



import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
	import jax
	import jax.numpy as jnp

	from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor





class       __lowerCAmelCase				(  unittest.TestCase ):




				def __init__(							self		,       _snake_case		,       _snake_case=3		,       _snake_case=32		,       _snake_case=3		,       _snake_case=10		,       _snake_case=[10, 20, 30, 40]		,       _snake_case=[1, 1, 2, 1]		,       _snake_case=True		,       _snake_case=True		,       _snake_case="relu"		,       _snake_case=3		,       _snake_case=None		,       ):



									"""simple docstring"""





									_lowerCAmelCase      			=					parent
									_lowerCAmelCase      			=					batch_size
									_lowerCAmelCase      			=					image_size
									_lowerCAmelCase      			=					num_channels
									_lowerCAmelCase      			=					embeddings_size
									_lowerCAmelCase      			=					hidden_sizes
									_lowerCAmelCase      			=					depths
									_lowerCAmelCase      			=					is_training
									_lowerCAmelCase      			=					use_labels
									_lowerCAmelCase      			=					hidden_act
									_lowerCAmelCase      			=					num_labels
									_lowerCAmelCase      			=					scope
									_lowerCAmelCase      			=					len(_snake_case	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

									_lowerCAmelCase      			=					self.get_config()

									return config, pixel_values




				def 							snake_case     (							self	):



									"""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		,       image_size=self.image_size		,       )




				def 							snake_case     (							self		,       _snake_case		,       _snake_case	):



									"""simple docstring"""





									_lowerCAmelCase      			=					FlaxResNetModel(config=_snake_case	)
									_lowerCAmelCase      			=					model(_snake_case	)

									# Output shape (b, c, h, w)
									self.parent.assertEqual(
									    result.last_hidden_state.shape		,       (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)		,       )




				def 							snake_case     (							self		,       _snake_case		,       _snake_case	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.num_labels
									_lowerCAmelCase      			=					FlaxResNetForImageClassification(config=_snake_case	)
									_lowerCAmelCase      			=					model(_snake_case	)
									self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.num_labels)	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.prepare_config_and_inputs()
									_lowerCAmelCase			,			_lowerCAmelCase      			=					config_and_inputs
									_lowerCAmelCase      			=					{"""pixel_values""": pixel_values}
									return config, inputs_dict





@require_flax
class       __lowerCAmelCase				(  lowerCamelCase__ ,		unittest.TestCase ):
				__lowerCamelCase				=       (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

				__lowerCamelCase				=       False
				__lowerCamelCase				=       False
				__lowerCamelCase				=       False




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					FlaxResNetModelTester(self	)
									_lowerCAmelCase      			=					ConfigTester(self		,       config_class=_snake_case		,       has_text_modality=_snake_case	)




				def 							snake_case     (							self	):



									"""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 							snake_case     (							self	):



									"""simple docstring"""





									return




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_snake_case	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_snake_case	)




				@unittest.skip(reason="""ResNet does not use inputs_embeds"""	)
				def 							snake_case     (							self	):



									"""simple docstring"""





									pass




				@unittest.skip(reason="""ResNet does not support input and output embeddings"""	)
				def 							snake_case     (							self	):



									"""simple docstring"""





									pass




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase			,			_lowerCAmelCase      			=					self.model_tester.prepare_config_and_inputs_for_common()

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

														_lowerCAmelCase      			=					["""pixel_values"""]
														self.assertListEqual(arg_names[:1]		,       _snake_case	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									def check_hidden_states_output(_snake_case		,       _snake_case		,       _snake_case	):
														_lowerCAmelCase      			=					model_class(_snake_case	)

														_lowerCAmelCase      			=					model(**self._prepare_for_class(_snake_case		,       _snake_case	)	)

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

														_lowerCAmelCase      			=					self.model_tester.num_stages
														self.assertEqual(len(_snake_case	)		,       expected_num_stages + 1	)




