"""simple docstring"""




import inspect
import unittest

from transformers import ResNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
					import torch
					from torch import nn

					from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
					from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor

class        __SCREAMING_SNAKE_CASE     :







				def __init__(    self    :Dict      ,__UpperCAmelCase    :int      ,__UpperCAmelCase    :Optional[int]=3      ,__UpperCAmelCase    :List[str]=32      ,__UpperCAmelCase    :Tuple=3      ,__UpperCAmelCase    :Dict=10      ,__UpperCAmelCase    :Union[str, Any]=[10, 20, 30, 40]      ,__UpperCAmelCase    :Dict=[1, 1, 2, 1]      ,__UpperCAmelCase    :Dict=True      ,__UpperCAmelCase    :Any=True      ,__UpperCAmelCase    :Optional[int]="relu"      ,__UpperCAmelCase    :str=3      ,__UpperCAmelCase    :List[str]=None      ,__UpperCAmelCase    :Optional[int]=["stage2", "stage3", "stage4"]      ,__UpperCAmelCase    :str=[2, 3, 4]      ,)      ->					List[str]:




									"""simple docstring"""



									lowerCamelCase__		:   int  				=					parent
									lowerCamelCase__		:   List[Any]  				=					batch_size
									lowerCamelCase__		:   str  				=					image_size
									lowerCamelCase__		:   Any  				=					num_channels
									lowerCamelCase__		:   Any  				=					embeddings_size
									lowerCamelCase__		:   List[Any]  				=					hidden_sizes
									lowerCamelCase__		:   Any  				=					depths
									lowerCamelCase__		:   Dict  				=					is_training
									lowerCamelCase__		:   Dict  				=					use_labels
									lowerCamelCase__		:   Union[str, Any]  				=					hidden_act
									lowerCamelCase__		:   Dict  				=					num_labels
									lowerCamelCase__		:   int  				=					scope
									lowerCamelCase__		:   Optional[Any]  				=					len(__UpperCAmelCase  )
									lowerCamelCase__		:   Optional[int]  				=					out_features
									lowerCamelCase__		:   Optional[int]  				=					out_indices







				def   lowercase_		(    self    :Any  )      ->					Union[str, Any]:




									"""simple docstring"""



									lowerCamelCase__		:   str  				=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

									lowerCamelCase__		:   List[Any]  				=					None
									if self.use_labels:
														lowerCamelCase__		:   Tuple  				=					ids_tensor([self.batch_size]      ,self.num_labels  )

									lowerCamelCase__		:   Optional[Any]  				=					self.get_config()

									return config, pixel_values, labels







				def   lowercase_		(    self    :Any  )      ->					str:




									"""simple docstring"""



									return ResNetConfig(
									    num_channels=self.num_channels      ,embeddings_size=self.embeddings_size      ,hidden_sizes=self.hidden_sizes      ,depths=self.depths      ,hidden_act=self.hidden_act      ,num_labels=self.num_labels      ,out_features=self.out_features      ,out_indices=self.out_indices      ,)







				def   lowercase_		(    self    :Optional[int]      ,__UpperCAmelCase    :Dict      ,__UpperCAmelCase    :Dict      ,__UpperCAmelCase    :Union[str, Any]  )      ->					Any:




									"""simple docstring"""



									lowerCamelCase__		:   str  				=					ResNetModel(config=__UpperCAmelCase  )
									model.to(__UpperCAmelCase  )
									model.eval()
									lowerCamelCase__		:   str  				=					model(__UpperCAmelCase  )
									# expected last hidden states: B, C, H // 32, W // 32
									self.parent.assertEqual(
									    result.last_hidden_state.shape      ,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)      ,)







				def   lowercase_		(    self    :Optional[int]      ,__UpperCAmelCase    :Dict      ,__UpperCAmelCase    :Union[str, Any]      ,__UpperCAmelCase    :Optional[Any]  )      ->					Optional[int]:




									"""simple docstring"""



									lowerCamelCase__		:   List[str]  				=					self.num_labels
									lowerCamelCase__		:   List[str]  				=					ResNetForImageClassification(__UpperCAmelCase  )
									model.to(__UpperCAmelCase  )
									model.eval()
									lowerCamelCase__		:   Any  				=					model(__UpperCAmelCase      ,labels=__UpperCAmelCase  )
									self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_labels)  )







				def   lowercase_		(    self    :Optional[Any]      ,__UpperCAmelCase    :Union[str, Any]      ,__UpperCAmelCase    :Optional[Any]      ,__UpperCAmelCase    :Any  )      ->					List[Any]:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					ResNetBackbone(config=__UpperCAmelCase  )
									model.to(__UpperCAmelCase  )
									model.eval()
									lowerCamelCase__		:   Any  				=					model(__UpperCAmelCase  )

									# verify feature maps
									self.parent.assertEqual(len(result.feature_maps  )      ,len(config.out_features  )  )
									self.parent.assertListEqual(list(result.feature_maps[0].shape  )      ,[self.batch_size, self.hidden_sizes[1], 4, 4]  )

