


"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case		=       logging.get_logger(__name__)

__snake_case		=       {
    '''microsoft/beit-base-patch16-224-pt22k''': (
        '''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Tuple    =		'beit'


     def __init__(      self    ,				__UpperCAmelCase=8192    ,				__UpperCAmelCase=768    ,				__UpperCAmelCase=12    ,				__UpperCAmelCase=12    ,				__UpperCAmelCase=3072    ,				__UpperCAmelCase="gelu"    ,				__UpperCAmelCase=0.0    ,				__UpperCAmelCase=0.0    ,				__UpperCAmelCase=0.02    ,				__UpperCAmelCase=1e-1_2    ,				__UpperCAmelCase=224    ,				__UpperCAmelCase=16    ,				__UpperCAmelCase=3    ,				__UpperCAmelCase=False    ,				__UpperCAmelCase=False    ,				__UpperCAmelCase=False    ,				__UpperCAmelCase=False    ,				__UpperCAmelCase=0.1    ,				__UpperCAmelCase=0.1    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=[3, 5, 7, 11]    ,				__UpperCAmelCase=[1, 2, 3, 6]    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=0.4    ,				__UpperCAmelCase=256    ,				__UpperCAmelCase=1    ,				__UpperCAmelCase=False    ,				__UpperCAmelCase=255    ,				**__UpperCAmelCase    ,				)       ->							Any:
          super().__init__(**UpperCamelCase_							)

          _a    	=				vocab_size
          _a    	=				hidden_size
          _a    	=				num_hidden_layers
          _a    	=				num_attention_heads
          _a    	=				intermediate_size
          _a    	=				hidden_act
          _a    	=				hidden_dropout_prob
          _a    	=				attention_probs_dropout_prob
          _a    	=				initializer_range
          _a    	=				layer_norm_eps

          _a    	=				image_size
          _a    	=				patch_size
          _a    	=				num_channels
          _a    	=				use_mask_token
          _a    	=				use_absolute_position_embeddings
          _a    	=				use_relative_position_bias
          _a    	=				use_shared_relative_position_bias
          _a    	=				layer_scale_init_value
          _a    	=				drop_path_rate
          _a    	=				use_mean_pooling
          # decode head attributes (semantic segmentation)
          _a    	=				out_indices
          _a    	=				pool_scales
          # auxiliary head attributes (semantic segmentation)
          _a    	=				use_auxiliary_head
          _a    	=				auxiliary_loss_weight
          _a    	=				auxiliary_channels
          _a    	=				auxiliary_num_convs
          _a    	=				auxiliary_concat_input
          _a    	=				semantic_loss_ignore_index







class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	int    =		version.parse('1.11'				)


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Mapping[str, Mapping[int, str]]:
          return OrderedDict(
              [
                  ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
              ]							)





     @property
     def 							_UpperCAmelCase      (      self							)       ->							float:
          return 1e-4



"""simple docstring"""






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case		=       logging.get_logger(__name__)
def       A_   (   _lowerCAmelCase     :					str,     _lowerCAmelCase     :					Optional[int]=False,     _lowerCAmelCase     :					Any=False       ):






     """simple docstring"""

     _a    	=				'''backbone.''' if is_semantic else ''''''

     _a    	=				[]
     for i in range(config.num_hidden_layers       ):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((f'{prefix}blocks.{i}.norm1.weight', f'beit.encoder.layer.{i}.layernorm_before.weight')       )
          rename_keys.append((f'{prefix}blocks.{i}.norm1.bias', f'beit.encoder.layer.{i}.layernorm_before.bias')       )
          rename_keys.append(
              (f'{prefix}blocks.{i}.attn.proj.weight', f'beit.encoder.layer.{i}.attention.output.dense.weight')       )
          rename_keys.append(
              (f'{prefix}blocks.{i}.attn.proj.bias', f'beit.encoder.layer.{i}.attention.output.dense.bias')       )
          rename_keys.append((f'{prefix}blocks.{i}.norm2.weight', f'beit.encoder.layer.{i}.layernorm_after.weight')       )
          rename_keys.append((f'{prefix}blocks.{i}.norm2.bias', f'beit.encoder.layer.{i}.layernorm_after.bias')       )
          rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.weight', f'beit.encoder.layer.{i}.intermediate.dense.weight')       )
          rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.bias', f'beit.encoder.layer.{i}.intermediate.dense.bias')       )
          rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.weight', f'beit.encoder.layer.{i}.output.dense.weight')       )
          rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.bias', f'beit.encoder.layer.{i}.output.dense.bias')       )

     # projection layer + position embeddings
     rename_keys.extend(
         [
             (f'{prefix}cls_token', '''beit.embeddings.cls_token'''),
             (f'{prefix}patch_embed.proj.weight', '''beit.embeddings.patch_embeddings.projection.weight'''),
             (f'{prefix}patch_embed.proj.bias', '''beit.embeddings.patch_embeddings.projection.bias'''),
             (f'{prefix}pos_embed', '''beit.embeddings.position_embeddings'''),
         ]       )

     if has_lm_head:
          # mask token + layernorm
          rename_keys.extend(
              [
                  ('''mask_token''', '''beit.embeddings.mask_token'''),
                  ('''norm.weight''', '''layernorm.weight'''),
                  ('''norm.bias''', '''layernorm.bias'''),
              ]       )
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
                  ('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
                  ('''head.weight''', '''classifier.weight'''),
                  ('''head.bias''', '''classifier.bias'''),
              ]       )

     return rename_keys
def       A_   (   _lowerCAmelCase     :					int,     _lowerCAmelCase     :					Union[str, Any],     _lowerCAmelCase     :					Any=False,     _lowerCAmelCase     :					List[Any]=False       ):






