



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__      :				Optional[Any]   	=			logging.get_logger(__name__)

def   _lowerCAmelCase   (						A__						,  A__=False						,  A__=False	):
      lowercase__     =  'backbone.' if is_semantic else ''

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

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

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

            lowercase__     =  gamma_a
            lowercase__     =  gamma_a

def   _lowerCAmelCase   (						A__						,  A__						,  A__	):
      lowercase__     =  dct.pop(A__	)
      lowercase__     =  val

def   _lowerCAmelCase   (						):
      lowercase__     =  'http://images.cocodataset.org/val2017/000000039769.jpg'
      lowercase__     =  Image.open(requests.get(A__						,  stream=A__	).raw	)
      return im

@torch.no_grad()
def   _lowerCAmelCase   (						A__						,  A__						,  A__=False	):
      lowercase__     =  False if 'rvlcdip' in checkpoint_url else True
      lowercase__     =  BeitConfig(use_absolute_position_embeddings=A__						,  use_mask_token=A__	)

      # size of the architecture
      if "large" in checkpoint_url or "dit-l" in checkpoint_url:
            lowercase__     =  1_024
            lowercase__     =  4_096
            lowercase__     =  24
            lowercase__     =  16

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

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

      lowercase__     =  create_rename_keys(A__						,  has_lm_head=A__	)
      for src, dest in rename_keys:
            rename_key(A__						,  A__						,  A__	)
      read_in_q_k_v(A__						,  A__						,  has_lm_head=A__	)

      # load HuggingFace model
      lowercase__     =  BeitForMaskedImageModeling(A__	) if has_lm_head else BeitForImageClassification(A__	)
      model.eval()
      model.load_state_dict(A__	)

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

      lowercase__     =  image_processor(images=A__						,  return_tensors='pt'	)
      lowercase__     =  encoding['pixel_values']

      lowercase__     =  model(A__	)
      lowercase__     =  outputs.logits

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

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

      if push_to_hub:
            if has_lm_head:
                  lowercase__     =  'dit-base' if 'base' in checkpoint_url else 'dit-large'
            else:
                  lowercase__     =  '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(A__						,  A__	)						,  organization='nielsr'						,  commit_message='Add image processor'						,  use_temp_dir=A__						,  )
            model.push_to_hub(
                repo_path_or_name=Path(A__						,  A__	)						,  organization='nielsr'						,  commit_message='Add model'						,  use_temp_dir=A__						,  )


if __name__ == "__main__":
       a__      :				List[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)





from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors

def   _lowerCAmelCase   (						A__	):
      lowercase__     =  prime_factors(A__	)
      if is_square_free(A__	):
            return -1 if len(A__	) % 2 else 1
      return 0


if __name__ == "__main__":
       import doctest

       doctest.testmod()
