code: 
  
  
  
  
  
  
  """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 	__lowerCamelCase       :
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		def __init__(     self							:			List[Any]		,    a_							:			Union[str, Any]		,    a_							:			List[str]=3		,    a_							:			Optional[Any]=32		,    a_							:			str=3		,    a_							:			int=10		,    a_							:			Optional[Any]=[10, 20, 30, 40]		,    a_							:			Optional[int]=[1, 1, 2, 1]		,    a_							:			List[str]=True		,    a_							:			List[str]=True		,    a_							:			Any="relu"		,    a_							:			str=3		,    a_							:			int=None		,    a_							:			Any=["stage2", "stage3", "stage4"]		,    a_							:			Optional[int]=[2, 3, 4]		,    ):
  				lowerCAmelCase_     :							Any             =    parent
  				lowerCAmelCase_     :							List[Any]             =    batch_size
  				lowerCAmelCase_     :							Any             =    image_size
  				lowerCAmelCase_     :							Dict             =    num_channels
  				lowerCAmelCase_     :							Optional[int]             =    embeddings_size
  				lowerCAmelCase_     :							Tuple             =    hidden_sizes
  				lowerCAmelCase_     :							List[str]             =    depths
  				lowerCAmelCase_     :							Any             =    is_training
  				lowerCAmelCase_     :							str             =    use_labels
  				lowerCAmelCase_     :							Dict             =    hidden_act
  				lowerCAmelCase_     :							List[Any]             =    num_labels
  				lowerCAmelCase_     :							List[Any]             =    scope
  				lowerCAmelCase_     :							Optional[Any]             =    len(a_			)
  				lowerCAmelCase_     :							List[Any]             =    out_features
  				lowerCAmelCase_     :							List[Any]             =    out_indices
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Union[str, Any]			):
  				lowerCAmelCase_     :							str             =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)
  
  				lowerCAmelCase_     :							Tuple             =    None
  				if self.use_labels:
  						lowerCAmelCase_     :							List[Any]             =    ids_tensor([self.batch_size]		,    self.num_labels			)
  
  				lowerCAmelCase_     :							List[Any]             =    self.get_config()
  
  				return config, pixel_values, labels
  
  
  
  
  
  		def      lowerCamelCase (     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      lowerCamelCase (     self							:			Optional[Any]		,    a_							:			str		,    a_							:			Union[str, Any]		,    a_							:			Any			):
  				lowerCAmelCase_     :							Optional[int]             =    ResNetModel(config=a_			)
  				model.to(a_			)
  				model.eval()
  				lowerCAmelCase_     :							Dict             =    model(a_			)
  				# 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      lowerCamelCase (     self							:			str		,    a_							:			str		,    a_							:			Dict		,    a_							:			List[Any]			):
  				lowerCAmelCase_     :							Optional[int]             =    self.num_labels
  				lowerCAmelCase_     :							Any             =    ResNetForImageClassification(a_			)
  				model.to(a_			)
  				model.eval()
  				lowerCAmelCase_     :							List[Any]             =    model(a_		,    labels=a_			)
  				self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.num_labels)			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Any		,    a_							:			Tuple		,    a_							:			Union[str, Any]		,    a_							:			List[Any]			):
  				lowerCAmelCase_     :							Optional[Any]             =    ResNetBackbone(config=a_			)
  				model.to(a_			)
  				model.eval()
  				lowerCAmelCase_     :							Dict             =    model(a_			)
  
  				# 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_     :							str             =    ResNetBackbone(config=a_			)
  				model.to(a_			)
  				model.eval()
  				lowerCAmelCase_     :							Optional[int]             =    model(a_			)
  
  				# 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      lowerCamelCase (     self							:			Union[str, Any]			):
  				lowerCAmelCase_     :							int             =    self.prepare_config_and_inputs()
  				lowerCAmelCase_       ,			lowerCAmelCase_       ,			lowerCAmelCase_     :							Tuple             =    config_and_inputs
  				lowerCAmelCase_     :							Tuple             =    {"pixel_values": pixel_values}
  				return config, inputs_dict
  
  
  
  
  
  
  
