



'''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 __A			:



 '''simple docstring'''







 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 ,   )    ->     Union[str, 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

      # 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 					a__  (self       )    ->     Tuple:


      """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 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      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 ,   A ,   A ,   A       )    ->     Tuple:


      """simple docstring"""



      _a      =  ViTMSNModel(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _a      =  model(__lowerCAmelCase       )
      self.parent.assertEqual(result.last_hidden_state.shape ,   (self.batch_size, self.seq_length, self.hidden_size)       )



 def 					a__  (self ,   A ,   A ,   A       )    ->     str:


      """simple docstring"""



      _a      =  self.type_sequence_label_size
      _a      =  ViTMSNForImageClassification(__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _a      =  model(__lowerCAmelCase ,   labels=__lowerCAmelCase       )
      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(__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()

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



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


      """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 __A			(  lowerCAmelCase__   ,   lowerCAmelCase__   ,   unittest.TestCase						):



 '''simple docstring'''







 __lowerCamelCase    :       List[Any]          =       (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
 __lowerCamelCase    :       List[Any]          =       (
     {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
     if is_torch_available()
     else {}
 )

 __lowerCamelCase    :       Optional[Any]          =       False
 __lowerCamelCase    :       List[str]          =       False
 __lowerCamelCase    :       Union[str, Any]          =       False
 __lowerCamelCase    :       Optional[int]          =       False



 def 					a__  (self       )    ->     Optional[int]:


      """simple docstring"""



      _a      =  ViTMSNModelTester(self       )
      _a      =  ConfigTester(self ,   config_class=__lowerCAmelCase ,   has_text_modality=__lowerCAmelCase ,   hidden_size=37       )



 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      self.config_tester.run_common_tests()



 @unittest.skip(reason='''ViTMSN does not use inputs_embeds'''       )
 def 					a__  (self       )    ->     Union[str, Any]:


      """simple docstring"""



      pass



 def 					a__  (self       )    ->     List[str]:


      """simple docstring"""



      _a  ,    _a      =  self.model_tester.prepare_config_and_inputs_for_common()

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



 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      _a  ,    _a      =  self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _a      =  model_class(__lowerCAmelCase       )
           _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] ,   __lowerCAmelCase       )



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


      """simple docstring"""



      _a      =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCAmelCase       )



 def 					a__  (self       )    ->     Optional[int]:


      """simple docstring"""



      _a      =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase       )



 @slow
 def 					a__  (self       )    ->     Optional[int]:


      """simple docstring"""



      for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a      =  ViTMSNModel.from_pretrained(__lowerCAmelCase       )
           self.assertIsNotNone(__lowerCAmelCase       )





def lowerCAmelCase  ():



     """simple docstring"""



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






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



 '''simple docstring'''







 @cached_property
 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small'''       ) if is_vision_available() else None



 @slow
 def 					a__  (self       )    ->     Any:


      """simple docstring"""



      torch.manual_seed(2       )
      _a      =  ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''       ).to(__lowerCAmelCase       )

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

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

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

      _a      =  torch.tensor([-0.0803, -0.4454, -0.2375]       ).to(__lowerCAmelCase       )

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




'''simple docstring'''




from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig


__lowercase          =  logging.get_logger(__name__)

__lowercase          =  '''T5Config'''






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     List[str]      =       '''mt5'''
			UpperCAmelCase_			:     Tuple      =       MTaConfig







class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     List[Any]      =       '''mt5'''
			UpperCAmelCase_			:     int      =       MTaConfig







class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     Tuple      =       '''mt5'''
			UpperCAmelCase_			:     Union[str, Any]      =       MTaConfig




