



"""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   :			str ,      __UpperCamelCase   :			str ,      __UpperCamelCase   :			Optional[Any]=1_3 ,      __UpperCamelCase   :			List[str]=3_0 ,      __UpperCamelCase   :			List[Any]=2 ,      __UpperCamelCase   :			List[Any]=3 ,      __UpperCamelCase   :			List[Any]=True ,      __UpperCamelCase   :			Dict=True ,      __UpperCamelCase   :			List[Any]=3_2 ,      __UpperCamelCase   :			List[str]=5 ,      __UpperCamelCase   :			Dict=4 ,      __UpperCamelCase   :			Any=3_7 ,      __UpperCamelCase   :			Dict="gelu" ,      __UpperCamelCase   :			List[str]=0.1 ,      __UpperCamelCase   :			str=0.1 ,      __UpperCamelCase   :			Any=1_0 ,      __UpperCamelCase   :			Tuple=0.0_2 ,      __UpperCamelCase   :			Union[str, Any]=None ,      )->int:
							_UpperCAmelCase							=			parent
							_UpperCAmelCase							=			batch_size
							_UpperCAmelCase							=			image_size
							_UpperCAmelCase							=			patch_size
							_UpperCAmelCase							=			num_channels
							_UpperCAmelCase							=			is_training
							_UpperCAmelCase							=			use_labels
							_UpperCAmelCase							=			hidden_size
							_UpperCAmelCase							=			num_hidden_layers
							_UpperCAmelCase							=			num_attention_heads
							_UpperCAmelCase							=			intermediate_size
							_UpperCAmelCase							=			hidden_act
							_UpperCAmelCase							=			hidden_dropout_prob
							_UpperCAmelCase							=			attention_probs_dropout_prob
							_UpperCAmelCase							=			type_sequence_label_size
							_UpperCAmelCase							=			initializer_range
							_UpperCAmelCase							=			scope

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



			def        lowercase__  (						self   :			List[str]      )->Tuple:
							_UpperCAmelCase							=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

							_UpperCAmelCase							=			self.get_config()

							return config, pixel_values, labels



			def        lowercase__  (						self   :			int      )->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        lowercase__  (						self   :			List[Any] ,      __UpperCamelCase   :			Optional[Any] ,      __UpperCamelCase   :			Tuple ,      __UpperCamelCase   :			str      )->List[str]:
							_UpperCAmelCase							=			ViTMSNModel(config=__lowerCamelCase      )
							model.to(__lowerCamelCase      )
							model.eval()
							_UpperCAmelCase							=			model(__lowerCamelCase      )
							self.parent.assertEqual(result.last_hidden_state.shape ,      (self.batch_size, self.seq_length, self.hidden_size)      )



			def        lowercase__  (						self   :			List[str] ,      __UpperCamelCase   :			int ,      __UpperCamelCase   :			Optional[int] ,      __UpperCamelCase   :			Tuple      )->Tuple:
							_UpperCAmelCase							=			self.type_sequence_label_size
							_UpperCAmelCase							=			ViTMSNForImageClassification(__lowerCamelCase      )
							model.to(__lowerCamelCase      )
							model.eval()
							_UpperCAmelCase							=			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
							_UpperCAmelCase							=			1
							_UpperCAmelCase							=			ViTMSNForImageClassification(__lowerCamelCase      )
							model.to(__lowerCamelCase      )
							model.eval()

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



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



@require_torch
class 	_a       (    lowercase__	,							lowercase__	,							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        lowercase__  (						self   :			int      )->List[Any]:
							_UpperCAmelCase							=			ViTMSNModelTester(self      )
							_UpperCAmelCase							=			ConfigTester(self ,      config_class=__lowerCamelCase ,      has_text_modality=__lowerCamelCase ,      hidden_size=3_7      )



			def        lowercase__  (						self   :			Union[str, Any]      )->int:
							self.config_tester.run_common_tests()



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



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

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



			def        lowercase__  (						self   :			int      )->Dict:
							_UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs_for_common()

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

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



			def        lowercase__  (						self   :			Tuple      )->Any:
							_UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowerCamelCase      )



			def        lowercase__  (						self   :			Optional[Any]      )->List[str]:
							_UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase      )



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



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







def        lowercase		(					):






				'''simple docstring'''



				_UpperCAmelCase							=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
				return image
@require_torch
@require_vision
class 	_a       (    unittest.TestCase):
			"""simple docstring"""



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



			@slow
			def        lowercase__  (						self   :			Tuple      )->str:
							torch.manual_seed(2      )
							_UpperCAmelCase							=			ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''      ).to(__lowerCamelCase      )

							_UpperCAmelCase							=			self.default_image_processor
							_UpperCAmelCase							=			prepare_img()
							_UpperCAmelCase							=			image_processor(images=__lowerCamelCase ,      return_tensors='''pt'''      ).to(__lowerCamelCase      )

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

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

							_UpperCAmelCase							=			torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5]      ).to(__lowerCamelCase      )

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







from __future__ import annotations

from typing import Any

def       lowercase_		(		_A			:    list[Any]						):

      """simple docstring"""




      create_state_space_tree(_A    ,					[]    ,					0						)

def       lowercase_		(		_A			:    list[Any]    ,					_A			:    list[Any]    ,					_A			:    int						):

      """simple docstring"""




      if index == len(_A						):
            print(_A						)
            return

      create_state_space_tree(_A    ,					_A    ,					index + 1						)
      current_subsequence.append(sequence[index]						)
      create_state_space_tree(_A    ,					_A    ,					index + 1						)
      current_subsequence.pop()


if __name__ == "__main__":
    A      :    list[Any]		=					[3, 1, 2, 4]
    generate_all_subsequences(seq)

    seq.clear()
    seq.extend(["A", "B", "C"])
    generate_all_subsequences(seq)


