




import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor

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

      return name

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Any			,			__UpperCAmelCase						:	Any							)					-> Dict:
      for key in orig_state_dict.copy().keys():
            SCREAMING_SNAKE_CASE_							=			orig_state_dict.pop(__UpperCAmelCase							)

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

            else:
                  SCREAMING_SNAKE_CASE_							=			val

      return orig_state_dict

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	List[Any]			,			__UpperCAmelCase						:	int							)					-> int:
      SCREAMING_SNAKE_CASE_							=			ViTMAEConfig()
      if "large" in checkpoint_url:
            SCREAMING_SNAKE_CASE_							=			10_24
            SCREAMING_SNAKE_CASE_							=			40_96
            SCREAMING_SNAKE_CASE_							=			24
            SCREAMING_SNAKE_CASE_							=			16
      elif "huge" in checkpoint_url:
            SCREAMING_SNAKE_CASE_							=			14
            SCREAMING_SNAKE_CASE_							=			12_80
            SCREAMING_SNAKE_CASE_							=			51_20
            SCREAMING_SNAKE_CASE_							=			32
            SCREAMING_SNAKE_CASE_							=			16

      SCREAMING_SNAKE_CASE_							=			ViTMAEForPreTraining(__UpperCAmelCase							)

      SCREAMING_SNAKE_CASE_							=			torch.hub.load_state_dict_from_url(__UpperCAmelCase			,			map_location='cpu'							)['model']

      SCREAMING_SNAKE_CASE_							=			ViTMAEImageProcessor(size=config.image_size							)

      SCREAMING_SNAKE_CASE_							=			convert_state_dict(__UpperCAmelCase			,			__UpperCAmelCase							)

      model.load_state_dict(__UpperCAmelCase							)
      model.eval()

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

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

      # forward pass
      torch.manual_seed(2							)
      SCREAMING_SNAKE_CASE_							=			model(**__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			outputs.logits

      if "large" in checkpoint_url:
            SCREAMING_SNAKE_CASE_							=			torch.tensor(
                [[-0.7_3_0_9, -0.7_1_2_8, -1.0_1_6_9], [-1.0_1_6_1, -0.9_0_5_8, -1.1_8_7_8], [-1.0_4_7_8, -0.9_4_1_1, -1.1_9_1_1]]							)
      elif "huge" in checkpoint_url:
            SCREAMING_SNAKE_CASE_							=			torch.tensor(
                [[-1.1_5_9_9, -0.9_1_9_9, -1.2_2_2_1], [-1.1_9_5_2, -0.9_2_6_9, -1.2_3_0_7], [-1.2_1_4_3, -0.9_3_3_7, -1.2_2_6_2]]							)
      else:
            SCREAMING_SNAKE_CASE_							=			torch.tensor(
                [[-0.9_1_9_2, -0.8_4_8_1, -1.1_2_5_9], [-1.1_3_4_9, -1.0_0_3_4, -1.2_5_9_9], [-1.1_7_5_7, -1.0_4_2_9, -1.2_7_2_6]]							)

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

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

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


if __name__ == "__main__":
   lowerCamelCase__ :   str					=							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.'
   )

   lowerCamelCase__ :   str					=							parser.parse_args()
   convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase__ :   Union[str, 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:
   lowerCamelCase__ :   Any					=							['LayoutLMv2TokenizerFast']

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__ :   Dict					=							['LayoutLMv2FeatureExtractor']
   lowerCamelCase__ :   Dict					=							['LayoutLMv2ImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__ :   Union[str, 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

   lowerCamelCase__ :   Any					=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)