



"""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()
lowercase__		:    List[str]				   =     logging.get_logger(__name__)

lowercase__		:    Dict				   =     [
    ("""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"""),
]

lowercase__		:    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_   (   lowerCAmelCase__				:    Tuple						)						->       Any:

  """simple docstring"""




  lowerCAmelCase_       :    Dict								=			torch.load(lowerCAmelCase__   ,       map_location='cpu'						)
  return sd

def     UpperCamelCase_   (   lowerCAmelCase__				:    Tuple   ,       lowerCAmelCase__				:    List[Any]   ,       lowerCAmelCase__				:    Any=rename_keys_prefix						)						->       Optional[int]:

  """simple docstring"""




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

@torch.no_grad()
def     UpperCamelCase_   (   lowerCAmelCase__				:    List[str]   ,       lowerCAmelCase__				:    Tuple						)						->       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_       :    Union[str, Any]								=			'pretraining'
    if "vcr" in checkpoint_path:
      lowerCAmelCase_       :    List[str]								=			{'visual_embedding_dim': 512}
    elif "vqa_advanced" in checkpoint_path:
      lowerCAmelCase_       :    Optional[Any]								=			{'visual_embedding_dim': 2048}
    elif "vqa" in checkpoint_path:
      lowerCAmelCase_       :    Optional[Any]								=			{'visual_embedding_dim': 2048}
    elif "nlvr" in checkpoint_path:
      lowerCAmelCase_       :    str								=			{'visual_embedding_dim': 1024}
    else:
      raise NotImplementedError(f"No implementation found for `{checkpoint_path}`."						)
  else:
    if "vcr" in checkpoint_path:
      lowerCAmelCase_       :    List[Any]								=			{'visual_embedding_dim': 512}
      lowerCAmelCase_       :    Dict								=			'multichoice'
    elif "vqa_advanced" in checkpoint_path:
      lowerCAmelCase_       :    Any								=			{'visual_embedding_dim': 2048}
      lowerCAmelCase_       :    List[Any]								=			'vqa_advanced'
    elif "vqa" in checkpoint_path:
      lowerCAmelCase_       :    Optional[int]								=			{'visual_embedding_dim': 2048, 'num_labels': 3129}
      lowerCAmelCase_       :    int								=			'vqa'
    elif "nlvr" in checkpoint_path:
      lowerCAmelCase_       :    Dict								=			{
          'visual_embedding_dim': 1024,
          'num_labels': 2,
      }
      lowerCAmelCase_       :    List[Any]								=			'nlvr'

  lowerCAmelCase_       :    Optional[int]								=			VisualBertConfig(**lowerCAmelCase__						)

  # Load State Dict
  lowerCAmelCase_       :    Optional[int]								=			load_state_dict(lowerCAmelCase__						)

  lowerCAmelCase_       :    Optional[Any]								=			get_new_dict(lowerCAmelCase__   ,       lowerCAmelCase__						)

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

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


if __name__ == "__main__":
      lowercase__		:    int				   =     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.""")
      lowercase__		:    Dict				   =     parser.parse_args()
      convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)










"""simple docstring"""







import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


lowercase__		:    int				   =     HUGGINGFACE_HUB_CACHE


lowercase__		:    Tuple				   =     """config.json"""
lowercase__		:    Union[str, Any]				   =     """diffusion_pytorch_model.bin"""
lowercase__		:    List[Any]				   =     """diffusion_flax_model.msgpack"""
lowercase__		:    List[str]				   =     """model.onnx"""
lowercase__		:    List[Any]				   =     """diffusion_pytorch_model.safetensors"""
lowercase__		:    Dict				   =     """weights.pb"""
lowercase__		:    List[Any]				   =     """https://huggingface.co"""
lowercase__		:    List[Any]				   =     default_cache_path
lowercase__		:    Tuple				   =     """diffusers_modules"""
lowercase__		:    Tuple				   =     os.getenv("""HF_MODULES_CACHE""", os.path.join(hf_cache_home, """modules"""))
lowercase__		:    List[str]				   =     ["""fp16""", """non-ema"""]
lowercase__		:    Optional[Any]				   =     """.self_attn"""





