
from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

lowercase  :					Any						    =							{
    """uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json""",
    """uclanlp/visualbert-vqa-pre""": """https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json""",
    """uclanlp/visualbert-vqa-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"""
    ),
    """uclanlp/visualbert-vcr""": """https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json""",
    """uclanlp/visualbert-vcr-pre""": """https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json""",
    """uclanlp/visualbert-vcr-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"""
    ),
    """uclanlp/visualbert-nlvr2""": """https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json""",
    """uclanlp/visualbert-nlvr2-pre""": """https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json""",
    """uclanlp/visualbert-nlvr2-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"""
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}

class __snake_case		(      lowerCAmelCase     ):
    _a		:						Union[str, Any]=				"visual_bert"
    def __init__(  self		,snake_case=30522		,snake_case=768		,snake_case=512		,snake_case=12		,snake_case=12		,snake_case=3072		,snake_case="gelu"		,snake_case=0.1		,snake_case=0.1		,snake_case=512		,snake_case=2		,snake_case=0.02		,snake_case=1e-12		,snake_case=False		,snake_case=True		,snake_case=1		,snake_case=0		,snake_case=2		,**snake_case		,):



           '''simple docstring'''







           super().__init__(pad_token_id=snake_case		,bos_token_id=snake_case		,eos_token_id=snake_case		,**snake_case      )

           lowercase							:     Tuple       	=      vocab_size
           lowercase							:     int       	=      max_position_embeddings
           lowercase							:     Optional[Any]       	=      hidden_size
           lowercase							:     int       	=      visual_embedding_dim
           lowercase							:     Tuple       	=      num_hidden_layers
           lowercase							:     str       	=      num_attention_heads
           lowercase							:     Optional[Any]       	=      intermediate_size
           lowercase							:     str       	=      hidden_act
           lowercase							:     Tuple       	=      hidden_dropout_prob
           lowercase							:     List[Any]       	=      attention_probs_dropout_prob
           lowercase							:     Union[str, Any]       	=      initializer_range
           lowercase							:     int       	=      type_vocab_size
           lowercase							:     Union[str, Any]       	=      layer_norm_eps
           lowercase							:     Union[str, Any]       	=      bypass_transformer
           lowercase							:     int       	=      special_visual_initialize


import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Optional[Any]:
       if "cls_token" in name:
              lowercase							:     List[Any]       	=      name.replace("""cls_token"""					,	"""vit.embeddings.cls_token"""				)
       if "mask_token" in name:
              lowercase							:     Any       	=      name.replace("""mask_token"""					,	"""decoder.mask_token"""				)
       if "decoder_pos_embed" in name:
              lowercase							:     str       	=      name.replace("""decoder_pos_embed"""					,	"""decoder.decoder_pos_embed"""				)
       if "pos_embed" in name and "decoder" not in name:
              lowercase							:     List[str]       	=      name.replace("""pos_embed"""					,	"""vit.embeddings.position_embeddings"""				)
       if "patch_embed.proj" in name:
              lowercase							:     Tuple       	=      name.replace("""patch_embed.proj"""					,	"""vit.embeddings.patch_embeddings.projection"""				)
       if "patch_embed.norm" in name:
              lowercase							:     int       	=      name.replace("""patch_embed.norm"""					,	"""vit.embeddings.norm"""				)
       if "decoder_blocks" in name:
              lowercase							:     Tuple       	=      name.replace("""decoder_blocks"""					,	"""decoder.decoder_layers"""				)
       if "blocks" in name:
              lowercase							:     List[Any]       	=      name.replace("""blocks"""					,	"""vit.encoder.layer"""				)
       if "attn.proj" in name:
              lowercase							:     List[str]       	=      name.replace("""attn.proj"""					,	"""attention.output.dense"""				)
       if "attn" in name:
              lowercase							:     Union[str, Any]       	=      name.replace("""attn"""					,	"""attention.self"""				)
       if "norm1" in name:
              lowercase							:     Optional[Any]       	=      name.replace("""norm1"""					,	"""layernorm_before"""				)
       if "norm2" in name:
              lowercase							:     Union[str, Any]       	=      name.replace("""norm2"""					,	"""layernorm_after"""				)
       if "mlp.fc1" in name:
              lowercase							:     Dict       	=      name.replace("""mlp.fc1"""					,	"""intermediate.dense"""				)
       if "mlp.fc2" in name:
              lowercase							:     Dict       	=      name.replace("""mlp.fc2"""					,	"""output.dense"""				)
       if "decoder_embed" in name:
              lowercase							:     List[str]       	=      name.replace("""decoder_embed"""					,	"""decoder.decoder_embed"""				)
       if "decoder_norm" in name:
              lowercase							:     Dict       	=      name.replace("""decoder_norm"""					,	"""decoder.decoder_norm"""				)
       if "decoder_pred" in name:
              lowercase							:     List[str]       	=      name.replace("""decoder_pred"""					,	"""decoder.decoder_pred"""				)
       if "norm.weight" in name and "decoder" not in name:
              lowercase							:     Tuple       	=      name.replace("""norm.weight"""					,	"""vit.layernorm.weight"""				)
       if "norm.bias" in name and "decoder" not in name:
              lowercase							:     int       	=      name.replace("""norm.bias"""					,	"""vit.layernorm.bias"""				)

