



'''simple docstring'''





from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,)      ->      list[float]:
  __lowerCamelCase       ,__lowerCamelCase       :       Tuple           = coefficient_matrix.shape
  __lowerCamelCase       ,__lowerCamelCase       :       str           = constant_matrix.shape

  if rowsa != colsa:
    __lowerCamelCase       :       List[Any]           = F'Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'
    raise ValueError(_lowerCAmelCase       )

  if colsa != 1:
    __lowerCamelCase       :       Any           = F'Constant matrix must be nx1 but received {rowsa}x{colsa}'
    raise ValueError(_lowerCAmelCase       )

  if rowsa != rowsa:
    __lowerCamelCase       :       int           = (
        'Coefficient and constant matrices dimensions must be nxn and nx1 but '
        F'received {rowsa}x{colsa} and {rowsa}x{colsa}'
    )
    raise ValueError(_lowerCAmelCase       )

  if len(_lowerCAmelCase       ) != rowsa:
    __lowerCamelCase       :       Union[str, Any]           = (
        'Number of initial values must be equal to number of rows in coefficient '
        F'matrix but received {len(_lowerCAmelCase       )} and {rowsa}'
    )
    raise ValueError(_lowerCAmelCase       )

  if iterations <= 0:
    raise ValueError('Iterations must be at least 1'       )

  __lowerCamelCase       :       NDArray[floataa]           = np.concatenate(
      (coefficient_matrix, constant_matrix)       ,axis=1       )

  __lowerCamelCase       ,__lowerCamelCase       :       Union[str, Any]           = table.shape

  strictly_diagonally_dominant(_lowerCAmelCase       )

  # Iterates the whole matrix for given number of times
  for _ in range(_lowerCAmelCase       ):
    __lowerCamelCase       :       Union[str, Any]           = []
    for row in range(_lowerCAmelCase       ):
      __lowerCamelCase       :       str           = 0
      for col in range(_lowerCAmelCase       ):
        if col == row:
          __lowerCamelCase       :       Optional[int]           = table[row][col]
        elif col == cols - 1:
          __lowerCamelCase       :       List[str]           = table[row][col]
        else:
          temp += (-1) * table[row][col] * init_val[col]
      __lowerCamelCase       :       List[Any]           = (temp + val) / denom
      new_val.append(_lowerCAmelCase       )
    __lowerCamelCase       :       Tuple           = new_val

  return [float(_lowerCAmelCase       ) for i in new_val]

def        a_     (							_lowerCAmelCase       )      ->      bool:
  __lowerCamelCase       ,__lowerCamelCase       :       List[str]           = table.shape

  __lowerCamelCase       :       Union[str, Any]           = True

  for i in range(0       ,_lowerCAmelCase       ):
    __lowerCamelCase       :       Optional[int]           = 0
    for j in range(0       ,cols - 1       ):
      if i == j:
        continue
      else:
        total += table[i][j]

    if table[i][i] <= total:
      raise ValueError('Coefficient matrix is not strictly diagonally dominant'       )

  return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
   import doctest

   doctest.testmod()











'''simple docstring'''





import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
   from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import DPTImageProcessor






class 					lowerCamelCase_    :
  """simple docstring"""




