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    lowercase_    :



							def __init__(						self      ,				lowercase_      ,				lowercase_=3      ,				lowercase_=32      ,				lowercase_=3      ,				lowercase_=10      ,				lowercase_=[10, 20, 30, 40]      ,				lowercase_=[1, 1, 2, 1]      ,				lowercase_=True      ,				lowercase_=True      ,				lowercase_="relu"      ,				lowercase_=3      ,				lowercase_=None      ,				lowercase_=["stage2", "stage3", "stage4"]      ,				lowercase_=[2, 3, 4]      ,				):
													_snake_case						:     Any				=    parent
													_snake_case						:     str				=    batch_size
													_snake_case						:     List[Any]				=    image_size
													_snake_case						:     Tuple				=    num_channels
													_snake_case						:     Optional[int]				=    embeddings_size
													_snake_case						:     Dict				=    hidden_sizes
													_snake_case						:     Tuple				=    depths
													_snake_case						:     int				=    is_training
													_snake_case						:     Optional[Any]				=    use_labels
													_snake_case						:     Tuple				=    hidden_act
													_snake_case						:     Dict				=    num_labels
													_snake_case						:     Any				=    scope
													_snake_case						:     Tuple				=    len(lowercase_				)
													_snake_case						:     Any				=    out_features
													_snake_case						:     Optional[int]				=    out_indices



							def        UpperCamelCase       (						self				):
													_snake_case						:     Dict				=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

													_snake_case						:     Union[str, Any]				=    None
													if self.use_labels:
																			_snake_case						:     Any				=    ids_tensor([self.batch_size]      ,				self.num_labels				)

													_snake_case						:     Tuple				=    self.get_config()

													return config, pixel_values, labels



							def        UpperCamelCase       (						self				):
													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      ,				lowercase_      ,				lowercase_      ,				lowercase_				):
													_snake_case						:     Optional[int]				=    ResNetModel(config=lowercase_				)
													model.to(lowercase_				)
													model.eval()
													_snake_case						:     Tuple				=    model(lowercase_				)
													# 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        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_				):
													_snake_case						:     Optional[int]				=    self.num_labels
													_snake_case						:     Optional[int]				=    ResNetForImageClassification(lowercase_				)
													model.to(lowercase_				)
													model.eval()
													_snake_case						:     Optional[Any]				=    model(lowercase_      ,				labels=lowercase_				)
													self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.num_labels)				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_				):
													_snake_case						:     str				=    ResNetBackbone(config=lowercase_				)
													model.to(lowercase_				)
													model.eval()
													_snake_case						:     str				=    model(lowercase_				)

													# 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
													_snake_case						:     Optional[int]				=    None
													_snake_case						:     List[str]				=    ResNetBackbone(config=lowercase_				)
													model.to(lowercase_				)
													model.eval()
													_snake_case						:     int				=    model(lowercase_				)

													# 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				):
													_snake_case						:     str				=    self.prepare_config_and_inputs()
													_snake_case				,_snake_case				,_snake_case						:     Any				=    config_and_inputs
													_snake_case						:     Optional[Any]				=    {"pixel_values": pixel_values}
													return config, inputs_dict




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

							_lowerCamelCase						=		True
							_lowerCamelCase						=		False
							_lowerCamelCase						=		False
							_lowerCamelCase						=		False
							_lowerCamelCase						=		False



							def        UpperCamelCase       (						self				):
													_snake_case						:     int				=    ResNetModelTester(self				)
													_snake_case						:     List[Any]				=    ConfigTester(self      ,				config_class=lowercase_      ,				has_text_modality=lowercase_				)



							def        UpperCamelCase       (						self				):
													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				):
													return



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



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



							def        UpperCamelCase       (						self				):
													_snake_case				,_snake_case						:     str				=    self.model_tester.prepare_config_and_inputs_for_common()

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

																			_snake_case						:     List[Any]				=    ["pixel_values"]
																			self.assertListEqual(arg_names[:1]      ,				lowercase_				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     int				=    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*lowercase_				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     List[str]				=    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_backbone(*lowercase_				)



							def        UpperCamelCase       (						self				):
													_snake_case				,_snake_case						:     str				=    self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			_snake_case						:     List[Any]				=    model_class(config=lowercase_				)
																			for name, module in model.named_modules():
																									if isinstance(lowercase_      ,				(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				):
													def check_hidden_states_output(lowercase_      ,				lowercase_      ,				lowercase_				):
																			_snake_case						:     Optional[int]				=    model_class(lowercase_				)
																			model.to(lowercase_				)
																			model.eval()

																			with torch.no_grad():
																									_snake_case						:     Optional[int]				=    model(**self._prepare_for_class(lowercase_      ,				lowercase_				)				)

