


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_       :       int      						=  logging.get_logger(__name__)

lowerCamelCase_       :       Optional[int]      						=  [
    ("""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[str]      						=  [
    """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__":
       lowerCamelCase_       :       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_       :       Optional[int]      						=  parser.parse_args()
       convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)



"""simple docstring"""







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_rembert import RemBertTokenizer
else:
       lowerCamelCase_       :       Optional[int]      						=  None

lowerCamelCase_       :       Union[str, Any]      						=  logging.get_logger(__name__)
lowerCamelCase_       :       str      						=  {"""vocab_file""": """sentencepiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase_       :       int      						=  {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
    """tokenizer_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/tokenizer.json""",
    },
}

lowerCamelCase_       :       Optional[Any]      						=  {
    """google/rembert""": 2_5_6,
}

lowerCamelCase_       :       Optional[Any]      						=  """▁"""






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  VOCAB_FILES_NAMES
 __lowerCAmelCase			 =  PRETRAINED_VOCAB_FILES_MAP
 __lowerCAmelCase			 =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCAmelCase			 =  RemBertTokenizer


 def __init__(		self			,   __A=None			,   __A=None			,   __A=True			,   __A=True			,   __A=False			,   __A="[CLS]"			,   __A="[SEP]"			,   __A="<unk>"			,   __A="[SEP]"			,   __A="<pad>"			,   __A="[CLS]"			,   __A="[MASK]"			,   **__A			,   ) ->					Dict:
     # Mask token behave like a normal word, i.e. include the space before it
     a					=AddedToken(__A			,   lstrip=__A			,   rstrip=__A						) if isinstance(__A			,   __A						) else mask_token

     super().__init__(
         __A			,   tokenizer_file=__A			,   do_lower_case=__A			,   remove_space=__A			,   keep_accents=__A			,   bos_token=__A			,   eos_token=__A			,   unk_token=__A			,   sep_token=__A			,   pad_token=__A			,   cls_token=__A			,   mask_token=__A			,   **__A			,   )

     a					=do_lower_case
     a					=remove_space
     a					=keep_accents
     a					=vocab_file
     a					=False if not self.vocab_file else True


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = None						) ->					List[int]:
     a					=[self.sep_token_id]
     a					=[self.cls_token_id]
     if token_ids_a is None:
         return cls + token_ids_a + sep
     return cls + token_ids_a + sep + token_ids_a + sep


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = None			,   __A = False						) ->					List[int]:

     if already_has_special_tokens:
         if token_ids_a is not None:
             raise ValueError(
                 '''You should not supply a second sequence if the provided sequence of '''
                 '''ids is already formatted with special tokens for the model.'''						)
         return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

     if token_ids_a is not None:
         return [1] + ([0] * len(__A						)) + [1] + ([0] * len(__A						)) + [1]
     return [1] + ([0] * len(__A						)) + [1]


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = None						) ->					List[int]:
     a					=[self.sep_token_id]
     a					=[self.cls_token_id]

     if token_ids_a is None:
         return len(cls + token_ids_a + sep						) * [0]
     return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = None						) ->					Tuple[str]:
     if not os.path.isdir(__A						):
         logger.error('''Vocabulary path ({}) should be a directory'''.format(__A						)						)
         return
     a					=os.path.join(
         __A			,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

     if os.path.abspath(self.vocab_file						) != os.path.abspath(__A						):
         copyfile(self.vocab_file			,   __A						)

     return (out_vocab_file,)