




"""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 							snake_case_ :







			"""simple docstring"""




			def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_=3					,				lowerCamelCase_=3_2					,				lowerCamelCase_=3					,				lowerCamelCase_=1_0					,				lowerCamelCase_=[1_0, 2_0, 3_0, 4_0]					,				lowerCamelCase_=[1, 1, 2, 1]					,				lowerCamelCase_=True					,				lowerCamelCase_=True					,				lowerCamelCase_="relu"					,				lowerCamelCase_=3					,				lowerCamelCase_=None					,				lowerCamelCase_=["stage2", "stage3", "stage4"]					,				lowerCamelCase_=[2, 3, 4]					,				)							->      Any:
							UpperCamelCase										=  parent
							UpperCamelCase										=  batch_size
							UpperCamelCase										=  image_size
							UpperCamelCase										=  num_channels
							UpperCamelCase										=  embeddings_size
							UpperCamelCase										=  hidden_sizes
							UpperCamelCase										=  depths
							UpperCamelCase										=  is_training
							UpperCamelCase										=  use_labels
							UpperCamelCase										=  hidden_act
							UpperCamelCase										=  num_labels
							UpperCamelCase										=  scope
							UpperCamelCase										=  len(lowerCamelCase_)
							UpperCamelCase										=  out_features
							UpperCamelCase										=  out_indices







			def      UpperCAmelCase__ (		self)							->      str:
							UpperCamelCase										=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

							UpperCamelCase										=  None
							if self.use_labels:
											UpperCamelCase										=  ids_tensor([self.batch_size]					,				self.num_labels)

							UpperCamelCase										=  self.get_config()

							return config, pixel_values, labels







			def      UpperCAmelCase__ (		self)							->      Tuple:
							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      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      Any:
							UpperCamelCase										=  ResNetModel(config=lowerCamelCase_)
							model.to(lowerCamelCase_)
							model.eval()
							UpperCamelCase										=  model(lowerCamelCase_)
							# 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 // 3_2, self.image_size // 3_2)					,				)







			def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      Dict:
							UpperCamelCase										=  self.num_labels
							UpperCamelCase										=  ResNetForImageClassification(lowerCamelCase_)
							model.to(lowerCamelCase_)
							model.eval()
							UpperCamelCase										=  model(lowerCamelCase_					,				labels=lowerCamelCase_)
							self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.num_labels))







			def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      Dict:
							UpperCamelCase										=  ResNetBackbone(config=lowerCamelCase_)
							model.to(lowerCamelCase_)
							model.eval()
							UpperCamelCase										=  model(lowerCamelCase_)

							# 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
							UpperCamelCase										=  None
							UpperCamelCase										=  ResNetBackbone(config=lowerCamelCase_)
							model.to(lowerCamelCase_)
							model.eval()
							UpperCamelCase										=  model(lowerCamelCase_)

							# 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      UpperCAmelCase__ (		self)							->      Optional[Any]:
							UpperCamelCase										=  self.prepare_config_and_inputs()
							UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase										=  config_and_inputs
							UpperCamelCase										=  {'''pixel_values''': pixel_values}
							return config, inputs_dict

@require_torch
class 							snake_case_ (				lowerCamelCase_  ,   lowerCamelCase_  ,   unittest.TestCase ):







			"""simple docstring"""




			A_										=					(
			    (
			        ResNetModel,
			        ResNetForImageClassification,
			        ResNetBackbone,
			    )
			    if is_torch_available()
			    else ()
			)
			A_										=					(
			    {'''feature-extraction''': ResNetModel, '''image-classification''': ResNetForImageClassification}
			    if is_torch_available()
			    else {}
			)

			A_										=					True
			A_										=					False
			A_										=					False
			A_										=					False
			A_										=					False







			def      UpperCAmelCase__ (		self)							->      Optional[Any]:
							UpperCamelCase										=  ResNetModelTester(self)
							UpperCamelCase										=  ConfigTester(self					,				config_class=lowerCamelCase_					,				has_text_modality=lowerCamelCase_)







			def      UpperCAmelCase__ (		self)							->      str:
							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      UpperCAmelCase__ (		self)							->      List[Any]:
							return







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







			@unittest.skip(reason='''ResNet does not support input and output embeddings''')
			def      UpperCAmelCase__ (		self)							->      List[Any]:
							pass







			def      UpperCAmelCase__ (		self)							->      List[str]:
							UpperCamelCase					,  UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()

