




"""simple docstring"""






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   a__			:





						def __init__(							self      :	int     ,a__      :	str     ,a__      :	Any=None     ,a__      :	Union[str, Any]=None     ,a__      :	Any=None     ,a__      :	Dict="resnet50"     ,a__      :	int=3     ,a__      :	List[Any]=32     ,a__      :	int=3     ,a__      :	List[Any]=True     ,a__      :	Dict=True     ,)						->       Tuple:




											"""simple docstring"""







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





						def       __UpperCamelCase     (							self      :	Any)						->       str:




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
											_lowerCAmelCase:List[str]									=  self.get_config()

											return config, pixel_values





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




											"""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       __UpperCamelCase     (							self      :	List[str]     ,a__      :	Optional[Any]     ,a__      :	Dict)						->       Dict:




											"""simple docstring"""







											_lowerCAmelCase:Any									=  TimmBackbone(config=a__)
											model.to(a__)
											model.eval()
											with torch.no_grad():
																_lowerCAmelCase:str									=  model(a__)
											self.parent.assertEqual(
											    result.feature_map[-1].shape     ,(self.batch_size, model.channels[-1], 14, 14)     ,)







						def       __UpperCamelCase     (							self      :	Any)						->       str:




											"""simple docstring"""







											_lowerCAmelCase:List[Any]									=  self.prepare_config_and_inputs()
											_lowerCAmelCase							,   _lowerCAmelCase:Optional[int]									=  config_and_inputs
											_lowerCAmelCase:List[Any]									=  {'''pixel_values''': pixel_values}
											return config, inputs_dict




@require_torch
@require_timm
class   a__			(       UpperCamelCase_					,	UpperCamelCase_					,	UpperCamelCase_					,	unittest.TestCase  ):
						snake_case__             = (TimmBackbone,) if is_torch_available() else ()
						snake_case__             = {'''feature-extraction''': TimmBackbone} if is_torch_available() else {}
						snake_case__             = False
						snake_case__             = False
						snake_case__             = False
						snake_case__             = False





						def       __UpperCamelCase     (							self      :	Optional[Any])						->       Optional[Any]:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  TimmBackboneModelTester(self)
											_lowerCAmelCase:List[Any]									=  ConfigTester(self     ,config_class=a__     ,has_text_modality=a__)





						def       __UpperCamelCase     (							self      :	int)						->       List[str]:




											"""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       __UpperCamelCase     (							self      :	Dict)						->       Tuple:




											"""simple docstring"""







											_lowerCAmelCase:int									=  '''resnet18'''
											_lowerCAmelCase:List[str]									=  '''microsoft/resnet-18'''

											_lowerCAmelCase:Union[str, Any]									=  AutoBackbone.from_pretrained(a__     ,use_timm_backbone=a__)
											_lowerCAmelCase:Optional[int]									=  AutoBackbone.from_pretrained(a__)

											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])

											_lowerCAmelCase:str									=  AutoBackbone.from_pretrained(a__     ,use_timm_backbone=a__     ,out_indices=[1, 2, 3])
											_lowerCAmelCase:Dict									=  AutoBackbone.from_pretrained(a__     ,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       __UpperCamelCase     (							self      :	Union[str, Any])						->       Tuple:




											"""simple docstring"""







											pass





						@unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute''')
						def       __UpperCamelCase     (							self      :	Dict)						->       Optional[int]:




											"""simple docstring"""







											pass





						@unittest.skip('''TimmBackbone initialization is managed on the timm side''')
						def       __UpperCamelCase     (							self      :	List[Any])						->       str:




											"""simple docstring"""







											pass





						@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds''')
						def       __UpperCamelCase     (							self      :	Dict)						->       Union[str, Any]:




											"""simple docstring"""







											pass





						@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds''')
						def       __UpperCamelCase     (							self      :	Tuple)						->       List[Any]:




											"""simple docstring"""







											pass





						@unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint''')
						def       __UpperCamelCase     (							self      :	List[str])						->       str:




											"""simple docstring"""







											pass





						@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone''')
						def       __UpperCamelCase     (							self      :	Tuple)						->       Tuple:




											"""simple docstring"""







											pass





						@unittest.skip('''model weights aren\'t tied in TimmBackbone.''')
						def       __UpperCamelCase     (							self      :	Optional[int])						->       str:




											"""simple docstring"""







											pass





						@unittest.skip('''model weights aren\'t tied in TimmBackbone.''')
						def       __UpperCamelCase     (							self      :	Dict)						->       Any:




