code: 
  
  
  
  
  
  
  """simple docstring"""
  
  from __future__ import annotations
  
  import inspect
  import unittest
  from typing import List, Tuple
  
  from transformers import ConvNextConfig
  from transformers.testing_utils import require_tf, require_vision, slow
  from transformers.utils import cached_property, is_tf_available, is_vision_available
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
  					import tensorflow as tf
  
  					from transformers import TFConvNextForImageClassification, TFConvNextModel
  
  
  if is_vision_available():
  					from PIL import Image
  
  					from transformers import ConvNextImageProcessor
  
  
  class 	__lowerCamelCase       :
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		def __init__(     self							:			Union[str, Any]		,    a_							:			Tuple		,    a_							:			Optional[Any]=13		,    a_							:			List[str]=32		,    a_							:			Dict=3		,    a_							:			Dict=4		,    a_							:			Tuple=[10, 20, 30, 40]		,    a_							:			List[str]=[2, 2, 3, 2]		,    a_							:			Optional[int]=True		,    a_							:			List[str]=True		,    a_							:			str=37		,    a_							:			Tuple="gelu"		,    a_							:			int=10		,    a_							:			str=0.02		,    a_							:			Tuple=3		,    a_							:			List[Any]=None		,    ):
  				lowerCAmelCase_     :							str             =    parent
  				lowerCAmelCase_     :							Dict             =    batch_size
  				lowerCAmelCase_     :							Tuple             =    image_size
  				lowerCAmelCase_     :							Optional[Any]             =    num_channels
  				lowerCAmelCase_     :							str             =    num_stages
  				lowerCAmelCase_     :							int             =    hidden_sizes
  				lowerCAmelCase_     :							Any             =    depths
  				lowerCAmelCase_     :							Optional[int]             =    is_training
  				lowerCAmelCase_     :							int             =    use_labels
  				lowerCAmelCase_     :							List[str]             =    intermediate_size
  				lowerCAmelCase_     :							int             =    hidden_act
  				lowerCAmelCase_     :							Dict             =    type_sequence_label_size
  				lowerCAmelCase_     :							List[str]             =    initializer_range
  				lowerCAmelCase_     :							Optional[int]             =    scope
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[Any]			):
  				lowerCAmelCase_     :							Union[str, Any]             =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)
  
  				lowerCAmelCase_     :							List[str]             =    None
  				if self.use_labels:
  						lowerCAmelCase_     :							Dict             =    ids_tensor([self.batch_size]		,    self.type_sequence_label_size			)
  
  				lowerCAmelCase_     :							Tuple             =    self.get_config()
  
  				return config, pixel_values, labels
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Any			):
  				return ConvNextConfig(
  				    num_channels=self.num_channels		,    hidden_sizes=self.hidden_sizes		,    depths=self.depths		,    num_stages=self.num_stages		,    hidden_act=self.hidden_act		,    is_decoder=a_		,    initializer_range=self.initializer_range		,    )
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Dict		,    a_							:			Union[str, Any]		,    a_							:			Optional[int]		,    a_							:			Any			):
  				lowerCAmelCase_     :							str             =    TFConvNextModel(config=a_			)
  				lowerCAmelCase_     :							Any             =    model(a_		,    training=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_							:			Optional[int]		,    a_							:			Optional[Any]		,    a_							:			Any			):
  				lowerCAmelCase_     :							Dict             =    self.type_sequence_label_size
  				lowerCAmelCase_     :							Optional[int]             =    TFConvNextForImageClassification(a_			)
  				lowerCAmelCase_     :							Optional[Any]             =    model(a_		,    labels=a_		,    training=a_			)
  				self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.type_sequence_label_size)			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Dict			):
  				lowerCAmelCase_     :							Union[str, Any]             =    self.prepare_config_and_inputs()
  				lowerCAmelCase_       ,			lowerCAmelCase_       ,			lowerCAmelCase_     :							Union[str, Any]             =    config_and_inputs
  				lowerCAmelCase_     :							Optional[Any]             =    {"pixel_values": pixel_values}
  				return config, inputs_dict
  
