





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       :







       """simple docstring"""




       def __init__( self     :					List[Any],			_UpperCAmelCase     :					Optional[Any],			_UpperCAmelCase     :					List[Any]=1_3,			_UpperCAmelCase     :					Optional[Any]=3_0,			_UpperCAmelCase     :					List[str]=2,			_UpperCAmelCase     :					str=3,			_UpperCAmelCase     :					Optional[int]=True,			_UpperCAmelCase     :					Optional[int]=True,			_UpperCAmelCase     :					Optional[Any]=3_2,			_UpperCAmelCase     :					Any=5,			_UpperCAmelCase     :					Optional[Any]=4,			_UpperCAmelCase     :					List[Any]=3_7,			_UpperCAmelCase     :					Optional[int]="gelu",			_UpperCAmelCase     :					int=0.1,			_UpperCAmelCase     :					List[str]=0.1,			_UpperCAmelCase     :					List[str]=1_0,			_UpperCAmelCase     :					List[Any]=0.02,			_UpperCAmelCase     :					List[Any]=None,			)				->			Dict:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Tuple     =						parent
              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						batch_size
              SCREAMING_SNAKE_CASE__       :      str     =						image_size
              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						patch_size
              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						num_channels
              SCREAMING_SNAKE_CASE__       :      List[str]     =						is_training
              SCREAMING_SNAKE_CASE__       :      Any     =						use_labels
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						hidden_size
              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						num_hidden_layers
              SCREAMING_SNAKE_CASE__       :      str     =						num_attention_heads
              SCREAMING_SNAKE_CASE__       :      str     =						intermediate_size
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						hidden_act
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						hidden_dropout_prob
              SCREAMING_SNAKE_CASE__       :      Any     =						attention_probs_dropout_prob
              SCREAMING_SNAKE_CASE__       :      Any     =						type_sequence_label_size
              SCREAMING_SNAKE_CASE__       :      Any     =						initializer_range
              SCREAMING_SNAKE_CASE__       :      Any     =						scope

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






       def  A_     ( self     :					Any						)				->			Dict:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      List[str]     =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

              SCREAMING_SNAKE_CASE__       :      List[str]     =						None
              if self.use_labels:
                     SCREAMING_SNAKE_CASE__       :      int     =						ids_tensor([self.batch_size],			self.type_sequence_label_size						)

              SCREAMING_SNAKE_CASE__       :      int     =						self.get_config()

              return config, pixel_values, labels






       def  A_     ( self     :					int						)				->			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     :					Dict,			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					List[Any]						)				->			str:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						ViTMSNModel(config=_UpperCAmelCase						)
              model.to(_UpperCAmelCase						)
              model.eval()
              SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						model(_UpperCAmelCase						)
              self.parent.assertEqual(result.last_hidden_state.shape,			(self.batch_size, self.seq_length, self.hidden_size)						)






       def  A_     ( self     :					int,			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					Union[str, Any]						)				->			List[Any]:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      List[str]     =						self.type_sequence_label_size
              SCREAMING_SNAKE_CASE__       :      Tuple     =						ViTMSNForImageClassification(_UpperCAmelCase						)
              model.to(_UpperCAmelCase						)
              model.eval()
              SCREAMING_SNAKE_CASE__       :      int     =						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
              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						1
              SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						ViTMSNForImageClassification(_UpperCAmelCase						)
              model.to(_UpperCAmelCase						)
              model.eval()

              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
              SCREAMING_SNAKE_CASE__       :      List[str]     =						model(_UpperCAmelCase						)
              self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.type_sequence_label_size)						)






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





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.prepare_config_and_inputs()
              SCREAMING_SNAKE_CASE__       :      Tuple     =						config_and_inputs
              SCREAMING_SNAKE_CASE__       :      Any     =						{"pixel_values": pixel_values}
              return config, inputs_dict


@require_torch
class 						lowerCamelCase       (__lowerCamelCase							,     __lowerCamelCase							,     unittest.TestCase      ):







       """simple docstring"""




       UpperCAmelCase_      						=   (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
       UpperCAmelCase_      						=   (
           {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
           if is_torch_available()
           else {}
       )

       UpperCAmelCase_      						=   False
       UpperCAmelCase_      						=   False
       UpperCAmelCase_      						=   False
       UpperCAmelCase_      						=   False






       def  A_     ( self     :					Any						)				->			Optional[Any]:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      List[str]     =						ViTMSNModelTester(self						)
              SCREAMING_SNAKE_CASE__       :      str     =						ConfigTester(self,			config_class=_UpperCAmelCase,			has_text_modality=_UpperCAmelCase,			hidden_size=3_7						)






       def  A_     ( self     :					Optional[int]						)				->			Union[str, Any]:





