'''simple docstring'''


import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
snake_case__     :			Any      			=  logging.get_logger(__name__)


def 					_lowerCamelCase    (      lowerCamelCase_					:							Union[str, Any]	,     lowerCamelCase_					:							Tuple=False	,     lowerCamelCase_					:							Optional[Any]=False   ):



     """simple docstring"""





     UpperCAmelCase_							:   Optional[Any]   =      'backbone.' if is_semantic else ''

     UpperCAmelCase_							:   List[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'),
         ]   )

     if has_lm_head:
          # mask token + shared relative position bias + layernorm
          rename_keys.extend(
              [
                  ('mask_token', 'beit.embeddings.mask_token'),
                  (
                      'rel_pos_bias.relative_position_bias_table',
                      'beit.encoder.relative_position_bias.relative_position_bias_table',
                  ),
                  (
                      'rel_pos_bias.relative_position_index',
                      'beit.encoder.relative_position_bias.relative_position_index',
                  ),
                  ('norm.weight', 'layernorm.weight'),
                  ('norm.bias', 'layernorm.bias'),
              ]   )
     elif is_semantic:
          # semantic segmentation classification heads
          rename_keys.extend(
              [
                  ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
                  ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
                  ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
                  ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.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_					:							Any	,     lowerCamelCase_					:							Optional[int]	,     lowerCamelCase_					:							List[str]=False	,     lowerCamelCase_					:							Tuple=False   ):



     """simple docstring"""





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

          UpperCAmelCase_							:   Tuple   =      in_proj_weight[
              : config.hidden_size, :
          ]
          UpperCAmelCase_							:   Optional[int]   =      q_bias
          UpperCAmelCase_							:   Tuple   =      in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          UpperCAmelCase_							:   Union[str, Any]   =      in_proj_weight[
              -config.hidden_size :, :
          ]
          UpperCAmelCase_							:   int   =      v_bias

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

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

          # relative_position bias table + index
          if not has_lm_head:
               # each layer has its own relative position bias
               UpperCAmelCase_							:   Union[str, Any]   =      state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''   )
               UpperCAmelCase_							:   List[str]   =      state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''   )

               UpperCAmelCase_							:   List[str]   =      table
               UpperCAmelCase_							:   Tuple   =      index


def 					_lowerCamelCase    (      lowerCamelCase_					:							List[str]	,     lowerCamelCase_					:							int	,     lowerCamelCase_					:							Union[str, Any]   ):



     """simple docstring"""





     UpperCAmelCase_							:   Tuple   =      dct.pop(lowerCamelCase_   )
     UpperCAmelCase_							:   Tuple   =      val


def 					_lowerCamelCase    (      ):



     """simple docstring"""





     UpperCAmelCase_							:   Optional[Any]   =      'http://images.cocodataset.org/val2017/000000039769.jpg'
     UpperCAmelCase_							:   Optional[Any]   =      Image.open(requests.get(lowerCamelCase_	,     stream=lowerCamelCase_   ).raw   )
     return im


@torch.no_grad()
def 					_lowerCamelCase    (      lowerCamelCase_					:							Optional[int]	,     lowerCamelCase_					:							Dict   ):



     """simple docstring"""





     UpperCAmelCase_							:   Any   =      BeitConfig()
     UpperCAmelCase_							:   List[str]   =      False
     UpperCAmelCase_							:   str   =      False
     UpperCAmelCase_							:   Tuple   =      'huggingface/label-files'
     # set config parameters based on URL
     if checkpoint_url[-9:-4] == "pt22k":
          # masked image modeling
          UpperCAmelCase_							:   Dict   =      True
          UpperCAmelCase_							:   Tuple   =      True
          UpperCAmelCase_							:   int   =      True
     elif checkpoint_url[-9:-4] == "ft22k":
          # intermediate fine-tuning on ImageNet-22k
          UpperCAmelCase_							:   List[str]   =      True
          UpperCAmelCase_							:   Optional[Any]   =      21841
          UpperCAmelCase_							:   Tuple   =      'imagenet-22k-id2label.json'
          UpperCAmelCase_							:   Any   =      json.load(open(hf_hub_download(lowerCamelCase_	,     lowerCamelCase_	,     repo_type='dataset'   )	,     'r'   )   )
          UpperCAmelCase_							:   str   =      {int(lowerCamelCase_   ): v for k, v in idalabel.items()}
          # this dataset contains 21843 labels but the model only has 21841
          # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
          del idalabel[9205]
          del idalabel[15027]
          UpperCAmelCase_							:   Tuple   =      idalabel
          UpperCAmelCase_							:   Optional[int]   =      {v: k for k, v in idalabel.items()}
     elif checkpoint_url[-8:-4] == "to1k":
          # fine-tuning on ImageNet-1k
          UpperCAmelCase_							:   int   =      True
          UpperCAmelCase_							:   Optional[Any]   =      1000
          UpperCAmelCase_							:   Optional[int]   =      'imagenet-1k-id2label.json'
          UpperCAmelCase_							:   Optional[Any]   =      json.load(open(hf_hub_download(lowerCamelCase_	,     lowerCamelCase_	,     repo_type='dataset'   )	,     'r'   )   )
          UpperCAmelCase_							:   int   =      {int(lowerCamelCase_   ): v for k, v in idalabel.items()}
          UpperCAmelCase_							:   Tuple   =      idalabel
          UpperCAmelCase_							:   Union[str, Any]   =      {v: k for k, v in idalabel.items()}
          if "384" in checkpoint_url:
               UpperCAmelCase_							:   int   =      384
          if "512" in checkpoint_url:
               UpperCAmelCase_							:   str   =      512
     elif "ade20k" in checkpoint_url:
          # fine-tuning
          UpperCAmelCase_							:   List[str]   =      True
          UpperCAmelCase_							:   Optional[Any]   =      150
          UpperCAmelCase_							:   str   =      'ade20k-id2label.json'
          UpperCAmelCase_							:   Dict   =      json.load(open(hf_hub_download(lowerCamelCase_	,     lowerCamelCase_	,     repo_type='dataset'   )	,     'r'   )   )
          UpperCAmelCase_							:   str   =      {int(lowerCamelCase_   ): v for k, v in idalabel.items()}
          UpperCAmelCase_							:   int   =      idalabel
          UpperCAmelCase_							:   int   =      {v: k for k, v in idalabel.items()}
          UpperCAmelCase_							:   int   =      640
          UpperCAmelCase_							:   Union[str, Any]   =      True
     else:
          raise ValueError('Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\''   )

