


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							:							List[Any]    							=					logging.get_logger(__name__)

def       _a	(					SCREAMING_SNAKE_CASE						:					Union[str, Any]     ,			SCREAMING_SNAKE_CASE						:					Optional[Any]=False     ,			SCREAMING_SNAKE_CASE						:					Any=False  ):







 """simple docstring"""



 UpperCamelCase__       :     Tuple 							= '''backbone.''' if is_semantic else ''''''

 UpperCamelCase__       :     Optional[int] 							= []
 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       _a	(					SCREAMING_SNAKE_CASE						:					Optional[Any]     ,			SCREAMING_SNAKE_CASE						:					Optional[Any]     ,			SCREAMING_SNAKE_CASE						:					Any=False     ,			SCREAMING_SNAKE_CASE						:					Optional[int]=False  ):







 """simple docstring"""



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

  UpperCamelCase__       :     List[str] 							= in_proj_weight[
      : config.hidden_size, :
  ]
  UpperCamelCase__       :     List[Any] 							= q_bias
  UpperCamelCase__       :     List[str] 							= in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  UpperCamelCase__       :     int 							= in_proj_weight[
      -config.hidden_size :, :
  ]
  UpperCamelCase__       :     Optional[int] 							= v_bias

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

  UpperCamelCase__       :     Optional[int] 							= gamma_a
  UpperCamelCase__       :     List[str] 							= gamma_a

  # relative_position bias table + index
  if not has_lm_head:
   # each layer has its own relative position bias
   UpperCamelCase__       :     Any 							= state_dict.pop(F"{prefix}blocks.{i}.attn.relative_position_bias_table"  )
   UpperCamelCase__       :     Optional[int] 							= state_dict.pop(F"{prefix}blocks.{i}.attn.relative_position_index"  )

   UpperCamelCase__       :     int 							= table
   UpperCamelCase__       :     List[Any] 							= index

def       _a	(					SCREAMING_SNAKE_CASE						:					Dict     ,			SCREAMING_SNAKE_CASE						:					List[Any]     ,			SCREAMING_SNAKE_CASE						:					int  ):







 """simple docstring"""



 UpperCamelCase__       :     Dict 							= dct.pop(_A  )
 UpperCamelCase__       :     List[Any] 							= val

def       _a	(					):







 """simple docstring"""



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

@torch.no_grad()
def       _a	(					SCREAMING_SNAKE_CASE						:					List[Any]     ,			SCREAMING_SNAKE_CASE						:					Optional[Any]  ):







 """simple docstring"""



 UpperCamelCase__       :     List[Any] 							= BeitConfig()
 UpperCamelCase__       :     Dict 							= False
 UpperCamelCase__       :     Union[str, Any] 							= False
 UpperCamelCase__       :     List[str] 							= '''huggingface/label-files'''
 # set config parameters based on URL
 if checkpoint_url[-9:-4] == "pt22k":
  # masked image modeling
  UpperCamelCase__       :     Any 							= True
  UpperCamelCase__       :     Optional[Any] 							= True
  UpperCamelCase__       :     Union[str, Any] 							= True
 elif checkpoint_url[-9:-4] == "ft22k":
  # intermediate fine-tuning on ImageNet-22k
  UpperCamelCase__       :     List[str] 							= True
  UpperCamelCase__       :     Optional[Any] 							= 21841
  UpperCamelCase__       :     Union[str, Any] 							= '''imagenet-22k-id2label.json'''
  UpperCamelCase__       :     Optional[Any] 							= json.load(open(hf_hub_download(_A     ,			_A     ,			repo_type='''dataset'''  )     ,			'''r'''  )  )
  UpperCamelCase__       :     List[str] 							= {int(_A  ): 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__       :     str 							= idalabel
  UpperCamelCase__       :     List[Any] 							= {v: k for k, v in idalabel.items()}
 elif checkpoint_url[-8:-4] == "to1k":
  # fine-tuning on ImageNet-1k
  UpperCamelCase__       :     List[Any] 							= True
  UpperCamelCase__       :     Optional[int] 							= 1000
  UpperCamelCase__       :     int 							= '''imagenet-1k-id2label.json'''
  UpperCamelCase__       :     Any 							= json.load(open(hf_hub_download(_A     ,			_A     ,			repo_type='''dataset'''  )     ,			'''r'''  )  )
  UpperCamelCase__       :     Any 							= {int(_A  ): v for k, v in idalabel.items()}
  UpperCamelCase__       :     Optional[Any] 							= idalabel
  UpperCamelCase__       :     Any 							= {v: k for k, v in idalabel.items()}
  if "384" in checkpoint_url:
   UpperCamelCase__       :     Any 							= 384
  if "512" in checkpoint_url:
   UpperCamelCase__       :     Union[str, Any] 							= 512
 elif "ade20k" in checkpoint_url:
  # fine-tuning
  UpperCamelCase__       :     List[Any] 							= True
  UpperCamelCase__       :     str 							= 150
  UpperCamelCase__       :     Union[str, Any] 							= '''ade20k-id2label.json'''
  UpperCamelCase__       :     str 							= json.load(open(hf_hub_download(_A     ,			_A     ,			repo_type='''dataset'''  )     ,			'''r'''  )  )
  UpperCamelCase__       :     Union[str, Any] 							= {int(_A  ): v for k, v in idalabel.items()}
  UpperCamelCase__       :     Tuple 							= idalabel
  UpperCamelCase__       :     int 							= {v: k for k, v in idalabel.items()}
  UpperCamelCase__       :     int 							= 640
  UpperCamelCase__       :     Optional[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__       :     Optional[Any] 							= 1024
  UpperCamelCase__       :     Tuple 							= 4096
  UpperCamelCase__       :     int 							= 24
  UpperCamelCase__       :     Tuple 							= 16
  if "ade20k" in checkpoint_url:
   UpperCamelCase__       :     List[Any] 							= 640
   UpperCamelCase__       :     Dict 							= [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__       :     str 							= torch.hub.load_state_dict_from_url(_A     ,			map_location='''cpu'''     ,			check_hash=_A  )
 UpperCamelCase__       :     int 							= state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

