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()
lowerCAmelCase			:							List[Any]     =logging.get_logger(__name__)

lowerCAmelCase			:							Optional[Any]     =[
    ("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"),
]

lowerCAmelCase			:							List[Any]     =[
    "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__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Union[str, Any] 						=						torch.load(__A   , map_location="""cpu"""	)
    return sd







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







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







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







    '''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     :		List[str] 						=						"""pretraining"""
        if "vcr" in checkpoint_path:
            _lowerCamelCase     :		str 						=						{"""visual_embedding_dim""": 512}
        elif "vqa_advanced" in checkpoint_path:
            _lowerCamelCase     :		Dict 						=						{"""visual_embedding_dim""": 2_048}
        elif "vqa" in checkpoint_path:
            _lowerCamelCase     :		Optional[int] 						=						{"""visual_embedding_dim""": 2_048}
        elif "nlvr" in checkpoint_path:
            _lowerCamelCase     :		Tuple 						=						{"""visual_embedding_dim""": 1_024}
        else:
            raise NotImplementedError(F"""No implementation found for `{checkpoint_path}`."""	)
    else:
        if "vcr" in checkpoint_path:
            _lowerCamelCase     :		int 						=						{"""visual_embedding_dim""": 512}
            _lowerCamelCase     :		Optional[int] 						=						"""multichoice"""
        elif "vqa_advanced" in checkpoint_path:
            _lowerCamelCase     :		List[Any] 						=						{"""visual_embedding_dim""": 2_048}
            _lowerCamelCase     :		Optional[Any] 						=						"""vqa_advanced"""
        elif "vqa" in checkpoint_path:
            _lowerCamelCase     :		Optional[Any] 						=						{"""visual_embedding_dim""": 2_048, """num_labels""": 3_129}
            _lowerCamelCase     :		Optional[int] 						=						"""vqa"""
        elif "nlvr" in checkpoint_path:
            _lowerCamelCase     :		List[str] 						=						{
                """visual_embedding_dim""": 1_024,
                """num_labels""": 2,
            }
            _lowerCamelCase     :		Optional[int] 						=						"""nlvr"""

    _lowerCamelCase     :		Any 						=						VisualBertConfig(**__A	)

    # Load State Dict
    _lowerCamelCase     :		Optional[int] 						=						load_state_dict(__A	)

    _lowerCamelCase     :		int 						=						get_new_dict(__A   , __A	)

    if model_type == "pretraining":
        _lowerCamelCase     :		Optional[int] 						=						VisualBertForPreTraining(__A	)
    elif model_type == "vqa":
        _lowerCamelCase     :		Dict 						=						VisualBertForQuestionAnswering(__A	)
    elif model_type == "nlvr":
        _lowerCamelCase     :		Tuple 						=						VisualBertForVisualReasoning(__A	)
    elif model_type == "multichoice":
        _lowerCamelCase     :		Optional[Any] 						=						VisualBertForMultipleChoice(__A	)

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


if __name__ == "__main__":
    lowerCAmelCase			:							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.")
    lowerCAmelCase			:							Tuple     =parser.parse_args()
    convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)





lowerCAmelCase			:							Tuple     =0  # The first color of the flag.
lowerCAmelCase			:							Union[str, Any]     =1  # The second color of the flag.
lowerCAmelCase			:							Any     =2  # The third color of the flag.
lowerCAmelCase			:							List[str]     =(red, white, blue)







def 						A__  (					__A	):







    '''simple docstring'''
    if not sequence:
        return []
    if len(__A	) == 1:
        return list(__A	)
    _lowerCamelCase     :		int 						=						0
    _lowerCamelCase     :		Dict 						=						len(__A	) - 1
    _lowerCamelCase     :		str 						=						0
    while mid <= high:
        if sequence[mid] == colors[0]:
            _lowerCamelCase						,							_lowerCamelCase     :		Tuple 						=						sequence[mid], sequence[low]
            low += 1
            mid += 1
        elif sequence[mid] == colors[1]:
            mid += 1
        elif sequence[mid] == colors[2]:
            _lowerCamelCase						,							_lowerCamelCase     :		str 						=						sequence[high], sequence[mid]
            high -= 1
        else:
            _lowerCamelCase     :		int 						=						F"""The elements inside the sequence must contains only {colors} values"""
            raise ValueError(__A	)
    return sequence


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    lowerCAmelCase			:							List[str]     =input("Enter numbers separated by commas:\n").strip()
    lowerCAmelCase			:							Dict     =[int(item.strip()) for item in user_input.split(",")]
    print(F"""{dutch_national_flag_sort(unsorted)}""")




