


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  snake_case__:







						'''simple docstring'''




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

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




						def   lowercase_       ( self							)    ->			List[Any]:
										lowerCAmelCase_       :					Tuple			 = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

										lowerCAmelCase_       :					Any			 = None
										if self.use_labels:
														lowerCAmelCase_       :					Optional[Any]			 = ids_tensor([self.batch_size]      ,  self.type_sequence_label_size							)

										lowerCAmelCase_       :					Tuple			 = self.get_config()

										return config, pixel_values, labels




						def   lowercase_       ( 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   lowercase_       ( self      ,  __lowercase      ,  __lowercase      ,  __lowercase							)    ->			Tuple:
										lowerCAmelCase_       :					Any			 = ViTMSNModel(config=lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()
										lowerCAmelCase_       :					Dict			 = model(lowerCAmelCase__							)
										self.parent.assertEqual(result.last_hidden_state.shape      ,  (self.batch_size, self.seq_length, self.hidden_size)							)




						def   lowercase_       ( self      ,  __lowercase      ,  __lowercase      ,  __lowercase							)    ->			str:
										lowerCAmelCase_       :					Optional[Any]			 = self.type_sequence_label_size
										lowerCAmelCase_       :					Union[str, Any]			 = ViTMSNForImageClassification(lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()
										lowerCAmelCase_       :					Dict			 = 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
										lowerCAmelCase_       :					Tuple			 = 1
										lowerCAmelCase_       :					str			 = ViTMSNForImageClassification(lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()

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




						def   lowercase_       ( self							)    ->			Optional[Any]:
										lowerCAmelCase_       :					List[Any]			 = self.prepare_config_and_inputs()
										lowerCAmelCase_       :					str			 = config_and_inputs
										lowerCAmelCase_       :					Tuple			 = {"pixel_values": pixel_values}
										return config, inputs_dict







@require_torch
class  snake_case__(							UpperCamelCase__,  UpperCamelCase__,  unittest.TestCase							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__					:     Optional[int]    =							(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
						SCREAMING_SNAKE_CASE__					:     Optional[int]    =							(
						    {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
						    if is_torch_available()
						    else {}
						)

						SCREAMING_SNAKE_CASE__					:     int    =							False
						SCREAMING_SNAKE_CASE__					:     Any    =							False
						SCREAMING_SNAKE_CASE__					:     Optional[int]    =							False
						SCREAMING_SNAKE_CASE__					:     List[Any]    =							False




						def   lowercase_       ( self							)    ->			str:
										lowerCAmelCase_       :					int			 = ViTMSNModelTester(self							)
										lowerCAmelCase_       :					Optional[Any]			 = ConfigTester(self      ,  config_class=lowerCAmelCase__      ,  has_text_modality=lowerCAmelCase__      ,  hidden_size=3_7							)




						def   lowercase_       ( self							)    ->			str:
										self.config_tester.run_common_tests()




						@unittest.skip(reason='''ViTMSN does not use inputs_embeds'''							)
						def   lowercase_       ( self							)    ->			Optional[Any]:
										pass




						def   lowercase_       ( self							)    ->			str:
										lowerCAmelCase_       :					Union[str, Any]			 = self.model_tester.prepare_config_and_inputs_for_common()

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




						def   lowercase_       ( self							)    ->			Union[str, Any]:
										lowerCAmelCase_       :					Optional[int]			 = self.model_tester.prepare_config_and_inputs_for_common()

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

														lowerCAmelCase_       :					str			 = ["pixel_values"]
														self.assertListEqual(arg_names[:1]      ,  lowerCAmelCase__							)




						def   lowercase_       ( self							)    ->			List[Any]:
										lowerCAmelCase_       :					List[str]			 = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__							)




						def   lowercase_       ( self							)    ->			Tuple:
										lowerCAmelCase_       :					List[Any]			 = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__							)




						@slow
						def   lowercase_       ( self							)    ->			List[Any]:




										for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowerCAmelCase_       :					Optional[int]			 = ViTMSNModel.from_pretrained(lowerCAmelCase__							)
														self.assertIsNotNone(lowerCAmelCase__							)






def 	lowerCAmelCase       (						)->       Optional[int]:
				lowerCAmelCase_       :					Tuple			 = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
				return image







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







						'''simple docstring'''




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




						@slow
						def   lowercase_       ( self							)    ->			Union[str, Any]:
										torch.manual_seed(2							)
										lowerCAmelCase_       :					Tuple			 = ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''							).to(lowerCAmelCase__							)

										lowerCAmelCase_       :					Optional[int]			 = self.default_image_processor
										lowerCAmelCase_       :					Tuple			 = prepare_img()
										lowerCAmelCase_       :					Union[str, Any]			 = image_processor(images=lowerCAmelCase__      ,  return_tensors='''pt'''							).to(lowerCAmelCase__							)

										# forward pass
										with torch.no_grad():
														lowerCAmelCase_       :					Tuple			 = model(**lowerCAmelCase__							)

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

										lowerCAmelCase_       :					Tuple			 = torch.tensor([-0.08_03, -0.44_54, -0.23_75]							).to(lowerCAmelCase__							)

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






def      _A       (		SCREAMING_SNAKE_CASE     :   int = 50						):





  """simple docstring"""



  a__	:					Any										=[1] * (length + 1)

  for row_length in range(3       ,       length + 1						):
    for block_length in range(3       ,       row_length + 1						):
      for block_start in range(row_length - block_length						):
        ways_number[row_length] += ways_number[
            row_length - block_start - block_length - 1
        ]

      ways_number[row_length] += 1

  return ways_number[length]


if __name__ == "__main__":
     print(F"""{solution() = }""")






