



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=3_2 ,       __lowercase=3 ,       __lowercase=True ,       __lowercase=True ,       ) ->				int:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				int:
									"""simple docstring"""




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

									return config, pixel_values

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[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 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase  ) ->				Any:
									"""simple docstring"""




									a__       :						Dict							=  TimmBackbone(config=__lowercase  )
									model.to(__lowercase  )
									model.eval()
									with torch.no_grad():
												a__       :						int							=  model(__lowercase  )
									self.parent.assertEqual(
									    result.feature_map[-1].shape ,       (self.batch_size, model.channels[-1], 1_4, 1_4) ,       )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Tuple:
									"""simple docstring"""




									a__       :						Any							=  self.prepare_config_and_inputs()
									a__	,						a__       :						List[str]							=  config_and_inputs
									a__       :						Dict							=  {"""pixel_values""": pixel_values}
									return config, inputs_dict




@require_torch
@require_timm
class        snake_case__    (A__ ,     A__ ,     A__ ,     unittest.TestCase		):







						"""simple docstring"""




						__lowerCAmelCase					:List[Any]      =					(TimmBackbone,) if is_torch_available() else ()
						__lowerCAmelCase					:Optional[Any]      =					{"feature-extraction": TimmBackbone} if is_torch_available() else {}
						__lowerCAmelCase					:Dict      =					False
						__lowerCAmelCase					:List[str]      =					False
						__lowerCAmelCase					:Dict      =					False
						__lowerCAmelCase					:Any      =					False

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  TimmBackboneModelTester(self  )
									a__       :						Optional[int]							=  ConfigTester(self ,       config_class=__lowercase ,       has_text_modality=__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Any:
									"""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 							SCREAMING_SNAKE_CASE__(       self  ) ->				Tuple:
									"""simple docstring"""




									a__       :						Optional[int]							=  """resnet18"""
									a__       :						Tuple							=  """microsoft/resnet-18"""

									a__       :						Any							=  AutoBackbone.from_pretrained(__lowercase ,       use_timm_backbone=__lowercase  )
									a__       :						Optional[int]							=  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]  )

									a__       :						str							=  AutoBackbone.from_pretrained(__lowercase ,       use_timm_backbone=__lowercase ,       out_indices=[1, 2, 3]  )
									a__       :						Union[str, Any]							=  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 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									pass

						@unittest.skip("""TimmBackbone doesn't have num_hidden_layers attribute"""  )
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




									pass

						@unittest.skip("""TimmBackbone initialization is managed on the timm side"""  )
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									pass

						@unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""  )
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									pass

						@unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""  )
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									pass

						@unittest.skip("""TimmBackbone model cannot be created without specifying a backbone checkpoint"""  )
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									pass

						@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""  )
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									pass

						@unittest.skip("""model weights aren't tied in TimmBackbone."""  )
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Tuple:
									"""simple docstring"""




									pass

						@unittest.skip("""model weights aren't tied in TimmBackbone."""  )
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									pass

						@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""  )
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									pass

						@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""  )
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									pass

						@unittest.skip("""TimmBackbone doesn't have hidden size info in its configuration."""  )
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									pass

						@unittest.skip("""TimmBackbone doesn't support output_attentions."""  )
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									pass

						@unittest.skip("""Safetensors is not supported by timm."""  )
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									pass

						@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""  )
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




									pass

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									a__	,						a__       :						str							=  self.model_tester.prepare_config_and_inputs_for_common()

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

												a__       :						List[Any]							=  ["""pixel_values"""]
												self.assertListEqual(arg_names[:1] ,       __lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




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

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

									a__       :						str							=  self._prepare_for_class(__lowercase ,       __lowercase  )
									a__       :						Optional[Any]							=  model(**__lowercase  )
									a__       :						Union[str, Any]							=  outputs[0][-1]

									# Encoder-/Decoder-only models
									a__       :						str							=  outputs.hidden_states[0]
									hidden_states.retain_grad()

									if self.has_attentions:
												a__       :						Optional[Any]							=  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 							SCREAMING_SNAKE_CASE__(       self  ) ->				int:
									"""simple docstring"""




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

									for model_class in self.all_model_classes:
												a__       :						int							=  model_class(__lowercase  )
												model.to(__lowercase  )
												model.eval()
												a__       :						List[str]							=  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
												a__       :						List[Any]							=  copy.deepcopy(__lowercase  )
												a__       :						Any							=  None
												a__       :						Optional[Any]							=  model_class(__lowercase  )
												model.to(__lowercase  )
												model.eval()
												a__       :						Optional[Any]							=  model(**__lowercase  )

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

												# Check backbone can be initialized with fresh weights
												a__       :						Optional[int]							=  copy.deepcopy(__lowercase  )
												a__       :						List[Any]							=  False
												a__       :						Optional[int]							=  model_class(__lowercase  )
												model.to(__lowercase  )
												model.eval()
												a__       :						Optional[int]							=  model(**__lowercase  )









def      lowerCAmelCase_					(  _lowercase    :       int)							->	int:







			"""simple docstring"""

			if not isinstance(_lowercase						,       _lowercase):
						raise TypeError("""only integers accepted as input""")
			else:
						a__       :						Any							=  str(abs(_lowercase))
						a__       :						str							=  [list(_lowercase) for char in range(len(_lowercase))]
						for index in range(len(_lowercase)):
									num_transpositions[index].pop(_lowercase)
						return max(
						    int("""""".join(list(_lowercase))) for transposition in num_transpositions)


if __name__ == "__main__":
		__import__("doctest").testmod()




