

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							:
			'''simple docstring'''







			def __init__(			self							: Dict      ,      a							: Tuple      ,      a							: List[str]=None      ,      a							: Union[str, Any]=None      ,      a							: Any=None      ,      a							: Dict="resnet50"      ,      a							: int=3      ,      a							: str=32      ,      a							: Tuple=3      ,      a							: List[str]=True      ,      a							: Any=True      ,      )							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       parent
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       out_indices if out_indices is not None else [4]
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       stage_names
				SCREAMING_SNAKE_CASE   :      str				     =       out_features
				SCREAMING_SNAKE_CASE   :      Dict				     =       backbone
				SCREAMING_SNAKE_CASE   :      str				     =       batch_size
				SCREAMING_SNAKE_CASE   :      int				     =       image_size
				SCREAMING_SNAKE_CASE   :      str				     =       num_channels
				SCREAMING_SNAKE_CASE   :      int				     =       use_pretrained_backbone
				SCREAMING_SNAKE_CASE   :      Any				     =       is_training
			def 							__UpperCamelCase (			self							: str	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.get_config()

				return config, pixel_values
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Optional[Any]:

				"""simple docstring"""

				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							: Optional[int]      ,      a							: Optional[int]	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       TimmBackbone(config=a	)
				model.to(a	)
				model.eval()
				with torch.no_grad():
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       model(a	)
				self.parent.assertEqual(
				    result.feature_map[-1].shape      ,      (self.batch_size, model.channels[-1], 14, 14)      ,      )






			def 							__UpperCamelCase (			self							: Dict	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[int]				     =       config_and_inputs
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       {"pixel_values": pixel_values}
				return config, inputs_dict






@require_torch
@require_timm
class _UpperCamelCase							(							__A							,	__A							,	__A							,	unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =(TimmBackbone,) if is_torch_available() else ()
			lowerCamelCase__           ={'feature-extraction': TimmBackbone} if is_torch_available() else {}
			lowerCamelCase__           =False
			lowerCamelCase__           =False
			lowerCamelCase__           =False
			lowerCamelCase__           =False
			def 							__UpperCamelCase (			self							: Any	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       TimmBackboneModelTester(self	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       ConfigTester(self      ,      config_class=a      ,      has_text_modality=a	)
			def 							__UpperCamelCase (			self							: int	)							->       Optional[int]:

				"""simple docstring"""

				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							: Any	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       "resnet18"
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       "microsoft/resnet-18"

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       AutoBackbone.from_pretrained(a      ,      use_timm_backbone=a	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       AutoBackbone.from_pretrained(a	)

				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]	)

				SCREAMING_SNAKE_CASE   :      List[str]				     =       AutoBackbone.from_pretrained(a      ,      use_timm_backbone=a      ,      out_indices=[1, 2, 3]	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       AutoBackbone.from_pretrained(a      ,      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							: int	)							->       Tuple:

				"""simple docstring"""

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

				"""simple docstring"""

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

				"""simple docstring"""

				pass
			@unittest.skip("TimmBackbone models doesn't have inputs_embeds"	)
			def 							__UpperCamelCase (			self							: Any	)							->       int:

				"""simple docstring"""

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

				"""simple docstring"""

				pass
			@unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint"	)
			def 							__UpperCamelCase (			self							: str	)							->       str:

				"""simple docstring"""

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

				"""simple docstring"""

				pass
			@unittest.skip("model weights aren't tied in TimmBackbone."	)
			def 							__UpperCamelCase (			self							: Any	)							->       Any:

				"""simple docstring"""

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

				"""simple docstring"""

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

				"""simple docstring"""

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

				"""simple docstring"""

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

				"""simple docstring"""

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

				"""simple docstring"""

				pass
			@unittest.skip("Safetensors is not supported by timm."	)
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Any:

				"""simple docstring"""

				pass
			@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."	)
			def 							__UpperCamelCase (			self							: str	)							->       List[str]:

				"""simple docstring"""

				pass
			def 							__UpperCamelCase (			self							: List[Any]	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Tuple				     =       self.model_tester.prepare_config_and_inputs_for_common()

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

					SCREAMING_SNAKE_CASE   :      Any				     =       ["pixel_values"]
					self.assertListEqual(arg_names[:1]      ,      a	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[str]				     =       self.model_tester.prepare_config_and_inputs_for_common()
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       True
				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.has_attentions

				# no need to test all models as different heads yield the same functionality
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.all_model_classes[0]
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       model_class(a	)
				model.to(a	)

