


"""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 					lowerCAmelCase	:




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__						,       lowerCAmelCase__=13						,       lowerCAmelCase__=30						,       lowerCAmelCase__=2						,       lowerCAmelCase__=3						,       lowerCAmelCase__=True						,       lowerCAmelCase__=True						,       lowerCAmelCase__=32						,       lowerCAmelCase__=5						,       lowerCAmelCase__=4						,       lowerCAmelCase__=37						,       lowerCAmelCase__="gelu"						,       lowerCAmelCase__=0.1						,       lowerCAmelCase__=0.1						,       lowerCAmelCase__=10						,       lowerCAmelCase__=0.02						,       lowerCAmelCase__=None						,       )     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					parent
								SCREAMING_SNAKE_CASE													=					batch_size
								SCREAMING_SNAKE_CASE													=					image_size
								SCREAMING_SNAKE_CASE													=					patch_size
								SCREAMING_SNAKE_CASE													=					num_channels
								SCREAMING_SNAKE_CASE													=					is_training
								SCREAMING_SNAKE_CASE													=					use_labels
								SCREAMING_SNAKE_CASE													=					hidden_size
								SCREAMING_SNAKE_CASE													=					num_hidden_layers
								SCREAMING_SNAKE_CASE													=					num_attention_heads
								SCREAMING_SNAKE_CASE													=					intermediate_size
								SCREAMING_SNAKE_CASE													=					hidden_act
								SCREAMING_SNAKE_CASE													=					hidden_dropout_prob
								SCREAMING_SNAKE_CASE													=					attention_probs_dropout_prob
								SCREAMING_SNAKE_CASE													=					type_sequence_label_size
								SCREAMING_SNAKE_CASE													=					initializer_range
								SCREAMING_SNAKE_CASE													=					scope

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







		def 							__A						(	self				)     ->							Tuple:
								SCREAMING_SNAKE_CASE													=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

								SCREAMING_SNAKE_CASE													=					self.get_config()

								return config, pixel_values, labels







		def 							__A						(	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 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							str:
								SCREAMING_SNAKE_CASE													=					ViTMSNModel(config=lowerCAmelCase__				)
								model.to(lowerCAmelCase__				)
								model.eval()
								SCREAMING_SNAKE_CASE													=					model(lowerCAmelCase__				)
								self.parent.assertEqual(result.last_hidden_state.shape						,       (self.batch_size, self.seq_length, self.hidden_size)				)







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							List[str]:
								SCREAMING_SNAKE_CASE													=					self.type_sequence_label_size
								SCREAMING_SNAKE_CASE													=					ViTMSNForImageClassification(lowerCAmelCase__				)
								model.to(lowerCAmelCase__				)
								model.eval()
								SCREAMING_SNAKE_CASE													=					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
								SCREAMING_SNAKE_CASE													=					1
								SCREAMING_SNAKE_CASE													=					ViTMSNForImageClassification(lowerCAmelCase__				)
								model.to(lowerCAmelCase__				)
								model.eval()

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







		def 							__A						(	self				)     ->							List[str]:
								SCREAMING_SNAKE_CASE													=					self.prepare_config_and_inputs()
								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					config_and_inputs
								SCREAMING_SNAKE_CASE													=					{'pixel_values': pixel_values}
								return config, inputs_dict





@require_torch
class 					lowerCAmelCase	(					lowerCamelCase_	,    lowerCamelCase_	,    unittest.TestCase    ):




		'''simple docstring'''







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

		SCREAMING_SNAKE_CASE_						:     Dict								=     False
		SCREAMING_SNAKE_CASE_						:     Dict								=     False
		SCREAMING_SNAKE_CASE_						:     List[Any]								=     False
		SCREAMING_SNAKE_CASE_						:     List[str]								=     False







		def 							__A						(	self				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					ViTMSNModelTester(self				)
								SCREAMING_SNAKE_CASE													=					ConfigTester(self						,       config_class=lowerCAmelCase__						,       has_text_modality=lowerCAmelCase__						,       hidden_size=37				)







		def 							__A						(	self				)     ->							List[Any]:
								self.config_tester.run_common_tests()







