'''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()
_A     :   List[str]								=   logging.get_logger(__name__)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_=False  ,       SCREAMING_SNAKE_CASE_=False							):
     lowercase_					:       Any     				=					'backbone.' if is_semantic else ''

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

          lowercase_					:       List[str]     				=					in_proj_weight[
              : config.hidden_size, :
          ]
          lowercase_					:       str     				=					q_bias
          lowercase_					:       Union[str, Any]     				=					in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          lowercase_					:       List[Any]     				=					in_proj_weight[
              -config.hidden_size :, :
          ]
          lowercase_					:       Union[str, Any]     				=					v_bias

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

          lowercase_					:       int     				=					gamma_a
          lowercase_					:       List[Any]     				=					gamma_a

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       Any     				=					dct.pop(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       int     				=					val

def _UpperCamelCase   (       ):
     lowercase_					:       Dict     				=					'http://images.cocodataset.org/val2017/000000039769.jpg'
     lowercase_					:       Optional[Any]     				=					Image.open(requests.get(SCREAMING_SNAKE_CASE_  ,       stream=SCREAMING_SNAKE_CASE_							).raw							)
     return im

@torch.no_grad()
def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_=False							):
     lowercase_					:       Optional[Any]     				=					False if 'rvlcdip' in checkpoint_url else True
     lowercase_					:       Dict     				=					BeitConfig(use_absolute_position_embeddings=SCREAMING_SNAKE_CASE_  ,       use_mask_token=SCREAMING_SNAKE_CASE_							)

     # size of the architecture
     if "large" in checkpoint_url or "dit-l" in checkpoint_url:
          lowercase_					:       str     				=					1_024
          lowercase_					:       Tuple     				=					4_096
          lowercase_					:       Optional[Any]     				=					24
          lowercase_					:       Optional[int]     				=					16

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

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

     lowercase_					:       Dict     				=					create_rename_keys(SCREAMING_SNAKE_CASE_  ,       has_lm_head=SCREAMING_SNAKE_CASE_							)
     for src, dest in rename_keys:
          rename_key(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
     read_in_q_k_v(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       has_lm_head=SCREAMING_SNAKE_CASE_							)

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

     # Check outputs on an image
     lowercase_					:       List[str]     				=					BeitImageProcessor(
         size=config.image_size  ,       resample=PILImageResampling.BILINEAR  ,       do_center_crop=SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Optional[int]     				=					prepare_img()

     lowercase_					:       List[str]     				=					image_processor(images=SCREAMING_SNAKE_CASE_  ,       return_tensors='pt'							)
     lowercase_					:       List[Any]     				=					encoding['pixel_values']

     lowercase_					:       Tuple     				=					model(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Dict     				=					outputs.logits

     # verify logits
     lowercase_					:       Dict     				=					[1, 16] if 'rvlcdip' in checkpoint_url else [1, 196, 8_192]
     assert logits.shape == torch.Size(SCREAMING_SNAKE_CASE_							), "Shape of logits not as expected"

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

     if push_to_hub:
          if has_lm_head:
               lowercase_					:       Optional[int]     				=					'dit-base' if 'base' in checkpoint_url else 'dit-large'
          else:
               lowercase_					:       List[str]     				=					'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(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)  ,       organization='nielsr'  ,       commit_message='Add image processor'  ,       use_temp_dir=SCREAMING_SNAKE_CASE_  ,       )
          model.push_to_hub(
              repo_path_or_name=Path(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)  ,       organization='nielsr'  ,       commit_message='Add model'  ,       use_temp_dir=SCREAMING_SNAKE_CASE_  ,       )


if __name__ == "__main__":
 _A     :   Union[str, 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',
 )
 _A     :   str								=   parser.parse_args()
 convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





'''simple docstring'''




import os
import string
import sys


_A								=   1 << 8

_A								=   {
    'tab': ord('\t'),
    'newline': ord('\r'),
    'esc': 2_7,
    'up': 6_5 + ARROW_KEY_FLAG,
    'down': 6_6 + ARROW_KEY_FLAG,
    'right': 6_7 + ARROW_KEY_FLAG,
    'left': 6_8 + ARROW_KEY_FLAG,
    'mod_int': 9_1,
    'undefined': sys.maxsize,
    'interrupt': 3,
    'insert': 5_0,
    'delete': 5_1,
    'pg_up': 5_3,
    'pg_down': 5_4,
}

