





'''simple docstring'''






from __future__ import annotations




class  _A :

		def __init__(   self					:   Optional[int]  ,      __magic_name__					:   list[list[int]]			)					->    str:


					"""simple docstring"""

					__snake_case					:      str							      =     TypeError(
					    """Matrices must be formed from a list of zero or more lists containing at """
					    """least one and the same number of values, each of which must be of type """
					    """int or float."""			)
					if len(__magic_name__			) != 0:
								__snake_case					:      List[str]							      =     len(rows[0]			)
								if cols == 0:
											raise error
								for row in rows:
											if len(__magic_name__			) != cols:
														raise error
											for value in row:
														if not isinstance(__magic_name__  ,      (int, float)			):
																	raise error
								__snake_case					:      Any							      =     rows
					else:
								__snake_case					:      List[Any]							      =     []

		def 	lowercase__     (   self					:   int			)					->    list[list[int]]:


					"""simple docstring"""

					return [[row[i] for row in self.rows] for i in range(len(self.rows[0]			)			)]

		@property
		def 	lowercase__     (   self					:   Union[str, Any]			)					->    int:


					"""simple docstring"""

					return len(self.rows			)

		@property
		def 	lowercase__     (   self					:   Any			)					->    int:


					"""simple docstring"""

					return len(self.rows[0]			)

		@property
		def 	lowercase__     (   self					:   str			)					->    tuple[int, int]:


					"""simple docstring"""

					return (self.num_rows, self.num_columns)

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


					"""simple docstring"""

					return self.order[0] == self.order[1]

		def 	lowercase__     (   self					:   List[str]			)					->    Matrix:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     [
					    [0 if column_num != row_num else 1 for column_num in range(self.num_rows			)]
					    for row_num in range(self.num_rows			)
					]
					return Matrix(__magic_name__			)

		def 	lowercase__     (   self					:   str			)					->    int:


					"""simple docstring"""

					if not self.is_square:
								return 0
					if self.order == (0, 0):
								return 1
					if self.order == (1, 1):
								return int(self.rows[0][0]			)
					if self.order == (2, 2):
								return int(
								    (self.rows[0][0] * self.rows[1][1])
								    - (self.rows[0][1] * self.rows[1][0])			)
					else:
								return sum(
								    self.rows[0][column] * self.cofactors().rows[0][column]
								    for column in range(self.num_columns			)			)

		def 	lowercase__     (   self					:   Any			)					->    bool:


					"""simple docstring"""

					return bool(self.determinant()			)

		def 	lowercase__     (   self					:   str  ,      __magic_name__					:   int  ,      __magic_name__					:   int			)					->    int:


					"""simple docstring"""

					__snake_case					:      Optional[int]							      =     [
					    [
					        self.rows[other_row][other_column]
					        for other_column in range(self.num_columns			)
					        if other_column != column
					    ]
					    for other_row in range(self.num_rows			)
					    if other_row != row
					]
					return Matrix(__magic_name__			).determinant()

		def 	lowercase__     (   self					:   Optional[int]  ,      __magic_name__					:   int  ,      __magic_name__					:   int			)					->    int:


					"""simple docstring"""

					if (row + column) % 2 == 0:
								return self.get_minor(__magic_name__  ,      __magic_name__			)
					return -1 * self.get_minor(__magic_name__  ,      __magic_name__			)

		def 	lowercase__     (   self					:   int			)					->    Matrix:


					"""simple docstring"""

					return Matrix(
					    [
					        [self.get_minor(__magic_name__  ,      __magic_name__			) for column in range(self.num_columns			)]
					        for row in range(self.num_rows			)
					    ]			)

		def 	lowercase__     (   self					:   str			)					->    Matrix:


					"""simple docstring"""

					return Matrix(
					    [
					        [
					            self.minors().rows[row][column]
					            if (row + column) % 2 == 0
					            else self.minors().rows[row][column] * -1
					            for column in range(self.minors().num_columns			)
					        ]
					        for row in range(self.minors().num_rows			)
					    ]			)

		def 	lowercase__     (   self					:   List[str]			)					->    Matrix:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     [
					    [self.cofactors().rows[column][row] for column in range(self.num_columns			)]
					    for row in range(self.num_rows			)
					]
					return Matrix(__magic_name__			)

		def 	lowercase__     (   self					:   Any			)					->    Matrix:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     self.determinant()
					if not determinant:
								raise TypeError("""Only matrices with a non-zero determinant have an inverse"""			)
					return self.adjugate() * (1 / determinant)

		def __repr__(   self					:   Optional[int]			)					->    str:


					"""simple docstring"""

					return str(self.rows			)

		def __str__(   self					:   Optional[Any]			)					->    str:


