





import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType



@require_torch
class 				_lowerCAmelCase			:


  '''simple docstring'''
  a_							:				Dict					    =()
  a_							:				List[Any]					    =True







  def UpperCamelCase_       (					self					:    Optional[int]							):


    '''simple docstring'''





    _snake_case      : Any														=		self.config_class

    # test default config
    _snake_case      : List[Any]														=		config_class()
    self.assertIsNotNone(UpperCamelCase							)
    _snake_case      : Optional[int]														=		['stem'] + [f"""stage{idx}""" for idx in range(1					, len(config.depths							) + 1							)]
    self.assertEqual(config.stage_names					, UpperCamelCase							)
    self.assertTrue(set(config.out_features							).issubset(set(config.stage_names							)							)							)

    # Test out_features and out_indices are correctly set
    # out_features and out_indices both None
    _snake_case      : List[str]														=		config_class(out_features=UpperCamelCase					, out_indices=UpperCamelCase							)
    self.assertEqual(config.out_features					, [config.stage_names[-1]]							)
    self.assertEqual(config.out_indices					, [len(config.stage_names							) - 1]							)

    # out_features and out_indices both set
    _snake_case      : List[str]														=		config_class(out_features=['stem', 'stage1']					, out_indices=[0, 1]							)
    self.assertEqual(config.out_features					, ['stem', 'stage1']							)
    self.assertEqual(config.out_indices					, [0, 1]							)

    # Only out_features set
    _snake_case      : Optional[int]														=		config_class(out_features=['stage1', 'stage3']							)
    self.assertEqual(config.out_features					, ['stage1', 'stage3']							)
    self.assertEqual(config.out_indices					, [1, 3]							)

    # Only out_indices set
    _snake_case      : Tuple														=		config_class(out_indices=[0, 2]							)
    self.assertEqual(config.out_features					, [config.stage_names[0], config.stage_names[2]]							)
    self.assertEqual(config.out_indices					, [0, 2]							)

    # Error raised when out_indices do not correspond to out_features
    with self.assertRaises(UpperCamelCase							):
      _snake_case      : Optional[Any]														=		config_class(out_features=['stage1', 'stage2']					, out_indices=[0, 2]							)







  def UpperCamelCase_       (					self					:    List[Any]							):


    '''simple docstring'''





    _snake_case							,    _snake_case      : List[str]														=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      _snake_case      : str														=		model_class(UpperCamelCase							)
      _snake_case      : Dict														=		inspect.signature(model.forward							)
      # signature.parameters is an OrderedDict => so arg_names order is deterministic
      _snake_case      : Optional[int]														=		[*signature.parameters.keys()]
      _snake_case      : Optional[Any]														=		['pixel_values']
      self.assertListEqual(arg_names[:1]					, UpperCamelCase							)







  def UpperCamelCase_       (					self					:    int							):


    '''simple docstring'''





    _snake_case							,    _snake_case      : Dict														=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      _snake_case      : Dict														=		model_class(UpperCamelCase							)
      self.assertEqual(len(model.channels							)					, len(config.out_features							)							)
      _snake_case      : Tuple														=		model.num_features
      _snake_case      : str														=		[config.stage_names.index(UpperCamelCase							) for feat in config.out_features]
      _snake_case      : List[Any]														=		[num_features[idx] for idx in out_indices]
      self.assertListEqual(model.channels					, UpperCamelCase							)

      _snake_case      : List[str]														=		copy.deepcopy(UpperCamelCase							)
      _snake_case      : Dict														=		None
      _snake_case      : str														=		model_class(UpperCamelCase							)
      self.assertEqual(len(model.channels							)					, 1							)
      self.assertListEqual(model.channels					, [num_features[-1]]							)

      _snake_case      : Optional[Any]														=		copy.deepcopy(UpperCamelCase							)
      _snake_case      : List[Any]														=		None
      _snake_case      : Optional[int]														=		model_class(UpperCamelCase							)
      self.assertEqual(len(model.channels							)					, 1							)
      self.assertListEqual(model.channels					, [num_features[-1]]							)







  def UpperCamelCase_       (					self					:    Any							):


    '''simple docstring'''





    _snake_case							,    _snake_case      : List[str]														=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      _snake_case      : str														=		model_class(UpperCamelCase							)
      model.to(UpperCamelCase							)
      model.eval()
      _snake_case      : List[str]														=		model(**UpperCamelCase							)

      self.assertEqual(len(result.feature_maps							)					, len(config.out_features							)							)
      self.assertEqual(len(model.channels							)					, len(config.out_features							)							)
      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
      _snake_case      : List[str]														=		copy.deepcopy(UpperCamelCase							)
      _snake_case      : Tuple														=		None
      _snake_case      : Union[str, Any]														=		model_class(UpperCamelCase							)
      model.to(UpperCamelCase							)
      model.eval()
      _snake_case      : Optional[Any]														=		model(**UpperCamelCase							)