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







		def 							__A						(	self				)     ->							Any:
								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					self.model_tester.prepare_config_and_inputs_for_common()

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







		def 							__A						(	self				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					self.model_tester.prepare_config_and_inputs_for_common()

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

														SCREAMING_SNAKE_CASE													=					['pixel_values']
														self.assertListEqual(arg_names[:1]						,       lowerCAmelCase__				)







		def 							__A						(	self				)     ->							Any:
								SCREAMING_SNAKE_CASE													=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCAmelCase__				)







		def 							__A						(	self				)     ->							Dict:
								SCREAMING_SNAKE_CASE													=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__				)







		@slow
		def 							__A						(	self				)     ->							Optional[int]:
								for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														SCREAMING_SNAKE_CASE													=					ViTMSNModel.from_pretrained(lowerCAmelCase__				)
														self.assertIsNotNone(lowerCAmelCase__				)





def     lowercase			()   ->						Union[str, Any]:
						SCREAMING_SNAKE_CASE													=					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				)     ->							Dict:
								return ViTImageProcessor.from_pretrained('facebook/vit-msn-small'				) if is_vision_available() else None







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

								SCREAMING_SNAKE_CASE													=					self.default_image_processor
								SCREAMING_SNAKE_CASE													=					prepare_img()
								SCREAMING_SNAKE_CASE													=					image_processor(images=lowerCAmelCase__						,       return_tensors='pt'				).to(lowerCAmelCase__				)

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

								# verify the logits
								SCREAMING_SNAKE_CASE													=					torch.Size((1, 1_000)				)
								self.assertEqual(outputs.logits.shape						,       lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					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				)				)






"""simple docstring"""




from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload
class 					lowerCAmelCase	:




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__ = None				)     ->							None:
								if components is None:
														SCREAMING_SNAKE_CASE													=					[]
								SCREAMING_SNAKE_CASE													=					list(lowerCAmelCase__				)







		def __len__(	self				)     ->							int:
								return len(self.__components				)







		def __str__(	self				)     ->							str:
								return "(" + ",".join(map(lowerCAmelCase__						,       self.__components				)				) + ")"







		def __add__(	self						,       lowerCAmelCase__				)     ->							Vector:
								SCREAMING_SNAKE_CASE													=					len(self				)
								if size == len(lowerCAmelCase__				):
														SCREAMING_SNAKE_CASE													=					[self.__components[i] + other.component(lowerCAmelCase__				) for i in range(lowerCAmelCase__				)]
														return Vector(lowerCAmelCase__				)
								else:
														raise Exception('must have the same size'				)







		def __sub__(	self						,       lowerCAmelCase__				)     ->							Vector:
								SCREAMING_SNAKE_CASE													=					len(self				)
								if size == len(lowerCAmelCase__				):
														SCREAMING_SNAKE_CASE													=					[self.__components[i] - other.component(lowerCAmelCase__				) for i in range(lowerCAmelCase__				)]
														return Vector(lowerCAmelCase__				)
								else:  # error case
														raise Exception('must have the same size'				)







		@overload
		def __mul__(	self						,       lowerCAmelCase__				)     ->							Vector:
								...







		@overload
		def __mul__(	self						,       lowerCAmelCase__				)     ->							float:
								...







		def __mul__(	self						,       lowerCAmelCase__				)     ->							float | Vector:
								if isinstance(lowerCAmelCase__						,       (float, int)				):
														SCREAMING_SNAKE_CASE													=					[c * other for c in self.__components]
														return Vector(lowerCAmelCase__				)
								elif isinstance(lowerCAmelCase__						,       lowerCAmelCase__				) and len(self				) == len(lowerCAmelCase__				):
														SCREAMING_SNAKE_CASE													=					len(self				)
														SCREAMING_SNAKE_CASE													=					[self.__components[i] * other.component(lowerCAmelCase__				) for i in range(lowerCAmelCase__				)]
														return sum(lowerCAmelCase__				)
								else:  # error case
														raise Exception('invalid operand!'				)







		def 							__A						(	self				)     ->							Vector:
								return Vector(self.__components				)







