

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

								# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								__lowerCamelCase    :    Optional[Any] 	=			(image_size // patch_size) ** 2
								__lowerCamelCase    :    Any 	=			num_patches + 1
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    str 	=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

								__lowerCamelCase    :    Dict 	=			self.get_config()

								return config, pixel_values, labels
							def    lowerCAmelCase					(		self		:      List[Any]):
								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    lowerCAmelCase					(		self		:      str       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Optional[int]):
								__lowerCamelCase    :    str 	=			ViTMSNModel(config=SCREAMING_SNAKE_CASE__)
								model.to(SCREAMING_SNAKE_CASE__)
								model.eval()
								__lowerCamelCase    :    List[str] 	=			model(SCREAMING_SNAKE_CASE__)
								self.parent.assertEqual(result.last_hidden_state.shape       ,(self.batch_size, self.seq_length, self.hidden_size))
							def    lowerCAmelCase					(		self		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      Optional[int]):
								__lowerCamelCase    :    Optional[int] 	=			self.type_sequence_label_size
								__lowerCamelCase    :    Optional[Any] 	=			ViTMSNForImageClassification(SCREAMING_SNAKE_CASE__)
								model.to(SCREAMING_SNAKE_CASE__)
								model.eval()
								__lowerCamelCase    :    Optional[Any] 	=			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
								__lowerCamelCase    :    int 	=			1
								__lowerCamelCase    :    Union[str, Any] 	=			ViTMSNForImageClassification(SCREAMING_SNAKE_CASE__)
								model.to(SCREAMING_SNAKE_CASE__)
								model.eval()

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


							def    lowerCAmelCase					(		self		:      List[str]):
								__lowerCamelCase    :    Tuple 	=			self.prepare_config_and_inputs()
								__lowerCamelCase    :    Optional[int] 	=			config_and_inputs
								__lowerCamelCase    :    int 	=			{'pixel_values': pixel_values}
								return config, inputs_dict






@require_torch
class 	A_		(	SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							unittest.TestCase    ):
							_UpperCAmelCase   :  Dict										=				(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
							_UpperCAmelCase   :  Any										=				(
							    {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
							    if is_torch_available()
							    else {}
							)

							_UpperCAmelCase   :  List[Any]										=				False
							_UpperCAmelCase   :  List[str]										=				False
							_UpperCAmelCase   :  str										=				False
							_UpperCAmelCase   :  Optional[int]										=				False
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								__lowerCamelCase    :    Optional[Any] 	=			ViTMSNModelTester(self)
								__lowerCamelCase    :    Optional[int] 	=			ConfigTester(self       ,config_class=SCREAMING_SNAKE_CASE__       ,has_text_modality=SCREAMING_SNAKE_CASE__       ,hidden_size=3_7)
							def    lowerCAmelCase					(		self		:      List[str]):
								self.config_tester.run_common_tests()
							@unittest.skip(reason='ViTMSN does not use inputs_embeds')
							def    lowerCAmelCase					(		self		:      int):
								pass
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    List[Any] 	=			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									__lowerCamelCase    :    Union[str, Any] 	=			model_class(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(model.get_input_embeddings()       ,(nn.Module))
									__lowerCamelCase    :    Any 	=			model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__       ,nn.Linear))
							def    lowerCAmelCase					(		self		:      Dict):
								__lowerCamelCase    :    Dict 	=			self.model_tester.prepare_config_and_inputs_for_common()

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

									__lowerCamelCase    :    int 	=			['pixel_values']
									self.assertListEqual(arg_names[:1]       ,SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    List[Any] 	=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Optional[Any] 	=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__)


							@slow
							def    lowerCAmelCase					(		self		:      Tuple):
								for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__lowerCamelCase    :    List[Any] 	=			ViTMSNModel.from_pretrained(SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)






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






@require_torch
@require_vision
class 	A_		(	unittest.TestCase    ):
							@cached_property
							def    lowerCAmelCase					(		self		:      Dict):
								return ViTImageProcessor.from_pretrained('facebook/vit-msn-small') if is_vision_available() else None


							@slow
							def    lowerCAmelCase					(		self		:      Optional[int]):
								torch.manual_seed(2)
								__lowerCamelCase    :    Optional[int] 	=			ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small').to(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    List[str] 	=			self.default_image_processor
								__lowerCamelCase    :    str 	=			prepare_img()
								__lowerCamelCase    :    Any 	=			image_processor(images=SCREAMING_SNAKE_CASE__       ,return_tensors='pt').to(SCREAMING_SNAKE_CASE__)

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

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

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

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







from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

a		=TypeVar("""T""")
a		=TypeVar("""U""")






class 	A_		(	Generic[T, U]    ):
							def __init__(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      T | None       ,SCREAMING_SNAKE_CASE__		:      U | None):
								__lowerCamelCase    :    Optional[Any] 	=			key
								__lowerCamelCase    :    Tuple 	=			val
								__lowerCamelCase    :    DoubleLinkedListNode[T, U] | None 	=			None
								__lowerCamelCase    :    DoubleLinkedListNode[T, U] | None 	=			None


							def __repr__(		self		:      Optional[int]):
								return (
								    F"Node: key: {self.key}, val: {self.val}, "
								    F"has next: {bool(self.next)}, has prev: {bool(self.prev)}"
								)






class 	A_		(	Generic[T, U]    ):
							def __init__(		self		:      List[Any]):
								__lowerCamelCase    :    DoubleLinkedListNode[T, U] 	=			DoubleLinkedListNode(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    DoubleLinkedListNode[T, U] 	=			DoubleLinkedListNode(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase	,			__lowerCamelCase    :    List[Any] 	=			self.rear, self.head
							def __repr__(		self		:      Optional[int]):
								__lowerCamelCase    :    int 	=			['DoubleLinkedList']
								__lowerCamelCase    :    Tuple 	=			self.head
								while node.next is not None:
									rep.append(str(SCREAMING_SNAKE_CASE__))
									__lowerCamelCase    :    Union[str, Any] 	=			node.next
								rep.append(str(self.rear))
								return ",\n    ".join(SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      DoubleLinkedListNode[T, U]):
								__lowerCamelCase    :    str 	=			self.rear.prev