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

      _snake_case      : Optional[int]														=		copy.deepcopy(UpperCamelCase							)
      _snake_case      : Tuple														=		None
      _snake_case      : List[Any]														=		model_class(UpperCamelCase							)
      model.to(UpperCamelCase							)
      model.eval()
      _snake_case      : List[Any]														=		model(**UpperCamelCase							)

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

      # Check backbone can be initialized with fresh weights
      _snake_case      : Dict														=		copy.deepcopy(UpperCamelCase							)
      _snake_case      : List[str]														=		False
      _snake_case      : Any														=		model_class(UpperCamelCase							)
      model.to(UpperCamelCase							)
      model.eval()
      _snake_case      : Tuple														=		model(**UpperCamelCase							)







  def UpperCamelCase_       (					self					:    int							):


    '''simple docstring'''





    _snake_case							,    _snake_case      : Union[str, Any]														=		self.model_tester.prepare_config_and_inputs_for_common()

    for backbone_class in self.all_model_classes:
      _snake_case      : Optional[int]														=		backbone_class(UpperCamelCase							)

      self.assertTrue(hasattr(UpperCamelCase					, 'backbone_type'							)							)
      self.assertTrue(hasattr(UpperCamelCase					, 'stage_names'							)							)
      self.assertTrue(hasattr(UpperCamelCase					, 'num_features'							)							)
      self.assertTrue(hasattr(UpperCamelCase					, 'out_indices'							)							)
      self.assertTrue(hasattr(UpperCamelCase					, 'out_features'							)							)
      self.assertTrue(hasattr(UpperCamelCase					, 'out_feature_channels'							)							)
      self.assertTrue(hasattr(UpperCamelCase					, 'channels'							)							)

      self.assertIsInstance(backbone.backbone_type					, UpperCamelCase							)
      # Verify num_features has been initialized in the backbone init
      self.assertIsNotNone(backbone.num_features							)
      self.assertTrue(len(backbone.channels							) == len(backbone.out_indices							)							)
      self.assertTrue(len(backbone.stage_names							) == len(backbone.num_features							)							)
      self.assertTrue(len(backbone.channels							) <= len(backbone.num_features							)							)
      self.assertTrue(len(backbone.out_feature_channels							) == len(backbone.stage_names							)							)







  def UpperCamelCase_       (					self					:    List[Any]							):


    '''simple docstring'''





    _snake_case							,    _snake_case      : Union[str, Any]														=		self.model_tester.prepare_config_and_inputs_for_common()
    _snake_case      : List[Any]														=		inputs_dict['pixel_values'].shape[0]

    for backbone_class in self.all_model_classes:
      _snake_case      : List[str]														=		backbone_class(UpperCamelCase							)
      backbone.to(UpperCamelCase							)
      backbone.eval()

      _snake_case      : Union[str, Any]														=		backbone(**UpperCamelCase							)

      # Test default outputs and verify feature maps
      self.assertIsInstance(outputs.feature_maps					, UpperCamelCase							)
      self.assertTrue(len(outputs.feature_maps							) == len(backbone.channels							)							)
      for feature_map, n_channels in zip(outputs.feature_maps					, backbone.channels							):
        self.assertTrue(feature_map.shape[:2]					, (batch_size, n_channels)							)
      self.assertIsNone(outputs.hidden_states							)
      self.assertIsNone(outputs.attentions							)

      # Test output_hidden_states=True
      _snake_case      : str														=		backbone(**UpperCamelCase					, output_hidden_states=UpperCamelCase							)
      self.assertIsNotNone(outputs.hidden_states							)
      self.assertTrue(len(outputs.hidden_states							)					, len(backbone.stage_names							)							)
      for hidden_state, n_channels in zip(outputs.hidden_states					, backbone.channels							):
        self.assertTrue(hidden_state.shape[:2]					, (batch_size, n_channels)							)

      # Test output_attentions=True
      if self.has_attentions:
        _snake_case      : Tuple														=		backbone(**UpperCamelCase					, output_attentions=UpperCamelCase							)
        self.assertIsNotNone(outputs.attentions							)








from __future__ import annotations
def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase					,     __lowerCamelCase		:							str							=						m - 1, 0
 while i >= 0 and j < n:
  if key == mat[i][j]:
   print(f'Key {key} found at row- {i + 1} column- {j + 1}'						)
   return
  if key < mat[i][j]:
   i -= 1
  else:
   j += 1
 print(f'Key {key} not found'						)
def       UpperCamelCase__     (  ):
 __lowerCamelCase		:							List[Any]							=						[[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
 __lowerCamelCase		:							int							=						int(input('Enter the element to be searched:'						)						)
 print(SCREAMING_SNAKE_CASE__						)
 search_in_a_sorted_matrix(SCREAMING_SNAKE_CASE__			,       len(SCREAMING_SNAKE_CASE__						)			,       len(mat[0]						)			,       SCREAMING_SNAKE_CASE__						)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()

