



'''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 	__magic_name__						:







							def __init__(							self					:       Optional[int]	,_UpperCAmelCase					:       Optional[int]	,_UpperCAmelCase					:       Union[str, Any]=13	,_UpperCAmelCase					:       Optional[int]=30	,_UpperCAmelCase					:       Union[str, Any]=2	,_UpperCAmelCase					:       List[str]=3	,_UpperCAmelCase					:       Any=True	,_UpperCAmelCase					:       Tuple=True	,_UpperCAmelCase					:       Tuple=32	,_UpperCAmelCase					:       Union[str, Any]=5	,_UpperCAmelCase					:       str=4	,_UpperCAmelCase					:       int=37	,_UpperCAmelCase					:       int="gelu"	,_UpperCAmelCase					:       List[str]=0.1	,_UpperCAmelCase					:       Optional[Any]=0.1	,_UpperCAmelCase					:       List[Any]=10	,_UpperCAmelCase					:       str=0.02	,_UpperCAmelCase					:       List[str]=None	,):
									_a     :      int        =							parent
									_a     :      str        =							batch_size
									_a     :      Tuple        =							image_size
									_a     :      str        =							patch_size
									_a     :      List[str]        =							num_channels
									_a     :      Union[str, Any]        =							is_training
									_a     :      Union[str, Any]        =							use_labels
									_a     :      List[Any]        =							hidden_size
									_a     :      Tuple        =							num_hidden_layers
									_a     :      List[str]        =							num_attention_heads
									_a     :      List[Any]        =							intermediate_size
									_a     :      Union[str, Any]        =							hidden_act
									_a     :      Optional[Any]        =							hidden_dropout_prob
									_a     :      Dict        =							attention_probs_dropout_prob
									_a     :      Optional[Any]        =							type_sequence_label_size
									_a     :      List[str]        =							initializer_range
									_a     :      List[Any]        =							scope

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







							def 							__lowercase			(							self					:       str		):
									_a     :      Tuple        =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

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







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       Dict	,_UpperCAmelCase					:       Union[str, Any]	,_UpperCAmelCase					:       Any		):
									_a     :      Optional[int]        =							self.type_sequence_label_size
									_a     :      Any        =							ViTMSNForImageClassification(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a     :      Optional[Any]        =							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
									_a     :      Tuple        =							1
									_a     :      Optional[Any]        =							ViTMSNForImageClassification(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()

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







							def 							__lowercase			(							self					:       Dict		):
									_a     :      int        =							self.prepare_config_and_inputs()
									_a       ,					_a       ,					_a     :      List[str]        =							config_and_inputs
									_a     :      Dict        =							{'pixel_values': pixel_values}
									return config, inputs_dict

@require_torch
class 	__magic_name__						(    _UpperCamelCase			,						_UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    Any   				=					(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
							lowerCAmelCase       :    Optional[int]   				=					(
							    {'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
							    if is_torch_available()
							    else {}
							)

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







							def 							__lowercase			(							self					:       Tuple		):
									_a     :      List[Any]        =							ViTMSNModelTester(self		)
									_a     :      Dict        =							ConfigTester(self	,config_class=_UpperCAmelCase	,has_text_modality=_UpperCAmelCase	,hidden_size=37		)







							def 							__lowercase			(							self					:       Optional[Any]		):
									self.config_tester.run_common_tests()







							@unittest.skip(reason='ViTMSN does not use inputs_embeds'		)
							def 							__lowercase			(							self					:       str		):
									pass







							def 							__lowercase			(							self					:       List[str]		):
									_a       ,					_a     :      Any        =							self.model_tester.prepare_config_and_inputs_for_common()

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







							def 							__lowercase			(							self					:       Optional[int]		):
									_a       ,					_a     :      Optional[int]        =							self.model_tester.prepare_config_and_inputs_for_common()

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

											_a     :      List[Any]        =							['pixel_values']
											self.assertListEqual(arg_names[:1]	,_UpperCAmelCase		)







							def 							__lowercase			(							self					:       int		):
									_a     :      List[str]        =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Dict		):
									_a     :      Dict        =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase		)







							@slow
							def 							__lowercase			(							self					:       List[Any]		):
									for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_a     :      List[str]        =							ViTMSNModel.from_pretrained(_UpperCAmelCase		)
											self.assertIsNotNone(_UpperCAmelCase		)



def      __lowerCamelCase     (      )			->      Tuple:
		_a     :      Tuple        =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
		return image

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







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







							@slow
							def 							__lowercase			(							self					:       Tuple		):
									torch.manual_seed(2		)
									_a     :      List[str]        =							ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'		).to(_UpperCAmelCase		)

									_a     :      List[str]        =							self.default_image_processor
									_a     :      Dict        =							prepare_img()
									_a     :      Union[str, Any]        =							image_processor(images=_UpperCAmelCase	,return_tensors='pt'		).to(_UpperCAmelCase		)