																			_snake_case						:     List[Any]				=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																			_snake_case						:     Union[str, Any]				=    self.model_tester.num_stages
																			self.assertEqual(len(lowercase_				)      ,				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]      ,				)

													_snake_case				,_snake_case						:     Union[str, Any]				=    self.model_tester.prepare_config_and_inputs_for_common()
													_snake_case						:     Union[str, Any]				=    ["basic", "bottleneck"]
													for model_class in self.all_model_classes:
																			for layer_type in layers_type:
																									_snake_case						:     Optional[Any]				=    layer_type
																									_snake_case						:     Union[str, Any]				=    True
																									check_hidden_states_output(lowercase_      ,				lowercase_      ,				lowercase_				)

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

																									check_hidden_states_output(lowercase_      ,				lowercase_      ,				lowercase_				)



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



							def        UpperCamelCase       (						self				):
													_snake_case						:     List[str]				=    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*lowercase_				)



							@slow
							def        UpperCamelCase       (						self				):
													for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			_snake_case						:     Union[str, Any]				=    ResNetModel.from_pretrained(lowercase_				)
																			self.assertIsNotNone(lowercase_				)




def 					snake_case						()       ->       Tuple:

						'''simple docstring'''



						_snake_case						:     Optional[int]				=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
						return image



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



							@cached_property
							def        UpperCamelCase       (						self				):
													return (
													    AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
													    if is_vision_available()
													    else None
													)



							@slow
							def        UpperCamelCase       (						self				):
													_snake_case						:     List[str]				=    ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(lowercase_				)

													_snake_case						:     Union[str, Any]				=    self.default_image_processor
													_snake_case						:     Optional[Any]				=    prepare_img()
													_snake_case						:     Tuple				=    image_processor(images=lowercase_      ,				return_tensors="pt"				).to(lowercase_				)

													# forward pass
													with torch.no_grad():
																			_snake_case						:     Tuple				=    model(**lowercase_				)

													# verify the logits
													_snake_case						:     List[str]				=    torch.Size((1, 1_000)				)
													self.assertEqual(outputs.logits.shape      ,				lowercase_				)

													_snake_case						:     str				=    torch.tensor([-11.1_069, -9.7_877, -8.3_777]				).to(lowercase_				)

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




@require_torch
class    lowercase_    (     __snake_case						, unittest.TestCase		):
							_lowerCamelCase						=		(ResNetBackbone,) if is_torch_available() else ()
							_lowerCamelCase						=		False
							_lowerCamelCase						=		ResNetConfig



							def        UpperCamelCase       (						self				):
													_snake_case						:     int				=    ResNetModelTester(self				)
from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
__SCREAMING_SNAKE_CASE						:    Optional[int]     = [[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 					snake_case						(__lowercase						)       ->       list[int]:

						'''simple docstring'''



						return [int(__lowercase						) for c in F"""{ruleset:08}"""[:8]]
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase						)       ->       list[int]:

						'''simple docstring'''



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

						'''simple docstring'''



						_snake_case						:     str				=    Image.new("RGB"					,			(len(cells[0]						), len(__lowercase						))						)
						_snake_case						:     int				=    img.load()
						# Generates image
						for w in range(img.width						):
												for h in range(img.height						):
																		_snake_case						:     List[Any]				=    255 - int(255 * cells[h][w]						)
																		_snake_case						:     Union[str, Any]				=    (color, color, color)
						return img


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:    Any     = bin(int(input('Rule:\n').strip()))[2:]
				__SCREAMING_SNAKE_CASE						:    Tuple     = format_ruleset(int(rule_num))
				for time in range(1_6):
								CELLS.append(new_generation(CELLS, rule, time))
				__SCREAMING_SNAKE_CASE						:    List[Any]     = generate_image(CELLS)
				# Uncomment to save the image
				# img.save(f"rule_{rule_num}.png")
				img.show()