





'''simple docstring'''

import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
			import torch

			from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin

class       UpperCAmelCase :




		def __init__(self      :	int							,			A__      :	Dict							,			A__      :	Any=None							,			A__      :	Tuple=None							,			A__      :	Any=None							,			A__      :	int="resnet50"							,			A__      :	Optional[int]=3							,			A__      :	Optional[int]=3_2							,			A__      :	Optional[Any]=3							,			A__      :	Union[str, Any]=True							,			A__      :	Union[str, Any]=True							,			)					-> Optional[int]:
						lowercase         = parent
						lowercase         = out_indices if out_indices is not None else [4]
						lowercase         = stage_names
						lowercase         = out_features
						lowercase         = backbone
						lowercase         = batch_size
						lowercase         = image_size
						lowercase         = num_channels
						lowercase         = use_pretrained_backbone
						lowercase         = is_training




		def 				UpperCAmelCase__   (self      :	List[str]				)					-> int:
						lowercase         = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)
						lowercase         = self.get_config()

						return config, pixel_values




		def 				UpperCAmelCase__   (self      :	Any				)					-> str:
						return TimmBackboneConfig(
						    image_size=self.image_size							,			num_channels=self.num_channels							,			out_features=self.out_features							,			out_indices=self.out_indices							,			stage_names=self.stage_names							,			use_pretrained_backbone=self.use_pretrained_backbone							,			backbone=self.backbone							,			)




		def 				UpperCAmelCase__   (self      :	Tuple							,			A__      :	str							,			A__      :	int				)					-> List[str]:
						lowercase         = TimmBackbone(config=_lowerCAmelCase				)
						model.to(_lowerCAmelCase				)
						model.eval()
						with torch.no_grad():
										lowercase         = model(_lowerCAmelCase				)
						self.parent.assertEqual(
						    result.feature_map[-1].shape							,			(self.batch_size, model.channels[-1], 1_4, 1_4)							,			)





		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> List[Any]:
						lowercase         = self.prepare_config_and_inputs()
						lowercase   ,							lowercase         = config_and_inputs
						lowercase         = {"pixel_values": pixel_values}
						return config, inputs_dict




@require_torch
@require_timm
class       UpperCAmelCase (						_lowerCAmelCase			,      _lowerCAmelCase			,      _lowerCAmelCase			,      unittest.TestCase  ):
		UpperCAmelCase					:	str 			=		(TimmBackbone,) if is_torch_available() else ()
		UpperCAmelCase					:	Dict 			=		{'feature-extraction': TimmBackbone} if is_torch_available() else {}
		UpperCAmelCase					:	Union[str, Any] 			=		False
		UpperCAmelCase					:	List[Any] 			=		False
		UpperCAmelCase					:	Tuple 			=		False
		UpperCAmelCase					:	Dict 			=		False




		def 				UpperCAmelCase__   (self      :	str				)					-> List[str]:
						lowercase         = TimmBackboneModelTester(self				)
						lowercase         = ConfigTester(self							,			config_class=_lowerCAmelCase							,			has_text_modality=_lowerCAmelCase				)




		def 				UpperCAmelCase__   (self      :	str				)					-> List[str]:
						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      :	str				)					-> Union[str, Any]:
						lowercase         = "resnet18"
						lowercase         = "microsoft/resnet-18"

						lowercase         = AutoBackbone.from_pretrained(_lowerCAmelCase							,			use_timm_backbone=_lowerCAmelCase				)
						lowercase         = AutoBackbone.from_pretrained(_lowerCAmelCase				)

						self.assertEqual(len(timm_model.out_features				)							,			len(transformers_model.out_features				)				)
						self.assertEqual(len(timm_model.stage_names				)							,			len(transformers_model.stage_names				)				)
						self.assertEqual(timm_model.channels							,			transformers_model.channels				)
						# Out indices are set to the last layer by default. For timm models, we don't know
						# the number of layers in advance, so we set it to (-1,), whereas for transformers
						# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
						self.assertEqual(timm_model.out_indices							,			(-1,)				)
						self.assertEqual(transformers_model.out_indices							,			[len(timm_model.stage_names				) - 1]				)

						lowercase         = AutoBackbone.from_pretrained(_lowerCAmelCase							,			use_timm_backbone=_lowerCAmelCase							,			out_indices=[1, 2, 3]				)
						lowercase         = AutoBackbone.from_pretrained(_lowerCAmelCase							,			out_indices=[1, 2, 3]				)

						self.assertEqual(timm_model.out_indices							,			transformers_model.out_indices				)
						self.assertEqual(len(timm_model.out_features				)							,			len(transformers_model.out_features				)				)
						self.assertEqual(timm_model.channels							,			transformers_model.channels				)




		@unittest.skip("TimmBackbone doesn't support feed forward chunking"				)
		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Dict:
						pass




		@unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute"				)
		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Optional[Any]:
						pass




		@unittest.skip("TimmBackbone initialization is managed on the timm side"				)
		def 				UpperCAmelCase__   (self      :	Tuple				)					-> Optional[Any]:
						pass