									# forward pass
									with torch.no_grad():
											_a     :      List[str]        =							model(**_UpperCAmelCase		)

									# verify the logits
									_a     :      Optional[int]        =							torch.Size((1, 1000)		)
									self.assertEqual(outputs.logits.shape	,_UpperCAmelCase		)

									_a     :      Any        =							torch.tensor([-0.08_03, -0.44_54, -0.23_75]		).to(_UpperCAmelCase		)

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








'''simple docstring'''


from typing import Any





class     lowercase__  :





							'''simple docstring'''


							def __init__(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Dict     		=      data
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      None






							def __repr__(    self  ):
											return f"""Node({self.data})"""





class     lowercase__  :





							'''simple docstring'''


							def __init__(    self  ):
											_SCREAMING_SNAKE_CASE :       Any     		=      None






							def __iter__(    self  ):
											_SCREAMING_SNAKE_CASE :       Any     		=      self.head
											while node:
															yield node.data
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      node.next






							def __len__(    self  ):
											return sum(1 for _ in self  )






							def __repr__(    self  ):
											return "->".join([str(__snake_case  ) for item in self]  )






							def __getitem__(    self    ,					__snake_case  ):
											if not 0 <= index < len(self  ):
															raise ValueError("""list index out of range."""  )
											for i, node in enumerate(self  ):
															if i == index:
																			return node
											return None






							def __setitem__(    self    ,					__snake_case    ,					__snake_case  ):
											if not 0 <= index < len(self  ):
															raise ValueError("""list index out of range."""  )
											_SCREAMING_SNAKE_CASE :       Any     		=      self.head
											for _ in range(__snake_case  ):
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      current.next
											_SCREAMING_SNAKE_CASE :       Dict     		=      data






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											self.insert_nth(len(self  )    ,					__snake_case  )






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											self.insert_nth(0    ,					__snake_case  )






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case  ):
											if not 0 <= index <= len(self  ):
															raise IndexError("""list index out of range"""  )
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      Node(__snake_case  )
											if self.head is None:
															_SCREAMING_SNAKE_CASE :       str     		=      new_node
											elif index == 0:
															_SCREAMING_SNAKE_CASE :       Tuple     		=      self.head  # link new_node to head
															_SCREAMING_SNAKE_CASE :       str     		=      new_node
											else:
															_SCREAMING_SNAKE_CASE :       Tuple     		=      self.head
															for _ in range(index - 1  ):
																			_SCREAMING_SNAKE_CASE :       List[str]     		=      temp.next
															_SCREAMING_SNAKE_CASE :       Tuple     		=      temp.next
															_SCREAMING_SNAKE_CASE :       Dict     		=      new_node






							def UpperCAmelCase_	(    self  ):  # print every node data
											print(self  )






							def UpperCAmelCase_	(    self  ):
											return self.delete_nth(0  )






							def UpperCAmelCase_	(    self  ):  # delete from tail
											return self.delete_nth(len(self  ) - 1  )






							def UpperCAmelCase_	(    self    ,					__snake_case = 0  ):
											if not 0 <= index <= len(self  ) - 1:  # test if index is valid
															raise IndexError("""List index out of range."""  )
											_SCREAMING_SNAKE_CASE :       Tuple     		=      self.head  # default first node
											if index == 0:
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.head.next
											else:
															_SCREAMING_SNAKE_CASE :       Tuple     		=      self.head
															for _ in range(index - 1  ):
																			_SCREAMING_SNAKE_CASE :       Any     		=      temp.next
															_SCREAMING_SNAKE_CASE :       Any     		=      temp.next
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      temp.next.next
											return delete_node.data






							def UpperCAmelCase_	(    self  ):
											return self.head is None






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Dict     		=      None
											_SCREAMING_SNAKE_CASE :       int     		=      self.head

											while current:
															# Store the current node's next node.
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      current.next
															# Make the current node's next point backwards
															_SCREAMING_SNAKE_CASE :       Tuple     		=      prev
															# Make the previous node be the current node
															_SCREAMING_SNAKE_CASE :       int     		=      current
															# Make the current node the next node (to progress iteration)
															_SCREAMING_SNAKE_CASE :       Any     		=      next_node
											# Return prev in order to put the head at the end
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      prev







def 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Dict     		=      LinkedList()
				assert linked_list.is_empty() is True
				assert str(SCREAMING_SNAKE_CASE__     ) == ""

				try:
								linked_list.delete_head()
								raise AssertionError  # This should not happen.
				except IndexError:
								assert True  # This should happen.

				try:
								linked_list.delete_tail()
								raise AssertionError  # This should not happen.
				except IndexError:
								assert True  # This should happen.

