'''simple docstring'''
import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



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

    return name







def  _UpperCamelCase (     _a	:	Tuple				,     _a	:	Tuple			):




    """simple docstring"""



    for key in orig_state_dict.copy().keys():
        __UpperCamelCase	:	Optional[Any]		    =  orig_state_dict.pop(_a			)

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

        else:
            __UpperCamelCase	:	Tuple		    =  val

    return orig_state_dict







def  _UpperCamelCase (     _a	:	Any				,     _a	:	List[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  ViTMAEConfig()
    if "large" in checkpoint_url:
        __UpperCamelCase	:	Dict		    =  1_0_2_4
        __UpperCamelCase	:	Tuple		    =  4_0_9_6
        __UpperCamelCase	:	Tuple		    =  2_4
        __UpperCamelCase	:	Any		    =  1_6
    elif "huge" in checkpoint_url:
        __UpperCamelCase	:	List[Any]		    =  1_4
        __UpperCamelCase	:	Dict		    =  1_2_8_0
        __UpperCamelCase	:	str		    =  5_1_2_0
        __UpperCamelCase	:	List[str]		    =  3_2
        __UpperCamelCase	:	str		    =  1_6

    __UpperCamelCase	:	str		    =  ViTMAEForPreTraining(_a			)

    __UpperCamelCase	:	Optional[Any]		    =  torch.hub.load_state_dict_from_url(_a				,     map_location='cpu'			)['model']

    __UpperCamelCase	:	Union[str, Any]		    =  ViTMAEImageProcessor(size=config.image_size			)

    __UpperCamelCase	:	List[Any]		    =  convert_state_dict(_a				,     _a			)

    model.load_state_dict(_a			)
    model.eval()

    __UpperCamelCase	:	Optional[int]		    =  'https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'

    __UpperCamelCase	:	Union[str, Any]		    =  Image.open(requests.get(_a				,     stream=_a			).raw			)
    __UpperCamelCase	:	Any		    =  ViTMAEImageProcessor(size=config.image_size			)
    __UpperCamelCase	:	List[str]		    =  image_processor(images=_a				,     return_tensors='pt'			)

    # forward pass
    torch.manual_seed(2			)
    __UpperCamelCase	:	Optional[int]		    =  model(**_a			)
    __UpperCamelCase	:	Union[str, Any]		    =  outputs.logits

    if "large" in checkpoint_url:
        __UpperCamelCase	:	Union[str, 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:
        __UpperCamelCase	:	Optional[int]		    =  torch.tensor(
            [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]			)
    else:
        __UpperCamelCase	:	Optional[Any]		    =  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]				,     _a				,     atol=1E-4			)

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

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


if __name__ == "__main__":
  a=  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.'''
  )

  a=  parser.parse_args()
  convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a=  {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  [
      '''VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''ViTMSNModel''',
      '''ViTMSNForImageClassification''',
      '''ViTMSNPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit_msn import (
        VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTMSNForImageClassification,
        ViTMSNModel,
        ViTMSNPreTrainedModel,
    )

else:
  import sys

  a=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