		@unittest.skip("TimmBackbone models doesn't have inputs_embeds"				)
		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Optional[Any]:
						pass




		@unittest.skip("TimmBackbone models doesn't have inputs_embeds"				)
		def 				UpperCAmelCase__   (self      :	List[str]				)					-> Union[str, Any]:
						pass




		@unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint"				)
		def 				UpperCAmelCase__   (self      :	Any				)					-> Optional[Any]:
						pass




		@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"				)
		def 				UpperCAmelCase__   (self      :	int				)					-> Optional[int]:
						pass




		@unittest.skip("model weights aren't tied in TimmBackbone."				)
		def 				UpperCAmelCase__   (self      :	Tuple				)					-> Union[str, Any]:
						pass




		@unittest.skip("model weights aren't tied in TimmBackbone."				)
		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> str:
						pass




		@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"				)
		def 				UpperCAmelCase__   (self      :	Dict				)					-> str:
						pass




		@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"				)
		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> str:
						pass




		@unittest.skip("TimmBackbone doesn't have hidden size info in its configuration."				)
		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Any:
						pass




		@unittest.skip("TimmBackbone doesn't support output_attentions."				)
		def 				UpperCAmelCase__   (self      :	Any				)					-> str:
						pass




		@unittest.skip("Safetensors is not supported by timm."				)
		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> List[str]:
						pass




		@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."				)
		def 				UpperCAmelCase__   (self      :	Tuple				)					-> int:
						pass




		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> Optional[int]:
						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()

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

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




		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> str:
						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()
						lowercase         = True
						lowercase         = self.has_attentions

						# no need to test all models as different heads yield the same functionality
						lowercase         = self.all_model_classes[0]
						lowercase         = model_class(_lowerCAmelCase				)
						model.to(_lowerCAmelCase				)

						lowercase         = self._prepare_for_class(_lowerCAmelCase							,			_lowerCAmelCase				)
						lowercase         = model(**_lowerCAmelCase				)
						lowercase         = outputs[0][-1]

						# Encoder-/Decoder-only models
						lowercase         = outputs.hidden_states[0]
						hidden_states.retain_grad()

						if self.has_attentions:
										lowercase         = outputs.attentions[0]
										attentions.retain_grad()

						output.flatten()[0].backward(retain_graph=_lowerCAmelCase				)

						self.assertIsNotNone(hidden_states.grad				)

						if self.has_attentions:
										self.assertIsNotNone(attentions.grad				)





		def 				UpperCAmelCase__   (self      :	int				)					-> Tuple:
						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										lowercase         = model_class(_lowerCAmelCase				)
										model.to(_lowerCAmelCase				)
										model.eval()
										lowercase         = model(**_lowerCAmelCase				)

										self.assertEqual(len(result.feature_maps				)							,			len(config.out_indices				)				)
										self.assertEqual(len(model.channels				)							,			len(config.out_indices				)				)

										# Check output of last stage is taken if out_features=None, out_indices=None
										lowercase         = copy.deepcopy(_lowerCAmelCase				)
										lowercase         = None
										lowercase         = model_class(_lowerCAmelCase				)
										model.to(_lowerCAmelCase				)
										model.eval()
										lowercase         = model(**_lowerCAmelCase				)

										self.assertEqual(len(result.feature_maps				)							,			1				)
										self.assertEqual(len(model.channels				)							,			1				)

										# Check backbone can be initialized with fresh weights
										lowercase         = copy.deepcopy(_lowerCAmelCase				)
										lowercase         = False
										lowercase         = model_class(_lowerCAmelCase				)
										model.to(_lowerCAmelCase				)
										model.eval()
										lowercase         = model(**_lowerCAmelCase				)











'''simple docstring'''

import functools







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				if not isinstance(lowerCAmelCase_		,							lowerCAmelCase_							) or not all(isinstance(lowerCAmelCase_		,							lowerCAmelCase_							) for day in days							):
								raise ValueError("The parameter days should be a list of integers"							)

				if len(lowerCAmelCase_							) != 3 or not all(isinstance(lowerCAmelCase_		,							lowerCAmelCase_							) for cost in costs							):
								raise ValueError("The parameter costs should be a list of three integers"							)

				if len(lowerCAmelCase_							) == 0:
								return 0

				if min(lowerCAmelCase_							) <= 0:
								raise ValueError("All days elements should be greater than 0"							)

				if max(lowerCAmelCase_							) >= 366:
								raise ValueError("All days elements should be less than 366"							)

				lowercase         = set(lowerCAmelCase_							)

				@functools.cache
				def dynamic_programming(lowerCAmelCase_							) -> int:
								if index > 365:
												return 0

								if index not in days_set:
												return dynamic_programming(index + 1							)

								return min(
								    costs[0] + dynamic_programming(index + 1							)		,							costs[1] + dynamic_programming(index + 7							)		,							costs[2] + dynamic_programming(index + 30							)		,							)

				return dynamic_programming(1							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()




