




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()
SCREAMING_SNAKE_CASE__			: str    =  logging.get_logger(__name__)

def 	A ( _SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE=False					,_SCREAMING_SNAKE_CASE=False       )      ->					Dict:
       lowerCamelCase  : List[str]     					=   "backbone." if is_semantic else ""

       lowerCamelCase  : Dict     					=   []
       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 ( _SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE=False					,_SCREAMING_SNAKE_CASE=False       )      ->					int:

       for i in range(config.num_hidden_layers       ):
              lowerCamelCase  : Dict     					=   "backbone." if is_semantic else ""
              # queries, keys and values
              lowerCamelCase  : List[str]     					=   state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''       )
              lowerCamelCase  : int     					=   state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''       )
              lowerCamelCase  : List[Any]     					=   state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''       )

              lowerCamelCase  : str     					=   in_proj_weight[
                  : config.hidden_size, :
              ]
              lowerCamelCase  : Any     					=   q_bias
              lowerCamelCase  : Tuple     					=   in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              lowerCamelCase  : Dict     					=   in_proj_weight[
                  -config.hidden_size :, :
              ]
              lowerCamelCase  : Union[str, Any]     					=   v_bias

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

              lowerCamelCase  : Optional[Any]     					=   gamma_a
              lowerCamelCase  : Optional[int]     					=   gamma_a

def 	A ( _SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE       )      ->					Optional[int]:
       lowerCamelCase  : List[str]     					=   dct.pop(__UpperCAmelCase       )
       lowerCamelCase  : Dict     					=   val

def 	A ( )      ->					Union[str, Any]:
       lowerCamelCase  : List[str]     					=   "http://images.cocodataset.org/val2017/000000039769.jpg"
       lowerCamelCase  : Optional[int]     					=   Image.open(requests.get(__UpperCAmelCase					,stream=__UpperCAmelCase       ).raw       )
       return im

@torch.no_grad()
def 	A ( _SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE=False       )      ->					Dict:
       lowerCamelCase  : Tuple     					=   False if "rvlcdip" in checkpoint_url else True
       lowerCamelCase  : Optional[Any]     					=   BeitConfig(use_absolute_position_embeddings=__UpperCAmelCase					,use_mask_token=__UpperCAmelCase       )

       # size of the architecture
       if "large" in checkpoint_url or "dit-l" in checkpoint_url:
              lowerCamelCase  : List[Any]     					=   1024
              lowerCamelCase  : Optional[int]     					=   4096
              lowerCamelCase  : Union[str, Any]     					=   24
              lowerCamelCase  : Tuple     					=   16

       # labels
       if "rvlcdip" in checkpoint_url:
              lowerCamelCase  : Any     					=   16
              lowerCamelCase  : List[str]     					=   "huggingface/label-files"
              lowerCamelCase  : List[Any]     					=   "rvlcdip-id2label.json"
              lowerCamelCase  : Optional[int]     					=   json.load(open(hf_hub_download(__UpperCAmelCase					,__UpperCAmelCase					,repo_type="dataset"       )					,"r"       )       )
              lowerCamelCase  : Dict     					=   {int(__UpperCAmelCase       ): v for k, v in idalabel.items()}
              lowerCamelCase  : int     					=   idalabel
              lowerCamelCase  : Union[str, Any]     					=   {v: k for k, v in idalabel.items()}

       # load state_dict of original model, remove and rename some keys
       lowerCamelCase  : Union[str, Any]     					=   torch.hub.load_state_dict_from_url(__UpperCAmelCase					,map_location="cpu"       )["model"]

       lowerCamelCase  : Any     					=   create_rename_keys(__UpperCAmelCase					,has_lm_head=__UpperCAmelCase       )
       for src, dest in rename_keys:
              rename_key(__UpperCAmelCase					,__UpperCAmelCase					,__UpperCAmelCase       )
       read_in_q_k_v(__UpperCAmelCase					,__UpperCAmelCase					,has_lm_head=__UpperCAmelCase       )

       # load HuggingFace model
       lowerCamelCase  : Optional[Any]     					=   BeitForMaskedImageModeling(__UpperCAmelCase       ) if has_lm_head else BeitForImageClassification(__UpperCAmelCase       )
       model.eval()
       model.load_state_dict(__UpperCAmelCase       )

       # Check outputs on an image
       lowerCamelCase  : int     					=   BeitImageProcessor(
           size=config.image_size					,resample=PILImageResampling.BILINEAR					,do_center_crop=__UpperCAmelCase       )
       lowerCamelCase  : str     					=   prepare_img()

       lowerCamelCase  : Tuple     					=   image_processor(images=__UpperCAmelCase					,return_tensors="pt"       )
       lowerCamelCase  : List[str]     					=   encoding["pixel_values"]

       lowerCamelCase  : int     					=   model(__UpperCAmelCase       )
       lowerCamelCase  : Optional[Any]     					=   outputs.logits

       # verify logits
       lowerCamelCase  : Dict     					=   [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192]
       assert logits.shape == torch.Size(__UpperCAmelCase       ), "Shape of logits not as expected"

       Path(__UpperCAmelCase       ).mkdir(exist_ok=__UpperCAmelCase       )
       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 push_to_hub:
              if has_lm_head:
                     lowerCamelCase  : Optional[Any]     					=   "dit-base" if "base" in checkpoint_url else "dit-large"
              else:
                     lowerCamelCase  : int     					=   "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(__UpperCAmelCase					,__UpperCAmelCase       )					,organization="nielsr"					,commit_message="Add image processor"					,use_temp_dir=__UpperCAmelCase					,)
              model.push_to_hub(
                  repo_path_or_name=Path(__UpperCAmelCase					,__UpperCAmelCase       )					,organization="nielsr"					,commit_message="Add model"					,use_temp_dir=__UpperCAmelCase					,)


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__			: Any    =  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',
       )
       SCREAMING_SNAKE_CASE__			: Any    =  parser.parse_args()
       convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)












'''simple docstring'''

from numpy import exp, pi, sqrt


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase = 0.0, __UpperCAmelCase = 1.0      )						->   int:

					'''simple docstring'''

					return 1 / sqrt(2 * pi * sigma**2      ) * exp(-((x - mu) ** 2) / (2 * sigma**2)      )


if __name__ == "__main__":
							import doctest

							doctest.testmod()

