRoFormerModel""",
      """FlaxRoFormerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
  from .tokenization_roformer import RoFormerTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_roformer_fast import RoFormerTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roformer import (
        ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        RoFormerForCausalLM,
        RoFormerForMaskedLM,
        RoFormerForMultipleChoice,
        RoFormerForQuestionAnswering,
        RoFormerForSequenceClassification,
        RoFormerForTokenClassification,
        RoFormerLayer,
        RoFormerModel,
        RoFormerPreTrainedModel,
        load_tf_weights_in_roformer,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_roformer import (
        TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRoFormerForCausalLM,
        TFRoFormerForMaskedLM,
        TFRoFormerForMultipleChoice,
        TFRoFormerForQuestionAnswering,
        TFRoFormerForSequenceClassification,
        TFRoFormerForTokenClassification,
        TFRoFormerLayer,
        TFRoFormerModel,
        TFRoFormerPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_roformer import (
        FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaxRoFormerForMaskedLM,
        FlaxRoFormerForMultipleChoice,
        FlaxRoFormerForQuestionAnswering,
        FlaxRoFormerForSequenceClassification,
        FlaxRoFormerForTokenClassification,
        FlaxRoFormerModel,
        FlaxRoFormerPreTrainedModel,
    )


else:
  import sys

  _UpperCamelCase					      =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



