






import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
__A      =				logging.get_logger(__name__)

__A      =				[
    ('bert.bert', 'visual_bert'),
    ('bert.cls', 'cls'),
    ('bert.classifier', 'cls'),
    ('token_type_embeddings_visual', 'visual_token_type_embeddings'),
    ('position_embeddings_visual', 'visual_position_embeddings'),
    ('projection', 'visual_projection'),
]

__A      =				[
    'nlvr2_coco_pre_trained.th',
    'nlvr2_fine_tuned.th',
    'nlvr2_pre_trained.th',
    'vcr_coco_pre_train.th',
    'vcr_fine_tune.th',
    'vcr_pre_train.th',
    'vqa_coco_pre_trained.th',
    'vqa_fine_tuned.th',
    'vqa_pre_trained.th',
]




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= torch.load(_lowercase					,			map_location='''cpu''' )
					return sd




def      __A					(     _lowercase					,			_lowercase					,			_lowercase=rename_keys_prefix ):

					'''simple docstring'''







					_A								= OrderedDict()
					_A								= torch.arange(config.max_position_embeddings ).expand((1, -1) )
					# detector_d = OrderedDict()
					for key in d:
										if "detector" in key:
															# detector_d[key.replace('detector.','')] = d[key]
															continue
										_A								= key
										for name_pair in rename_keys_prefix:
															_A								= new_key.replace(name_pair[0]					,			name_pair[1] )
										_A								= d[key]
										if key == "bert.cls.predictions.decoder.weight":
															# Old bert code didn't have `decoder.bias`, but was added separately
															_A								= new_d['''cls.predictions.bias''']
					return new_d




@torch.no_grad()
def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					assert (
					    checkpoint_path.split('''/''' )[-1] in ACCEPTABLE_CHECKPOINTS
					), f"""The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."""

					# Get Config
					if "pre" in checkpoint_path:
										_A								= '''pretraining'''
										if "vcr" in checkpoint_path:
															_A								= {'''visual_embedding_dim''': 5_12}
										elif "vqa_advanced" in checkpoint_path:
															_A								= {'''visual_embedding_dim''': 20_48}
										elif "vqa" in checkpoint_path:
															_A								= {'''visual_embedding_dim''': 20_48}
										elif "nlvr" in checkpoint_path:
															_A								= {'''visual_embedding_dim''': 10_24}
										else:
															raise NotImplementedError(f"""No implementation found for `{checkpoint_path}`.""" )
					else:
										if "vcr" in checkpoint_path:
															_A								= {'''visual_embedding_dim''': 5_12}
															_A								= '''multichoice'''
										elif "vqa_advanced" in checkpoint_path:
															_A								= {'''visual_embedding_dim''': 20_48}
															_A								= '''vqa_advanced'''
										elif "vqa" in checkpoint_path:
															_A								= {'''visual_embedding_dim''': 20_48, '''num_labels''': 31_29}
															_A								= '''vqa'''
										elif "nlvr" in checkpoint_path:
															_A								= {
															    '''visual_embedding_dim''': 10_24,
															    '''num_labels''': 2,
															}
															_A								= '''nlvr'''

					_A								= VisualBertConfig(**_lowercase )

					# Load State Dict
					_A								= load_state_dict(_lowercase )

					_A								= get_new_dict(_lowercase					,			_lowercase )

					if model_type == "pretraining":
										_A								= VisualBertForPreTraining(_lowercase )
					elif model_type == "vqa":
										_A								= VisualBertForQuestionAnswering(_lowercase )
					elif model_type == "nlvr":
										_A								= VisualBertForVisualReasoning(_lowercase )
					elif model_type == "multichoice":
										_A								= VisualBertForMultipleChoice(_lowercase )

					model.load_state_dict(_lowercase )
					# Save Checkpoints
					Path(_lowercase ).mkdir(exist_ok=_lowercase )
					model.save_pretrained(_lowercase )


if __name__ == "__main__":
				__A      =				argparse.ArgumentParser()
				# Required parameters
				parser.add_argument('orig_checkpoint_path', type=str, help='A path to .th on local filesystem.')
				parser.add_argument('pytorch_dump_folder_path', type=str, help='Path to the output PyTorch model.')
				__A      =				parser.parse_args()
				convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)











def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''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					,			_lowercase					,			_lowercase					,			_lowercase )
										else:
															_A								= max(
															    mf_knapsack(i - 1					,			_lowercase					,			_lowercase					,			_lowercase )					,			mf_knapsack(i - 1					,			_lowercase					,			_lowercase					,			j - wt[i - 1] ) + val[i - 1]					,			)
										_A								= val
					return f[i][j]




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''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      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







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

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

					_A							,_A								= knapsack(_lowercase					,			_lowercase					,			_lowercase					,			_lowercase )
					_A								= set()
					_construct_solution(_lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase )

					return optimal_val, example_optional_set




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







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


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)