											"""simple docstring"""







											pass





						@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone''')
						def       __UpperCamelCase     (							self      :	str)						->       Union[str, Any]:




											"""simple docstring"""







											pass





						@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone''')
						def       __UpperCamelCase     (							self      :	str)						->       str:




											"""simple docstring"""







											pass





						@unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.''')
						def       __UpperCamelCase     (							self      :	Union[str, Any])						->       Optional[int]:




											"""simple docstring"""







											pass





						@unittest.skip('''TimmBackbone doesn\'t support output_attentions.''')
						def       __UpperCamelCase     (							self      :	Union[str, Any])						->       Tuple:




											"""simple docstring"""







											pass





						@unittest.skip('''Safetensors is not supported by timm.''')
						def       __UpperCamelCase     (							self      :	Dict)						->       Tuple:




											"""simple docstring"""







											pass





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




											"""simple docstring"""







											pass





						def       __UpperCamelCase     (							self      :	Tuple)						->       Any:




											"""simple docstring"""







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

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

																_lowerCAmelCase:str									=  ['''pixel_values''']
																self.assertListEqual(arg_names[:1]     ,a__)





						def       __UpperCamelCase     (							self      :	str)						->       str:




											"""simple docstring"""







											_lowerCAmelCase							,   _lowerCAmelCase:List[Any]									=  self.model_tester.prepare_config_and_inputs_for_common()
											_lowerCAmelCase:Tuple									=  True
											_lowerCAmelCase:Optional[Any]									=  self.has_attentions

											# no need to test all models as different heads yield the same functionality
											_lowerCAmelCase:str									=  self.all_model_classes[0]
											_lowerCAmelCase:Dict									=  model_class(a__)
											model.to(a__)

											_lowerCAmelCase:str									=  self._prepare_for_class(a__     ,a__)
											_lowerCAmelCase:int									=  model(**a__)
											_lowerCAmelCase:str									=  outputs[0][-1]

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

											if self.has_attentions:
																_lowerCAmelCase:Dict									=  outputs.attentions[0]
																attentions.retain_grad()

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

											self.assertIsNotNone(hidden_states.grad)

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







						def       __UpperCamelCase     (							self      :	int)						->       str:




											"""simple docstring"""







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

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

																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
																_lowerCAmelCase:Tuple									=  copy.deepcopy(a__)
																_lowerCAmelCase:List[Any]									=  None
																_lowerCAmelCase:List[str]									=  model_class(a__)
																model.to(a__)
																model.eval()
																_lowerCAmelCase:List[Any]									=  model(**a__)

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

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












"""simple docstring"""






def 							UpperCAmelCase		(		snake_case			:					str      ,       snake_case			:					str    ):
					assert x is not None
					assert y is not None

					_lowerCAmelCase:Optional[Any]									=  len(snake_case    )
					_lowerCAmelCase:Tuple									=  len(snake_case    )

					# declaring the array for storing the dp values
					_lowerCAmelCase:Optional[Any]									=  [[0] * (n + 1) for _ in range(m + 1    )]  # noqa: E741

					for i in range(1      ,       m + 1    ):
										for j in range(1      ,       n + 1    ):
															_lowerCAmelCase:Optional[int]									=  1 if x[i - 1] == y[j - 1] else 0

															_lowerCAmelCase:int									=  max(l[i - 1][j]      ,       l[i][j - 1]      ,       l[i - 1][j - 1] + match    )

					_lowerCAmelCase:List[Any]									=  ''''''
					_lowerCAmelCase							,   _lowerCAmelCase:Dict									=  m, n
					while i > 0 and j > 0:
										_lowerCAmelCase:Any									=  1 if x[i - 1] == y[j - 1] else 0

										if l[i][j] == l[i - 1][j - 1] + match:
															if match == 1:
																				_lowerCAmelCase:Any									=  x[i - 1] + seq
															i -= 1
															j -= 1
										elif l[i][j] == l[i - 1][j]:
															i -= 1
										else:
															j -= 1

					return l[m][n], seq


if __name__ == "__main__":
				UpperCamelCase__						=			'''AGGTAB'''
				UpperCamelCase__						=			'''GXTXAYB'''
				UpperCamelCase__						=			4
				UpperCamelCase__						=			'''GTAB'''

				UpperCamelCase__						,							UpperCamelCase__						=			longest_common_subsequence(a, b)
				print('''len =''', ln, ''', sub-sequence =''', subseq)
				import doctest

				doctest.testmod()