					"""simple docstring"""

					if self.num_rows == 0:
								return "[]"
					if self.num_rows == 1:
								return "[[" + ". ".join(str(self.rows[0]			)			) + "]]"
					return (
					    "["
					    + "\n ".join(
					        [
					            """[""" + """. """.join([str(__magic_name__			) for value in row]			) + """.]"""
					            for row in self.rows
					        ]			)
					    + "]"
					)

		def 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   list[int]  ,      __magic_name__					:   int | None = None			)					->    None:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     TypeError("""Row must be a list containing all ints and/or floats"""			)
					if not isinstance(__magic_name__  ,      __magic_name__			):
								raise type_error
					for value in row:
								if not isinstance(__magic_name__  ,      (int, float)			):
											raise type_error
					if len(__magic_name__			) != self.num_columns:
								raise ValueError(
								    """Row must be equal in length to the other rows in the matrix"""			)
					if position is None:
								self.rows.append(__magic_name__			)
					else:
								__snake_case					:      Optional[Any]							      =     self.rows[0:position] + [row] + self.rows[position:]

		def 	lowercase__     (   self					:   List[Any]  ,      __magic_name__					:   list[int]  ,      __magic_name__					:   int | None = None			)					->    None:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     TypeError(
					    """Column must be a list containing all ints and/or floats"""			)
					if not isinstance(__magic_name__  ,      __magic_name__			):
								raise type_error
					for value in column:
								if not isinstance(__magic_name__  ,      (int, float)			):
											raise type_error
					if len(__magic_name__			) != self.num_rows:
								raise ValueError(
								    """Column must be equal in length to the other columns in the matrix"""			)
					if position is None:
								__snake_case					:      str							      =     [self.rows[i] + [column[i]] for i in range(self.num_rows			)]
					else:
								__snake_case					:      List[Any]							      =     [
								    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
								    for i in range(self.num_rows			)
								]

		def __eq__(   self					:   Tuple  ,      __magic_name__					:   object			)					->    bool:


					"""simple docstring"""

					if not isinstance(__magic_name__  ,      __magic_name__			):
								return NotImplemented
					return self.rows == other.rows

		def __ne__(   self					:   List[Any]  ,      __magic_name__					:   object			)					->    bool:


					"""simple docstring"""

					return not self == other

		def __neg__(   self					:   Dict			)					->    Matrix:


					"""simple docstring"""

					return self * -1

		def __add__(   self					:   List[Any]  ,      __magic_name__					:   Matrix			)					->    Matrix:


					"""simple docstring"""

					if self.order != other.order:
								raise ValueError("""Addition requires matrices of the same order"""			)
					return Matrix(
					    [
					        [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns			)]
					        for i in range(self.num_rows			)
					    ]			)

		def __sub__(   self					:   Optional[Any]  ,      __magic_name__					:   Matrix			)					->    Matrix:


					"""simple docstring"""

					if self.order != other.order:
								raise ValueError("""Subtraction requires matrices of the same order"""			)
					return Matrix(
					    [
					        [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns			)]
					        for i in range(self.num_rows			)
					    ]			)

		def __mul__(   self					:   Union[str, Any]  ,      __magic_name__					:   Matrix | int | float			)					->    Matrix:


					"""simple docstring"""

					if isinstance(__magic_name__  ,      (int, float)			):
								return Matrix(
								    [[int(element * other			) for element in row] for row in self.rows]			)
					elif isinstance(__magic_name__  ,      __magic_name__			):
								if self.num_columns != other.num_rows:
											raise ValueError(
											    """The number of columns in the first matrix must """
											    """be equal to the number of rows in the second"""			)
								return Matrix(
								    [
								        [Matrix.dot_product(__magic_name__  ,      __magic_name__			) for column in other.columns()]
								        for row in self.rows
								    ]			)
					else:
								raise TypeError(
								    """A Matrix can only be multiplied by an int, float, or another matrix"""			)

		def __pow__(   self					:   Tuple  ,      __magic_name__					:   int			)					->    Matrix:


					"""simple docstring"""

					if not isinstance(__magic_name__  ,      __magic_name__			):
								raise TypeError("""A Matrix can only be raised to the power of an int"""			)
					if not self.is_square:
								raise ValueError("""Only square matrices can be raised to a power"""			)
					if other == 0:
								return self.identity()
					if other < 0:
								if self.is_invertable():
											return self.inverse() ** (-other)
								raise ValueError(
								    """Only invertable matrices can be raised to a negative power"""			)
					__snake_case					:      int							      =     self
					for _ in range(other - 1			):
								result *= self
					return result