				@unittest.skip(reason="""ResNet does not use feedforward chunking"""	)
				def 							snake_case     (							self	):



									"""simple docstring"""





									pass




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase			,			_lowerCAmelCase      			=					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__	):
																			_lowerCAmelCase      			=					self._prepare_for_class(_snake_case		,       _snake_case	)
																			_lowerCAmelCase      			=					model_class(_snake_case	)

																			@jax.jit
																			def model_jitted(_snake_case		,       **_snake_case	):
																								return model(pixel_values=_snake_case		,       **_snake_case	)

																			with self.subTest("""JIT Enabled"""	):
																								_lowerCAmelCase      			=					model_jitted(**_snake_case	).to_tuple()

																			with self.subTest("""JIT Disabled"""	):
																								with jax.disable_jit():
																													_lowerCAmelCase      			=					model_jitted(**_snake_case	).to_tuple()

																			self.assertEqual(len(_snake_case	)		,       len(_snake_case	)	)
																			for jitted_output, output in zip(_snake_case		,       _snake_case	):
																								self.assertEqual(jitted_output.shape		,       output.shape	)


def 	_UpperCAmelCase       (   ):





					"""simple docstring"""

					_lowerCAmelCase      			=					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
					return image





@require_flax
class       __lowerCAmelCase				(  unittest.TestCase ):




				@cached_property
				def 							snake_case     (							self	):



									"""simple docstring"""





									return AutoImageProcessor.from_pretrained("""microsoft/resnet-50"""	) if is_vision_available() else None




				@slow
				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					FlaxResNetForImageClassification.from_pretrained("""microsoft/resnet-50"""	)

									_lowerCAmelCase      			=					self.default_image_processor
									_lowerCAmelCase      			=					prepare_img()
									_lowerCAmelCase      			=					image_processor(images=_snake_case		,       return_tensors="""np"""	)

									_lowerCAmelCase      			=					model(**_snake_case	)

									# verify the logits
									_lowerCAmelCase      			=					(1, 1000)
									self.assertEqual(outputs.logits.shape		,       _snake_case	)

									_lowerCAmelCase      			=					jnp.array([-11.1069, -9.7877, -8.3777]	)

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








from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
A__			   = [[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 	_UpperCAmelCase       (   snake_case    ):





					"""simple docstring"""

					return [int(snake_case    ) for c in F'{ruleset:08}'[:8]]


def 	_UpperCAmelCase       (   snake_case			,		snake_case			,		snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					len(cells[0]    )  # 31
					_lowerCAmelCase      			=					[]
					for i in range(snake_case    ):
										# Get the neighbors of each cell
										# Handle neighbours outside bounds by using 0 as their value
										_lowerCAmelCase      			=					0 if i == 0 else cells[time][i - 1]
										_lowerCAmelCase      			=					0 if i == population - 1 else cells[time][i + 1]
										# Define a new cell and add it to the new generation
										_lowerCAmelCase      			=					7 - int(F'{left_neighbor}{cells[time][i]}{right_neighbor}'			,		2    )
										next_generation.append(rule[situation]    )
					return next_generation


def 	_UpperCAmelCase       (   snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					Image.new("""RGB"""			,		(len(cells[0]    ), len(snake_case    ))    )
					_lowerCAmelCase      			=					img.load()
					# Generates image
					for w in range(img.width    ):
										for h in range(img.height    ):
															_lowerCAmelCase      			=					2_55 - int(2_55 * cells[h][w]    )
															_lowerCAmelCase      			=					(color, color, color)
					return img


if __name__ == "__main__":
	A__			   = bin(int(input("""Rule:\n""").strip()))[2:]
	A__			   = format_ruleset(int(rule_num))
	for time in range(16):
		CELLS.append(new_generation(CELLS, rule, time))
	A__			   = generate_image(CELLS)
	# Uncomment to save the image
	# img.save(f"rule_{rule_num}.png")
	img.show()