				for i in range(10     ):
								assert len(SCREAMING_SNAKE_CASE__     ) == i
								linked_list.insert_nth(SCREAMING_SNAKE_CASE__							,      i + 1     )
				assert str(SCREAMING_SNAKE_CASE__     ) == "->".join(str(SCREAMING_SNAKE_CASE__     ) for i in range(1							,      11     )     )

				linked_list.insert_head(0     )
				linked_list.insert_tail(11     )
				assert str(SCREAMING_SNAKE_CASE__     ) == "->".join(str(SCREAMING_SNAKE_CASE__     ) for i in range(0							,      12     )     )

				assert linked_list.delete_head() == 0
				assert linked_list.delete_nth(9     ) == 10
				assert linked_list.delete_tail() == 11
				assert len(SCREAMING_SNAKE_CASE__     ) == 9
				assert str(SCREAMING_SNAKE_CASE__     ) == "->".join(str(SCREAMING_SNAKE_CASE__     ) for i in range(1							,      10     )     )

				assert all(linked_list[i] == i + 1 for i in range(0							,      9     )     ) is True

				for i in range(0							,      9     ):
								_SCREAMING_SNAKE_CASE :       List[str]     		=      -i
				assert all(linked_list[i] == -i for i in range(0							,      9     )     ) is True

				linked_list.reverse()
				assert str(SCREAMING_SNAKE_CASE__     ) == "->".join(str(SCREAMING_SNAKE_CASE__     ) for i in range(-8							,      1     )     )







def 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      [
				    -9,
				    100,
				    Node(7734_5112     ),
				    """dlrow olleH""",
				    7,
				    5555,
				    0,
				    -1_9_2.5_5_5_5_5,
				    """Hello, world!""",
				    7_7.9,
				    Node(10     ),
				    None,
				    None,
				    1_2.2_0,
				]
				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      LinkedList()

				for i in test_input:
								linked_list.insert_tail(SCREAMING_SNAKE_CASE__     )

				# Check if it's empty or not
				assert linked_list.is_empty() is False
				assert (
				    str(SCREAMING_SNAKE_CASE__     ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
				    "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
				)

				# Delete the head
				_SCREAMING_SNAKE_CASE :       Any     		=      linked_list.delete_head()
				assert result == -9
				assert (
				    str(SCREAMING_SNAKE_CASE__     ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
				    "Hello, world!->77.9->Node(10)->None->None->12.2"
				)

				# Delete the tail
				_SCREAMING_SNAKE_CASE :       List[Any]     		=      linked_list.delete_tail()
				assert result == 1_2.2
				assert (
				    str(SCREAMING_SNAKE_CASE__     ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
				    "Hello, world!->77.9->Node(10)->None->None"
				)

				# Delete a node in specific location in linked list
				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      linked_list.delete_nth(10     )
				assert result is None
				assert (
				    str(SCREAMING_SNAKE_CASE__     ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
				    "Hello, world!->77.9->Node(10)->None"
				)

				# Add a Node instance to its head
				linked_list.insert_head(Node("""Hello again, world!"""     )     )
				assert (
				    str(SCREAMING_SNAKE_CASE__     )
				    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
				    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
				)

				# Add None to its tail
				linked_list.insert_tail(SCREAMING_SNAKE_CASE__     )
				assert (
				    str(SCREAMING_SNAKE_CASE__     )
				    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
				    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
				)

				# Reverse the linked list
				linked_list.reverse()
				assert (
				    str(SCREAMING_SNAKE_CASE__     )
				    == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
				    "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
				)







def 			snake_case_   (  ):







				"""simple docstring"""





				from doctest import testmod

				testmod()

				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      LinkedList()
				linked_list.insert_head(input("""Inserting 1st at head """     ).strip()     )
				linked_list.insert_head(input("""Inserting 2nd at head """     ).strip()     )
				print("""\nPrint list:"""     )
				linked_list.print_list()
				linked_list.insert_tail(input("""\nInserting 1st at tail """     ).strip()     )
				linked_list.insert_tail(input("""Inserting 2nd at tail """     ).strip()     )
				print("""\nPrint list:"""     )
				linked_list.print_list()
				print("""\nDelete head"""     )
				linked_list.delete_head()
				print("""Delete tail"""     )
				linked_list.delete_tail()
				print("""\nPrint list:"""     )
				linked_list.print_list()
				print("""\nReverse linked list"""     )
				linked_list.reverse()
				print("""\nPrint list:"""     )
				linked_list.print_list()
				print("""\nString representation of linked list:"""     )
				print(SCREAMING_SNAKE_CASE__     )
				print("""\nReading/changing Node data using indexing:"""     )
				print(f"""Element at Position 1: {linked_list[1]}"""     )
				_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      input("""Enter New Value: """     ).strip()
				print("""New list:"""     )
				print(SCREAMING_SNAKE_CASE__     )
				print(f"""length of linked_list is : {len(SCREAMING_SNAKE_CASE__     )}"""     )


if __name__ == "__main__":
				main()