       return name







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       for key in orig_state_dict.copy().keys():
              lowercase							:     List[Any]       	=      orig_state_dict.pop(SCREAMING_SNAKE_CASE__				)

              if "qkv" in key:
                     lowercase							:     int       	=      key.split("""."""				)
                     lowercase							:     List[str]       	=      int(key_split[1]				)
                     if "decoder_blocks" in key:
                            lowercase							:     Tuple       	=      config.decoder_hidden_size
                            lowercase							:     int       	=      """decoder.decoder_layers."""
                            if "weight" in key:
                                   lowercase							:     List[Any]       	=      val[:dim, :]
                                   lowercase							:     Tuple       	=      val[dim : dim * 2, :]
                                   lowercase							:     List[Any]       	=      val[-dim:, :]
                            elif "bias" in key:
                                   lowercase							:     str       	=      val[:dim]
                                   lowercase							:     Dict       	=      val[dim : dim * 2]
                                   lowercase							:     Union[str, Any]       	=      val[-dim:]
                     else:
                            lowercase							:     Tuple       	=      config.hidden_size
                            lowercase							:     Union[str, Any]       	=      """vit.encoder.layer."""
                            if "weight" in key:
                                   lowercase							:     Tuple       	=      val[:dim, :]
                                   lowercase							:     List[str]       	=      val[dim : dim * 2, :]
                                   lowercase							:     Dict       	=      val[-dim:, :]
                            elif "bias" in key:
                                   lowercase							:     Any       	=      val[:dim]
                                   lowercase							:     str       	=      val[dim : dim * 2]
                                   lowercase							:     Union[str, Any]       	=      val[-dim:]

              else:
                     lowercase							:     Union[str, Any]       	=      val

       return orig_state_dict







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    int:
       lowercase							:     int       	=      ViTMAEConfig()
       if "large" in checkpoint_url:
              lowercase							:     Dict       	=      1_024
              lowercase							:     str       	=      4_096
              lowercase							:     Optional[Any]       	=      24
              lowercase							:     Optional[Any]       	=      16
       elif "huge" in checkpoint_url:
              lowercase							:     int       	=      14
              lowercase							:     List[Any]       	=      1_280
              lowercase							:     int       	=      5_120
              lowercase							:     List[Any]       	=      32
              lowercase							:     Any       	=      16

       lowercase							:     List[str]       	=      ViTMAEForPreTraining(SCREAMING_SNAKE_CASE__				)

       lowercase							:     Optional[Any]       	=      torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__					,	map_location="""cpu"""				)["""model"""]

       lowercase							:     Tuple       	=      ViTMAEImageProcessor(size=config.image_size				)

       lowercase							:     Optional[int]       	=      convert_state_dict(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       model.load_state_dict(SCREAMING_SNAKE_CASE__				)
       model.eval()

       lowercase							:     Union[str, Any]       	=      """https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""

       lowercase							:     Union[str, Any]       	=      Image.open(requests.get(SCREAMING_SNAKE_CASE__					,	stream=SCREAMING_SNAKE_CASE__				).raw				)
       lowercase							:     Optional[Any]       	=      ViTMAEImageProcessor(size=config.image_size				)
       lowercase							:     List[Any]       	=      image_processor(images=SCREAMING_SNAKE_CASE__					,	return_tensors="""pt"""				)

       # forward pass
       torch.manual_seed(2				)
       lowercase							:     int       	=      model(**SCREAMING_SNAKE_CASE__				)
       lowercase							:     str       	=      outputs.logits

       if "large" in checkpoint_url:
              lowercase							:     List[Any]       	=      torch.tensor(
                  [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]]				)
       elif "huge" in checkpoint_url:
              lowercase							:     Tuple       	=      torch.tensor(
                  [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]				)
       else:
              lowercase							:     List[str]       	=      torch.tensor(
                  [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]]				)

       # verify logits
       assert torch.allclose(logits[0, :3, :3]					,	SCREAMING_SNAKE_CASE__					,	atol=1e-4				)

       print(f"Saving model to {pytorch_dump_folder_path}"				)
       model.save_pretrained(SCREAMING_SNAKE_CASE__				)

       print(f"Saving image processor to {pytorch_dump_folder_path}"				)
       image_processor.save_pretrained(SCREAMING_SNAKE_CASE__				)


if __name__ == "__main__":
  lowercase  :					Union[str, Any]						    =							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--checkpoint_url""",
      default="""https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth""",
      type=str,
      help="""URL of the checkpoint you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )

  lowercase  :					List[Any]						    =							parser.parse_args()
  convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