  def __init__( self      :	Tuple			,	_a      :	List[Any]			,	_a      :	Dict=2			,	_a      :	Dict=32			,	_a      :	int=16			,	_a      :	str=3			,	_a      :	Optional[int]=True			,	_a      :	List[Any]=True			,	_a      :	int=32			,	_a      :	int=4			,	_a      :	Optional[Any]=[0, 1, 2, 3]			,	_a      :	int=4			,	_a      :	Union[str, Any]=37			,	_a      :	List[str]="gelu"			,	_a      :	List[str]=0.1			,	_a      :	List[str]=0.1			,	_a      :	Union[str, Any]=0.02			,	_a      :	str=3			,	_a      :	int=[1, 384, 24, 24]			,	_a      :	Optional[Any]=True			,	_a      :	Tuple=None			,	)					->					Tuple:
    __lowerCamelCase       :       Dict           = parent
    __lowerCamelCase       :       List[Any]           = batch_size
    __lowerCamelCase       :       int           = image_size
    __lowerCamelCase       :       Any           = patch_size
    __lowerCamelCase       :       Tuple           = num_channels
    __lowerCamelCase       :       Dict           = is_training
    __lowerCamelCase       :       List[str]           = use_labels
    __lowerCamelCase       :       Union[str, Any]           = hidden_size
    __lowerCamelCase       :       List[Any]           = num_hidden_layers
    __lowerCamelCase       :       List[Any]           = backbone_out_indices
    __lowerCamelCase       :       Tuple           = num_attention_heads
    __lowerCamelCase       :       Optional[Any]           = intermediate_size
    __lowerCamelCase       :       Any           = hidden_act
    __lowerCamelCase       :       List[str]           = hidden_dropout_prob
    __lowerCamelCase       :       Tuple           = attention_probs_dropout_prob
    __lowerCamelCase       :       List[str]           = initializer_range
    __lowerCamelCase       :       Dict           = num_labels
    __lowerCamelCase       :       List[Any]           = backbone_featmap_shape
    __lowerCamelCase       :       Optional[int]           = scope
    __lowerCamelCase       :       str           = is_hybrid
    # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
    __lowerCamelCase       :       Union[str, Any]           = (image_size // patch_size) ** 2
    __lowerCamelCase       :       Optional[Any]           = num_patches + 1




  def 						_lowercase     ( self      :	Dict   )					->					Any:
    __lowerCamelCase       :       Optional[int]           = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

    __lowerCamelCase       :       List[str]           = None
    if self.use_labels:
      __lowerCamelCase       :       Dict           = ids_tensor([self.batch_size, self.image_size, self.image_size]			,	self.num_labels   )

    __lowerCamelCase       :       Dict           = self.get_config()

    return config, pixel_values, labels




  def 						_lowercase     ( self      :	Optional[Any]   )					->					List[str]:
    __lowerCamelCase       :       Optional[Any]           = {
        'global_padding': 'same',
        'layer_type': 'bottleneck',
        'depths': [3, 4, 9],
        'out_features': ['stage1', 'stage2', 'stage3'],
        'embedding_dynamic_padding': True,
        'hidden_sizes': [96, 192, 384, 768],
        'num_groups': 2,
    }

    return DPTConfig(
        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			,	backbone_out_indices=self.backbone_out_indices			,	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=_a			,	initializer_range=self.initializer_range			,	is_hybrid=self.is_hybrid			,	backbone_config=_a			,	backbone_featmap_shape=self.backbone_featmap_shape			,	)




  def 						_lowercase     ( self      :	Optional[int]			,	_a      :	int			,	_a      :	str			,	_a      :	Optional[int]   )					->					Optional[int]:
    __lowerCamelCase       :       Any           = DPTModel(config=_a   )
    model.to(_a   )
    model.eval()
    __lowerCamelCase       :       List[str]           = model(_a   )
    self.parent.assertEqual(result.last_hidden_state.shape			,	(self.batch_size, self.seq_length, self.hidden_size)   )




  def 						_lowercase     ( self      :	Optional[Any]			,	_a      :	Union[str, Any]			,	_a      :	Optional[Any]			,	_a      :	List[Any]   )					->					List[Any]:
    __lowerCamelCase       :       Dict           = self.num_labels
    __lowerCamelCase       :       List[Any]           = DPTForDepthEstimation(_a   )
    model.to(_a   )
    model.eval()
    __lowerCamelCase       :       List[str]           = model(_a   )
    self.parent.assertEqual(result.predicted_depth.shape			,	(self.batch_size, self.image_size, self.image_size)   )