  @require_torch
  class 	__lowerCamelCase       (				A__				,							A__				,							unittest.TestCase    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       Optional[int]      =							(
  		    (
  		        ResNetModel,
  		        ResNetForImageClassification,
  		        ResNetBackbone,
  		    )
  		    if is_torch_available()
  		    else ()
  		)
  		a_			:       Optional[Any]      =							(
  		    {"""feature-extraction""": ResNetModel, """image-classification""": ResNetForImageClassification}
  		    if is_torch_available()
  		    else {}
  		)
  
  		a_			:       Tuple      =							True
  		a_			:       Tuple      =							False
  		a_			:       Tuple      =							False
  		a_			:       List[Any]      =							False
  		a_			:       Union[str, Any]      =							False
  
  
  
  
  
  		def      lowerCamelCase (     self							:			str			):
  				lowerCAmelCase_     :							List[Any]             =    ResNetModelTester(self			)
  				lowerCAmelCase_     :							Tuple             =    ConfigTester(self		,    config_class=a_		,    has_text_modality=a_			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Any			):
  				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      lowerCamelCase (     self							:			Optional[int]			):
  				return
  
  
  
  
  
  		@unittest.skip(reason="ResNet does not use inputs_embeds"			)
  		def      lowerCamelCase (     self							:			Optional[Any]			):
  				pass
  
  
  
  
  
  		@unittest.skip(reason="ResNet does not support input and output embeddings"			)
  		def      lowerCamelCase (     self							:			Union[str, Any]			):
  				pass
  
  
  
  
  
  		def      lowerCamelCase (     self							:			List[str]			):
  				lowerCAmelCase_       ,			lowerCAmelCase_     :							Optional[int]             =    self.model_tester.prepare_config_and_inputs_for_common()
  
  				for model_class in self.all_model_classes:
  						lowerCAmelCase_     :							int             =    model_class(a_			)
  						lowerCAmelCase_     :							List[Any]             =    inspect.signature(model.forward			)
  						# signature.parameters is an OrderedDict => so arg_names order is deterministic
  						lowerCAmelCase_     :							Optional[Any]             =    [*signature.parameters.keys()]
  
  						lowerCAmelCase_     :							Dict             =    ["pixel_values"]
  						self.assertListEqual(arg_names[:1]		,    a_			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			int			):
  				lowerCAmelCase_     :							int             =    self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_model(*a_			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			int			):
  				lowerCAmelCase_     :							Union[str, Any]             =    self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_backbone(*a_			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[int]			):
  				lowerCAmelCase_       ,			lowerCAmelCase_     :							str             =    self.model_tester.prepare_config_and_inputs_for_common()
  
  				for model_class in self.all_model_classes:
  						lowerCAmelCase_     :							Dict             =    model_class(config=a_			)
  						for name, module in model.named_modules():
  								if isinstance(a_		,    (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      lowerCamelCase (     self							:			str			):
  				def check_hidden_states_output(a_							:			Any		,    a_							:			Any		,    a_							:			Union[str, Any]			):
  						lowerCAmelCase_     :							List[str]             =    model_class(a_			)
  						model.to(a_			)
  						model.eval()
  
  						with torch.no_grad():
  								lowerCAmelCase_     :							Optional[int]             =    model(**self._prepare_for_class(a_		,    a_			)			)
  
  						lowerCAmelCase_     :							Optional[int]             =    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
  
  						lowerCAmelCase_     :							Any             =    self.model_tester.num_stages
  						self.assertEqual(len(a_			)		,    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_     :							int             =    self.model_tester.prepare_config_and_inputs_for_common()
  				lowerCAmelCase_     :							str             =    ["basic", "bottleneck"]
  				for model_class in self.all_model_classes:
  						for layer_type in layers_type:
  								lowerCAmelCase_     :							Optional[Any]             =    layer_type
  								lowerCAmelCase_     :							Optional[int]             =    True
  								check_hidden_states_output(a_		,    a_		,    a_			)
  
  								# check that output_hidden_states also work using config
  								del inputs_dict["output_hidden_states"]
  								lowerCAmelCase_     :							Optional[Any]             =    True
  
  								check_hidden_states_output(a_		,    a_		,    a_			)
  
  
  
  
  
  		@unittest.skip(reason="ResNet does not use feedforward chunking"			)
  		def      lowerCamelCase (     self							:			Optional[Any]			):
  				pass
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Any			):
  				lowerCAmelCase_     :							str             =    self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_for_image_classification(*a_			)
  
