

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

UpperCamelCase__  :      Tuple			=    [
    ("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__  :      int			=    [
    "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 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Dict       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						torch.load(_SCREAMING_SNAKE_CASE ,   map_location='cpu'       )
    return sd


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Any ,   _SCREAMING_SNAKE_CASE					:		Tuple ,   _SCREAMING_SNAKE_CASE					:		Optional[Any]=rename_keys_prefix       ):





    """simple docstring"""


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


@torch.no_grad()
def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		int       ):





    """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:
        SCREAMING_SNAKE_CASE_									=						'pretraining'
        if "vcr" in checkpoint_path:
            SCREAMING_SNAKE_CASE_									=						{'visual_embedding_dim': 512}
        elif "vqa_advanced" in checkpoint_path:
            SCREAMING_SNAKE_CASE_									=						{'visual_embedding_dim': 2_048}
        elif "vqa" in checkpoint_path:
            SCREAMING_SNAKE_CASE_									=						{'visual_embedding_dim': 2_048}
        elif "nlvr" in checkpoint_path:
            SCREAMING_SNAKE_CASE_									=						{'visual_embedding_dim': 1_024}
        else:
            raise NotImplementedError(f"""No implementation found for `{checkpoint_path}`."""       )
    else:
        if "vcr" in checkpoint_path:
            SCREAMING_SNAKE_CASE_									=						{'visual_embedding_dim': 512}
            SCREAMING_SNAKE_CASE_									=						'multichoice'
        elif "vqa_advanced" in checkpoint_path:
            SCREAMING_SNAKE_CASE_									=						{'visual_embedding_dim': 2_048}
            SCREAMING_SNAKE_CASE_									=						'vqa_advanced'
        elif "vqa" in checkpoint_path:
            SCREAMING_SNAKE_CASE_									=						{'visual_embedding_dim': 2_048, 'num_labels': 3_129}
            SCREAMING_SNAKE_CASE_									=						'vqa'
        elif "nlvr" in checkpoint_path:
            SCREAMING_SNAKE_CASE_									=						{
                'visual_embedding_dim': 1_024,
                'num_labels': 2,
            }
            SCREAMING_SNAKE_CASE_									=						'nlvr'

    SCREAMING_SNAKE_CASE_									=						VisualBertConfig(**_SCREAMING_SNAKE_CASE       )

    # Load State Dict
    SCREAMING_SNAKE_CASE_									=						load_state_dict(_SCREAMING_SNAKE_CASE       )

    SCREAMING_SNAKE_CASE_									=						get_new_dict(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )

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

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


if __name__ == "__main__":
       UpperCamelCase__  :      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)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase__  :      Any			=    {
    "configuration_mvp": ["MVP_PRETRAINED_CONFIG_ARCHIVE_MAP", "MvpConfig", "MvpOnnxConfig"],
    "tokenization_mvp": ["MvpTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__  :      Optional[int]			=    ["MvpTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__  :      str			=    [
           "MVP_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MvpForCausalLM",
           "MvpForConditionalGeneration",
           "MvpForQuestionAnswering",
           "MvpForSequenceClassification",
           "MvpModel",
           "MvpPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
       from .tokenization_mvp import MvpTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mvp_fast import MvpTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mvp import (
                  MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MvpForCausalLM,
                  MvpForConditionalGeneration,
                  MvpForQuestionAnswering,
                  MvpForSequenceClassification,
                  MvpModel,
                  MvpPreTrainedModel,
              )

else:
       import sys

       UpperCamelCase__  :      Dict			=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


