


'''simple docstring'''



import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor




def 			_a			(		_lowercase      :			Dict						):





     '''simple docstring'''






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





     '''simple docstring'''






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

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

          else:
               __UpperCAmelCase			:				Dict													=    val

     return orig_state_dict




def 			_a			(		_lowercase      :			List[Any]    ,      _lowercase      :			str						):





     '''simple docstring'''






     __UpperCAmelCase			:				Optional[int]													=    ViTMAEConfig()
     if "large" in checkpoint_url:
          __UpperCAmelCase			:				List[str]													=    1024
          __UpperCAmelCase			:				int													=    4096
          __UpperCAmelCase			:				int													=    24
          __UpperCAmelCase			:				Union[str, Any]													=    16
     elif "huge" in checkpoint_url:
          __UpperCAmelCase			:				List[str]													=    14
          __UpperCAmelCase			:				str													=    1280
          __UpperCAmelCase			:				List[Any]													=    5120
          __UpperCAmelCase			:				Union[str, Any]													=    32
          __UpperCAmelCase			:				List[str]													=    16

     __UpperCAmelCase			:				Any													=    ViTMAEForPreTraining(_lowercase						)

     __UpperCAmelCase			:				Optional[int]													=    torch.hub.load_state_dict_from_url(_lowercase    ,      map_location='''cpu'''						)['''model''']

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

     __UpperCAmelCase			:				int													=    convert_state_dict(_lowercase    ,      _lowercase						)

     model.load_state_dict(_lowercase						)
     model.eval()

     __UpperCAmelCase			:				List[str]													=    '''https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'''

     __UpperCAmelCase			:				Any													=    Image.open(requests.get(_lowercase    ,      stream=_lowercase						).raw						)
     __UpperCAmelCase			:				Optional[Any]													=    ViTMAEImageProcessor(size=config.image_size						)
     __UpperCAmelCase			:				int													=    image_processor(images=_lowercase    ,      return_tensors='''pt'''						)

     # forward pass
     torch.manual_seed(2						)
     __UpperCAmelCase			:				Dict													=    model(**_lowercase						)
     __UpperCAmelCase			:				Dict													=    outputs.logits

     if "large" in checkpoint_url:
          __UpperCAmelCase			:				Dict													=    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			:				Union[str, Any]													=    torch.tensor(
              [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]						)
     else:
          __UpperCAmelCase			:				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]    ,      _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__":
      __UpperCAmelCase    :int				       =     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."
      )

      __UpperCAmelCase    :Optional[Any]				       =     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_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__UpperCAmelCase    :Optional[Any]				       =     {
    "configuration_layoutlmv2": ["LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv2Config"],
    "processing_layoutlmv2": ["LayoutLMv2Processor"],
    "tokenization_layoutlmv2": ["LayoutLMv2Tokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase    :Any				       =     ["LayoutLMv2TokenizerFast"]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase    :int				       =     ["LayoutLMv2FeatureExtractor"]
      __UpperCAmelCase    :Optional[int]				       =     ["LayoutLMv2ImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase    :List[Any]				       =     [
          "LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST",
          "LayoutLMv2ForQuestionAnswering",
          "LayoutLMv2ForSequenceClassification",
          "LayoutLMv2ForTokenClassification",
          "LayoutLMv2Layer",
          "LayoutLMv2Model",
          "LayoutLMv2PreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
      from .processing_layoutlmva import LayoutLMvaProcessor
      from .tokenization_layoutlmva import LayoutLMvaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_layoutlmva import (
                LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                LayoutLMvaForQuestionAnswering,
                LayoutLMvaForSequenceClassification,
                LayoutLMvaForTokenClassification,
                LayoutLMvaLayer,
                LayoutLMvaModel,
                LayoutLMvaPreTrainedModel,
            )
else:
      import sys

      __UpperCAmelCase    :Any				       =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)