_A								=   KEYMAP['up']
_A								=   KEYMAP['left']

if sys.platform == "win32":
 _A								=   []
 _A								=   {
     b'\xe0H': KEYMAP['up'] - ARROW_KEY_FLAG,
     b'\x00H': KEYMAP['up'] - ARROW_KEY_FLAG,
     b'\xe0P': KEYMAP['down'] - ARROW_KEY_FLAG,
     b'\x00P': KEYMAP['down'] - ARROW_KEY_FLAG,
     b'\xe0M': KEYMAP['right'] - ARROW_KEY_FLAG,
     b'\x00M': KEYMAP['right'] - ARROW_KEY_FLAG,
     b'\xe0K': KEYMAP['left'] - ARROW_KEY_FLAG,
     b'\x00K': KEYMAP['left'] - ARROW_KEY_FLAG,
 }

for i in range(1_0):
 _A								=   ord(str(i))

def _UpperCamelCase   (       ):
     if os.name == "nt":
          import msvcrt

          lowercase_					:       Optional[Any]     				=					'mbcs'
          # Flush the keyboard buffer
          while msvcrt.kbhit():
               msvcrt.getch()
          if len(SCREAMING_SNAKE_CASE_							) == 0:
               # Read the keystroke
               lowercase_					:       int     				=					msvcrt.getch()

               # If it is a prefix char, get second part
               if ch in (b"\x00", b"\xe0"):
                    lowercase_					:       int     				=					ch + msvcrt.getch()
                    # Translate actual Win chars to bullet char types
                    try:
                         lowercase_					:       List[Any]     				=					chr(WIN_KEYMAP[cha]							)
                         WIN_CH_BUFFER.append(chr(KEYMAP['mod_int']							)							)
                         WIN_CH_BUFFER.append(SCREAMING_SNAKE_CASE_							)
                         if ord(SCREAMING_SNAKE_CASE_							) in (
                             KEYMAP["insert"] - 1 << 9,
                             KEYMAP["delete"] - 1 << 9,
                             KEYMAP["pg_up"] - 1 << 9,
                             KEYMAP["pg_down"] - 1 << 9,
                         ):
                              WIN_CH_BUFFER.append(chr(126							)							)
                         lowercase_					:       Optional[int]     				=					chr(KEYMAP['esc']							)
                    except KeyError:
                         lowercase_					:       Tuple     				=					cha[1]
               else:
                    lowercase_					:       Tuple     				=					ch.decode(SCREAMING_SNAKE_CASE_							)
          else:
               lowercase_					:       Dict     				=					WIN_CH_BUFFER.pop(0							)
     elif os.name == "posix":
          import termios
          import tty

          lowercase_					:       int     				=					sys.stdin.fileno()
          lowercase_					:       Union[str, Any]     				=					termios.tcgetattr(SCREAMING_SNAKE_CASE_							)
          try:
               tty.setraw(SCREAMING_SNAKE_CASE_							)
               lowercase_					:       Any     				=					sys.stdin.read(1							)
          finally:
               termios.tcsetattr(SCREAMING_SNAKE_CASE_  ,       termios.TCSADRAIN  ,       SCREAMING_SNAKE_CASE_							)
     return ch






def _UpperCamelCase   (       ):
     lowercase_					:       Union[str, Any]     				=					get_raw_chars()
     if ord(SCREAMING_SNAKE_CASE_							) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
          return char

     elif ord(SCREAMING_SNAKE_CASE_							) == KEYMAP["esc"]:
          lowercase_					:       Any     				=					get_raw_chars()
          if ord(SCREAMING_SNAKE_CASE_							) == KEYMAP["mod_int"]:
               lowercase_					:       Dict     				=					get_raw_chars()
               if ord(SCREAMING_SNAKE_CASE_							) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(SCREAMING_SNAKE_CASE_							) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
                    return chr(ord(SCREAMING_SNAKE_CASE_							) + ARROW_KEY_FLAG							)
               else:
                    return KEYMAP["undefined"]
          else:
               return get_raw_chars()

     else:
          if char in string.printable:
               return char
          else:
               return KEYMAP["undefined"]




