

from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers.models.layoutlm.modeling_tf_layoutlm import (
			    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
			    TFLayoutLMForMaskedLM,
			    TFLayoutLMForQuestionAnswering,
			    TFLayoutLMForSequenceClassification,
			    TFLayoutLMForTokenClassification,
			    TFLayoutLMModel,
			)






class _UpperCamelCase							:
		'''simple docstring'''







		def __init__(			self							: List[str]      ,      a							: str      ,      a							: Dict=13      ,      a							: List[Any]=7      ,      a							: int=True      ,      a							: int=True      ,      a							: List[Any]=True      ,      a							: Optional[Any]=True      ,      a							: Optional[int]=99      ,      a							: Dict=32      ,      a							: Union[str, Any]=2      ,      a							: Dict=4      ,      a							: Union[str, Any]=37      ,      a							: str="gelu"      ,      a							: Any=0.1      ,      a							: int=0.1      ,      a							: Optional[int]=512      ,      a							: Optional[int]=16      ,      a							: Dict=2      ,      a							: List[str]=0.02      ,      a							: Tuple=3      ,      a							: List[Any]=4      ,      a							: Tuple=None      ,      a							: int=1000      ,      )							->       Union[str, Any]:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       parent
			SCREAMING_SNAKE_CASE   :      List[Any]				     =       batch_size
			SCREAMING_SNAKE_CASE   :      List[Any]				     =       seq_length
			SCREAMING_SNAKE_CASE   :      int				     =       is_training
			SCREAMING_SNAKE_CASE   :      Any				     =       use_input_mask
			SCREAMING_SNAKE_CASE   :      List[str]				     =       use_token_type_ids
			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       use_labels
			SCREAMING_SNAKE_CASE   :      int				     =       vocab_size
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       hidden_size
			SCREAMING_SNAKE_CASE   :      Tuple				     =       num_hidden_layers
			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       num_attention_heads
			SCREAMING_SNAKE_CASE   :      Tuple				     =       intermediate_size
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       hidden_act
			SCREAMING_SNAKE_CASE   :      int				     =       hidden_dropout_prob
			SCREAMING_SNAKE_CASE   :      Tuple				     =       attention_probs_dropout_prob
			SCREAMING_SNAKE_CASE   :      int				     =       max_position_embeddings
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       type_vocab_size
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       type_sequence_label_size
			SCREAMING_SNAKE_CASE   :      Any				     =       initializer_range
			SCREAMING_SNAKE_CASE   :      int				     =       num_labels
			SCREAMING_SNAKE_CASE   :      int				     =       num_choices
			SCREAMING_SNAKE_CASE   :      Any				     =       scope
			SCREAMING_SNAKE_CASE   :      List[Any]				     =       range_bbox
		def 							__UpperCamelCase (			self							: List[str]	)							->       str:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      List[str]				     =       ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size	)

			# convert bbox to numpy since TF does not support item assignment
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       ids_tensor([self.batch_size, self.seq_length, 4]      ,      self.range_bbox	).numpy()
			# Ensure that bbox is legal
			for i in range(bbox.shape[0]	):
				for j in range(bbox.shape[1]	):
					if bbox[i, j, 3] < bbox[i, j, 1]:
						SCREAMING_SNAKE_CASE   :      List[str]				     =       bbox[i, j, 3]
						SCREAMING_SNAKE_CASE   :      Dict				     =       bbox[i, j, 1]
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       t
					if bbox[i, j, 2] < bbox[i, j, 0]:
						SCREAMING_SNAKE_CASE   :      Dict				     =       bbox[i, j, 2]
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       bbox[i, j, 0]
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       t
			SCREAMING_SNAKE_CASE   :      Tuple				     =       tf.convert_to_tensor(a	)

			SCREAMING_SNAKE_CASE   :      List[str]				     =       None
			if self.use_input_mask:
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       random_attention_mask([self.batch_size, self.seq_length]	)

			SCREAMING_SNAKE_CASE   :      Dict				     =       None
			if self.use_token_type_ids:
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       ids_tensor([self.batch_size, self.seq_length]      ,      self.type_vocab_size	)

			SCREAMING_SNAKE_CASE   :      List[str]				     =       None
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       None
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       None
			if self.use_labels:
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       ids_tensor([self.batch_size]      ,      self.type_sequence_label_size	)
				SCREAMING_SNAKE_CASE   :      Any				     =       ids_tensor([self.batch_size, self.seq_length]      ,      self.num_labels	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       ids_tensor([self.batch_size]      ,      self.num_choices	)