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

											UpperCamelCase										=  ['''pixel_values''']
											self.assertListEqual(arg_names[:1]					,				lowerCamelCase_)







			def      UpperCAmelCase__ (		self)							->      Optional[Any]:
							UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase_)







			def      UpperCAmelCase__ (		self)							->      Any:
							UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*lowerCamelCase_)







			def      UpperCAmelCase__ (		self)							->      Union[str, Any]:
							UpperCamelCase					,  UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											UpperCamelCase										=  model_class(config=lowerCamelCase_)
											for name, module in model.named_modules():
															if isinstance(lowerCamelCase_					,				(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      UpperCAmelCase__ (		self)							->      Dict:
							def check_hidden_states_output(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_):
											UpperCamelCase										=  model_class(lowerCamelCase_)
											model.to(lowerCamelCase_)
											model.eval()

											with torch.no_grad():
															UpperCamelCase										=  model(**self._prepare_for_class(lowerCamelCase_					,				lowerCamelCase_))

											UpperCamelCase										=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

											UpperCamelCase										=  self.model_tester.num_stages
											self.assertEqual(len(lowerCamelCase_)					,				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]					,				)

							UpperCamelCase					,  UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()
							UpperCamelCase										=  ['''basic''', '''bottleneck''']
							for model_class in self.all_model_classes:
											for layer_type in layers_type:
															UpperCamelCase										=  layer_type
															UpperCamelCase										=  True
															check_hidden_states_output(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															UpperCamelCase										=  True

															check_hidden_states_output(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)







			@unittest.skip(reason='''ResNet does not use feedforward chunking''')
			def      UpperCAmelCase__ (		self)							->      Optional[Any]:
							pass







			def      UpperCAmelCase__ (		self)							->      str:
							UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_)







			@slow
			def      UpperCAmelCase__ (		self)							->      str:
							for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											UpperCamelCase										=  ResNetModel.from_pretrained(lowerCamelCase_)
											self.assertIsNotNone(lowerCamelCase_)





def     __snake_case   (							):


				"""simple docstring"""







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

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







			"""simple docstring"""




			@cached_property
			def      UpperCAmelCase__ (		self)							->      Dict:
							return (
							    AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
							    if is_vision_available()
							    else None
							)







			@slow
			def      UpperCAmelCase__ (		self)							->      Tuple:
							UpperCamelCase										=  ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(lowerCamelCase_)

							UpperCamelCase										=  self.default_image_processor
							UpperCamelCase										=  prepare_img()
							UpperCamelCase										=  image_processor(images=lowerCamelCase_					,				return_tensors='''pt''').to(lowerCamelCase_)

							# forward pass
							with torch.no_grad():
											UpperCamelCase										=  model(**lowerCamelCase_)

							# verify the logits
							UpperCamelCase										=  torch.Size((1, 1_0_0_0))
							self.assertEqual(outputs.logits.shape					,				lowerCamelCase_)

							UpperCamelCase										=  torch.tensor([-11.1069, -9.7877, -8.3777]).to(lowerCamelCase_)

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

@require_torch
class 							snake_case_ (				lowerCamelCase_  ,   unittest.TestCase ):







			"""simple docstring"""




			A_										=					(ResNetBackbone,) if is_torch_available() else ()
			A_										=					False
			A_										=					ResNetConfig







			def      UpperCAmelCase__ (		self)							->      List[str]:
							UpperCamelCase										=  ResNetModelTester(self)





"""simple docstring"""







class 							snake_case_ :







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_=None)							->      Tuple:
       UpperCamelCase										=  data
       UpperCamelCase										=  None







   def __repr__(		self)							->      Union[str, Any]:
       UpperCamelCase										=  ''''''
       UpperCamelCase										=  self
       while temp:
           string_rep += F'<{temp.data}> ---> '
           UpperCamelCase										=  temp.next
       string_rep += "<END>"
       return string_rep





def     __snake_case   (							_lowercase						):


    """simple docstring"""







    if not elements_list:
        raise Exception('''The Elements List is empty'''						)

    # Set first element as Head
    UpperCamelCase										=  Node(elements_list[0]						)
    UpperCamelCase										=  head
    # Loop through elements from position 1
    for data in elements_list[1:]:
        UpperCamelCase										=  Node(_lowercase						)
        UpperCamelCase										=  current.next
    return head


SCREAMING_SNAKE_CASE_      =					[1, 3, 5, 32, 44, 12, 43]
print(f'List: {list_data}')
print('Creating Linked List from List.')
SCREAMING_SNAKE_CASE_      =					make_linked_list(list_data)
print('Linked List:')


print(linked_list)