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self._prepare_for_class(a      ,      a	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       model(**a	)
				SCREAMING_SNAKE_CASE   :      str				     =       outputs[0][-1]

				# Encoder-/Decoder-only models
				SCREAMING_SNAKE_CASE   :      List[str]				     =       outputs.hidden_states[0]
				hidden_states.retain_grad()

				if self.has_attentions:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       outputs.attentions[0]
					attentions.retain_grad()

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

				self.assertIsNotNone(hidden_states.grad	)

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






			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE   :      str				     =       model_class(a	)
					model.to(a	)
					model.eval()
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       model(**a	)

					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
					SCREAMING_SNAKE_CASE   :      Dict				     =       copy.deepcopy(a	)
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       None
					SCREAMING_SNAKE_CASE   :      List[str]				     =       model_class(a	)
					model.to(a	)
					model.eval()
					SCREAMING_SNAKE_CASE   :      Any				     =       model(**a	)

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

					# Check backbone can be initialized with fresh weights
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       copy.deepcopy(a	)
					SCREAMING_SNAKE_CASE   :      Any				     =       False
					SCREAMING_SNAKE_CASE   :      List[str]				     =       model_class(a	)
					model.to(a	)
					model.eval()
					SCREAMING_SNAKE_CASE   :      int				     =       model(**a	)


def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	global f  # a global dp table for knapsack
	if f[i][j] < 0:
		if j < wt[i - 1]:
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       mf_knapsack(i - 1							,			_a							,			_a							,			_a)
		else:
			SCREAMING_SNAKE_CASE   :      Any				     =       max(
			    mf_knapsack(i - 1							,			_a							,			_a							,			_a)							,			mf_knapsack(i - 1							,			_a							,			_a							,			j - wt[i - 1]) + val[i - 1]							,			)
		SCREAMING_SNAKE_CASE   :      Tuple				     =       val
	return f[i][j]

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [[0] * (w + 1) for _ in range(n + 1)]

	for i in range(1							,			n + 1):
		for w_ in range(1							,			w + 1):
			if wt[i - 1] <= w_:
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]							,			dp[i - 1][w_])
			else:
				SCREAMING_SNAKE_CASE   :      int				     =       dp[i - 1][w_]

	return dp[n][w_], dp

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	if not (isinstance(_a							,			(list, tuple)) and isinstance(_a							,			(list, tuple))):
		raise ValueError(
		    "Both the weights and values vectors must be either lists or tuples")

	SCREAMING_SNAKE_CASE   :      int				     =       len(_a)
	if num_items != len(_a):
		SCREAMING_SNAKE_CASE   :      List[str]				     =       (
		    "The number of weights must be the same as the number of values.\n"
		    f"But got {num_items} weights and {len(_a)} values"
		)
		raise ValueError(_a)
	for i in range(_a):
		if not isinstance(wt[i]							,			_a):
			SCREAMING_SNAKE_CASE   :      int				     =       (
			    "All weights must be integers but got weight of "
			    f"type {type(wt[i])} at index {i}"
			)
			raise TypeError(_a)

	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Tuple				     =       knapsack(_a							,			_a							,			_a							,			_a)
	SCREAMING_SNAKE_CASE   :      set				     =       set()
	_construct_solution(_a							,			_a							,			_a							,			_a							,			_a)

	return optimal_val, example_optional_set

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a							,			_a):
	# for the current item i at a maximum weight j to be part of an optimal subset,
	# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
	# where i - 1 means considering only the previous items at the given maximum weight
	if i > 0 and j > 0:
		if dp[i - 1][j] == dp[i][j]:
			_construct_solution(_a							,			_a							,			i - 1							,			_a							,			_a)
		else:
			optimal_set.add(_a)
			_construct_solution(_a							,			_a							,			i - 1							,			j - wt[i - 1]							,			_a)


if __name__ == "__main__":
			a_						     =    [3, 2, 4, 4]
			a_						     =    [4, 3, 2, 3]
			a_						     =    4
			a_						     =    6
			a_						     =    [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
			a_     ,	a_						     =    knapsack(w, wt, val, n)
			print(optimal_solution)
			print(mf_knapsack(n, wt, val, w))  # switched the n and w

			# testing the dynamic programming problem with example
			# the optimal subset for the above example are items 3 and 4
			a_     ,	a_						     =    knapsack_with_example_solution(w, wt, val)
			assert optimal_solution == 8
			assert optimal_subset == {3, 4}
			print('optimal_value = ', optimal_solution)
			print('An optimal subset corresponding to the optimal value', optimal_subset)