






import inspect
import unittest

from transformers import YolosConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import YolosForObjectDetection, YolosModel
  from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			Dict       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			Any=13       ,				lowerCamelCase		:			List[Any]=[30, 30]       ,				lowerCamelCase		:			Optional[Any]=2       ,				lowerCamelCase		:			Tuple=3       ,				lowerCamelCase		:			int=True       ,				lowerCamelCase		:			Dict=True       ,				lowerCamelCase		:			Tuple=32       ,				lowerCamelCase		:			Optional[int]=5       ,				lowerCamelCase		:			Tuple=4       ,				lowerCamelCase		:			Dict=37       ,				lowerCamelCase		:			int="gelu"       ,				lowerCamelCase		:			Dict=0.1       ,				lowerCamelCase		:			str=0.1       ,				lowerCamelCase		:			Dict=10       ,				lowerCamelCase		:			Any=0.02       ,				lowerCamelCase		:			str=3       ,				lowerCamelCase		:			int=None       ,				lowerCamelCase		:			int=8       ,				lowerCamelCase		:			Any=10       ,				)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    image_size
         _UpperCAmelCase							=    patch_size
         _UpperCAmelCase							=    num_channels
         _UpperCAmelCase							=    is_training
         _UpperCAmelCase							=    use_labels
         _UpperCAmelCase							=    hidden_size
         _UpperCAmelCase							=    num_hidden_layers
         _UpperCAmelCase							=    num_attention_heads
         _UpperCAmelCase							=    intermediate_size
         _UpperCAmelCase							=    hidden_act
         _UpperCAmelCase							=    hidden_dropout_prob
         _UpperCAmelCase							=    attention_probs_dropout_prob
         _UpperCAmelCase							=    type_sequence_label_size
         _UpperCAmelCase							=    initializer_range
         _UpperCAmelCase							=    num_labels
         _UpperCAmelCase							=    scope
         _UpperCAmelCase							=    n_targets
         _UpperCAmelCase							=    num_detection_tokens
         # we set the expected sequence length (which is used in several tests)
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
         _UpperCAmelCase							=    (image_size[1] // patch_size) * (image_size[0] // patch_size)
         _UpperCAmelCase							=    num_patches + 1 + self.num_detection_tokens


  def  lowerCamelCase       (      self		:			List[Any]							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]							)

         _UpperCAmelCase							=    None
         if self.use_labels:
                # labels is a list of Dict (each Dict being the labels for a given example in the batch)
                _UpperCAmelCase							=    []
                for i in range(self.batch_size							):
                       _UpperCAmelCase							=    {}
                       _UpperCAmelCase							=    torch.randint(
                           high=self.num_labels       ,				size=(self.n_targets,)       ,				device=lowerCamelCase							)
                       _UpperCAmelCase							=    torch.rand(self.n_targets       ,				4       ,				device=lowerCamelCase							)
                       labels.append(lowerCamelCase							)

         _UpperCAmelCase							=    self.get_config()

         return config, pixel_values, labels


  def  lowerCamelCase       (      self		:			List[str]							)						->	Dict:


         """simple docstring"""
         return YolosConfig(
             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       ,				is_decoder=lowerCamelCase       ,				initializer_range=self.initializer_range       ,				num_detection_tokens=self.num_detection_tokens       ,				num_labels=self.num_labels       ,				)


  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			str							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    YolosModel(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase							)
         self.parent.assertEqual(
             result.last_hidden_state.shape       ,				(self.batch_size, self.expected_seq_len, self.hidden_size)							)


  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			Tuple							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    YolosForObjectDetection(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()

         _UpperCAmelCase							=    model(pixel_values=lowerCamelCase							)
         _UpperCAmelCase							=    model(lowerCamelCase							)

         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_detection_tokens, self.num_labels + 1)							)
         self.parent.assertEqual(result.pred_boxes.shape       ,				(self.batch_size, self.num_detection_tokens, 4)							)

         _UpperCAmelCase							=    model(pixel_values=lowerCamelCase       ,				labels=lowerCamelCase							)

         self.parent.assertEqual(result.loss.shape       ,				()							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_detection_tokens, self.num_labels + 1)							)
         self.parent.assertEqual(result.pred_boxes.shape       ,				(self.batch_size, self.num_detection_tokens, 4)							)





  def  lowerCamelCase       (      self		:			Dict							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.prepare_config_and_inputs()
         _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    config_and_inputs
         _UpperCAmelCase							=    {"""pixel_values""": pixel_values}
         return config, inputs_dict



