


"""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    _lowerCamelCase   (     lowerCamelCase__	:							Dict       ,		lowerCamelCase__	:							str=False       ,		lowerCamelCase__	:							str=False  ):
 lowercase__       :       List[str]				=						"""backbone.""" if is_semantic else """"""

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

  lowercase__       :       Dict				=						in_proj_weight[
      : config.hidden_size, :
  ]
  lowercase__       :       Optional[int]				=						q_bias
  lowercase__       :       int				=						in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  lowercase__       :       str				=						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__       :       Tuple				=						state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''  )
  lowercase__       :       Union[str, Any]				=						state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''  )

  lowercase__       :       Any				=						gamma_a
  lowercase__       :       Any				=						gamma_a


def    _lowerCamelCase   (     lowerCamelCase__	:							List[Any]       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							str  ):
 lowercase__       :       Optional[int]				=						dct.pop(lowerCamelCase__  )
 lowercase__       :       List[str]				=						val


def    _lowerCamelCase   (     ):
 lowercase__       :       int				=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 lowercase__       :       Optional[int]				=						Image.open(requests.get(lowerCamelCase__       ,		stream=lowerCamelCase__  ).raw  )
 return im


@torch.no_grad()
def    _lowerCamelCase   (     lowerCamelCase__	:							Tuple       ,		lowerCamelCase__	:							str       ,		lowerCamelCase__	:							Optional[int]=False  ):
 lowercase__       :       Union[str, Any]				=						False if """rvlcdip""" in checkpoint_url else True
 lowercase__       :       Optional[Any]				=						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:
  lowercase__       :       Optional[int]				=						10_24
  lowercase__       :       str				=						40_96
  lowercase__       :       Union[str, Any]				=						24
  lowercase__       :       str				=						16

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

 # load state_dict of original model, remove and rename some keys
 lowercase__       :       Optional[Any]				=						torch.hub.load_state_dict_from_url(lowerCamelCase__       ,		map_location="""cpu"""  )["""model"""]

 lowercase__       :       Any				=						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
 lowercase__       :       Dict				=						BeitForMaskedImageModeling(lowerCamelCase__  ) if has_lm_head else BeitForImageClassification(lowerCamelCase__  )
 model.eval()
 model.load_state_dict(lowerCamelCase__  )

 # Check outputs on an image
 lowercase__       :       int				=						BeitImageProcessor(
     size=config.image_size       ,		resample=PILImageResampling.BILINEAR       ,		do_center_crop=lowerCamelCase__  )
 lowercase__       :       List[Any]				=						prepare_img()

 lowercase__       :       Union[str, Any]				=						image_processor(images=lowerCamelCase__       ,		return_tensors="""pt"""  )
 lowercase__       :       Dict				=						encoding["""pixel_values"""]

 lowercase__       :       Tuple				=						model(lowerCamelCase__  )
 lowercase__       :       Optional[int]				=						outputs.logits

 # verify logits
 lowercase__       :       Optional[Any]				=						[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:
   lowercase__       :       Any				=						"""dit-base""" if """base""" in checkpoint_url else """dit-large"""
  else:
   lowercase__       :       Optional[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(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)



"""simple docstring"""


import random


def    _lowerCamelCase   (     lowerCamelCase__	:							Tuple       ,		lowerCamelCase__	:							Dict       ,		lowerCamelCase__	:							str  ):
 lowercase__       :       List[Any]				=						a[left_index]
 lowercase__       :       List[Any]				=						left_index + 1
 for j in range(left_index + 1       ,		lowerCamelCase__  ):
  if a[j] < pivot:
   lowercase__	,       lowercase__       :       Dict				=						a[i], a[j]
   i += 1
 lowercase__	,       lowercase__       :       Any				=						a[i - 1], a[left_index]
 return i - 1


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							Tuple       ,		lowerCamelCase__	:							Optional[int]  ):
 if left < right:
  lowercase__       :       str				=						random.randint(lowerCamelCase__       ,		right - 1  )
  lowercase__	,       lowercase__       :       Any				=						(
      a[left],
      a[pivot],
  )  # switches the pivot with the left most bound
  lowercase__       :       str				=						partition(lowerCamelCase__       ,		lowerCamelCase__       ,		lowerCamelCase__  )
  quick_sort_random(
      lowerCamelCase__       ,		lowerCamelCase__       ,		lowerCamelCase__  )  # recursive quicksort to the left of the pivot point
  quick_sort_random(
      lowerCamelCase__       ,		pivot_index + 1       ,		lowerCamelCase__  )  # recursive quicksort to the right of the pivot point


def    _lowerCamelCase   (     ):
 lowercase__       :       List[Any]				=						input("""Enter numbers separated by a comma:\n"""  ).strip()
 lowercase__       :       int				=						[int(lowerCamelCase__  ) for item in user_input.split(""","""  )]

 quick_sort_random(lowerCamelCase__       ,		0       ,		len(lowerCamelCase__  )  )

 print(lowerCamelCase__  )


if __name__ == "__main__":
   main()