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							float:
								if isinstance(lowerCAmelCase__						,       lowerCAmelCase__				) and -len(self.__components				) <= i < len(self.__components				):
														return self.__components[i]
								else:
														raise Exception('index out of range'				)







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							None:
								assert -len(self.__components				) <= pos < len(self.__components				)
								SCREAMING_SNAKE_CASE													=					value







		def 							__A						(	self				)     ->							float:
								if len(self.__components				) == 0:
														raise Exception('Vector is empty'				)
								SCREAMING_SNAKE_CASE													=					[c**2 for c in self.__components]
								return math.sqrt(sum(lowerCAmelCase__				)				)







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__ = False				)     ->							float:
								SCREAMING_SNAKE_CASE													=					self * other
								SCREAMING_SNAKE_CASE													=					self.euclidean_length() * other.euclidean_length()
								if deg:
														return math.degrees(math.acos(num / den				)				)
								else:
														return math.acos(num / den				)





def     lowercase			(SCREAMING_SNAKE_CASE_		:				int  )   ->						Vector:
						assert isinstance(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
						return Vector([0] * dimension  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				int  )   ->						Vector:
						assert isinstance(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ) and (isinstance(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ))
						SCREAMING_SNAKE_CASE													=					[0] * dimension
						SCREAMING_SNAKE_CASE													=					1
						return Vector(SCREAMING_SNAKE_CASE_  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				float ,			SCREAMING_SNAKE_CASE_		:				Vector ,			SCREAMING_SNAKE_CASE_		:				Vector  )   ->						Vector:
						assert (
						    isinstance(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
						    and isinstance(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
						    and (isinstance(SCREAMING_SNAKE_CASE_ ,			(int, float)  ))
						)
						return x * scalar + y


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				int  )   ->						Vector:
						random.seed(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					[random.randint(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ) for _ in range(SCREAMING_SNAKE_CASE_  )]
						return Vector(SCREAMING_SNAKE_CASE_  )
class 					lowerCAmelCase	:




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							None:
								SCREAMING_SNAKE_CASE													=					matrix
								SCREAMING_SNAKE_CASE													=					w
								SCREAMING_SNAKE_CASE													=					h







		def __str__(	self				)     ->							str:
								SCREAMING_SNAKE_CASE													=					''
								for i in range(self.__height				):
														ans += "|"
														for j in range(self.__width				):
																				if j < self.__width - 1:
																										ans += str(self.__matrix[i][j]				) + ","
																				else:
																										ans += str(self.__matrix[i][j]				) + "|\n"
								return ans







		def __add__(	self						,       lowerCAmelCase__				)     ->							Matrix:
								if self.__width == other.width() and self.__height == other.height():
														SCREAMING_SNAKE_CASE													=					[]
														for i in range(self.__height				):
																				SCREAMING_SNAKE_CASE													=					[
																				    self.__matrix[i][j] + other.component(lowerCAmelCase__						,       lowerCAmelCase__				)
																				    for j in range(self.__width				)
																				]
																				matrix.append(lowerCAmelCase__				)
														return Matrix(lowerCAmelCase__						,       self.__width						,       self.__height				)
								else:
														raise Exception('matrix must have the same dimension!'				)







		def __sub__(	self						,       lowerCAmelCase__				)     ->							Matrix:
								if self.__width == other.width() and self.__height == other.height():
														SCREAMING_SNAKE_CASE													=					[]
														for i in range(self.__height				):
																				SCREAMING_SNAKE_CASE													=					[
																				    self.__matrix[i][j] - other.component(lowerCAmelCase__						,       lowerCAmelCase__				)
																				    for j in range(self.__width				)
																				]
																				matrix.append(lowerCAmelCase__				)
														return Matrix(lowerCAmelCase__						,       self.__width						,       self.__height				)
								else:
														raise Exception('matrices must have the same dimension!'				)







		@overload
		def __mul__(	self						,       lowerCAmelCase__				)     ->							Matrix:
								...







