

import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	BertModel    ,			SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	str      )							->      List[Any]:





    '''simple docstring'''






    A__          =   ('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

    A__          =   (
        ('layer.', 'layer_'),
        ('word_embeddings.weight', 'word_embeddings'),
        ('position_embeddings.weight', 'position_embeddings'),
        ('token_type_embeddings.weight', 'token_type_embeddings'),
        ('.', '/'),
        ('LayerNorm/weight', 'LayerNorm/gamma'),
        ('LayerNorm/bias', 'LayerNorm/beta'),
        ('weight', 'kernel'),
    )

    if not os.path.isdir(SCREAMING_SNAKE_CASE__      ):
        os.makedirs(SCREAMING_SNAKE_CASE__      )

    A__          =   model.state_dict()

    def to_tf_var_name(SCREAMING_SNAKE_CASE__			:	str      ):
        for patt, repl in iter(SCREAMING_SNAKE_CASE__      ):
            A__          =   name.replace(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
        return f'bert/{name}'

    def create_tf_var(SCREAMING_SNAKE_CASE__			:	np.ndarray    ,			SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	tf.Session      ):
        A__          =   tf.dtypes.as_dtype(tensor.dtype      )
        A__          =   tf.get_variable(dtype=SCREAMING_SNAKE_CASE__    ,			shape=tensor.shape    ,			name=SCREAMING_SNAKE_CASE__    ,			initializer=tf.zeros_initializer()      )
        session.run(tf.variables_initializer([tf_var]      )      )
        session.run(SCREAMING_SNAKE_CASE__      )
        return tf_var

    tf.reset_default_graph()
    with tf.Session() as session:
        for var_name in state_dict:
            A__          =   to_tf_var_name(SCREAMING_SNAKE_CASE__      )
            A__          =   state_dict[var_name].numpy()
            if any(x in var_name for x in tensors_to_transpose      ):
                A__          =   torch_tensor.T
            A__          =   create_tf_var(tensor=SCREAMING_SNAKE_CASE__    ,			name=SCREAMING_SNAKE_CASE__    ,			session=SCREAMING_SNAKE_CASE__      )
            tf.keras.backend.set_value(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
            A__          =   session.run(SCREAMING_SNAKE_CASE__      )
            print(f'Successfully created {tf_name}: {np.allclose(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )}'      )

        A__          =   tf.train.Saver(tf.trainable_variables()      )
        saver.save(SCREAMING_SNAKE_CASE__    ,			os.path.join(SCREAMING_SNAKE_CASE__    ,			model_name.replace('-'    ,			'_'      ) + '.ckpt'      )      )
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Any=None      )							->      Any:





    '''simple docstring'''






    A__          =   argparse.ArgumentParser()
    parser.add_argument('--model_name'    ,			type=SCREAMING_SNAKE_CASE__    ,			required=SCREAMING_SNAKE_CASE__    ,			help='model name e.g. bert-base-uncased'      )
    parser.add_argument(
        '--cache_dir'    ,			type=SCREAMING_SNAKE_CASE__    ,			default=SCREAMING_SNAKE_CASE__    ,			required=SCREAMING_SNAKE_CASE__    ,			help='Directory containing pytorch model'      )
    parser.add_argument('--pytorch_model_path'    ,			type=SCREAMING_SNAKE_CASE__    ,			required=SCREAMING_SNAKE_CASE__    ,			help='/path/to/<pytorch-model-name>.bin'      )
    parser.add_argument('--tf_cache_dir'    ,			type=SCREAMING_SNAKE_CASE__    ,			required=SCREAMING_SNAKE_CASE__    ,			help='Directory in which to save tensorflow model'      )
    A__          =   parser.parse_args(SCREAMING_SNAKE_CASE__      )

    A__          =   BertModel.from_pretrained(
        pretrained_model_name_or_path=args.model_name    ,			state_dict=torch.load(args.pytorch_model_path      )    ,			cache_dir=args.cache_dir    ,			)

    convert_pytorch_checkpoint_to_tf(model=SCREAMING_SNAKE_CASE__    ,			ckpt_dir=args.tf_cache_dir    ,			model_name=args.model_name      )


if __name__ == "__main__":
     main()




def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	Any      )							->      Optional[int]:





    '''simple docstring'''






    global f  # a global dp table for knapsack
    if f[i][j] < 0:
        if j < wt[i - 1]:
            A__          =   mf_knapsack(i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
        else:
            A__          =   max(
                mf_knapsack(i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )    ,			mf_knapsack(i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			j - wt[i - 1]      ) + val[i - 1]    ,			)
        A__          =   val
    return f[i][j]
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]      )							->      Tuple:





    '''simple docstring'''






    A__          =   [[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_:
                A__          =   max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]    ,			dp[i - 1][w_]      )
            else:
                A__          =   dp[i - 1][w_]

    return dp[n][w_], dp
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	list    ,			SCREAMING_SNAKE_CASE__			:	list      )							->      Union[str, Any]:





    '''simple docstring'''






    if not (isinstance(SCREAMING_SNAKE_CASE__    ,			(list, tuple)      ) and isinstance(SCREAMING_SNAKE_CASE__    ,			(list, tuple)      )):
        raise ValueError(
            'Both the weights and values vectors must be either lists or tuples'      )

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

    A__      ,						A__          =   knapsack(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    A__          =   set()
    _construct_solution(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )

    return optimal_val, example_optional_set
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	list    ,			SCREAMING_SNAKE_CASE__			:	list    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	set      )							->      Optional[int]:





    '''simple docstring'''






    if i > 0 and j > 0:
        if dp[i - 1][j] == dp[i][j]:
            _construct_solution(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
        else:
            optimal_set.add(SCREAMING_SNAKE_CASE__      )
            _construct_solution(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			i - 1    ,			j - wt[i - 1]    ,			SCREAMING_SNAKE_CASE__      )


if __name__ == "__main__":
     lowercase_       		=  [3, 2, 4, 4]
     lowercase_       		=  [4, 3, 2, 3]
     lowercase_       		=  4
     lowercase_       		=  6
     lowercase_       		=  [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
     lowercase_		,			lowercase_       		=  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
     lowercase_		,			lowercase_       		=  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)