		@classmethod
		def 	lowercase__     (   cls					:   Dict  ,      __magic_name__					:   list[int]  ,      __magic_name__					:   list[int]			)					->    int:


					"""simple docstring"""

					return sum(row[i] * column[i] for i in range(len(__magic_name__			)			)			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()






import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
				import torch

				from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin




class 							lowerCamelCase   :


	def __init__(    self				,     lowercase__				,     lowercase__=None				,     lowercase__=None				,     lowercase__=None				,     lowercase__="resnet50"				,     lowercase__=3				,     lowercase__=3_2				,     lowercase__=3				,     lowercase__=True				,     lowercase__=True				,     ):
							__UpperCAmelCase			:							List[str]	    =	parent
							__UpperCAmelCase			:							Dict	    =	out_indices if out_indices is not None else [4]
							__UpperCAmelCase			:							List[Any]	    =	stage_names
							__UpperCAmelCase			:							int	    =	out_features
							__UpperCAmelCase			:							Union[str, Any]	    =	backbone
							__UpperCAmelCase			:							Optional[Any]	    =	batch_size
							__UpperCAmelCase			:							Optional[Any]	    =	image_size
							__UpperCAmelCase			:							Optional[int]	    =	num_channels
							__UpperCAmelCase			:							List[Any]	    =	use_pretrained_backbone
							__UpperCAmelCase			:							Dict	    =	is_training


	def  A(    self):
							__UpperCAmelCase			:							Optional[Any]	    =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
							__UpperCAmelCase			:							Tuple	    =	self.get_config()

							return config, pixel_values


	def  A(    self):
							return TimmBackboneConfig(
							    image_size=self.image_size				,     num_channels=self.num_channels				,     out_features=self.out_features				,     out_indices=self.out_indices				,     stage_names=self.stage_names				,     use_pretrained_backbone=self.use_pretrained_backbone				,     backbone=self.backbone				,     )


	def  A(    self				,     lowercase__				,     lowercase__):
							__UpperCAmelCase			:							Tuple	    =	TimmBackbone(config=lowercase__)
							model.to(lowercase__)
							model.eval()
							with torch.no_grad():
													__UpperCAmelCase			:							Union[str, Any]	    =	model(lowercase__)
							self.parent.assertEqual(
							    result.feature_map[-1].shape				,     (self.batch_size, model.channels[-1], 1_4, 1_4)				,     )


	def  A(    self):
							__UpperCAmelCase			:							Dict	    =	self.prepare_config_and_inputs()
							__UpperCAmelCase    ,      __UpperCAmelCase			:							List[Any]	    =	config_and_inputs
							__UpperCAmelCase			:							List[Any]	    =	{'''pixel_values''': pixel_values}
							return config, inputs_dict






@require_torch
@require_timm
class 							lowerCamelCase   (  _UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase							,						unittest.TestCase ):
	_lowerCAmelCase     :		Dict										=						(TimmBackbone,) if is_torch_available() else ()
	_lowerCAmelCase     :		str										=						{'''feature-extraction''': TimmBackbone} if is_torch_available() else {}
	_lowerCAmelCase     :		List[str]										=						False
	_lowerCAmelCase     :		str										=						False
	_lowerCAmelCase     :		List[Any]										=						False
	_lowerCAmelCase     :		List[str]										=						False


	def  A(    self):
							__UpperCAmelCase			:							List[Any]	    =	TimmBackboneModelTester(self)
							__UpperCAmelCase			:							int	    =	ConfigTester(self				,     config_class=lowercase__				,     has_text_modality=lowercase__)


	def  A(    self):
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()


	def  A(    self):
							__UpperCAmelCase			:							int	    =	'''resnet18'''
							__UpperCAmelCase			:							List[str]	    =	'''microsoft/resnet-18'''

							__UpperCAmelCase			:							Any	    =	AutoBackbone.from_pretrained(lowercase__				,     use_timm_backbone=lowercase__)
							__UpperCAmelCase			:							Union[str, Any]	    =	AutoBackbone.from_pretrained(lowercase__)

							self.assertEqual(len(timm_model.out_features)				,     len(transformers_model.out_features))
							self.assertEqual(len(timm_model.stage_names)				,     len(transformers_model.stage_names))
							self.assertEqual(timm_model.channels				,     transformers_model.channels)
							# Out indices are set to the last layer by default. For timm models, we don't know
							# the number of layers in advance, so we set it to (-1,), whereas for transformers
							# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
							self.assertEqual(timm_model.out_indices				,     (-1,))
							self.assertEqual(transformers_model.out_indices				,     [len(timm_model.stage_names) - 1])