  def 						_lowercase     ( self      :	List[str]			,	_a      :	Optional[Any]			,	_a      :	Tuple			,	_a      :	Tuple   )					->					List[Any]:
    __lowerCamelCase       :       Union[str, Any]           = self.num_labels
    __lowerCamelCase       :       Optional[Any]           = DPTForSemanticSegmentation(_a   )
    model.to(_a   )
    model.eval()
    __lowerCamelCase       :       int           = model(_a			,	labels=_a   )
    self.parent.assertEqual(
        result.logits.shape			,	(self.batch_size, self.num_labels, self.image_size, self.image_size)   )




  def 						_lowercase     ( self      :	Optional[Any]   )					->					Union[str, Any]:
    __lowerCamelCase       :       Tuple           = self.prepare_config_and_inputs()
    __lowerCamelCase       ,__lowerCamelCase       ,__lowerCamelCase       :       str           = config_and_inputs
    __lowerCamelCase       :       Optional[Any]           = {'pixel_values': pixel_values}
    return config, inputs_dict






@require_torch
class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   unittest.TestCase     ):
  """simple docstring"""




  a_												=(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
  a_												=(
      {
          """depth-estimation""": DPTForDepthEstimation,
          """feature-extraction""": DPTModel,
          """image-segmentation""": DPTForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  a_												=False
  a_												=False
  a_												=False




  def 						_lowercase     ( self      :	Dict   )					->					Any:
    __lowerCamelCase       :       Optional[Any]           = DPTModelTester(self   )
    __lowerCamelCase       :       Union[str, Any]           = ConfigTester(self			,	config_class=_a			,	has_text_modality=_a			,	hidden_size=37   )




  def 						_lowercase     ( self      :	Tuple   )					->					Union[str, Any]:
    self.config_tester.run_common_tests()




  @unittest.skip(reason='DPT does not use inputs_embeds'   )
  def 						_lowercase     ( self      :	Dict   )					->					Union[str, Any]:
    pass




  def 						_lowercase     ( self      :	Dict   )					->					str:
    __lowerCamelCase       ,__lowerCamelCase       :       Optional[Any]           = self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      __lowerCamelCase       :       Union[str, Any]           = model_class(_a   )
      self.assertIsInstance(model.get_input_embeddings()			,	(nn.Module)   )
      __lowerCamelCase       :       List[Any]           = model.get_output_embeddings()
      self.assertTrue(x is None or isinstance(_a			,	nn.Linear   )   )




  def 						_lowercase     ( self      :	List[str]   )					->					Any:
    __lowerCamelCase       ,__lowerCamelCase       :       Any           = self.model_tester.prepare_config_and_inputs_for_common()

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

      __lowerCamelCase       :       Optional[int]           = ['pixel_values']
      self.assertListEqual(arg_names[:1]			,	_a   )




  def 						_lowercase     ( self      :	Dict   )					->					Any:
    __lowerCamelCase       :       Dict           = self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_a   )




  def 						_lowercase     ( self      :	Union[str, Any]   )					->					int:
    __lowerCamelCase       :       Dict           = self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_depth_estimation(*_a   )




  def 						_lowercase     ( self      :	List[Any]   )					->					Any:
    __lowerCamelCase       :       Union[str, Any]           = self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_semantic_segmentation(*_a   )




  def 						_lowercase     ( self      :	Optional[int]   )					->					Dict:
    for model_class in self.all_model_classes:
      if model_class.__name__ == "DPTForDepthEstimation":
        continue

      __lowerCamelCase       ,__lowerCamelCase       :       int           = self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCamelCase       :       Optional[int]           = True

      if model_class in get_values(_a   ):
        continue

      __lowerCamelCase       :       Tuple           = model_class(_a   )
      model.to(_a   )
      model.train()
      __lowerCamelCase       :       str           = self._prepare_for_class(_a			,	_a			,	return_labels=_a   )
      __lowerCamelCase       :       Dict           = model(**_a   ).loss
      loss.backward()




  def 						_lowercase     ( self      :	Union[str, Any]   )					->					str:
    for model_class in self.all_model_classes:
      if model_class.__name__ == "DPTForDepthEstimation":
        continue