		@overload
		def __mul__(	self						,       lowerCAmelCase__				)     ->							Vector:
								...







		def __mul__(	self						,       lowerCAmelCase__				)     ->							Vector | Matrix:
								if isinstance(lowerCAmelCase__						,       lowerCAmelCase__				):  # matrix-vector
														if len(lowerCAmelCase__				) == self.__width:
																				SCREAMING_SNAKE_CASE													=					zero_vector(self.__height				)
																				for i in range(self.__height				):
																										SCREAMING_SNAKE_CASE													=					[
																										    self.__matrix[i][j] * other.component(lowerCAmelCase__				)
																										    for j in range(self.__width				)
																										]
																										ans.change_component(lowerCAmelCase__						,       sum(lowerCAmelCase__				)				)
																				return ans
														else:
																				raise Exception(
																				    'vector must have the same size as the '
																				    'number of columns of the matrix!'				)
								elif isinstance(lowerCAmelCase__						,       (int, float)				):  # matrix-scalar
														SCREAMING_SNAKE_CASE													=					[
														    [self.__matrix[i][j] * other for j in range(self.__width				)]
														    for i in range(self.__height				)
														]
														return Matrix(lowerCAmelCase__						,       self.__width						,       self.__height				)
								return None







		def 							__A						(	self				)     ->							int:
								return self.__height







		def 							__A						(	self				)     ->							int:
								return self.__width







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							float:
								if 0 <= x < self.__height and 0 <= y < self.__width:
														return self.__matrix[x][y]
								else:
														raise Exception('change_component: indices out of bounds'				)







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							None:
								if 0 <= x < self.__height and 0 <= y < self.__width:
														SCREAMING_SNAKE_CASE													=					value
								else:
														raise Exception('change_component: indices out of bounds'				)







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							float:
								if self.__height != self.__width:
														raise Exception('Matrix is not square'				)
								SCREAMING_SNAKE_CASE													=					self.__matrix[:x] + self.__matrix[x + 1 :]
								for i in range(len(lowerCAmelCase__				)				):
														SCREAMING_SNAKE_CASE													=					minor[i][:y] + minor[i][y + 1 :]
								return Matrix(lowerCAmelCase__						,       self.__width - 1						,       self.__height - 1				).determinant()







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							float:
								if self.__height != self.__width:
														raise Exception('Matrix is not square'				)
								if 0 <= x < self.__height and 0 <= y < self.__width:
														return (-1) ** (x + y) * self.minor(lowerCAmelCase__						,       lowerCAmelCase__				)
								else:
														raise Exception('Indices out of bounds'				)







		def 							__A						(	self				)     ->							float:
								if self.__height != self.__width:
														raise Exception('Matrix is not square'				)
								if self.__height < 1:
														raise Exception('Matrix has no element'				)
								elif self.__height == 1:
														return self.__matrix[0][0]
								elif self.__height == 2:
														return (
														    self.__matrix[0][0] * self.__matrix[1][1]
														    - self.__matrix[0][1] * self.__matrix[1][0]
														)
								else:
														SCREAMING_SNAKE_CASE													=					[
														    self.__matrix[0][y] * self.cofactor(0						,       lowerCAmelCase__				) for y in range(self.__width				)
														]
														return sum(lowerCAmelCase__				)





def     lowercase			(SCREAMING_SNAKE_CASE_		:				int  )   ->						Matrix:
						SCREAMING_SNAKE_CASE													=					[[0] * n for _ in range(SCREAMING_SNAKE_CASE_  )]
						return Matrix(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				int  )   ->						Matrix:
						random.seed(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					[
						    [random.randint(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ) for _ in range(SCREAMING_SNAKE_CASE_  )] for _ in range(SCREAMING_SNAKE_CASE_  )
						]
						return Matrix(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )


