def 						A__  (					__A   , __A   , __A   , __A	):







    '''simple docstring'''
    global f  # a global dp table for knapsack
    if f[i][j] < 0:
        if j < wt[i - 1]:
            _lowerCamelCase     :		List[str] 						=						mf_knapsack(i - 1   , __A   , __A   , __A	)
        else:
            _lowerCamelCase     :		Any 						=						max(
                mf_knapsack(i - 1   , __A   , __A   , __A	)   , mf_knapsack(i - 1   , __A   , __A   , j - wt[i - 1]	) + val[i - 1]   , )
        _lowerCamelCase     :		Tuple 						=						val
    return f[i][j]







def 						A__  (					__A   , __A   , __A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		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_:
                _lowerCamelCase     :		Tuple 						=						max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]   , dp[i - 1][w_]	)
            else:
                _lowerCamelCase     :		Union[str, Any] 						=						dp[i - 1][w_]

    return dp[n][w_], dp







def 						A__  (					__A   , __A   , __A	):







    '''simple docstring'''
    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"""	)

    _lowerCamelCase     :		List[str] 						=						len(__A	)
    if num_items != len(__A	):
        _lowerCamelCase     :		Union[str, Any] 						=						(
            """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	):
            _lowerCamelCase     :		Tuple 						=						(
                """All weights must be integers but got weight of """
                F"""type {type(wt[i]	)} at index {i}"""
            )
            raise TypeError(__A	)

    _lowerCamelCase						,							_lowerCamelCase     :		Union[str, Any] 						=						knapsack(__A   , __A   , __A   , __A	)
    _lowerCamelCase     :		set 						=						set()
    _construct_solution(__A   , __A   , __A   , __A   , __A	)

    return optimal_val, example_optional_set







def 						A__  (					__A   , __A   , __A   , __A   , __A	):







    '''simple docstring'''
    # 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__":
    lowerCAmelCase			:							Any     =[3, 2, 4, 4]
    lowerCAmelCase			:							str     =[4, 3, 2, 3]
    lowerCAmelCase			:							Dict     =4
    lowerCAmelCase			:							str     =6
    lowerCAmelCase			:							List[str]     =[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
    lowerCAmelCase , lowerCAmelCase			:							Union[str, Any]     =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
    lowerCAmelCase , lowerCAmelCase			:							Optional[Any]     =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)





import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


lowerCAmelCase			:							int     ={
    "cola": 2,
    "mnli": 3,
    "mrpc": 2,
    "sst-2": 2,
    "sts-b": 1,
    "qqp": 2,
    "qnli": 2,
    "rte": 2,
    "wnli": 2,
}


logging.set_verbosity_info()







def 						A__  (					__A   , __A   , __A   , __A=None	):







    '''simple docstring'''
    # Initialise PyTorch model
    _lowerCamelCase     :		Tuple 						=						XLNetConfig.from_json_file(__A	)

    _lowerCamelCase     :		List[Any] 						=						finetuning_task.lower() if finetuning_task is not None else """"""
    if finetuning_task in GLUE_TASKS_NUM_LABELS:
        print(F"""Building PyTorch XLNetForSequenceClassification model from configuration: {config}"""	)
        _lowerCamelCase     :		int 						=						finetuning_task
        _lowerCamelCase     :		Union[str, Any] 						=						GLUE_TASKS_NUM_LABELS[finetuning_task]
        _lowerCamelCase     :		int 						=						XLNetForSequenceClassification(__A	)
    elif "squad" in finetuning_task:
        _lowerCamelCase     :		Dict 						=						finetuning_task
        _lowerCamelCase     :		Optional[Any] 						=						XLNetForQuestionAnswering(__A	)
    else:
        _lowerCamelCase     :		Any 						=						XLNetLMHeadModel(__A	)

    # Load weights from tf checkpoint
    load_tf_weights_in_xlnet(__A   , __A   , __A	)

    # Save pytorch-model
    _lowerCamelCase     :		Optional[Any] 						=						os.path.join(__A   , __A	)
    _lowerCamelCase     :		Any 						=						os.path.join(__A   , __A	)
    print(F"""Save PyTorch model to {os.path.abspath(__A	)}"""	)
    torch.save(model.state_dict()   , __A	)
    print(F"""Save configuration file to {os.path.abspath(__A	)}"""	)
    with open(__A   , """w"""   , encoding="""utf-8"""	) as f:
        f.write(config.to_json_string()	)


if __name__ == "__main__":
    lowerCAmelCase			:							Dict     =argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
    )
    parser.add_argument(
        "--xlnet_config_file",
        default=None,
        type=str,
        required=True,
        help=(
            "The config json file corresponding to the pre-trained XLNet model. \n"
            "This specifies the model architecture."
        ),
    )
    parser.add_argument(
        "--pytorch_dump_folder_path",
        default=None,
        type=str,
        required=True,
        help="Path to the folder to store the PyTorch model or dataset/vocab.",
    )
    parser.add_argument(
        "--finetuning_task",
        default=None,
        type=str,
        help="Name of a task on which the XLNet TensorFlow model was fine-tuned",
    )
    lowerCAmelCase			:							Union[str, Any]     =parser.parse_args()
    print(args)

    convert_xlnet_checkpoint_to_pytorch(
        args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
    )




