





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()
__UpperCAmelCase       :   Union[str, Any]				       =							logging.get_logger(__name__)







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__=False					,					SCREAMING_SNAKE_CASE__=False)   ->    Any:
  __snake_case:						Dict   =						"""backbone.""" if is_semantic else """"""

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

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

    # gamma_1 and gamma_2
    # we call them lambda because otherwise they are renamed when using .from_pretrained
    __snake_case:						Optional[int]   =						state_dict.pop(F'''{prefix}blocks.{i}.gamma_1''')
    __snake_case:						Optional[Any]   =						state_dict.pop(F'''{prefix}blocks.{i}.gamma_2''')

    __snake_case:						Tuple   =						gamma_a
    __snake_case:						str   =						gamma_a







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    Union[str, Any]:
  __snake_case:						Tuple   =						dct.pop(SCREAMING_SNAKE_CASE__)
  __snake_case:						Tuple   =						val







def 			A__							(    )   ->    Optional[int]:
  __snake_case:						int   =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
  __snake_case:						Any   =						Image.open(requests.get(SCREAMING_SNAKE_CASE__					,					stream=SCREAMING_SNAKE_CASE__).raw)
  return im







@torch.no_grad()
def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__=False)   ->    List[str]:
  __snake_case:						List[str]   =						False if """rvlcdip""" in checkpoint_url else True
  __snake_case:						str   =						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:
    __snake_case:						List[Any]   =						1024
    __snake_case:						str   =						4096
    __snake_case:						List[str]   =						24
    __snake_case:						Tuple   =						16

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

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

  __snake_case:						Any   =						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
  __snake_case:						List[str]   =						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
  __snake_case:						List[str]   =						BeitImageProcessor(
      size=config.image_size					,					resample=PILImageResampling.BILINEAR					,					do_center_crop=SCREAMING_SNAKE_CASE__)
  __snake_case:						Optional[int]   =						prepare_img()

  __snake_case:						str   =						image_processor(images=SCREAMING_SNAKE_CASE__					,					return_tensors="""pt""")
  __snake_case:						List[str]   =						encoding["""pixel_values"""]

  __snake_case:						List[Any]   =						model(SCREAMING_SNAKE_CASE__)
  __snake_case:						Tuple   =						outputs.logits

  # verify logits
  __snake_case:						Any   =						[1, 16] if """rvlcdip""" in checkpoint_url else [1, 196, 8192]
  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:
      __snake_case:						Tuple   =						"""dit-base""" if """base""" in checkpoint_url else """dit-large"""
    else:
      __snake_case:						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(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__":
      __UpperCAmelCase       :   str				       =							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",
      )
      __UpperCAmelCase       :   Union[str, Any]				       =							parser.parse_args()
      convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)













import math







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    bool:

  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5					,					int(math.sqrt(SCREAMING_SNAKE_CASE__) + 1)					,					6):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True







def 			A__							(    SCREAMING_SNAKE_CASE__ = 1_0001)   ->    int:

  try:
    __snake_case:						List[str]   =						int(SCREAMING_SNAKE_CASE__)
  except (TypeError, ValueError):
    raise TypeError("""Parameter nth must be int or castable to int.""") from None
  if nth <= 0:
    raise ValueError("""Parameter nth must be greater than or equal to one.""")
  __snake_case:						list[int]   =						[]
  __snake_case:						List[str]   =						2
  while len(SCREAMING_SNAKE_CASE__) < nth:
    if is_prime(SCREAMING_SNAKE_CASE__):
      primes.append(SCREAMING_SNAKE_CASE__)
      num += 1
    else:
      num += 1
  return primes[len(SCREAMING_SNAKE_CASE__) - 1]


if __name__ == "__main__":
      print(f'{solution() = }')