@require_torch
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
  _lowerCamelCase    =  (
      {'''feature-extraction''': YolosModel, '''object-detection''': YolosForObjectDetection} if is_torch_available() else {}
  )

  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False


  def  lowerCamelCase       (      self		:			Optional[Any]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Tuple=False							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    super()._prepare_for_class(lowerCamelCase       ,				lowerCamelCase       ,				return_labels=lowerCamelCase							)

         if return_labels:
                if model_class.__name__ == "YolosForObjectDetection":
                       _UpperCAmelCase							=    []
                       for i in range(self.model_tester.batch_size							):
                              _UpperCAmelCase							=    {}
                              _UpperCAmelCase							=    torch.ones(
                                  size=(self.model_tester.n_targets,)       ,				device=lowerCamelCase       ,				dtype=torch.long							)
                              _UpperCAmelCase							=    torch.ones(
                                  self.model_tester.n_targets       ,				4       ,				device=lowerCamelCase       ,				dtype=torch.float							)
                              labels.append(lowerCamelCase							)
                       _UpperCAmelCase							=    labels

         return inputs_dict


  def  lowerCamelCase       (      self		:			Dict							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    YolosModelTester(self							)
         _UpperCAmelCase							=    ConfigTester(self       ,				config_class=lowerCamelCase       ,				has_text_modality=lowerCamelCase       ,				hidden_size=37							)


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Union[str, Any]:


         """simple docstring"""
         self.config_tester.run_common_tests()


  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	List[str]:


         """simple docstring"""
         # YOLOS does not use inputs_embeds
         pass


  def  lowerCamelCase       (      self		:			List[Any]							)						->	str:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()

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


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()

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

                _UpperCAmelCase							=    ["""pixel_values"""]
                self.assertListEqual(arg_names[:1]       ,				lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			str							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()
         _UpperCAmelCase							=    True

         # in YOLOS, the seq_len is different
         _UpperCAmelCase							=    self.model_tester.expected_seq_len
         for model_class in self.all_model_classes:
                _UpperCAmelCase							=    True
                _UpperCAmelCase							=    False
                _UpperCAmelCase							=    True
                _UpperCAmelCase							=    model_class(lowerCamelCase							)
                model.to(lowerCamelCase							)
                model.eval()
                with torch.no_grad():
                       _UpperCAmelCase							=    model(**self._prepare_for_class(lowerCamelCase       ,				lowerCamelCase							)							)
                _UpperCAmelCase							=    outputs.attentions
                self.assertEqual(len(lowerCamelCase							)       ,				self.model_tester.num_hidden_layers							)

                # check that output_attentions also work using config
                del inputs_dict["output_attentions"]
                _UpperCAmelCase							=    True
                _UpperCAmelCase							=    model_class(lowerCamelCase							)
                model.to(lowerCamelCase							)
                model.eval()
                with torch.no_grad():
                       _UpperCAmelCase							=    model(**self._prepare_for_class(lowerCamelCase       ,				lowerCamelCase							)							)
                _UpperCAmelCase							=    outputs.attentions
                self.assertEqual(len(lowerCamelCase							)       ,				self.model_tester.num_hidden_layers							)

                self.assertListEqual(
                    list(attentions[0].shape[-3:]							)       ,				[self.model_tester.num_attention_heads, seq_len, seq_len]       ,				)
                _UpperCAmelCase							=    len(lowerCamelCase							)

                # Check attention is always last and order is fine
                _UpperCAmelCase							=    True
                _UpperCAmelCase							=    True
                _UpperCAmelCase							=    model_class(lowerCamelCase							)
                model.to(lowerCamelCase							)
                model.eval()
                with torch.no_grad():
                       _UpperCAmelCase							=    model(**self._prepare_for_class(lowerCamelCase       ,				lowerCamelCase							)							)

                _UpperCAmelCase							=    1
                self.assertEqual(out_len + added_hidden_states       ,				len(lowerCamelCase							)							)