			SCREAMING_SNAKE_CASE   :      List[Any]				     =       LayoutLMConfig(
			    vocab_size=self.vocab_size      ,      hidden_size=self.hidden_size      ,      num_hidden_layers=self.num_hidden_layers      ,      num_attention_heads=self.num_attention_heads      ,      intermediate_size=self.intermediate_size      ,      hidden_act=self.hidden_act      ,      hidden_dropout_prob=self.hidden_dropout_prob      ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,      max_position_embeddings=self.max_position_embeddings      ,      type_vocab_size=self.type_vocab_size      ,      initializer_range=self.initializer_range      ,      )

			return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
		def 							__UpperCamelCase (			self							: Dict      ,      a							: Union[str, Any]      ,      a							: Dict      ,      a							: Tuple      ,      a							: int      ,      a							: Optional[Any]      ,      a							: Dict      ,      a							: Union[str, Any]      ,      a							: Union[str, Any]	)							->       str:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      List[Any]				     =       TFLayoutLMModel(config=a	)

			SCREAMING_SNAKE_CASE   :      Dict				     =       model(a      ,      a      ,      attention_mask=a      ,      token_type_ids=a	)
			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       model(a      ,      a      ,      token_type_ids=a	)
			SCREAMING_SNAKE_CASE   :      List[Any]				     =       model(a      ,      a	)
			self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.seq_length, self.hidden_size)	)
			self.parent.assertEqual(result.pooler_output.shape      ,      (self.batch_size, self.hidden_size)	)
		def 							__UpperCamelCase (			self							: List[str]      ,      a							: Optional[int]      ,      a							: List[str]      ,      a							: Any      ,      a							: str      ,      a							: List[Any]      ,      a							: str      ,      a							: Tuple      ,      a							: Union[str, Any]	)							->       List[Any]:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       TFLayoutLMForMaskedLM(config=a	)

			SCREAMING_SNAKE_CASE   :      int				     =       model(a      ,      a      ,      attention_mask=a      ,      token_type_ids=a      ,      labels=a	)
			self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.seq_length, self.vocab_size)	)
		def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: Tuple      ,      a							: Optional[Any]      ,      a							: Dict      ,      a							: Any      ,      a							: List[str]      ,      a							: List[Any]      ,      a							: List[str]      ,      a							: Union[str, Any]	)							->       List[str]:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      Any				     =       self.num_labels
			SCREAMING_SNAKE_CASE   :      str				     =       TFLayoutLMForSequenceClassification(config=a	)

			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       model(a      ,      a      ,      attention_mask=a      ,      token_type_ids=a	)
			self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.num_labels)	)
		def 							__UpperCamelCase (			self							: List[Any]      ,      a							: List[str]      ,      a							: Optional[int]      ,      a							: Optional[Any]      ,      a							: Union[str, Any]      ,      a							: List[Any]      ,      a							: Optional[Any]      ,      a							: Tuple      ,      a							: str	)							->       str:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      List[str]				     =       self.num_labels
			SCREAMING_SNAKE_CASE   :      int				     =       TFLayoutLMForTokenClassification(config=a	)

			SCREAMING_SNAKE_CASE   :      Dict				     =       model(a      ,      a      ,      attention_mask=a      ,      token_type_ids=a      ,      labels=a	)
			self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.seq_length, self.num_labels)	)
		def 							__UpperCamelCase (			self							: Tuple      ,      a							: str      ,      a							: Optional[int]      ,      a							: Dict      ,      a							: str      ,      a							: Tuple      ,      a							: List[Any]      ,      a							: Tuple      ,      a							: Optional[Any]	)							->       Union[str, Any]:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      Any				     =       TFLayoutLMForQuestionAnswering(config=a	)

			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       model(a      ,      a      ,      attention_mask=a      ,      token_type_ids=a	)
			self.parent.assertEqual(result.start_logits.shape      ,      (self.batch_size, self.seq_length)	)
			self.parent.assertEqual(result.end_logits.shape      ,      (self.batch_size, self.seq_length)	)






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

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      Dict				     =       self.prepare_config_and_inputs()
			(
			    SCREAMING_SNAKE_CASE
			)   :      List[str]				     =       config_and_inputs
			SCREAMING_SNAKE_CASE   :      Dict				     =       {
			    "input_ids": input_ids,
			    "bbox": bbox,
			    "token_type_ids": token_type_ids,
			    "attention_mask": input_mask,
			}
			return config, inputs_dict






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







		lowerCamelCase__           =(
		    (
		        TFLayoutLMModel,
		        TFLayoutLMForMaskedLM,
		        TFLayoutLMForTokenClassification,
		        TFLayoutLMForSequenceClassification,
		        TFLayoutLMForQuestionAnswering,
		    )
		    if is_tf_available()
		    else ()
		)
		lowerCamelCase__           =(
		    {
		        'feature-extraction': TFLayoutLMModel,
		        'fill-mask': TFLayoutLMForMaskedLM,
		        'text-classification': TFLayoutLMForSequenceClassification,
		        'token-classification': TFLayoutLMForTokenClassification,
		        'zero-shot': TFLayoutLMForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		lowerCamelCase__           =False
		lowerCamelCase__           =True
		lowerCamelCase__           =10
		def 							__UpperCamelCase (			self							: Optional[Any]	)							->       Any:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      str				     =       TFLayoutLMModelTester(self	)
			SCREAMING_SNAKE_CASE   :      List[Any]				     =       ConfigTester(self      ,      config_class=a      ,      hidden_size=37	)
		def 							__UpperCamelCase (			self							: int	)							->       Dict:

			"""simple docstring"""

			self.config_tester.run_common_tests()
		def 							__UpperCamelCase (			self							: Tuple	)							->       Dict:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      str				     =       self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*a	)
		def 							__UpperCamelCase (			self							: Dict	)							->       str:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      Any				     =       self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_masked_lm(*a	)
		def 							__UpperCamelCase (			self							: List[Any]	)							->       Dict:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      int				     =       self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_sequence_classification(*a	)
		def 							__UpperCamelCase (			self							: List[Any]	)							->       str:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      str				     =       self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_token_classification(*a	)
		def 							__UpperCamelCase (			self							: Optional[int]	)							->       Union[str, Any]:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      List[str]				     =       self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_question_answering(*a	)
		@slow
		def 							__UpperCamelCase (			self							: Optional[Any]	)							->       Optional[Any]:

			"""simple docstring"""

			for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       TFLayoutLMModel.from_pretrained(a	)
				self.assertIsNotNone(a	)






		@unittest.skip("Onnx compliancy broke with TF 2.10"	)
		def 							__UpperCamelCase (			self							: Optional[Any]	)							->       Optional[int]:

			"""simple docstring"""

			pass

def  lowerCamelCase__ (					):
	# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
	# fmt: off
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       tf.convert_to_tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]])  # noqa: E231
	SCREAMING_SNAKE_CASE   :      str				     =       tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],])  # noqa: E231
	SCREAMING_SNAKE_CASE   :      Dict				     =       tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]])  # noqa: E231
	SCREAMING_SNAKE_CASE   :      List[str]				     =       tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]])  # noqa: E231
	# these are sequence labels (i.e. at the token level)
	SCREAMING_SNAKE_CASE   :      str				     =       tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]])  # noqa: E231
	# fmt: on

	return input_ids, attention_mask, bbox, token_type_ids, labels






@require_tf
class _UpperCamelCase							(							unittest.TestCase   ):
		'''simple docstring'''







		@slow
		def 							__UpperCamelCase (			self							: List[Any]	)							->       Dict:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      Any				     =       TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"	)

			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       prepare_layoutlm_batch_inputs()

			# forward pass
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       model(input_ids=a      ,      bbox=a      ,      attention_mask=a      ,      token_type_ids=a	)

			# test the sequence output on [0, :3, :3]
			SCREAMING_SNAKE_CASE   :      List[str]				     =       tf.convert_to_tensor(
			    [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]      ,      )

			self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]      ,      a      ,      atol=1e-3	)	)

			# test the pooled output on [1, :3]
			SCREAMING_SNAKE_CASE   :      List[str]				     =       tf.convert_to_tensor([-0.6580, -0.0214, 0.8552]	)

			self.assertTrue(np.allclose(outputs.pooler_output[1, :3]      ,      a      ,      atol=1e-3	)	)
		@slow
		def 							__UpperCamelCase (			self							: Optional[Any]	)							->       Optional[Any]:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      int				     =       TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"      ,      num_labels=2	)

			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       prepare_layoutlm_batch_inputs()

			# forward pass
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       model(
			    input_ids=a      ,      bbox=a      ,      attention_mask=a      ,      token_type_ids=a      ,      labels=tf.convert_to_tensor([1, 1]	)      ,      )

			# test whether we get a loss as a scalar
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       outputs.loss
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       (2,)
			self.assertEqual(loss.shape      ,      a	)

			# test the shape of the logits
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       outputs.logits
			SCREAMING_SNAKE_CASE   :      Tuple				     =       (2, 2)
			self.assertEqual(logits.shape      ,      a	)
		@slow
		def 							__UpperCamelCase (			self							: Optional[Any]	)							->       Tuple:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      int				     =       TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"      ,      num_labels=13	)

			SCREAMING_SNAKE_CASE   :      List[Any]				     =       prepare_layoutlm_batch_inputs()

			# forward pass
			SCREAMING_SNAKE_CASE   :      List[str]				     =       model(
			    input_ids=a      ,      bbox=a      ,      attention_mask=a      ,      token_type_ids=a      ,      labels=a	)

			# test the shape of the logits
			SCREAMING_SNAKE_CASE   :      Dict				     =       outputs.logits
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       tf.convert_to_tensor((2, 25, 13)	)
			self.assertEqual(logits.shape      ,      a	)






		@slow
		def 							__UpperCamelCase (			self							: Optional[int]	)							->       Any:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      List[str]				     =       TFLayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"	)

			SCREAMING_SNAKE_CASE   :      int				     =       prepare_layoutlm_batch_inputs()

			# forward pass
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       model(input_ids=a      ,      bbox=a      ,      attention_mask=a      ,      token_type_ids=a	)

			# test the shape of the logits
			SCREAMING_SNAKE_CASE   :      int				     =       tf.convert_to_tensor((2, 25)	)
			self.assertEqual(outputs.start_logits.shape      ,      a	)
			self.assertEqual(outputs.end_logits.shape      ,      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)