






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						:






	def __init__(		self					,UpperCamelCase					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase="resnet50"					,UpperCamelCase=3					,UpperCamelCase=32					,UpperCamelCase=3					,UpperCamelCase=True					,UpperCamelCase=True					,)						->    Optional[Any]:
					snake_case__      :List[str]         =   parent
					snake_case__      :Dict         =   out_indices if out_indices is not None else [4]
					snake_case__      :List[str]         =   stage_names
					snake_case__      :str         =   out_features
					snake_case__      :Union[str, Any]         =   backbone
					snake_case__      :int         =   batch_size
					snake_case__      :List[str]         =   image_size
					snake_case__      :Optional[Any]         =   num_channels
					snake_case__      :Any         =   use_pretrained_backbone
					snake_case__      :Tuple         =   is_training






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :Union[str, Any]         =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )
					snake_case__      :List[str]         =   self.get_config()

					return config, pixel_values






	def    lowerCAmelCase_							(		self       )						->    str:
					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    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase       )						->    int:
					snake_case__      :Any         =   TimmBackbone(config=UpperCamelCase       )
					model.to(UpperCamelCase       )
					model.eval()
					with torch.no_grad():
									snake_case__      :List[str]         =   model(UpperCamelCase       )
					self.parent.assertEqual(
					    result.feature_map[-1].shape					,(self.batch_size, model.channels[-1], 14, 14)					,)






	def    lowerCAmelCase_							(		self       )						->    Dict:
					snake_case__      :List[Any]         =   self.prepare_config_and_inputs()
					snake_case__      :List[Any]         =   config_and_inputs
					snake_case__      :str         =   {"pixel_values": pixel_values}
					return config, inputs_dict




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






	def    lowerCAmelCase_							(		self       )						->    int:
					snake_case__      :List[Any]         =   TimmBackboneModelTester(self       )
					snake_case__      :Optional[Any]         =   ConfigTester(self					,config_class=UpperCamelCase					,has_text_modality=UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					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    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :Optional[Any]         =   "resnet18"
					snake_case__      :int         =   "microsoft/resnet-18"

					snake_case__      :List[Any]         =   AutoBackbone.from_pretrained(UpperCamelCase					,use_timm_backbone=UpperCamelCase       )
					snake_case__      :List[Any]         =   AutoBackbone.from_pretrained(UpperCamelCase       )

					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]       )

					snake_case__      :str         =   AutoBackbone.from_pretrained(UpperCamelCase					,use_timm_backbone=UpperCamelCase					,out_indices=[1, 2, 3]       )
					snake_case__      :str         =   AutoBackbone.from_pretrained(UpperCamelCase					,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    lowerCAmelCase_							(		self       )						->    Tuple:
					pass






	@unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute"       )
	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					pass






	@unittest.skip("TimmBackbone initialization is managed on the timm side"       )
	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					pass






	@unittest.skip("TimmBackbone models doesn't have inputs_embeds"       )
	def    lowerCAmelCase_							(		self       )						->    str:
					pass






	@unittest.skip("TimmBackbone models doesn't have inputs_embeds"       )
	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					pass






	@unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint"       )
	def    lowerCAmelCase_							(		self       )						->    Dict:
					pass






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






	@unittest.skip("model weights aren't tied in TimmBackbone."       )
	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					pass






	@unittest.skip("model weights aren't tied in TimmBackbone."       )
	def    lowerCAmelCase_							(		self       )						->    int:
					pass






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






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






	@unittest.skip("TimmBackbone doesn't have hidden size info in its configuration."       )
	def    lowerCAmelCase_							(		self       )						->    Tuple:
					pass






	@unittest.skip("TimmBackbone doesn't support output_attentions."       )
	def    lowerCAmelCase_							(		self       )						->    Tuple:
					pass






	@unittest.skip("Safetensors is not supported by timm."       )
	def    lowerCAmelCase_							(		self       )						->    str:
					pass






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






	def    lowerCAmelCase_							(		self       )						->    Dict:
					snake_case__      :Optional[int]         =   self.model_tester.prepare_config_and_inputs_for_common()

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

									snake_case__      :Optional[Any]         =   ["pixel_values"]
									self.assertListEqual(arg_names[:1]					,UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :Optional[Any]         =   self.model_tester.prepare_config_and_inputs_for_common()
					snake_case__      :Any         =   True
					snake_case__      :str         =   self.has_attentions

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

					snake_case__      :Optional[int]         =   self._prepare_for_class(UpperCamelCase					,UpperCamelCase       )
					snake_case__      :Tuple         =   model(**UpperCamelCase       )
					snake_case__      :Union[str, Any]         =   outputs[0][-1]

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

					if self.has_attentions:
									snake_case__      :Optional[Any]         =   outputs.attentions[0]
									attentions.retain_grad()

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

					self.assertIsNotNone(hidden_states.grad       )

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






	def    lowerCAmelCase_							(		self       )						->    int:
					snake_case__      :Tuple         =   self.model_tester.prepare_config_and_inputs_for_common()

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

									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__      :Optional[Any]         =   copy.deepcopy(UpperCamelCase       )
									snake_case__      :List[str]         =   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__      :Tuple         =   copy.deepcopy(UpperCamelCase       )
									snake_case__      :Tuple         =   False
									snake_case__      :Tuple         =   model_class(UpperCamelCase       )
									model.to(UpperCamelCase       )
									model.eval()
									snake_case__      :Optional[int]         =   model(**UpperCamelCase       )







from __future__ import annotations


class 					_snake_case						:






	def __init__(		self					,UpperCamelCase       )						->    None:
					snake_case__      :Union[str, Any]         =   data
					snake_case__      :Node | None         =   None
					snake_case__      :Node | None         =   None





def 					lowercase_ (    __snake_case     :							Node | None      )     -> None:  # In Order traversal of the tree





				'''simple docstring'''


				if tree:
								display(tree.left      )
								print(tree.data      )
								display(tree.right      )





def 					lowercase_ (    __snake_case     :							Node | None      )     -> int:





				'''simple docstring'''


				return 1 + max(depth_of_tree(tree.left      )	,	depth_of_tree(tree.right      )      ) if tree else 0





def 					lowercase_ (    __snake_case     :							Node      )     -> bool:





				'''simple docstring'''


				if not tree:
								return True
				if tree.left and tree.right:
								return is_full_binary_tree(tree.left      ) and is_full_binary_tree(tree.right      )
				else:
								return not tree.left and not tree.right





def 					lowercase_ (    )     -> None:  # Main function for testing.





				'''simple docstring'''


				snake_case__      :Dict         =   Node(1      )
				snake_case__      :int         =   Node(2      )
				snake_case__      :Optional[Any]         =   Node(3      )
				snake_case__      :Tuple         =   Node(4      )
				snake_case__      :str         =   Node(5      )
				snake_case__      :Optional[Any]         =   Node(6      )
				snake_case__      :List[Any]         =   Node(7      )
				snake_case__      :List[str]         =   Node(8      )
				snake_case__      :Tuple         =   Node(9      )

				print(is_full_binary_tree(__snake_case      )      )
				print(depth_of_tree(__snake_case      )      )
				print("Tree is: "      )
				display(__snake_case      )


if __name__ == "__main__":
	main()