



from __future__ import annotations


class   snake_case_ :




       """simple docstring"""






       def __init__(     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		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(lowercase) != 0:
                   UpperCAmelCase_  :	str				      =		len(rows[0])
                   if cols == 0:
                         raise error
                   for row in rows:
                         if len(lowercase) != cols:
                               raise error
                         for value in row:
                               if not isinstance(lowercase     ,(int, float)):
                                     raise error
                   UpperCAmelCase_  :	int				      =		rows
             else:
                   UpperCAmelCase_  :	Union[str, Any]				      =		[]



       def  A_   (     self):

             """simple docstring"""





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



       @property
       def  A_   (     self):

             """simple docstring"""





             return len(self.rows)



       @property
       def  A_   (     self):

             """simple docstring"""





             return len(self.rows[0])



       @property
       def  A_   (     self):

             """simple docstring"""





             return (self.num_rows, self.num_columns)



       @property
       def  A_   (     self):

             """simple docstring"""





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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	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(lowercase)



       def  A_   (     self):

             """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  A_   (     self):

             """simple docstring"""





             return bool(self.determinant())



       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	List[Any]				      =		[
                 [
                     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(lowercase).determinant()



       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





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



       def  A_   (     self):

             """simple docstring"""





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



       def  A_   (     self):

             """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  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		[
                 [self.cofactors().rows[column][row] for column in range(self.num_columns)]
                 for row in range(self.num_rows)
             ]
             return Matrix(lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		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):

             """simple docstring"""





             return str(self.rows)



       def __str__(     self):

             """simple docstring"""





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



       def  A_   (     self     ,lowercase     ,lowercase = None):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		TypeError("Row must be a list containing all ints and/or floats")
             if not isinstance(lowercase     ,lowercase):
                   raise type_error
             for value in row:
                   if not isinstance(lowercase     ,(int, float)):
                         raise type_error
             if len(lowercase) != 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(lowercase)
             else:
                   UpperCAmelCase_  :	Optional[int]				      =		self.rows[0:position] + [row] + self.rows[position:]



       def  A_   (     self     ,lowercase     ,lowercase = None):

             """simple docstring"""





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



       def __eq__(     self     ,lowercase):

             """simple docstring"""





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



       def __ne__(     self     ,lowercase):

             """simple docstring"""





             return not self == other



       def __neg__(     self):

             """simple docstring"""





             return self * -1



       def __add__(     self     ,lowercase):

             """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     ,lowercase):

             """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     ,lowercase):

             """simple docstring"""





             if isinstance(lowercase     ,(int, float)):
                   return Matrix(
                       [[int(element * other) for element in row] for row in self.rows])
             elif isinstance(lowercase     ,lowercase):
                   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(lowercase     ,lowercase) 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     ,lowercase):

             """simple docstring"""





             if not isinstance(lowercase     ,lowercase):
                   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")
             UpperCAmelCase_  :	int				      =		self
             for _ in range(other - 1):
                   result *= self
             return result



       @classmethod
       def  A_   (     cls     ,lowercase     ,lowercase):

             """simple docstring"""





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


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   snake_case_ :




       """simple docstring"""






       def __init__(     self     ,lowercase     ,lowercase=None     ,lowercase=None     ,lowercase=None     ,lowercase="resnet50"     ,lowercase=3     ,lowercase=32     ,lowercase=3     ,lowercase=True     ,lowercase=True     ,):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		parent
             UpperCAmelCase_  :	int				      =		out_indices if out_indices is not None else [4]
             UpperCAmelCase_  :	Any				      =		stage_names
             UpperCAmelCase_  :	List[Any]				      =		out_features
             UpperCAmelCase_  :	List[str]				      =		backbone
             UpperCAmelCase_  :	Union[str, Any]				      =		batch_size
             UpperCAmelCase_  :	int				      =		image_size
             UpperCAmelCase_  :	str				      =		num_channels
             UpperCAmelCase_  :	Optional[Any]				      =		use_pretrained_backbone
             UpperCAmelCase_  :	Optional[int]				      =		is_training



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
             UpperCAmelCase_  :	Dict				      =		self.get_config()

             return config, pixel_values



       def  A_   (     self):

             """simple docstring"""





             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):

             """simple docstring"""





