



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()
__A      		=     logging.get_logger(__name__)

__A      		=     [
    ('''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'''),
]

__A      		=     [
    '''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 				snake_case_(_UpperCamelCase					)							->     Union[str, Any]:



  """simple docstring"""






  _snake_case						=							torch.load(_UpperCamelCase			,	map_location='''cpu'''					)
  return sd



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase=rename_keys_prefix					)							->     List[Any]:



  """simple docstring"""






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



@torch.no_grad()
def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     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:
    _snake_case						=							'''pretraining'''
    if "vcr" in checkpoint_path:
      _snake_case						=							{'''visual_embedding_dim''': 512}
    elif "vqa_advanced" in checkpoint_path:
      _snake_case						=							{'''visual_embedding_dim''': 2_048}
    elif "vqa" in checkpoint_path:
      _snake_case						=							{'''visual_embedding_dim''': 2_048}
    elif "nlvr" in checkpoint_path:
      _snake_case						=							{'''visual_embedding_dim''': 1_024}
    else:
      raise NotImplementedError(F"""No implementation found for `{checkpoint_path}`."""					)
  else:
    if "vcr" in checkpoint_path:
      _snake_case						=							{'''visual_embedding_dim''': 512}
      _snake_case						=							'''multichoice'''
    elif "vqa_advanced" in checkpoint_path:
      _snake_case						=							{'''visual_embedding_dim''': 2_048}
      _snake_case						=							'''vqa_advanced'''
    elif "vqa" in checkpoint_path:
      _snake_case						=							{'''visual_embedding_dim''': 2_048, '''num_labels''': 3_129}
      _snake_case						=							'''vqa'''
    elif "nlvr" in checkpoint_path:
      _snake_case						=							{
          '''visual_embedding_dim''': 1_024,
          '''num_labels''': 2,
      }
      _snake_case						=							'''nlvr'''

  _snake_case						=							VisualBertConfig(**_UpperCamelCase					)

  # Load State Dict
  _snake_case						=							load_state_dict(_UpperCamelCase					)

  _snake_case						=							get_new_dict(_UpperCamelCase			,	_UpperCamelCase					)

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

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


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




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A      		=     {'''configuration_mra''': ['''MRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MraConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A      		=     [
     '''MRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MraForMaskedLM''',
     '''MraForMultipleChoice''',
     '''MraForQuestionAnswering''',
     '''MraForSequenceClassification''',
     '''MraForTokenClassification''',
     '''MraLayer''',
     '''MraModel''',
     '''MraPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mra import (
      MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
      MraForMaskedLM,
      MraForMultipleChoice,
      MraForQuestionAnswering,
      MraForSequenceClassification,
      MraForTokenClassification,
      MraLayer,
      MraModel,
      MraPreTrainedModel,
  )
else:
 import sys

 __A      		=     _LazyModule(__name__, globals()['''__file__'''], _import_structure)