     # size of the architecture
     if "base" in checkpoint_url:
          pass
     elif "large" in checkpoint_url:
          UpperCAmelCase_							:   Union[str, Any]   =      1024
          UpperCAmelCase_							:   Union[str, Any]   =      4096
          UpperCAmelCase_							:   str   =      24
          UpperCAmelCase_							:   Tuple   =      16
          if "ade20k" in checkpoint_url:
               UpperCAmelCase_							:   str   =      640
               UpperCAmelCase_							:   List[Any]   =      [7, 11, 15, 23]
     else:
          raise ValueError('Should either find \'base\' or \'large\' in checkpoint URL'   )

     # load state_dict of original model, remove and rename some keys
     UpperCAmelCase_							:   int   =      torch.hub.load_state_dict_from_url(lowerCamelCase_	,     map_location='cpu'	,     check_hash=lowerCamelCase_   )
     UpperCAmelCase_							:   Optional[Any]   =      state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']

     UpperCAmelCase_							:   Union[str, Any]   =      create_rename_keys(lowerCamelCase_	,     has_lm_head=lowerCamelCase_	,     is_semantic=lowerCamelCase_   )
     for src, dest in rename_keys:
          rename_key(lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_   )
     read_in_q_k_v(lowerCamelCase_	,     lowerCamelCase_	,     has_lm_head=lowerCamelCase_	,     is_semantic=lowerCamelCase_   )
     if is_semantic:
          # add prefix to decoder keys
          for key, val in state_dict.copy().items():
               UpperCAmelCase_							:   Optional[Any]   =      state_dict.pop(lowerCamelCase_   )
               if key.startswith('backbone.fpn'   ):
                    UpperCAmelCase_							:   Dict   =      key.replace('backbone.fpn'	,     'fpn'   )
               UpperCAmelCase_							:   List[Any]   =      val

    # load HuggingFace model
     if checkpoint_url[-9:-4] == "pt22k":
          UpperCAmelCase_							:   Optional[int]   =      BeitForMaskedImageModeling(lowerCamelCase_   )
     elif "ade20k" in checkpoint_url:
          UpperCAmelCase_							:   Optional[Any]   =      BeitForSemanticSegmentation(lowerCamelCase_   )
     else:
          UpperCAmelCase_							:   Tuple   =      BeitForImageClassification(lowerCamelCase_   )
     model.eval()
     model.load_state_dict(lowerCamelCase_   )

     # Check outputs on an image
     if is_semantic:
          UpperCAmelCase_							:   str   =      BeitImageProcessor(size=config.image_size	,     do_center_crop=lowerCamelCase_   )
          UpperCAmelCase_							:   Union[str, Any]   =      load_dataset('hf-internal-testing/fixtures_ade20k'	,     split='test'   )
          UpperCAmelCase_							:   Dict   =      Image.open(ds[0]['file']   )
     else:
          UpperCAmelCase_							:   str   =      BeitImageProcessor(
              size=config.image_size	,     resample=PILImageResampling.BILINEAR	,     do_center_crop=lowerCamelCase_   )
          UpperCAmelCase_							:   Optional[int]   =      prepare_img()

     UpperCAmelCase_							:   List[str]   =      image_processor(images=lowerCamelCase_	,     return_tensors='pt'   )
     UpperCAmelCase_							:   Dict   =      encoding['pixel_values']

     UpperCAmelCase_							:   List[Any]   =      model(lowerCamelCase_   )
     UpperCAmelCase_							:   List[Any]   =      outputs.logits

