
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					   =logging.get_logger(__name__)

_lowerCamelCase					   =[
    ("""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					   =[
    """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					(						lowerCamelCase							):
 lowerCamelCase		:	Any							=      torch.load(lowerCamelCase,	map_location="""cpu"""							)
 return sd





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





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

 lowerCamelCase		:	Dict							=      VisualBertConfig(**lowerCamelCase							)

 # Load State Dict
 lowerCamelCase		:	List[Any]							=      load_state_dict(lowerCamelCase							)

 lowerCamelCase		:	List[str]							=      get_new_dict(lowerCamelCase,	lowerCamelCase							)

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

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


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






import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """nvidia/segformer-b0-finetuned-ade-512-512""": (
        """https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"""
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Dict           =			"""segformer"""
      def __init__(      self    ,  __magic_name__=3    ,  __magic_name__=4    ,  __magic_name__=[2, 2, 2, 2]    ,  __magic_name__=[8, 4, 2, 1]    ,  __magic_name__=[3_2, 6_4, 1_6_0, 2_5_6]    ,  __magic_name__=[7, 3, 3, 3]    ,  __magic_name__=[4, 2, 2, 2]    ,  __magic_name__=[1, 2, 5, 8]    ,  __magic_name__=[4, 4, 4, 4]    ,  __magic_name__="gelu"    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=0.1    ,  __magic_name__=0.02    ,  __magic_name__=0.1    ,  __magic_name__=1e-6    ,  __magic_name__=2_5_6    ,  __magic_name__=2_5_5    ,  **__magic_name__    ,  ):
       super().__init__(**__magic_name__      )

       if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
        warnings.warn(
            """Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
            """ removed, as the behaviour will default to that of reshape_last_stage = True."""    ,  __magic_name__    ,  )

       lowerCamelCase		:	Optional[Any]							=      num_channels
       lowerCamelCase		:	str							=      num_encoder_blocks
       lowerCamelCase		:	Any							=      depths
       lowerCamelCase		:	List[Any]							=      sr_ratios
       lowerCamelCase		:	int							=      hidden_sizes
       lowerCamelCase		:	Union[str, Any]							=      patch_sizes
       lowerCamelCase		:	Optional[Any]							=      strides
       lowerCamelCase		:	Dict							=      mlp_ratios
       lowerCamelCase		:	str							=      num_attention_heads
       lowerCamelCase		:	Any							=      hidden_act
       lowerCamelCase		:	Tuple							=      hidden_dropout_prob
       lowerCamelCase		:	Union[str, Any]							=      attention_probs_dropout_prob
       lowerCamelCase		:	Dict							=      classifier_dropout_prob
       lowerCamelCase		:	Tuple							=      initializer_range
       lowerCamelCase		:	Dict							=      drop_path_rate
       lowerCamelCase		:	List[str]							=      layer_norm_eps
       lowerCamelCase		:	Any							=      decoder_hidden_size
       lowerCamelCase		:	str							=      kwargs.get("""reshape_last_stage"""    ,  __magic_name__      )
       lowerCamelCase		:	Dict							=      semantic_loss_ignore_index





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    str           =			version.parse("""1.11""")
      @property
      def 						UpperCamelCase__							(      self      ):
       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
           ]      )
      @property
      def 						UpperCamelCase__							(      self      ):
       return 1e-4






      @property
      def 						UpperCamelCase__							(      self      ):
       return 1_2