							__UpperCAmelCase			:							Union[str, Any]	    =	AutoBackbone.from_pretrained(lowercase__				,     use_timm_backbone=lowercase__				,     out_indices=[1, 2, 3])
							__UpperCAmelCase			:							Any	    =	AutoBackbone.from_pretrained(lowercase__				,     out_indices=[1, 2, 3])

							self.assertEqual(timm_model.out_indices				,     transformers_model.out_indices)
							self.assertEqual(len(timm_model.out_features)				,     len(transformers_model.out_features))
							self.assertEqual(timm_model.channels				,     transformers_model.channels)


	@unittest.skip('''TimmBackbone doesn\'t support feed forward chunking''')
	def  A(    self):
							pass


	@unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute''')
	def  A(    self):
							pass


	@unittest.skip('''TimmBackbone initialization is managed on the timm side''')
	def  A(    self):
							pass


	@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds''')
	def  A(    self):
							pass


	@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds''')
	def  A(    self):
							pass


	@unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint''')
	def  A(    self):
							pass


	@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone''')
	def  A(    self):
							pass


	@unittest.skip('''model weights aren\'t tied in TimmBackbone.''')
	def  A(    self):
							pass


	@unittest.skip('''model weights aren\'t tied in TimmBackbone.''')
	def  A(    self):
							pass


	@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone''')
	def  A(    self):
							pass


	@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone''')
	def  A(    self):
							pass


	@unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.''')
	def  A(    self):
							pass


	@unittest.skip('''TimmBackbone doesn\'t support output_attentions.''')
	def  A(    self):
							pass


	@unittest.skip('''Safetensors is not supported by timm.''')
	def  A(    self):
							pass


	@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
	def  A(    self):
							pass


	def  A(    self):
							__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[int]	    =	self.model_tester.prepare_config_and_inputs_for_common()

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

													__UpperCAmelCase			:							Dict	    =	['''pixel_values''']
													self.assertListEqual(arg_names[:1]				,     lowercase__)


	def  A(    self):
							__UpperCAmelCase    ,      __UpperCAmelCase			:							str	    =	self.model_tester.prepare_config_and_inputs_for_common()
							__UpperCAmelCase			:							Tuple	    =	True
							__UpperCAmelCase			:							Optional[int]	    =	self.has_attentions

							# no need to test all models as different heads yield the same functionality
							__UpperCAmelCase			:							Optional[Any]	    =	self.all_model_classes[0]
							__UpperCAmelCase			:							Optional[int]	    =	model_class(lowercase__)
							model.to(lowercase__)

							__UpperCAmelCase			:							Tuple	    =	self._prepare_for_class(lowercase__				,     lowercase__)
							__UpperCAmelCase			:							Optional[int]	    =	model(**lowercase__)
							__UpperCAmelCase			:							List[str]	    =	outputs[0][-1]

							# Encoder-/Decoder-only models
							__UpperCAmelCase			:							Dict	    =	outputs.hidden_states[0]
							hidden_states.retain_grad()

							if self.has_attentions:
													__UpperCAmelCase			:							Optional[int]	    =	outputs.attentions[0]
													attentions.retain_grad()

							output.flatten()[0].backward(retain_graph=lowercase__)

							self.assertIsNotNone(hidden_states.grad)

							if self.has_attentions:
													self.assertIsNotNone(attentions.grad)


	def  A(    self):
							__UpperCAmelCase    ,      __UpperCAmelCase			:							Dict	    =	self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													__UpperCAmelCase			:							Optional[Any]	    =	model_class(lowercase__)
													model.to(lowercase__)
													model.eval()
													__UpperCAmelCase			:							Union[str, Any]	    =	model(**lowercase__)

													self.assertEqual(len(result.feature_maps)				,     len(config.out_indices))
													self.assertEqual(len(model.channels)				,     len(config.out_indices))

													# Check output of last stage is taken if out_features=None, out_indices=None
													__UpperCAmelCase			:							List[str]	    =	copy.deepcopy(lowercase__)
													__UpperCAmelCase			:							str	    =	None
													__UpperCAmelCase			:							Optional[int]	    =	model_class(lowercase__)
													model.to(lowercase__)
													model.eval()
													__UpperCAmelCase			:							Optional[Any]	    =	model(**lowercase__)

													self.assertEqual(len(result.feature_maps)				,     1)
													self.assertEqual(len(model.channels)				,     1)

													# Check backbone can be initialized with fresh weights
													__UpperCAmelCase			:							Tuple	    =	copy.deepcopy(lowercase__)
													__UpperCAmelCase			:							Optional[int]	    =	False
													__UpperCAmelCase			:							Optional[int]	    =	model_class(lowercase__)
													model.to(lowercase__)
													model.eval()
													__UpperCAmelCase			:							List[Any]	    =	model(**lowercase__)