             UpperCAmelCase_  :	List[Any]				      =		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], 14, 14)     ,)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Tuple				      =		self.prepare_config_and_inputs()
             UpperCAmelCase_      ,      UpperCAmelCase_  :	Optional[int]				      =		config_and_inputs
             UpperCAmelCase_  :	Union[str, Any]				      =		{"pixel_values": pixel_values}
             return config, inputs_dict


@require_torch
@require_timm
class   snake_case_ (lowercase__ , lowercase__ , lowercase__ , unittest.TestCase  ):




       """simple docstring"""






       _lowerCamelCase			    =				(TimmBackbone,) if is_torch_available() else ()
       _lowerCamelCase			    =				{"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
       _lowerCamelCase			    =				False
       _lowerCamelCase			    =				False
       _lowerCamelCase			    =				False
       _lowerCamelCase			    =				False



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		TimmBackboneModelTester(self)
             UpperCAmelCase_  :	Tuple				      =		ConfigTester(self     ,config_class=lowercase     ,has_text_modality=lowercase)



       def  A_   (     self):

             """simple docstring"""





             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):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		"resnet18"
             UpperCAmelCase_  :	Tuple				      =		"microsoft/resnet-18"

             UpperCAmelCase_  :	int				      =		AutoBackbone.from_pretrained(lowercase     ,use_timm_backbone=lowercase)
             UpperCAmelCase_  :	Dict				      =		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_  :	Dict				      =		AutoBackbone.from_pretrained(lowercase     ,use_timm_backbone=lowercase     ,out_indices=[1, 2, 3])
             UpperCAmelCase_  :	List[str]				      =		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):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone initialization is managed on the timm side")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone models doesn't have inputs_embeds")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone models doesn't have inputs_embeds")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("model weights aren't tied in TimmBackbone.")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("model weights aren't tied in TimmBackbone.")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone")
       def  A_   (     self):

             """simple docstring"""





             pass



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

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone doesn't support output_attentions.")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("Safetensors is not supported by timm.")
       def  A_   (     self):

             """simple docstring"""





             pass



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

             """simple docstring"""





             pass



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_  :	int				      =		self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   UpperCAmelCase_  :	Union[str, Any]				      =		model_class(lowercase)
                   UpperCAmelCase_  :	Optional[Any]				      =		inspect.signature(model.forward)
                   # signature.parameters is an OrderedDict => so arg_names order is deterministic
                   UpperCAmelCase_  :	str				      =		[*signature.parameters.keys()]

                   UpperCAmelCase_  :	Any				      =		["pixel_values"]
                   self.assertListEqual(arg_names[:1]     ,lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_  :	Tuple				      =		self.model_tester.prepare_config_and_inputs_for_common()
             UpperCAmelCase_  :	Dict				      =		True
             UpperCAmelCase_  :	str				      =		self.has_attentions

             # no need to test all models as different heads yield the same functionality
             UpperCAmelCase_  :	str				      =		self.all_model_classes[0]
             UpperCAmelCase_  :	List[Any]				      =		model_class(lowercase)
             model.to(lowercase)

             UpperCAmelCase_  :	Any				      =		self._prepare_for_class(lowercase     ,lowercase)
             UpperCAmelCase_  :	Optional[int]				      =		model(**lowercase)
             UpperCAmelCase_  :	Optional[Any]				      =		outputs[0][-1]

             # Encoder-/Decoder-only models
             UpperCAmelCase_  :	List[Any]				      =		outputs.hidden_states[0]
             hidden_states.retain_grad()

             if self.has_attentions:
                   UpperCAmelCase_  :	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):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_  :	List[Any]				      =		self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   UpperCAmelCase_  :	Any				      =		model_class(lowercase)
                   model.to(lowercase)
                   model.eval()
                   UpperCAmelCase_  :	Tuple				      =		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_  :	Tuple				      =		copy.deepcopy(lowercase)
                   UpperCAmelCase_  :	List[Any]				      =		None
                   UpperCAmelCase_  :	Optional[Any]				      =		model_class(lowercase)
                   model.to(lowercase)
                   model.eval()
                   UpperCAmelCase_  :	Tuple				      =		model(**lowercase)

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

                   # Check backbone can be initialized with fresh weights
                   UpperCAmelCase_  :	str				      =		copy.deepcopy(lowercase)
                   UpperCAmelCase_  :	Optional[Any]				      =		False
                   UpperCAmelCase_  :	List[str]				      =		model_class(lowercase)
                   model.to(lowercase)
                   model.eval()
                   UpperCAmelCase_  :	Union[str, Any]				      =		model(**lowercase)