  
  
  
  
  
  
  @require_tf
  class 	__lowerCamelCase       (				A__				,							A__				,							unittest.TestCase    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       str      =							(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
  		a_			:       Union[str, Any]      =							(
  		    {"""feature-extraction""": TFConvNextModel, """image-classification""": TFConvNextForImageClassification}
  		    if is_tf_available()
  		    else {}
  		)
  
  		a_			:       Dict      =							False
  		a_			:       Tuple      =							False
  		a_			:       Any      =							False
  		a_			:       int      =							False
  		a_			:       Dict      =							False
  
  
  
  
  
  		def      lowerCamelCase (     self							:			int			):
  				lowerCAmelCase_     :							str             =    TFConvNextModelTester(self			)
  				lowerCAmelCase_     :							Union[str, Any]             =    ConfigTester(
  				    self		,    config_class=a_		,    has_text_modality=a_		,    hidden_size=37		,    )
  
  
  
  
  
  		@unittest.skip(reason="ConvNext does not use inputs_embeds"			)
  		def      lowerCamelCase (     self							:			Any			):
  				pass
  
  
  
  
  
  		@unittest.skipIf(
  		    not is_tf_available() or len(tf.config.list_physical_devices("GPU"			)			) == 0		,    reason="TF does not support backprop for grouped convolutions on CPU."		,    )
  		@slow
  		def      lowerCamelCase (     self							:			Dict			):
  				super().test_keras_fit()
  
  
  
  
  
  		@unittest.skip(reason="ConvNext does not support input and output embeddings"			)
  		def      lowerCamelCase (     self							:			str			):
  				pass
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Any			):
  				lowerCAmelCase_       ,			lowerCAmelCase_     :							int             =    self.model_tester.prepare_config_and_inputs_for_common()
  
  				for model_class in self.all_model_classes:
  						lowerCAmelCase_     :							Optional[int]             =    model_class(a_			)
  						lowerCAmelCase_     :							Union[str, Any]             =    inspect.signature(model.call			)
  						# signature.parameters is an OrderedDict => so arg_names order is deterministic
  						lowerCAmelCase_     :							List[Any]             =    [*signature.parameters.keys()]
  
  						lowerCAmelCase_     :							Union[str, Any]             =    ["pixel_values"]
  						self.assertListEqual(arg_names[:1]		,    a_			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[int]			):
  				lowerCAmelCase_     :							Optional[Any]             =    self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_model(*a_			)
  
  
  
  
  
  		@unittest.skipIf(
  		    not is_tf_available() or len(tf.config.list_physical_devices("GPU"			)			) == 0		,    reason="TF does not support backprop for grouped convolutions on CPU."		,    )
  		def      lowerCamelCase (     self							:			Optional[int]			):
  				super().test_dataset_conversion()
  
  
  
  
  
  		def      lowerCamelCase (     self							:			int			):
  				def check_hidden_states_output(a_							:			List[Any]		,    a_							:			Union[str, Any]		,    a_							:			Any			):
  						lowerCAmelCase_     :							Any             =    model_class(a_			)
  
  						lowerCAmelCase_     :							Tuple             =    model(**self._prepare_for_class(a_		,    a_			)			)
  						lowerCAmelCase_     :							List[str]             =    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
  
  						lowerCAmelCase_     :							Union[str, Any]             =    self.model_tester.num_stages
  						self.assertEqual(len(a_			)		,    expected_num_stages + 1			)
  
  						# ConvNext'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_     :							Tuple             =    self.model_tester.prepare_config_and_inputs_for_common()
  
  				for model_class in self.all_model_classes:
  						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_     :							List[str]             =    True
  
  						check_hidden_states_output(a_		,    a_		,    a_			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Dict			):
  				lowerCAmelCase_       ,			lowerCAmelCase_     :							List[Any]             =    self.model_tester.prepare_config_and_inputs_for_common()
  