              """simple docstring"""
              self.config_tester.run_common_tests()






       @unittest.skip(reason="ViTMSN does not use inputs_embeds"						)
       def  A_     ( self     :					List[str]						)				->			Tuple:





              """simple docstring"""
              pass






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





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Tuple     =						self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						model_class(_UpperCAmelCase						)
                     self.assertIsInstance(model.get_input_embeddings(),			(nn.Module)						)
                     SCREAMING_SNAKE_CASE__       :      List[Any]     =						model.get_output_embeddings()
                     self.assertTrue(x is None or isinstance(_UpperCAmelCase,			nn.Linear						)						)






       def  A_     ( self     :					List[Any]						)				->			Dict:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.model_tester.prepare_config_and_inputs_for_common()

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

                     SCREAMING_SNAKE_CASE__       :      str     =						["pixel_values"]
                     self.assertListEqual(arg_names[:1],			_UpperCAmelCase						)






       def  A_     ( self     :					Optional[int]						)				->			Tuple:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Dict     =						self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*_UpperCAmelCase						)






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





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase						)






       @slow
       def  A_     ( self     :					Optional[int]						)				->			List[Any]:





              """simple docstring"""
              for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						ViTMSNModel.from_pretrained(_UpperCAmelCase						)
                     self.assertIsNotNone(_UpperCAmelCase						)




def _a (  )	->    Dict:

       '''simple docstring'''






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







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







       """simple docstring"""




       @cached_property
       def  A_     ( self     :					List[str]						)				->			Optional[Any]:





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






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





              """simple docstring"""
              torch.manual_seed(2						)
              SCREAMING_SNAKE_CASE__       :      List[str]     =						ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"						).to(_UpperCAmelCase						)

              SCREAMING_SNAKE_CASE__       :      Dict     =						self.default_image_processor
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						prepare_img()
              SCREAMING_SNAKE_CASE__       :      Dict     =						image_processor(images=_UpperCAmelCase,			return_tensors="pt"						).to(_UpperCAmelCase						)

              # forward pass
              with torch.no_grad():
                     SCREAMING_SNAKE_CASE__       :      Dict     =						model(**_UpperCAmelCase						)

              # verify the logits
              SCREAMING_SNAKE_CASE__       :      Tuple     =						torch.Size((1, 1_0_0_0)						)
              self.assertEqual(outputs.logits.shape,			_UpperCAmelCase						)

              SCREAMING_SNAKE_CASE__       :      int     =						torch.tensor([-0.0803, -0.4454, -0.2375]						).to(_UpperCAmelCase						)

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













_lowerCamelCase :    dict[tuple[int, int, int], int]  							= {}




def _a (  SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  int       )	->    int:

							'''simple docstring'''






							if late == 3 or absent == 2:
														return 0

							# if we have no days left, and have not failed any other rules,
							# we have a prize string
							if days == 0:
														return 1

							# No easy solution, so now we need to do the recursive calculation

							# First, check if the combination is already in the cache, and
							# if yes, return the stored value from there since we already
							# know the number of possible prize strings from this point on
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						(days, absent, late)
							if key in cache:
														return cache[key]

							# now we calculate the three possible ways that can unfold from
							# this point on, depending on our attendance today

							# 1) if we are late (but not absent), the "absent" counter stays as
							# it is, but the "late" counter increases by one
							SCREAMING_SNAKE_CASE__       :      Tuple     =						_calculate(days - 1			,				SCREAMING_SNAKE_CASE__			,				late + 1       )

							# 2) if we are absent, the "absent" counter increases by 1, and the
							# "late" counter resets to 0
							SCREAMING_SNAKE_CASE__       :      Dict     =						_calculate(days - 1			,				absent + 1			,				0       )

							# 3) if we are on time, this resets the "late" counter and keeps the
							# absent counter
							SCREAMING_SNAKE_CASE__       :      Any     =						_calculate(days - 1			,				SCREAMING_SNAKE_CASE__			,				0       )

							SCREAMING_SNAKE_CASE__       :      str     =						state_late + state_absent + state_ontime

							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						prizestrings
							return prizestrings




def _a (  SCREAMING_SNAKE_CASE__      :  int = 30       )	->    int:

							'''simple docstring'''






							return _calculate(SCREAMING_SNAKE_CASE__			,				absent=0			,				late=0       )


if __name__ == "__main__":
		print(solution())