								# All nodes other than self.head are guaranteed to have non-None previous
								assert previous is not None

								__lowerCamelCase    :    Any 	=			node
								__lowerCamelCase    :    str 	=			previous
								__lowerCamelCase    :    str 	=			node
								__lowerCamelCase    :    List[str] 	=			self.rear


							def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      DoubleLinkedListNode[T, U]):

								if node.prev is None or node.next is None:
									return None

								__lowerCamelCase    :    Union[str, Any] 	=			node.next
								__lowerCamelCase    :    Dict 	=			node.prev
								__lowerCamelCase    :    Optional[int] 	=			None
								__lowerCamelCase    :    List[str] 	=			None
								return node






class 	A_		(	Generic[T, U]    ):
							_UpperCAmelCase   :  dict[Callable[[T], U], LRUCache[T, U]]										=				{}
							def __init__(		self		:      str       ,SCREAMING_SNAKE_CASE__		:      int):
								__lowerCamelCase    :    DoubleLinkedList[T, U] 	=			DoubleLinkedList()
								__lowerCamelCase    :    List[Any] 	=			capacity
								__lowerCamelCase    :    Optional[int] 	=			0
								__lowerCamelCase    :    int 	=			0
								__lowerCamelCase    :    Optional[Any] 	=			0
								__lowerCamelCase    :    dict[T, DoubleLinkedListNode[T, U]] 	=			{}
							def __repr__(		self		:      Optional[Any]):
								return (
								    F"CacheInfo(hits={self.hits}, misses={self.miss}, "
								    F"capacity={self.capacity}, current size={self.num_keys})"
								)
							def __contains__(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      T):
								return key in self.cache
							def    lowerCAmelCase					(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      T):
								# Note: pythonic interface would throw KeyError rather than return None

								if key in self.cache:
									self.hits += 1
									__lowerCamelCase    :    DoubleLinkedListNode[T, U] 	=			self.cache[key]
									__lowerCamelCase    :    List[str] 	=			self.list.remove(self.cache[key])
									assert node == value_node

									# node is guaranteed not None because it is in self.cache
									assert node is not None
									self.list.add(SCREAMING_SNAKE_CASE__)
									return node.val
								self.miss += 1
								return None
							def    lowerCAmelCase					(		self		:      Dict       ,SCREAMING_SNAKE_CASE__		:      T       ,SCREAMING_SNAKE_CASE__		:      U):

								if key not in self.cache:
									if self.num_keys >= self.capacity:
										# delete first node (oldest) when over capacity
										__lowerCamelCase    :    Optional[Any] 	=			self.list.head.next

										# guaranteed to have a non-None first node when num_keys > 0
										# explain to type checker via assertions
										assert first_node is not None
										assert first_node.key is not None
										assert (
										    self.list.remove(SCREAMING_SNAKE_CASE__) is not None
										)  # node guaranteed to be in list assert node.key is not None

										del self.cache[first_node.key]
										self.num_keys -= 1
									__lowerCamelCase    :    List[Any] 	=			DoubleLinkedListNode(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
									self.list.add(self.cache[key])
									self.num_keys += 1

								else:
									# bump node to the end of the list, update value
									__lowerCamelCase    :    List[str] 	=			self.list.remove(self.cache[key])
									assert node is not None  # node guaranteed to be in list
									__lowerCamelCase    :    List[Any] 	=			value
									self.list.add(SCREAMING_SNAKE_CASE__)


							@classmethod
							def    lowerCAmelCase					(		cls		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      int = 1_2_8):

								def cache_decorator_inner(SCREAMING_SNAKE_CASE__		:      Callable[[T], U]) -> Callable[..., U]:
									def cache_decorator_wrapper(*SCREAMING_SNAKE_CASE__		:      T) -> U:
										if func not in cls.decorator_function_to_instance_map:
											__lowerCamelCase    :    str 	=			LRUCache(SCREAMING_SNAKE_CASE__)

										__lowerCamelCase    :    Dict 	=			cls.decorator_function_to_instance_map[func].get(args[0])
										if result is None:
											__lowerCamelCase    :    str 	=			func(*SCREAMING_SNAKE_CASE__)
											cls.decorator_function_to_instance_map[func].put(args[0]       ,SCREAMING_SNAKE_CASE__)
										return result

									def cache_info() -> LRUCache[T, U]:
										return cls.decorator_function_to_instance_map[func]

									setattr(SCREAMING_SNAKE_CASE__       ,'cache_info'       ,SCREAMING_SNAKE_CASE__)  # noqa: B010

									return cache_decorator_wrapper

								return cache_decorator_inner


if __name__ == "__main__":
					import doctest

					doctest.testmod()




