

'''simple docstring'''



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()
_UpperCamelCase						:  Tuple							       =				logging.get_logger(__name__)

_UpperCamelCase						:  List[str]							       =				[
    ("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"),
]

_UpperCamelCase						:  Dict							       =				[
    "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       snake_case   (	snake_case      : str			) ->  int:


    """simple docstring"""


    lowerCAmelCase 						=							torch.load(snake_case					,		map_location='cpu'			)
    return sd


def       snake_case   (	snake_case      : Dict					,		snake_case      : Dict					,		snake_case      : List[str]=rename_keys_prefix			) ->  List[Any]:


    """simple docstring"""


    lowerCAmelCase 						=							OrderedDict()
    lowerCAmelCase 						=							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
        lowerCAmelCase 						=							key
        for name_pair in rename_keys_prefix:
            lowerCAmelCase 						=							new_key.replace(name_pair[0]					,		name_pair[1]			)
        lowerCAmelCase 						=							d[key]
        if key == "bert.cls.predictions.decoder.weight":
            # Old bert code didn't have `decoder.bias`, but was added separately
            lowerCAmelCase 						=							new_d['cls.predictions.bias']
    return new_d


@torch.no_grad()
def       snake_case   (	snake_case      : str					,		snake_case      : Dict			) ->  Any:


    """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:
        lowerCAmelCase 						=							'pretraining'
        if "vcr" in checkpoint_path:
            lowerCAmelCase 						=							{'visual_embedding_dim': 512}
        elif "vqa_advanced" in checkpoint_path:
            lowerCAmelCase 						=							{'visual_embedding_dim': 2048}
        elif "vqa" in checkpoint_path:
            lowerCAmelCase 						=							{'visual_embedding_dim': 2048}
        elif "nlvr" in checkpoint_path:
            lowerCAmelCase 						=							{'visual_embedding_dim': 1024}
        else:
            raise NotImplementedError(F'No implementation found for `{checkpoint_path}`.'			)
    else:
        if "vcr" in checkpoint_path:
            lowerCAmelCase 						=							{'visual_embedding_dim': 512}
            lowerCAmelCase 						=							'multichoice'
        elif "vqa_advanced" in checkpoint_path:
            lowerCAmelCase 						=							{'visual_embedding_dim': 2048}
            lowerCAmelCase 						=							'vqa_advanced'
        elif "vqa" in checkpoint_path:
            lowerCAmelCase 						=							{'visual_embedding_dim': 2048, 'num_labels': 3129}
            lowerCAmelCase 						=							'vqa'
        elif "nlvr" in checkpoint_path:
            lowerCAmelCase 						=							{
                'visual_embedding_dim': 1024,
                'num_labels': 2,
            }
            lowerCAmelCase 						=							'nlvr'

    lowerCAmelCase 						=							VisualBertConfig(**snake_case			)

    # Load State Dict
    lowerCAmelCase 						=							load_state_dict(snake_case			)

    lowerCAmelCase 						=							get_new_dict(snake_case					,		snake_case			)

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

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


if __name__ == "__main__":
     _UpperCamelCase						:  Union[str, Any]							       =				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.")
     _UpperCamelCase						:  Optional[int]							       =				parser.parse_args()
     convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)








'''simple docstring'''



import operator


def       snake_case   (	snake_case      : list					,		snake_case      : bool = False					,		snake_case      : list | None = None			) ->  list:


    """simple docstring"""


    lowerCAmelCase 						=							operator.lt if reverse else operator.gt
    lowerCAmelCase 						=							solution or []

    if not arr:
        return solution

    lowerCAmelCase 						=							[arr.pop(0			)]
    for i, item in enumerate(snake_case			):
        if _operator(snake_case					,		sublist[-1]			):
            sublist.append(snake_case			)
            arr.pop(snake_case			)

    #  merging sublist into solution list
    if not solution:
        solution.extend(snake_case			)
    else:
        while sublist:
            lowerCAmelCase 						=							sublist.pop(0			)
            for i, xx in enumerate(snake_case			):
                if not _operator(snake_case					,		snake_case			):
                    solution.insert(snake_case					,		snake_case			)
                    break
            else:
                solution.append(snake_case			)

    strand_sort(snake_case					,		snake_case					,		snake_case			)
    return solution


if __name__ == "__main__":
     assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
     assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]