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

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

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

 UpperCamelCase__       :     Tuple 							= image_processor(images=_A     ,			return_tensors='''pt'''  )
 UpperCamelCase__       :     int 							= encoding['''pixel_values''']

 UpperCamelCase__       :     Union[str, Any] 							= model(_A  )
 UpperCamelCase__       :     Optional[Any] 							= outputs.logits

 # verify logits
 UpperCamelCase__       :     Union[str, Any] 							= torch.Size([1, 1000]  )
 if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''  ):
  UpperCamelCase__       :     Optional[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__       :     List[str] 							= torch.Size([1, 21841]  )
  UpperCamelCase__       :     int 							= torch.tensor([2.2288, 2.4671, 0.7395]  )
  UpperCamelCase__       :     Tuple 							= 2397
 elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''  ):
  UpperCamelCase__       :     int 							= torch.Size([1, 21841]  )
  UpperCamelCase__       :     Tuple 							= torch.tensor([1.6881, -0.2787, 0.5901]  )
  UpperCamelCase__       :     Optional[Any] 							= 2396
 elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''  ):
  UpperCamelCase__       :     List[str] 							= torch.tensor([0.1241, 0.0798, -0.6569]  )
  UpperCamelCase__       :     List[str] 							= 285
 elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''  ):
  UpperCamelCase__       :     Tuple 							= torch.tensor([-1.2385, -1.0987, -1.0108]  )
  UpperCamelCase__       :     List[str] 							= 281
 elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''  ):
  UpperCamelCase__       :     List[str] 							= torch.tensor([-1.5303, -0.9484, -0.3147]  )
  UpperCamelCase__       :     str 							= 761
 elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''  ):
  UpperCamelCase__       :     List[str] 							= torch.tensor([0.4610, -0.0928, 0.2086]  )
  UpperCamelCase__       :     Union[str, Any] 							= 761
 elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''  ):
  UpperCamelCase__       :     Union[str, Any] 							= torch.tensor([-0.4804, 0.6257, -0.1837]  )
  UpperCamelCase__       :     List[Any] 							= 761
 elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''  ):
  UpperCamelCase__       :     Any 							= torch.tensor([[-0.5122, 0.5117, -0.2113]]  )
  UpperCamelCase__       :     Any 							= 761
 elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''  ):
  UpperCamelCase__       :     Any 							= torch.tensor([-0.3062, 0.7261, 0.4852]  )
  UpperCamelCase__       :     Union[str, Any] 							= 761
 elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''  ):
  UpperCamelCase__       :     Union[str, Any] 							= (1, 150, 160, 160)
  UpperCamelCase__       :     Dict 							= 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__       :     Optional[Any] 							= (1, 150, 160, 160)
  UpperCamelCase__       :     Union[str, Any] 							= 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]     ,			_A     ,			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]     ,			_A     ,			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(_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 __name__ == "__main__":
       __UpperCamelCase							:							int    							=					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							:							Optional[int]    							=					parser.parse_args()
       convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





def        __UpperCamelCase     (      _A					:							str		)				->int:



							"""simple docstring"""
							lowerCamelCase_								=lowerCamelCase_								=0
							for letter in data:
														lowerCamelCase_								=(hash_ << 4) + ord(_A		)
														lowerCamelCase_								=hash_ & 0XF0000000
														if x != 0:
																					hash_ ^= x >> 24
														hash_ &= ~x
							return hash_


if __name__ == "__main__":
	import doctest

	doctest.testmod()
