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()
UpperCAmelCase				 =   logging.get_logger(__name__)







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_=False    ,   lowerCAmelCase_=False							)						->						int:
     a	:     Union[str, Any]						      =    '''backbone.''' if is_semantic else ''''''

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

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

          # gamma_1 and gamma_2
          # we call them lambda because otherwise they are renamed when using .from_pretrained
          a	:     int						      =    state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""							)
          a	:     List[str]						      =    state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""							)

          a	:     Optional[Any]						      =    gamma_a
          a	:     Tuple						      =    gamma_a

          # relative_position bias table + index
          if not has_lm_head:
               # each layer has its own relative position bias
               a	:     Tuple						      =    state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_bias_table"""							)
               a	:     List[str]						      =    state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_index"""							)

               a	:     Optional[int]						      =    table
               a	:     Optional[Any]						      =    index







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Optional[Any]:
     a	:     Any						      =    dct.pop(lowerCAmelCase_							)
     a	:     Optional[Any]						      =    val







def 						UpperCAmelCase							(		)						->						Any:
     a	:     Any						      =    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     a	:     Tuple						      =    Image.open(requests.get(lowerCAmelCase_    ,   stream=lowerCAmelCase_							).raw							)
     return im







@torch.no_grad()
def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Optional[Any]:
     a	:     Optional[int]						      =    BeitConfig()
     a	:     List[str]						      =    False
     a	:     Optional[Any]						      =    False
     a	:     List[Any]						      =    '''huggingface/label-files'''
     # set config parameters based on URL
     if checkpoint_url[-9:-4] == "pt22k":
          # masked image modeling
          a	:     str						      =    True
          a	:     str						      =    True
          a	:     Dict						      =    True
     elif checkpoint_url[-9:-4] == "ft22k":
          # intermediate fine-tuning on ImageNet-22k
          a	:     Optional[int]						      =    True
          a	:     Union[str, Any]						      =    2_1_8_4_1
          a	:     Dict						      =    '''imagenet-22k-id2label.json'''
          a	:     List[str]						      =    json.load(open(hf_hub_download(lowerCAmelCase_    ,   lowerCAmelCase_    ,   repo_type='''dataset'''							)    ,   '''r'''							)							)
          a	:     Union[str, Any]						      =    {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[9_2_0_5]
          del idalabel[1_5_0_2_7]
          a	:     Union[str, Any]						      =    idalabel
          a	:     Optional[int]						      =    {v: k for k, v in idalabel.items()}
     elif checkpoint_url[-8:-4] == "to1k":
          # fine-tuning on ImageNet-1k
          a	:     Optional[Any]						      =    True
          a	:     Optional[Any]						      =    1_0_0_0
          a	:     Any						      =    '''imagenet-1k-id2label.json'''
          a	:     Union[str, Any]						      =    json.load(open(hf_hub_download(lowerCAmelCase_    ,   lowerCAmelCase_    ,   repo_type='''dataset'''							)    ,   '''r'''							)							)
          a	:     Any						      =    {int(lowerCAmelCase_							): v for k, v in idalabel.items()}
          a	:     Union[str, Any]						      =    idalabel
          a	:     int						      =    {v: k for k, v in idalabel.items()}
          if "384" in checkpoint_url:
               a	:     Tuple						      =    3_8_4
          if "512" in checkpoint_url:
               a	:     Dict						      =    5_1_2
     elif "ade20k" in checkpoint_url:
          # fine-tuning
          a	:     Tuple						      =    True
          a	:     str						      =    1_5_0
          a	:     int						      =    '''ade20k-id2label.json'''
          a	:     Optional[int]						      =    json.load(open(hf_hub_download(lowerCAmelCase_    ,   lowerCAmelCase_    ,   repo_type='''dataset'''							)    ,   '''r'''							)							)
          a	:     Optional[Any]						      =    {int(lowerCAmelCase_							): v for k, v in idalabel.items()}
          a	:     Optional[Any]						      =    idalabel
          a	:     List[Any]						      =    {v: k for k, v in idalabel.items()}
          a	:     List[Any]						      =    6_4_0
          a	:     str						      =    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:
          a	:     Optional[int]						      =    1_0_2_4
          a	:     Union[str, Any]						      =    4_0_9_6
          a	:     Union[str, Any]						      =    2_4
          a	:     Optional[Any]						      =    1_6
          if "ade20k" in checkpoint_url:
               a	:     int						      =    6_4_0
               a	:     List[Any]						      =    [7, 1_1, 1_5, 2_3]
     else:
          raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL'''							)

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

     a	:     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():
               a	:     List[str]						      =    state_dict.pop(lowerCAmelCase_							)
               if key.startswith('''backbone.fpn'''							):
                    a	:     Dict						      =    key.replace('''backbone.fpn'''    ,   '''fpn'''							)
               a	:     Optional[int]						      =    val

