




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()
_snake_case										=       logging.get_logger(__name__)

_snake_case										=       [
    ("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"),
]

_snake_case										=       [
    "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 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     int			=						torch.load(snake_case_,map_location="""cpu"""     )
      return sd







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







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

      _A							:     Tuple			=						VisualBertConfig(**snake_case_     )

      # Load State Dict
      _A							:     Any			=						load_state_dict(snake_case_     )

      _A							:     List[Any]			=						get_new_dict(snake_case_,snake_case_     )

      if model_type == "pretraining":
            _A							:     Tuple			=						VisualBertForPreTraining(snake_case_     )
      elif model_type == "vqa":
            _A							:     Tuple			=						VisualBertForQuestionAnswering(snake_case_     )
      elif model_type == "nlvr":
            _A							:     Union[str, Any]			=						VisualBertForVisualReasoning(snake_case_     )
      elif model_type == "multichoice":
            _A							:     List[Any]			=						VisualBertForMultipleChoice(snake_case_     )

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


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








'''simple docstring'''

import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowercase_					= logging.getLogger(__name__)

if __name__ == "__main__":
      lowercase_					= argparse.ArgumentParser(
          description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
      )
      parser.add_argument(
          """--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
      )
      parser.add_argument(
          """--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
      )
      parser.add_argument("""--vocab_size""", default=30_522, type=int)
      lowercase_					= parser.parse_args()

      logger.info(f"""Loading data from {args.data_file}""")
      with open(args.data_file, """rb""") as fp:
            lowercase_					= pickle.load(fp)

      logger.info("""Counting occurrences for MLM.""")
      lowercase_					= Counter()
      for tk_ids in data:
            counter.update(tk_ids)
      lowercase_					= [0] * args.vocab_size
      for k, v in counter.items():
            lowercase_					= v

      logger.info(f"""Dump to {args.token_counts_dump}""")
      with open(args.token_counts_dump, """wb""") as handle:
            pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)