     # verify logits
     UpperCAmelCase_							:   List[str]   =      torch.Size([1, 1000]   )
     if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'   ):
          UpperCAmelCase_							:   Union[str, Any]   =      torch.Size([1, 196, 8192]   )
     elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'   ):
          UpperCAmelCase_							:   Tuple   =      torch.Size([1, 196, 8192]   )
     elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'   ):
          UpperCAmelCase_							:   Optional[Any]   =      torch.Size([1, 21841]   )
          UpperCAmelCase_							:   Any   =      torch.tensor([2.2288, 2.4671, 0.7395]   )
          UpperCAmelCase_							:   Union[str, Any]   =      2397
     elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'   ):
          UpperCAmelCase_							:   str   =      torch.Size([1, 21841]   )
          UpperCAmelCase_							:   int   =      torch.tensor([1.6881, -0.2787, 0.5901]   )
          UpperCAmelCase_							:   List[str]   =      2396
     elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'   ):
          UpperCAmelCase_							:   Tuple   =      torch.tensor([0.1241, 0.0798, -0.6569]   )
          UpperCAmelCase_							:   Union[str, Any]   =      285
     elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'   ):
          UpperCAmelCase_							:   Any   =      torch.tensor([-1.2385, -1.0987, -1.0108]   )
          UpperCAmelCase_							:   Tuple   =      281
     elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'   ):
          UpperCAmelCase_							:   int   =      torch.tensor([-1.5303, -0.9484, -0.3147]   )
          UpperCAmelCase_							:   str   =      761
     elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'   ):
          UpperCAmelCase_							:   Union[str, Any]   =      torch.tensor([0.4610, -0.0928, 0.2086]   )
          UpperCAmelCase_							:   str   =      761
     elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'   ):
          UpperCAmelCase_							:   str   =      torch.tensor([-0.4804, 0.6257, -0.1837]   )
          UpperCAmelCase_							:   Optional[int]   =      761
     elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'   ):
          UpperCAmelCase_							:   List[str]   =      torch.tensor([[-0.5122, 0.5117, -0.2113]]   )
          UpperCAmelCase_							:   Dict   =      761
     elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'   ):
          UpperCAmelCase_							:   Union[str, Any]   =      torch.tensor([-0.3062, 0.7261, 0.4852]   )
          UpperCAmelCase_							:   List[str]   =      761
     elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'   ):
          UpperCAmelCase_							:   Dict   =      (1, 150, 160, 160)
          UpperCAmelCase_							:   Optional[Any]   =      torch.tensor(
              [
                  [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
                  [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
                  [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
              ]   )
     elif checkpoint_url[:-4].endswith('beit_large_patch16_640_pt22k_ft22ktoade20k'   ):
          UpperCAmelCase_							:   Dict   =      (1, 150, 160, 160)
          UpperCAmelCase_							:   int   =      torch.tensor(
              [
                  [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251], [-3.4198, -1.8004, -2.9062]],
                  [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755], [-4.2791, -3.1874, -4.1681]],
                  [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518], [4.5550, 6.2495, 6.5154]],
              ]   )
     else:
          raise ValueError('Can\'t verify logits as model is not supported'   )

     assert logits.shape == expected_shape, "Shape of logits not as expected"
     if not has_lm_head:
          if is_semantic:
               assert torch.allclose(
                   logits[0, :3, :3, :3]	,     lowerCamelCase_	,     atol=1e-3   ), "First elements of logits not as expected"
          else:
               print('Predicted class idx:'	,     logits.argmax(-1   ).item()   )
               assert torch.allclose(
                   logits[0, :3]	,     lowerCamelCase_	,     atol=1e-3   ), "First elements of logits not as expected"
               assert logits.argmax(-1   ).item() == expected_class_idx, "Predicted class index 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 __name__ == "__main__":
 snake_case__     :			List[str]      			=  argparse.ArgumentParser()

 parser.add_argument(
     '''--checkpoint_url''',
     default='''https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.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.'''
 )
 snake_case__     :			Optional[Any]      			=  parser.parse_args()
 convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







'''simple docstring'''


def 					_lowerCamelCase    (      lowerCamelCase_					:							str   ):



     """simple docstring"""





     UpperCAmelCase_							:   str   =      str(lowerCamelCase_   ).strip()
     if not oct_string:
          raise ValueError('Empty string was passed to the function'   )
     UpperCAmelCase_							:   Tuple   =      oct_string[0] == '-'
     if is_negative:
          UpperCAmelCase_							:   Tuple   =      oct_string[1:]
     if not oct_string.isdigit() or not all(0 <= int(lowerCamelCase_   ) <= 7 for char in oct_string   ):
          raise ValueError('Non-octal value was passed to the function'   )
     UpperCAmelCase_							:   str   =      0
     for char in oct_string:
          UpperCAmelCase_							:   List[Any]   =      8 * decimal_number + int(lowerCamelCase_   )
     if is_negative:
          UpperCAmelCase_							:   List[Any]   =      -decimal_number
     return decimal_number


if __name__ == "__main__":
 from doctest import testmod

 testmod()






