




"""simple docstring"""







import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor





def      __snake_case (				_lowercase    ):


    """simple docstring"""







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

    return name





def      __snake_case (				_lowercase			,_lowercase    ):


    """simple docstring"""







    for key in orig_state_dict.copy().keys():
        UpperCamelCase              =	orig_state_dict.pop(_lowercase    )

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

        else:
            UpperCamelCase              =	val

    return orig_state_dict





def      __snake_case (				_lowercase			,_lowercase    ):


    """simple docstring"""







    UpperCamelCase              =	ViTMAEConfig()
    if "large" in checkpoint_url:
        UpperCamelCase              =	1024
        UpperCamelCase              =	4096
        UpperCamelCase              =	24
        UpperCamelCase              =	16
    elif "huge" in checkpoint_url:
        UpperCamelCase              =	14
        UpperCamelCase              =	1280
        UpperCamelCase              =	5120
        UpperCamelCase              =	32
        UpperCamelCase              =	16

    UpperCamelCase              =	ViTMAEForPreTraining(_lowercase    )

    UpperCamelCase              =	torch.hub.load_state_dict_from_url(_lowercase			,map_location='''cpu'''    )['''model''']

    UpperCamelCase              =	ViTMAEImageProcessor(size=config.image_size    )

    UpperCamelCase              =	convert_state_dict(_lowercase			,_lowercase    )

    model.load_state_dict(_lowercase    )
    model.eval()

    UpperCamelCase              =	'''https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'''

    UpperCamelCase              =	Image.open(requests.get(_lowercase			,stream=_lowercase    ).raw    )
    UpperCamelCase              =	ViTMAEImageProcessor(size=config.image_size    )
    UpperCamelCase              =	image_processor(images=_lowercase			,return_tensors='''pt'''    )

    # forward pass
    torch.manual_seed(2    )
    UpperCamelCase              =	model(**_lowercase    )
    UpperCamelCase              =	outputs.logits

    if "large" in checkpoint_url:
        UpperCamelCase              =	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              =	torch.tensor(
            [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]    )
    else:
        UpperCamelCase              =	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]			,_lowercase			,atol=1e-4    )

    print(f'Saving model to {pytorch_dump_folder_path}'    )
    model.save_pretrained(_lowercase    )

    print(f'Saving image processor to {pytorch_dump_folder_path}'    )
    image_processor.save_pretrained(_lowercase    )


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

  SCREAMING_SNAKE_CASE_	     =       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	:  Union[str, Any]            =     {
    "configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A	:  Optional[int]            =     [
	    "TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TimesformerModel",
	    "TimesformerForVideoClassification",
	    "TimesformerPreTrainedModel",
	]

if TYPE_CHECKING:
	from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_timesformer import (
		    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimesformerForVideoClassification,
		    TimesformerModel,
		    TimesformerPreTrainedModel,
		)

else:
	import sys

	A	:  List[str]            =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