    # load HuggingFace model
     if checkpoint_url[-9:-4] == "pt22k":
          a	:     List[str]						      =    BeitForMaskedImageModeling(lowerCAmelCase_							)
     elif "ade20k" in checkpoint_url:
          a	:     str						      =    BeitForSemanticSegmentation(lowerCAmelCase_							)
     else:
          a	:     Optional[Any]						      =    BeitForImageClassification(lowerCAmelCase_							)
     model.eval()
     model.load_state_dict(lowerCAmelCase_							)

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

     a	:     List[Any]						      =    image_processor(images=lowerCAmelCase_    ,   return_tensors='''pt'''							)
     a	:     Union[str, Any]						      =    encoding['''pixel_values''']

     a	:     int						      =    model(lowerCAmelCase_							)
     a	:     str						      =    outputs.logits

     # verify logits
     a	:     Tuple						      =    torch.Size([1, 1_0_0_0]							)
     if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''							):
          a	:     List[Any]						      =    torch.Size([1, 1_9_6, 8_1_9_2]							)
     elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''							):
          a	:     Optional[Any]						      =    torch.Size([1, 1_9_6, 8_1_9_2]							)
     elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''							):
          a	:     Union[str, Any]						      =    torch.Size([1, 2_1_8_4_1]							)
          a	:     Optional[Any]						      =    torch.tensor([2.22_88, 2.46_71, 0.73_95]							)
          a	:     int						      =    2_3_9_7
     elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''							):
          a	:     List[Any]						      =    torch.Size([1, 2_1_8_4_1]							)
          a	:     Union[str, Any]						      =    torch.tensor([1.68_81, -0.27_87, 0.59_01]							)
          a	:     str						      =    2_3_9_6
     elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''							):
          a	:     Optional[int]						      =    torch.tensor([0.12_41, 0.07_98, -0.65_69]							)
          a	:     int						      =    2_8_5
     elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''							):
          a	:     Tuple						      =    torch.tensor([-1.23_85, -1.09_87, -1.01_08]							)
          a	:     List[str]						      =    2_8_1
     elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''							):
          a	:     Optional[int]						      =    torch.tensor([-1.53_03, -0.94_84, -0.31_47]							)
          a	:     Union[str, Any]						      =    7_6_1
     elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''							):
          a	:     Tuple						      =    torch.tensor([0.46_10, -0.09_28, 0.20_86]							)
          a	:     Any						      =    7_6_1
     elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''							):
          a	:     Any						      =    torch.tensor([-0.48_04, 0.62_57, -0.18_37]							)
          a	:     Optional[int]						      =    7_6_1
     elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''							):
          a	:     str						      =    torch.tensor([[-0.51_22, 0.51_17, -0.21_13]]							)
          a	:     Tuple						      =    7_6_1
     elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''							):
          a	:     Dict						      =    torch.tensor([-0.30_62, 0.72_61, 0.48_52]							)
          a	:     int						      =    7_6_1
     elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''							):
          a	:     List[str]						      =    (1, 1_5_0, 1_6_0, 1_6_0)
          a	:     List[Any]						      =    torch.tensor(
              [
                  [[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
                  [[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
                  [[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
              ]							)
     elif checkpoint_url[:-4].endswith('''beit_large_patch16_640_pt22k_ft22ktoade20k'''							):
          a	:     List[Any]						      =    (1, 1_5_0, 1_6_0, 1_6_0)
          a	:     Optional[Any]						      =    torch.tensor(
              [
                  [[-4.33_05, -2.30_49, -3.01_61], [-2.95_91, -1.53_05, -2.22_51], [-3.41_98, -1.80_04, -2.90_62]],
                  [[-5.89_22, -3.74_35, -4.39_78], [-4.20_63, -2.78_72, -3.47_55], [-4.27_91, -3.18_74, -4.16_81]],
                  [[0.98_95, 4.34_67, 4.76_63], [4.24_76, 5.68_30, 6.15_18], [4.55_50, 6.24_95, 6.51_54]],
              ]							)
     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__":
  UpperCAmelCase				 =   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."""
  )
  UpperCAmelCase				 =   parser.parse_args()
  convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
UpperCAmelCase				 =   {
    0: """underwear""",
    1: """pants""",
    2: """belt""",
    3: """suit""",
    4: """shoe""",
    5: """socks""",
    6: """shirt""",
    7: """tie""",
    8: """watch""",
}

UpperCAmelCase				 =   [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

UpperCAmelCase				 =   [0 for x in range(len(graph))]
UpperCAmelCase				 =   []







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_							)						->						List[Any]:
     a	:     Tuple						      =    1
     while stack:
          a	:     Optional[Any]						      =    stack.pop()
          print(lowerCAmelCase_    ,   clothes[current_clothing]							)
          order += 1







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Any:
     a	:     Union[str, Any]						      =    1
     for v in graph[u]:
          if not visited[v]:
               depth_first_search(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)

     stack.append(lowerCAmelCase_							)







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Optional[Any]:
     for v in range(len(lowerCAmelCase_							)							):
          if not visited[v]:
               depth_first_search(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)


if __name__ == "__main__":
  topological_sort(graph, visited)
  print(stack)
  print_stack(stack, clothes)