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_			:

  def __init__(		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   List[str]=13  ,				__SCREAMING_SNAKE_CASE			:   Tuple=30  ,				__SCREAMING_SNAKE_CASE			:   Tuple=2  ,				__SCREAMING_SNAKE_CASE			:   int=3  ,				__SCREAMING_SNAKE_CASE			:   str=True  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=True  ,				__SCREAMING_SNAKE_CASE			:   List[str]=32  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=5  ,				__SCREAMING_SNAKE_CASE			:   str=4  ,				__SCREAMING_SNAKE_CASE			:   Tuple=37  ,				__SCREAMING_SNAKE_CASE			:   int="gelu"  ,				__SCREAMING_SNAKE_CASE			:   str=0.1  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=0.1  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=10  ,				__SCREAMING_SNAKE_CASE			:   Tuple=0.02  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=None  ,				):
   __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 		_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			:   List[str]							):
   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 		_UpperCAmelCase (		self			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   int  ,				__SCREAMING_SNAKE_CASE			:   List[str]							):
   __a							=	ViTMSNModel(config=__SCREAMING_SNAKE_CASE							)
   model.to(__SCREAMING_SNAKE_CASE							)
   model.eval()
   __a							=	model(__SCREAMING_SNAKE_CASE							)
   self.parent.assertEqual(result.last_hidden_state.shape  ,				(self.batch_size, self.seq_length, self.hidden_size)							)

  def 		_UpperCAmelCase (		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]							):
   __a							=	self.type_sequence_label_size
   __a							=	ViTMSNForImageClassification(__SCREAMING_SNAKE_CASE							)
   model.to(__SCREAMING_SNAKE_CASE							)
   model.eval()
   __a							=	model(__SCREAMING_SNAKE_CASE  ,				labels=__SCREAMING_SNAKE_CASE							)
   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(__SCREAMING_SNAKE_CASE							)
   model.to(__SCREAMING_SNAKE_CASE							)
   model.eval()

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

  def 		_UpperCAmelCase (		self			:   Any							):
   __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_			(							a_      ,							a_      ,							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 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __a							=	ViTMSNModelTester(self							)
   __a							=	ConfigTester(self  ,				config_class=__SCREAMING_SNAKE_CASE  ,				has_text_modality=__SCREAMING_SNAKE_CASE  ,				hidden_size=37							)

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   self.config_tester.run_common_tests()

  @unittest.skip(reason="ViTMSN does not use inputs_embeds"							)
  def 		_UpperCAmelCase (		self			:   Tuple							):
   pass

  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(__SCREAMING_SNAKE_CASE							)
    self.assertIsInstance(model.get_input_embeddings()  ,				(nn.Module)							)
    __a							=	model.get_output_embeddings()
    self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE  ,				nn.Linear							)							)

  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(__SCREAMING_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]  ,				__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   int							):
   __a							=	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   int							):
   __a							=	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE							)

  @slow
  def 		_UpperCAmelCase (		self			:   Tuple							):
   for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __a							=	ViTMSNModel.from_pretrained(__SCREAMING_SNAKE_CASE							)
    self.assertIsNotNone(__SCREAMING_SNAKE_CASE							)



def 	__A		(      ):



 """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			:   List[Any]							):
   return ViTImageProcessor.from_pretrained("facebook/vit-msn-small"							) if is_vision_available() else None

  @slow
  def 		_UpperCAmelCase (		self			:   List[str]							):
   torch.manual_seed(2							)
   __a							=	ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"							).to(__SCREAMING_SNAKE_CASE							)

   __a							=	self.default_image_processor
   __a							=	prepare_img()
   __a							=	image_processor(images=__SCREAMING_SNAKE_CASE  ,				return_tensors="pt"							).to(__SCREAMING_SNAKE_CASE							)

   # forward pass
   with torch.no_grad():
    __a							=	model(**__SCREAMING_SNAKE_CASE							)

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

   __a							=	torch.tensor([-0.08_03, -0.44_54, -0.23_75]							).to(__SCREAMING_SNAKE_CASE							)

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



from typing import Any



def 	__A		(      _A       ):



 """simple docstring"""


 if not input_list:
  return []
 __a							=	[input_list.count(_A       ) for value in input_list]
 __a							=	max(_A       )  # Gets the maximum count in the input list.
 # Gets values of modes
 return sorted({input_list[i] for i, value in enumerate(_A       ) if value == y}       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()