                _UpperCAmelCase							=    outputs.attentions

                self.assertEqual(len(lowerCamelCase							)       ,				self.model_tester.num_hidden_layers							)
                self.assertListEqual(
                    list(self_attentions[0].shape[-3:]							)       ,				[self.model_tester.num_attention_heads, seq_len, seq_len]       ,				)


  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	List[Any]:


         """simple docstring"""
         def check_hidden_states_output(lowerCamelCase		:			Dict       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Dict							):
                _UpperCAmelCase							=    model_class(lowerCamelCase							)
                model.to(lowerCamelCase							)
                model.eval()

                with torch.no_grad():
                       _UpperCAmelCase							=    model(**self._prepare_for_class(lowerCamelCase       ,				lowerCamelCase							)							)

                _UpperCAmelCase							=    outputs.hidden_states

                _UpperCAmelCase							=    getattr(
                    self.model_tester       ,				"""expected_num_hidden_layers"""       ,				self.model_tester.num_hidden_layers + 1							)
                self.assertEqual(len(lowerCamelCase							)       ,				lowerCamelCase							)

                # YOLOS has a different seq_length
                _UpperCAmelCase							=    self.model_tester.expected_seq_len

                self.assertListEqual(
                    list(hidden_states[0].shape[-2:]							)       ,				[seq_length, self.model_tester.hidden_size]       ,				)

         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                _UpperCAmelCase							=    True
                check_hidden_states_output(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase							)

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                _UpperCAmelCase							=    True

                check_hidden_states_output(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_object_detection(*lowerCamelCase							)





  @slow
  def  lowerCamelCase       (      self		:			str							)						->	Dict:


         """simple docstring"""
         for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase							=    YolosModel.from_pretrained(lowerCamelCase							)
                self.assertIsNotNone(lowerCamelCase							)




def       _SCREAMING_SNAKE_CASE (							)				->						Union[str, Any]:
       _UpperCAmelCase							=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
       return image





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

  '''simple docstring'''







  @cached_property
  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Tuple:


         """simple docstring"""
         return AutoImageProcessor.from_pretrained("""hustvl/yolos-small"""							) if is_vision_available() else None





  @slow
  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    YolosForObjectDetection.from_pretrained("""hustvl/yolos-small"""							).to(lowerCamelCase							)

         _UpperCAmelCase							=    self.default_image_processor
         _UpperCAmelCase							=    prepare_img()
         _UpperCAmelCase							=    image_processor(images=lowerCamelCase       ,				return_tensors="""pt"""							).to(lowerCamelCase							)

         # forward pass
         with torch.no_grad():
                _UpperCAmelCase							=    model(inputs.pixel_values							)

         # verify outputs
         _UpperCAmelCase							=    torch.Size((1, 100, 92)							)
         self.assertEqual(outputs.logits.shape       ,				lowerCamelCase							)

         _UpperCAmelCase							=    torch.tensor(
             [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]       ,				device=lowerCamelCase       ,				)
         _UpperCAmelCase							=    torch.tensor(
             [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]       ,				device=lowerCamelCase							)
         self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]       ,				lowerCamelCase       ,				atol=1E-4							)							)
         self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]       ,				lowerCamelCase       ,				atol=1E-4							)							)

         # verify postprocessing
         _UpperCAmelCase							=    image_processor.post_process_object_detection(
             lowerCamelCase       ,				threshold=0.3       ,				target_sizes=[image.size[::-1]]							)[0]
         _UpperCAmelCase							=    torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]							).to(lowerCamelCase							)
         _UpperCAmelCase							=    [75, 75, 17, 63, 17]
         _UpperCAmelCase							=    torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]							).to(lowerCamelCase							)

         self.assertEqual(len(results["""scores"""]							)       ,				5							)
         self.assertTrue(torch.allclose(results["""scores"""]       ,				lowerCamelCase       ,				atol=1E-4							)							)
         self.assertSequenceEqual(results["""labels"""].tolist()       ,				lowerCamelCase							)
         self.assertTrue(torch.allclose(results["""boxes"""][0, :]       ,				lowerCamelCase							)							)







from __future__ import annotations
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			Optional[Any]       ,				lowerCamelCase		:			list[list[int]]							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    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(lowerCamelCase							) != 0:
                _UpperCAmelCase							=    len(rows[0]							)
                if cols == 0:
                       raise error
                for row in rows:
                       if len(lowerCamelCase							) != cols:
                              raise error
                       for value in row:
                              if not isinstance(lowerCamelCase       ,				(int, float)							):
                                     raise error
                _UpperCAmelCase							=    rows
         else:
                _UpperCAmelCase							=    []