      __lowerCamelCase       ,__lowerCamelCase       :       Optional[Any]           = self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCamelCase       :       Tuple           = False
      __lowerCamelCase       :       List[str]           = True

      if model_class in get_values(_a   ) or not model_class.supports_gradient_checkpointing:
        continue
      __lowerCamelCase       :       Optional[int]           = model_class(_a   )
      model.to(_a   )
      model.gradient_checkpointing_enable()
      model.train()
      __lowerCamelCase       :       List[Any]           = self._prepare_for_class(_a			,	_a			,	return_labels=_a   )
      __lowerCamelCase       :       str           = model(**_a   ).loss
      loss.backward()




  def 						_lowercase     ( self      :	Dict   )					->					Optional[Any]:
    __lowerCamelCase       ,__lowerCamelCase       :       int           = self.model_tester.prepare_config_and_inputs_for_common()

    __lowerCamelCase       :       Union[str, Any]           = _config_zero_init(_a   )
    for model_class in self.all_model_classes:
      __lowerCamelCase       :       List[Any]           = model_class(config=_a   )
      # Skip the check for the backbone
      __lowerCamelCase       :       Any           = []
      for name, module in model.named_modules():
        if module.__class__.__name__ == "DPTViTHybridEmbeddings":
          __lowerCamelCase       :       Dict           = [f'{name}.{key}' for key in module.state_dict().keys()]
          break

      for name, param in model.named_parameters():
        if param.requires_grad:
          if name in backbone_params:
            continue
          self.assertIn(
              ((param.data.mean() * 1e9).round() / 1e9).item()			,	[0.0, 1.0]			,	msg=f'Parameter {name} of model {model_class} seems not properly initialized'			,	)




  @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'   )
  def 						_lowercase     ( self      :	Dict   )					->					Optional[int]:
    pass




  @slow
  def 						_lowercase     ( self      :	Any   )					->					int:
    for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
      __lowerCamelCase       :       Union[str, Any]           = DPTModel.from_pretrained(_a   )
      self.assertIsNotNone(_a   )




  def 						_lowercase     ( self      :	List[Any]   )					->					List[Any]:
    # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
    __lowerCamelCase       ,__lowerCamelCase       :       str           = self.model_tester.prepare_config_and_inputs_for_common()
    __lowerCamelCase       :       List[Any]           = 'add'
    with self.assertRaises(_a   ):
      __lowerCamelCase       :       int           = DPTForDepthEstimation(_a   )




def        a_     (							)      ->      str:
  __lowerCamelCase       :       List[str]           = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
  return image






@require_torch
@require_vision
@slow
class 					lowerCamelCase_    (				unittest.TestCase     ):
  """simple docstring"""




  def 						_lowercase     ( self      :	Dict   )					->					Tuple:
    __lowerCamelCase       :       Any           = DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'   )
    __lowerCamelCase       :       Any           = DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'   ).to(_a   )

    __lowerCamelCase       :       Any           = prepare_img()
    __lowerCamelCase       :       Dict           = image_processor(images=_a			,	return_tensors='pt'   ).to(_a   )

    # forward pass
    with torch.no_grad():
      __lowerCamelCase       :       Any           = model(**_a   )
      __lowerCamelCase       :       Any           = outputs.predicted_depth

    # verify the predicted depth
    __lowerCamelCase       :       int           = torch.Size((1, 384, 384)   )
    self.assertEqual(predicted_depth.shape			,	_a   )

    __lowerCamelCase       :       Tuple           = torch.tensor(
        [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]   ).to(_a   )

    self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100			,	_a			,	atol=1e-4   )   )






