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

SCREAMING_SNAKE_CASE :	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"""),
]

SCREAMING_SNAKE_CASE :	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		(			_A					):



 """simple docstring"""


 __a       =  torch.load(_A							,				map_location="cpu"					)
 return sd



def   __A		(			_A							,				_A							,				_A=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		(			_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:
  __a       =  "pretraining"
  if "vcr" in checkpoint_path:
   __a       =  {"visual_embedding_dim": 512}
  elif "vqa_advanced" in checkpoint_path:
   __a       =  {"visual_embedding_dim": 2048}
  elif "vqa" in checkpoint_path:
   __a       =  {"visual_embedding_dim": 2048}
  elif "nlvr" in checkpoint_path:
   __a       =  {"visual_embedding_dim": 1024}
  else:
   raise NotImplementedError(f"""No implementation found for `{checkpoint_path}`."""					)
 else:
  if "vcr" in checkpoint_path:
   __a       =  {"visual_embedding_dim": 512}
   __a       =  "multichoice"
  elif "vqa_advanced" in checkpoint_path:
   __a       =  {"visual_embedding_dim": 2048}
   __a       =  "vqa_advanced"
  elif "vqa" in checkpoint_path:
   __a       =  {"visual_embedding_dim": 2048, "num_labels": 3129}
   __a       =  "vqa"
  elif "nlvr" in checkpoint_path:
   __a       =  {
       "visual_embedding_dim": 1024,
       "num_labels": 2,
   }
   __a       =  "nlvr"

 __a       =  VisualBertConfig(**_A					)

 # Load State Dict
 __a       =  load_state_dict(_A					)

 __a       =  get_new_dict(_A							,				_A					)

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

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


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



def   __A		(			_A					):



 """simple docstring"""


 __a       =  []
 for data in source_data:
  for i, el in enumerate(_A					):
   if len(_A					) < i + 1:
    data_lists.append([]					)
   data_lists[i].append(float(_A					)					)
 return data_lists



def   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  []
 for dlist, weight in zip(_A							,				_A					):
  __a       =  min(_A					)
  __a       =  max(_A					)

  __a       =  []
  # for weight 0 score is 1 - actual score
  if weight == 0:
   for item in dlist:
    try:
     score.append(1 - ((item - mind) / (maxd - mind))					)
    except ZeroDivisionError:
     score.append(1					)

  elif weight == 1:
   for item in dlist:
    try:
     score.append((item - mind) / (maxd - mind)					)
    except ZeroDivisionError:
     score.append(0					)

        # weight not 0 or 1
  else:
   __a       =  f"""Invalid weight of {weight:f} provided"""
   raise ValueError(_A					)

  score_lists.append(_A					)

 return score_lists



def   __A		(			_A					):



 """simple docstring"""


 __a       =  [0 for i in range(len(score_lists[0]					)					)]

 for slist in score_lists:
  for j, ele in enumerate(_A					):
   __a       =  final_scores[j] + ele

 return final_scores






def   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  get_data(_A					)
 __a       =  calculate_each_score(_A							,				_A					)
 __a       =  generate_final_scores(_A					)

 # append scores to source data
 for i, ele in enumerate(_A					):
  source_data[i].append(_A					)

 return source_data