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	list[list[int]]:


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


  @property
  def  lowerCamelCase       (      self		:			int							)						->	int:


         """simple docstring"""
         return len(self.rows							)


  @property
  def  lowerCamelCase       (      self		:			int							)						->	int:


         """simple docstring"""
         return len(self.rows[0]							)


  @property
  def  lowerCamelCase       (      self		:			Tuple							)						->	tuple[int, int]:


         """simple docstring"""
         return (self.num_rows, self.num_columns)


  @property
  def  lowerCamelCase       (      self		:			Tuple							)						->	bool:


         """simple docstring"""
         return self.order[0] == self.order[1]


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Matrix:


         """simple docstring"""
         _UpperCAmelCase							=    [
             [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(lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	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  lowerCamelCase       (      self		:			Optional[Any]							)						->	bool:


         """simple docstring"""
         return bool(self.determinant()							)


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			int							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    [
             [
                 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(lowerCamelCase							).determinant()


  def  lowerCamelCase       (      self		:			int       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			int							)						->	int:


         """simple docstring"""
         if (row + column) % 2 == 0:
                return self.get_minor(lowerCamelCase       ,				lowerCamelCase							)
         return -1 * self.get_minor(lowerCamelCase       ,				lowerCamelCase							)


  def  lowerCamelCase       (      self		:			str							)						->	Matrix:


         """simple docstring"""
         return Matrix(
             [
                 [self.get_minor(lowerCamelCase       ,				lowerCamelCase							) for column in range(self.num_columns							)]
                 for row in range(self.num_rows							)
             ]							)


  def  lowerCamelCase       (      self		:			Dict							)						->	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  lowerCamelCase       (      self		:			Optional[Any]							)						->	Matrix:


         """simple docstring"""
         _UpperCAmelCase							=    [
             [self.cofactors().rows[column][row] for column in range(self.num_columns							)]
             for row in range(self.num_rows							)
         ]
         return Matrix(lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Any							)						->	Matrix:


         """simple docstring"""
         _UpperCAmelCase							=    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		:			List[str]							)						->	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(lowerCamelCase							) for value in row]							) + """.]"""
                     for row in self.rows
                 ]							)
             + "]"
         )


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			list[int]       ,				lowerCamelCase		:			int | None = None							)						->	None:


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


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


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


  def __eq__(      self		:			Dict       ,				lowerCamelCase		:			object							)						->	bool:


         """simple docstring"""
         if not isinstance(lowerCamelCase       ,				lowerCamelCase							):
                return NotImplemented
         return self.rows == other.rows


  def __ne__(      self		:			List[str]       ,				lowerCamelCase		:			object							)						->	bool:


         """simple docstring"""
         return not self == other


  def __neg__(      self		:			Union[str, Any]							)						->	Matrix:


         """simple docstring"""
         return self * -1


  def __add__(      self		:			Any       ,				lowerCamelCase		:			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]       ,				lowerCamelCase		:			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		:			List[Any]       ,				lowerCamelCase		:			Matrix | int | float							)						->	Matrix:


         """simple docstring"""
         if isinstance(lowerCamelCase       ,				(int, float)							):
                return Matrix(
                    [[int(element * other							) for element in row] for row in self.rows]							)
         elif isinstance(lowerCamelCase       ,				lowerCamelCase							):
                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(lowerCamelCase       ,				lowerCamelCase							) 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		:			Any       ,				lowerCamelCase		:			int							)						->	Matrix:


         """simple docstring"""
         if not isinstance(lowerCamelCase       ,				lowerCamelCase							):
                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							=    self
         for _ in range(other - 1							):
                result *= self
         return result





  @classmethod
  def  lowerCamelCase       (      cls		:			Tuple       ,				lowerCamelCase		:			list[int]       ,				lowerCamelCase		:			list[int]							)						->	int:


         """simple docstring"""
         return sum(row[i] * column[i] for i in range(len(lowerCamelCase							)							)							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()