  
  
  
  
  		@slow
  		def      lowerCamelCase (     self							:			Union[str, Any]			):
  				for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  						lowerCAmelCase_     :							str             =    ResNetModel.from_pretrained(a_			)
  						self.assertIsNotNone(a_			)
  
  
  
  
  
  
  
  def 	__lowerCamelCase			(						)		->					Any:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							Optional[Any]             =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
  		return image
  
  
  @require_torch
  @require_vision
  class 	__lowerCamelCase       (				unittest.TestCase    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		@cached_property
  		def      lowerCamelCase (     self							:			Any			):
  				return (
  				    AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)
  				    if is_vision_available()
  				    else None
  				)
  
  
  
  
  
  		@slow
  		def      lowerCamelCase (     self							:			str			):
  				lowerCAmelCase_     :							List[Any]             =    ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			).to(a_			)
  
  				lowerCAmelCase_     :							List[str]             =    self.default_image_processor
  				lowerCAmelCase_     :							Union[str, Any]             =    prepare_img()
  				lowerCAmelCase_     :							Optional[int]             =    image_processor(images=a_		,    return_tensors="pt"			).to(a_			)
  
  				# forward pass
  				with torch.no_grad():
  						lowerCAmelCase_     :							Tuple             =    model(**a_			)
  
  				# verify the logits
  				lowerCAmelCase_     :							Dict             =    torch.Size((1, 10_00)			)
  				self.assertEqual(outputs.logits.shape		,    a_			)
  
  				lowerCAmelCase_     :							int             =    torch.tensor([-11.1069, -9.7877, -8.3777]			).to(a_			)
  
  				self.assertTrue(torch.allclose(outputs.logits[0, :3]		,    a_		,    atol=1e-4			)			)
  
  
  
  
  
  
  
  @require_torch
  class 	__lowerCamelCase       (				A__				,							unittest.TestCase    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       Union[str, Any]      =							(ResNetBackbone,) if is_torch_available() else ()
  		a_			:       int      =							False
  		a_			:       int      =							ResNetConfig
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Union[str, Any]			):
  				lowerCAmelCase_     :							Optional[int]             =    ResNetModelTester(self			)
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  from __future__ import annotations
  
  from PIL import Image
  
  # Define the first generation of cells
  # fmt: off
  lowercase__      =				[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
  
  def 	__lowerCamelCase			(						__UpperCamelCase )		->					list[int]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		return [int(__UpperCamelCase ) for c in f'''{ruleset:08}'''[:8]]
  
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase )		->					list[int]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							int             =    len(cells[0] )  # 31
  		lowerCAmelCase_     :							List[Any]             =    []
  		for i in range(__UpperCamelCase ):
  				# Get the neighbors of each cell
  				# Handle neighbours outside bounds by using 0 as their value
  				lowerCAmelCase_     :							List[str]             =    0 if i == 0 else cells[time][i - 1]
  				lowerCAmelCase_     :							Optional[int]             =    0 if i == population - 1 else cells[time][i + 1]
  				# Define a new cell and add it to the new generation
  				lowerCAmelCase_     :							List[Any]             =    7 - int(f'''{left_neighbor}{cells[time][i]}{right_neighbor}'''   , 2 )
  				next_generation.append(rule[situation] )
  		return next_generation
  
  def 	__lowerCamelCase			(						__UpperCamelCase )		->					Image.Image:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							List[str]             =    Image.new("RGB"   , (len(cells[0] ), len(__UpperCamelCase )) )
  		lowerCAmelCase_     :							List[str]             =    img.load()
  		# Generates image
  		for w in range(img.width ):
  				for h in range(img.height ):
  						lowerCAmelCase_     :							Union[str, Any]             =    255 - int(255 * cells[h][w] )
  						lowerCAmelCase_     :							List[str]             =    (color, color, color)
  		return img
  
  
  if __name__ == "__main__":
  					lowercase__      =				bin(int(input("""Rule:\n""").strip()))[2:]
  					lowercase__      =				format_ruleset(int(rule_num))
  					for time in range(16):
  										CELLS.append(new_generation(CELLS, rule, time))
  					lowercase__      =				generate_image(CELLS)
  					# Uncomment to save the image
  					# img.save(f"rule_{rule_num}.png")
  					img.show()
  