  				def check_equivalence(a_							:			Dict		,    a_							:			List[Any]		,    a_							:			int		,    a_							:			Tuple={}			):
  						lowerCAmelCase_     :							Any             =    model(a_		,    return_dict=a_		,    **a_			)
  						lowerCAmelCase_     :							List[Any]             =    model(a_		,    return_dict=a_		,    **a_			).to_tuple()
  
  						def recursive_check(a_							:			List[str]		,    a_							:			int			):
  								if isinstance(a_		,    (List, Tuple)			):
  										for tuple_iterable_value, dict_iterable_value in zip(a_		,    a_			):
  												recursive_check(a_		,    a_			)
  								elif tuple_object is None:
  										return
  								else:
  										self.assertTrue(
  										    all(tf.equal(a_		,    a_			)			)		,    msg=(
  										        "Tuple and dict output are not equal. Difference:"
  										        f''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object			)			)}'''
  										    )		,    )
  
  								recursive_check(a_		,    a_			)
  
  				for model_class in self.all_model_classes:
  						lowerCAmelCase_     :							Tuple             =    model_class(a_			)
  
  						lowerCAmelCase_     :							Tuple             =    self._prepare_for_class(a_		,    a_			)
  						lowerCAmelCase_     :							List[Any]             =    self._prepare_for_class(a_		,    a_			)
  						check_equivalence(a_		,    a_		,    a_			)
  
  						lowerCAmelCase_     :							Dict             =    self._prepare_for_class(a_		,    a_		,    return_labels=a_			)
  						lowerCAmelCase_     :							Any             =    self._prepare_for_class(a_		,    a_		,    return_labels=a_			)
  						check_equivalence(a_		,    a_		,    a_			)
  
  						lowerCAmelCase_     :							Optional[int]             =    self._prepare_for_class(a_		,    a_			)
  						lowerCAmelCase_     :							str             =    self._prepare_for_class(a_		,    a_			)
  						check_equivalence(a_		,    a_		,    a_		,    {"output_hidden_states": True}			)
  
  						lowerCAmelCase_     :							Union[str, Any]             =    self._prepare_for_class(a_		,    a_		,    return_labels=a_			)
  						lowerCAmelCase_     :							str             =    self._prepare_for_class(a_		,    a_		,    return_labels=a_			)
  						check_equivalence(a_		,    a_		,    a_		,    {"output_hidden_states": True}			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Union[str, Any]			):
  				lowerCAmelCase_     :							Dict             =    self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_for_image_classification(*a_			)
  
  
  
  
  
  		@slow
  		def      lowerCamelCase (     self							:			str			):
  				lowerCAmelCase_     :							Optional[Any]             =    TFConvNextModel.from_pretrained("facebook/convnext-tiny-224"			)
  				self.assertIsNotNone(a_			)
  
  
  
  
  
  
  
  def 	__lowerCamelCase			(						)		->					int:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							Dict             =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
  		return image
  
  
  
  @require_tf
  @require_vision
  class 	__lowerCamelCase       (				unittest.TestCase    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		@cached_property
  		def      lowerCamelCase (     self							:			List[str]			):
  				return ConvNextImageProcessor.from_pretrained("facebook/convnext-tiny-224"			) if is_vision_available() else None
  
  
  
  
  
  		@slow
  		def      lowerCamelCase (     self							:			Any			):
  				lowerCAmelCase_     :							Tuple             =    TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"			)
  
  				lowerCAmelCase_     :							List[Any]             =    self.default_image_processor
  				lowerCAmelCase_     :							Optional[int]             =    prepare_img()
  				lowerCAmelCase_     :							int             =    image_processor(images=a_		,    return_tensors="tf"			)
  
  				# forward pass
  				lowerCAmelCase_     :							Union[str, Any]             =    model(**a_			)
  
  				# verify the logits
  				lowerCAmelCase_     :							str             =    tf.TensorShape((1, 10_00)			)
  				self.assertEqual(outputs.logits.shape		,    a_			)
  
  				lowerCAmelCase_     :							Any             =    tf.constant([-0.0260, -0.4739, 0.1911]			)
  
  				tf.debugging.assert_near(outputs.logits[0, :3]		,    a_		,    atol=1e-4			)
  
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()
  