


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       lowerCamelCase__   :





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase=1_3		,     UpperCAmelCase=3_0		,     UpperCAmelCase=2		,     UpperCAmelCase=3		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=3_2		,     UpperCAmelCase=5		,     UpperCAmelCase=4		,     UpperCAmelCase=3_7		,     UpperCAmelCase="gelu"		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=1_0		,     UpperCAmelCase=0.02		,     UpperCAmelCase=None		,     )     ->   List[str]:
      _lowercase							      =parent
      _lowercase							      =batch_size
      _lowercase							      =image_size
      _lowercase							      =patch_size
      _lowercase							      =num_channels
      _lowercase							      =is_training
      _lowercase							      =use_labels
      _lowercase							      =hidden_size
      _lowercase							      =num_hidden_layers
      _lowercase							      =num_attention_heads
      _lowercase							      =intermediate_size
      _lowercase							      =hidden_act
      _lowercase							      =hidden_dropout_prob
      _lowercase							      =attention_probs_dropout_prob
      _lowercase							      =type_sequence_label_size
      _lowercase							      =initializer_range
      _lowercase							      =scope

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





     def      __A					(self       )     ->   Dict:
      _lowercase							      =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

      _lowercase							      =self.get_config()

      return config, pixel_values, labels





     def      __A					(self       )     ->   Dict:
      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      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   List[str]:
      _lowercase							      =ViTMSNModel(config=UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =model(UpperCAmelCase       )
      self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Union[str, Any]:
      _lowercase							      =self.type_sequence_label_size
      _lowercase							      =ViTMSNForImageClassification(UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =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
      _lowercase							      =1
      _lowercase							      =ViTMSNForImageClassification(UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()

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





     def      __A					(self       )     ->   str:
      _lowercase							      =self.prepare_config_and_inputs()
      _lowercase      ,    _lowercase      ,    _lowercase							      =config_and_inputs
      _lowercase							      ={'''pixel_values''': pixel_values}
      return config, inputs_dict







@require_torch
class       lowerCamelCase__   (       lowerCAmelCase   ,							lowerCAmelCase   ,							unittest.TestCase):
     SCREAMING_SNAKE_CASE__      			=			(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE__      			=			(
         {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
         if is_torch_available()
         else {}
     )

     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False





     def      __A					(self       )     ->   int:
      _lowercase							      =ViTMSNModelTester(self       )
      _lowercase							      =ConfigTester(self		,     config_class=UpperCAmelCase		,     has_text_modality=UpperCAmelCase		,     hidden_size=3_7       )





     def      __A					(self       )     ->   Optional[int]:
      self.config_tester.run_common_tests()





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





     def      __A					(self       )     ->   Dict:
      _lowercase      ,    _lowercase							      =self.model_tester.prepare_config_and_inputs_for_common()

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





     def      __A					(self       )     ->   List[Any]:
      _lowercase      ,    _lowercase							      =self.model_tester.prepare_config_and_inputs_for_common()

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

       _lowercase							      =['''pixel_values''']
       self.assertListEqual(arg_names[:1]		,     UpperCAmelCase       )





     def      __A					(self       )     ->   Optional[int]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCAmelCase       )





     def      __A					(self       )     ->   str:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase       )





     @slow
     def      __A					(self       )     ->   Optional[Any]:
      for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       _lowercase							      =ViTMSNModel.from_pretrained(UpperCAmelCase       )
       self.assertIsNotNone(UpperCAmelCase       )



def 				UpperCAmelCase_					(							)     ->			Optional[int]:







 """simple docstring"""


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







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





     @cached_property
     def      __A					(self       )     ->   Tuple:
      return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small'''       ) if is_vision_available() else None





     @slow
     def      __A					(self       )     ->   Optional[Any]:
      torch.manual_seed(2       )
      _lowercase							      =ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''       ).to(UpperCAmelCase       )

      _lowercase							      =self.default_image_processor
      _lowercase							      =prepare_img()
      _lowercase							      =image_processor(images=UpperCAmelCase		,     return_tensors='''pt'''       ).to(UpperCAmelCase       )

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

      # verify the logits
      _lowercase							      =torch.Size((1, 1_0_0_0)       )
      self.assertEqual(outputs.logits.shape		,     UpperCAmelCase       )

      _lowercase							      =torch.tensor([-0.0803, -0.4454, -0.2375]       ).to(UpperCAmelCase       )

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




"""simple docstring"""





from typing import TYPE_CHECKING

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


__UpperCamelCase  :      Dict							      =		{
    '''configuration_clip''': [
        '''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPConfig''',
        '''CLIPOnnxConfig''',
        '''CLIPTextConfig''',
        '''CLIPVisionConfig''',
    ],
    '''processing_clip''': ['''CLIPProcessor'''],
    '''tokenization_clip''': ['''CLIPTokenizer'''],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      int							      =		['''CLIPTokenizerFast''']

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Optional[int]							      =		['''CLIPFeatureExtractor''']
							__UpperCamelCase  :      Optional[Any]							      =		['''CLIPImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Tuple							      =		[
							    '''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''CLIPModel''',
							    '''CLIPPreTrainedModel''',
							    '''CLIPTextModel''',
							    '''CLIPTextModelWithProjection''',
							    '''CLIPVisionModel''',
							    '''CLIPVisionModelWithProjection''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Optional[int]							      =		[
							    '''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFCLIPModel''',
							    '''TFCLIPPreTrainedModel''',
							    '''TFCLIPTextModel''',
							    '''TFCLIPVisionModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Optional[Any]							      =		[
							    '''FlaxCLIPModel''',
							    '''FlaxCLIPPreTrainedModel''',
							    '''FlaxCLIPTextModel''',
							    '''FlaxCLIPTextPreTrainedModel''',
							    '''FlaxCLIPVisionModel''',
							    '''FlaxCLIPVisionPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_clip import (
							    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    CLIPConfig,
							    CLIPOnnxConfig,
							    CLIPTextConfig,
							    CLIPVisionConfig,
							)
							from .processing_clip import CLIPProcessor
							from .tokenization_clip import CLIPTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_clip_fast import CLIPTokenizerFast

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_clip import CLIPFeatureExtractor
														from .image_processing_clip import CLIPImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_clip import (
														    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CLIPModel,
														    CLIPPreTrainedModel,
														    CLIPTextModel,
														    CLIPTextModelWithProjection,
														    CLIPVisionModel,
														    CLIPVisionModelWithProjection,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_clip import (
														    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFCLIPModel,
														    TFCLIPPreTrainedModel,
														    TFCLIPTextModel,
														    TFCLIPVisionModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_clip import (
														    FlaxCLIPModel,
														    FlaxCLIPPreTrainedModel,
														    FlaxCLIPTextModel,
														    FlaxCLIPTextPreTrainedModel,
														    FlaxCLIPVisionModel,
														    FlaxCLIPVisionPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase  :      Tuple							      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