									# verify channels
									self.parent.assertEqual(len(model.channels  )      ,len(config.out_features  )  )
									self.parent.assertListEqual(model.channels      ,config.hidden_sizes[1:]  )

									# verify backbone works with out_features=None
									lowerCamelCase__		:   str  				=					None
									lowerCamelCase__		:   int  				=					ResNetBackbone(config=__UpperCAmelCase  )
									model.to(__UpperCAmelCase  )
									model.eval()
									lowerCamelCase__		:   Any  				=					model(__UpperCAmelCase  )

									# verify feature maps
									self.parent.assertEqual(len(result.feature_maps  )      ,1  )
									self.parent.assertListEqual(list(result.feature_maps[0].shape  )      ,[self.batch_size, self.hidden_sizes[-1], 1, 1]  )

									# verify channels
									self.parent.assertEqual(len(model.channels  )      ,1  )
									self.parent.assertListEqual(model.channels      ,[config.hidden_sizes[-1]]  )







				def   lowercase_		(    self    :str  )      ->					Union[str, Any]:




									"""simple docstring"""



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





@require_torch
class        __SCREAMING_SNAKE_CASE     (   _lowerCAmelCase						,       _lowerCAmelCase						,       unittest.TestCase							):
				UpperCAmelCase	=      (
				    (
				        ResNetModel,
				        ResNetForImageClassification,
				        ResNetBackbone,
				    )
				    if is_torch_available()
				    else ()
				)
				UpperCAmelCase	=      (
				    {'''feature-extraction''': ResNetModel, '''image-classification''': ResNetForImageClassification}
				    if is_torch_available()
				    else {}
				)

				UpperCAmelCase	=      True
				UpperCAmelCase	=      False
				UpperCAmelCase	=      False
				UpperCAmelCase	=      False
				UpperCAmelCase	=      False







				def   lowercase_		(    self    :Any  )      ->					Dict:




									"""simple docstring"""



									lowerCamelCase__		:   Dict  				=					ResNetModelTester(self  )
									lowerCamelCase__		:   Union[str, Any]  				=					ConfigTester(self      ,config_class=__UpperCAmelCase      ,has_text_modality=__UpperCAmelCase  )







				def   lowercase_		(    self    :List[str]  )      ->					Optional[Any]:




									"""simple docstring"""



									self.create_and_test_config_common_properties()
									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   lowercase_		(    self    :Optional[int]  )      ->					Union[str, Any]:




									"""simple docstring"""



									return







				@unittest.skip(reason='''ResNet does not use inputs_embeds'''  )
				def   lowercase_		(    self    :Optional[Any]  )      ->					List[str]:




									"""simple docstring"""



									pass







				@unittest.skip(reason='''ResNet does not support input and output embeddings'''  )
				def   lowercase_		(    self    :Optional[int]  )      ->					List[str]:




									"""simple docstring"""



									pass







				def   lowercase_		(    self    :Tuple  )      ->					int:




									"""simple docstring"""



									lowerCamelCase__				,						lowerCamelCase__		:   Any  				=					self.model_tester.prepare_config_and_inputs_for_common()

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

														lowerCamelCase__		:   Tuple  				=					['''pixel_values''']
														self.assertListEqual(arg_names[:1]      ,__UpperCAmelCase  )







				def   lowercase_		(    self    :Any  )      ->					int:




									"""simple docstring"""



									lowerCamelCase__		:   Dict  				=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__UpperCAmelCase  )







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




									"""simple docstring"""



									lowerCamelCase__		:   Any  				=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_backbone(*__UpperCAmelCase  )







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




									"""simple docstring"""



									lowerCamelCase__				,						lowerCamelCase__		:   int  				=					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														lowerCamelCase__		:   Optional[Any]  				=					model_class(config=__UpperCAmelCase  )
														for name, module in model.named_modules():
																			if isinstance(__UpperCAmelCase      ,(nn.BatchNormad, nn.GroupNorm)  ):
																								self.assertTrue(
																								    torch.all(module.weight == 1  )      ,msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""      ,)
																								self.assertTrue(
																								    torch.all(module.bias == 0  )      ,msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""      ,)







				def   lowercase_		(    self    :Dict  )      ->					List[Any]:




									"""simple docstring"""



									def check_hidden_states_output(__UpperCAmelCase    :List[Any]      ,__UpperCAmelCase    :Union[str, Any]      ,__UpperCAmelCase    :Union[str, Any]  ):
														lowerCamelCase__		:   Union[str, Any]  				=					model_class(__UpperCAmelCase  )
														model.to(__UpperCAmelCase  )
														model.eval()

														with torch.no_grad():
																			lowerCamelCase__		:   str  				=					model(**self._prepare_for_class(__UpperCAmelCase      ,__UpperCAmelCase  )  )

														lowerCamelCase__		:   Union[str, Any]  				=					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

														lowerCamelCase__		:   List[Any]  				=					self.model_tester.num_stages
														self.assertEqual(len(__UpperCAmelCase  )      ,expected_num_stages + 1  )

														# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
														self.assertListEqual(
														    list(hidden_states[0].shape[-2:]  )      ,[self.model_tester.image_size // 4, self.model_tester.image_size // 4]      ,)

									lowerCamelCase__				,						lowerCamelCase__		:   Optional[int]  				=					self.model_tester.prepare_config_and_inputs_for_common()
									lowerCamelCase__		:   Optional[int]  				=					['''basic''', '''bottleneck''']
									for model_class in self.all_model_classes:
														for layer_type in layers_type:
																			lowerCamelCase__		:   int  				=					layer_type
																			lowerCamelCase__		:   Optional[int]  				=					True
																			check_hidden_states_output(__UpperCAmelCase      ,__UpperCAmelCase      ,__UpperCAmelCase  )

																			# check that output_hidden_states also work using config
																			del inputs_dict["output_hidden_states"]
																			lowerCamelCase__		:   str  				=					True

																			check_hidden_states_output(__UpperCAmelCase      ,__UpperCAmelCase      ,__UpperCAmelCase  )







				@unittest.skip(reason='''ResNet does not use feedforward chunking'''  )
				def   lowercase_		(    self    :Tuple  )      ->					Optional[int]:




									"""simple docstring"""



									pass







				def   lowercase_		(    self    :Union[str, Any]  )      ->					Optional[Any]:




									"""simple docstring"""



									lowerCamelCase__		:   str  				=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase  )







				@slow
				def   lowercase_		(    self    :int  )      ->					Any:




									"""simple docstring"""



									for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowerCamelCase__		:   Optional[int]  				=					ResNetModel.from_pretrained(__UpperCAmelCase  )
														self.assertIsNotNone(__UpperCAmelCase  )





def 				__a       (			):







					"""simple docstring"""





					lowerCamelCase__		:   Dict  				=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
					return image

@require_torch
@require_vision
class        __SCREAMING_SNAKE_CASE     (   unittest.TestCase							):







				@cached_property
				def   lowercase_		(    self    :str  )      ->					int:




									"""simple docstring"""



									return (
									    AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )
									    if is_vision_available()
									    else None
									)







				@slow
				def   lowercase_		(    self    :Any  )      ->					int:




									"""simple docstring"""



									lowerCamelCase__		:   int  				=					ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(__UpperCAmelCase  )

									lowerCamelCase__		:   List[Any]  				=					self.default_image_processor
									lowerCamelCase__		:   Any  				=					prepare_img()
									lowerCamelCase__		:   Optional[int]  				=					image_processor(images=__UpperCAmelCase      ,return_tensors='''pt'''  ).to(__UpperCAmelCase  )

									# forward pass
									with torch.no_grad():
														lowerCamelCase__		:   Tuple  				=					model(**__UpperCAmelCase  )

									# verify the logits
									lowerCamelCase__		:   List[Any]  				=					torch.Size((1, 10_00)  )
									self.assertEqual(outputs.logits.shape      ,__UpperCAmelCase  )

									lowerCamelCase__		:   Dict  				=					torch.tensor([-11.1_069, -9.7_877, -8.3_777]  ).to(__UpperCAmelCase  )

									self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,__UpperCAmelCase      ,atol=1E-4  )  )





@require_torch
class        __SCREAMING_SNAKE_CASE     (   _lowerCAmelCase						,       unittest.TestCase							):
				UpperCAmelCase	=      (ResNetBackbone,) if is_torch_available() else ()
				UpperCAmelCase	=      False
				UpperCAmelCase	=      ResNetConfig







				def   lowercase_		(    self    :Tuple  )      ->					str:




									"""simple docstring"""



									lowerCamelCase__		:   Tuple  				=					ResNetModelTester(self  )







"""simple docstring"""




def 				__a       (			_lowercase							, _lowercase						):







					"""simple docstring"""





					if not root or root not in binary_tree_mirror_dictionary:
										return
					lowerCamelCase__				,						lowerCamelCase__		:   Tuple  				=					binary_tree_mirror_dictionary[root][:2]
					lowerCamelCase__		:   Optional[Any]  				=					[right_child, left_child]
					binary_tree_mirror_dict(_lowercase							, _lowercase						)
					binary_tree_mirror_dict(_lowercase							, _lowercase						)



def 				__a       (			_lowercase							, _lowercase = 1						):







					"""simple docstring"""





					if not binary_tree:
										raise ValueError('''binary tree cannot be empty'''						)
					if root not in binary_tree:
										lowerCamelCase__		:   Dict  				=					f"""root {root} is not present in the binary_tree"""
										raise ValueError(_lowercase						)
					lowerCamelCase__		:   Optional[Any]  				=					dict(_lowercase						)
					binary_tree_mirror_dict(_lowercase							, _lowercase						)
					return binary_tree_mirror_dictionary


if __name__ == "__main__":
					UpperCAmelCase       :    Any									=			{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
					print(f'''Binary tree: {binary_tree}''')
					UpperCAmelCase       :    List[Any]									=			binary_tree_mirror(binary_tree, 5)
					print(f'''Binary tree mirror: {binary_tree_mirror_dictionary}''')