     """simple docstring"""

     for i in range(config.num_hidden_layers       ):
          _a    	=				'''backbone.''' if is_semantic else ''''''
          # queries, keys and values
          _a    	=				state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight'       )
          _a    	=				state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias'       )
          _a    	=				state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias'       )

          _a    	=				in_proj_weight[
              : config.hidden_size, :
          ]
          _a    	=				q_bias
          _a    	=				in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          _a    	=				in_proj_weight[
              -config.hidden_size :, :
          ]
          _a    	=				v_bias

          # gamma_1 and gamma_2
          # we call them lambda because otherwise they are renamed when using .from_pretrained
          _a    	=				state_dict.pop(f'{prefix}blocks.{i}.gamma_1'       )
          _a    	=				state_dict.pop(f'{prefix}blocks.{i}.gamma_2'       )

          _a    	=				gamma_a
          _a    	=				gamma_a
def       A_   (   _lowerCAmelCase     :					Tuple,     _lowerCAmelCase     :					Any,     _lowerCAmelCase     :					List[Any]       ):






     """simple docstring"""

     _a    	=				dct.pop(_lowerCAmelCase       )
     _a    	=				val
def       A_   (   ):






     """simple docstring"""

     _a    	=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     _a    	=				Image.open(requests.get(_lowerCAmelCase,     stream=_lowerCAmelCase       ).raw       )
     return im
@torch.no_grad()
def       A_   (   _lowerCAmelCase     :					Union[str, Any],     _lowerCAmelCase     :					Optional[Any],     _lowerCAmelCase     :					Optional[int]=False       ):






     """simple docstring"""

     _a    	=				False if '''rvlcdip''' in checkpoint_url else True
     _a    	=				BeitConfig(use_absolute_position_embeddings=_lowerCAmelCase,     use_mask_token=_lowerCAmelCase       )

     # size of the architecture
     if "large" in checkpoint_url or "dit-l" in checkpoint_url:
          _a    	=				10_24
          _a    	=				40_96
          _a    	=				24
          _a    	=				16

     # labels
     if "rvlcdip" in checkpoint_url:
          _a    	=				16
          _a    	=				'''huggingface/label-files'''
          _a    	=				'''rvlcdip-id2label.json'''
          _a    	=				json.load(open(hf_hub_download(_lowerCAmelCase,     _lowerCAmelCase,     repo_type='''dataset'''       ),     '''r'''       )       )
          _a    	=				{int(_lowerCAmelCase       ): v for k, v in idalabel.items()}
          _a    	=				idalabel
          _a    	=				{v: k for k, v in idalabel.items()}

     # load state_dict of original model, remove and rename some keys
     _a    	=				torch.hub.load_state_dict_from_url(_lowerCAmelCase,     map_location='''cpu'''       )['''model''']

     _a    	=				create_rename_keys(_lowerCAmelCase,     has_lm_head=_lowerCAmelCase       )
     for src, dest in rename_keys:
          rename_key(_lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase       )
     read_in_q_k_v(_lowerCAmelCase,     _lowerCAmelCase,     has_lm_head=_lowerCAmelCase       )

     # load HuggingFace model
     _a    	=				BeitForMaskedImageModeling(_lowerCAmelCase       ) if has_lm_head else BeitForImageClassification(_lowerCAmelCase       )
     model.eval()
     model.load_state_dict(_lowerCAmelCase       )

     # Check outputs on an image
     _a    	=				BeitImageProcessor(
         size=config.image_size,     resample=PILImageResampling.BILINEAR,     do_center_crop=_lowerCAmelCase       )
     _a    	=				prepare_img()

     _a    	=				image_processor(images=_lowerCAmelCase,     return_tensors='''pt'''       )
     _a    	=				encoding['''pixel_values''']

     _a    	=				model(_lowerCAmelCase       )
     _a    	=				outputs.logits

     # verify logits
     _a    	=				[1, 16] if '''rvlcdip''' in checkpoint_url else [1, 1_96, 81_92]
     assert logits.shape == torch.Size(_lowerCAmelCase       ), "Shape of logits not as expected"

     Path(_lowerCAmelCase       ).mkdir(exist_ok=_lowerCAmelCase       )
     print(f'Saving model to {pytorch_dump_folder_path}'       )
     model.save_pretrained(_lowerCAmelCase       )
     print(f'Saving image processor to {pytorch_dump_folder_path}'       )
     image_processor.save_pretrained(_lowerCAmelCase       )

     if push_to_hub:
          if has_lm_head:
               _a    	=				'''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
          else:
               _a    	=				'''dit-base-finetuned-rvlcdip''' if '''dit-b''' in checkpoint_url else '''dit-large-finetuned-rvlcdip'''
          image_processor.push_to_hub(
              repo_path_or_name=Path(_lowerCAmelCase,     _lowerCAmelCase       ),     organization='''nielsr''',     commit_message='''Add image processor''',     use_temp_dir=_lowerCAmelCase,     )
          model.push_to_hub(
              repo_path_or_name=Path(_lowerCAmelCase,     _lowerCAmelCase       ),     organization='''nielsr''',     commit_message='''Add model''',     use_temp_dir=_lowerCAmelCase,     )


if __name__ == "__main__":
   __snake_case		=       argparse.ArgumentParser()

   parser.add_argument(
       '''--checkpoint_url''',
       default='''https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth''',
       type=str,
       help='''URL to the original PyTorch checkpoint (.pth file).''',
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
   )
   parser.add_argument(
       '''--push_to_hub''',
       action='''store_true''',
   )
   __snake_case		=       parser.parse_args()
   convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)