code: 
  
  
  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()
  lowerCAmelCase   :str								=    logging.get_logger(__name__)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Optional[int]	,	lowerCAmelCase       :			int=False	,	lowerCAmelCase       :			str=False	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					Any  			=  'backbone.' if is_semantic else ''
  
  	__magic_name__ :					Union[str, 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       :			Tuple	,	lowerCAmelCase       :			Optional[int]	,	lowerCAmelCase       :			int=False	,	lowerCAmelCase       :			str=False	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	for i in range(config.num_hidden_layers	):
  		__magic_name__ :					List[str]  			=  'backbone.' if is_semantic else ''
  		# queries, keys and values
  		__magic_name__ :					str  			=  state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight'	)
  		__magic_name__ :					str  			=  state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias'	)
  		__magic_name__ :					int  			=  state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias'	)
  
  		__magic_name__ :					Union[str, Any]  			=  in_proj_weight[
  		    : config.hidden_size, :
  		]
  		__magic_name__ :					Optional[int]  			=  q_bias
  		__magic_name__ :					List[Any]  			=  in_proj_weight[
  		    config.hidden_size : config.hidden_size * 2, :
  		]
  		__magic_name__ :					int  			=  in_proj_weight[
  		    -config.hidden_size :, :
  		]
  		__magic_name__ :					List[str]  			=  v_bias
  
  		# gamma_1 and gamma_2
  		# we call them lambda because otherwise they are renamed when using .from_pretrained
  		__magic_name__ :					Tuple  			=  state_dict.pop(f'{prefix}blocks.{i}.gamma_1'	)
  		__magic_name__ :					List[Any]  			=  state_dict.pop(f'{prefix}blocks.{i}.gamma_2'	)
  
  		__magic_name__ :					int  			=  gamma_a
  		__magic_name__ :					List[str]  			=  gamma_a
  
  		# relative_position bias table + index
  		if not has_lm_head:
  			# each layer has its own relative position bias
  			__magic_name__ :					int  			=  state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_bias_table'	)
  			__magic_name__ :					Any  			=  state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_index'	)
  
  			__magic_name__ :					Dict  			=  table
  			__magic_name__ :					str  			=  index
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Union[str, Any]	,	lowerCAmelCase       :			Dict	,	lowerCAmelCase       :			Optional[Any]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					List[Any]  			=  dct.pop(lowerCAmelCase	)
  	__magic_name__ :					Union[str, Any]  			=  val
  
  
  
  
  
  def lowerCamelCase    (    ):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					List[str]  			=  'http://images.cocodataset.org/val2017/000000039769.jpg'
  	__magic_name__ :					Dict  			=  Image.open(requests.get(lowerCAmelCase	,	stream=lowerCAmelCase	).raw	)
  	return im
  
  
  
  
  
  @torch.no_grad()
  def lowerCamelCase    (    lowerCAmelCase       :			Tuple	,	lowerCAmelCase       :			Dict	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					Optional[Any]  			=  BeitConfig()
  	__magic_name__ :					List[Any]  			=  False
  	__magic_name__ :					Optional[Any]  			=  False
  	__magic_name__ :					Any  			=  'huggingface/label-files'
  	# set config parameters based on URL
  	if checkpoint_url[-9:-4] == "pt22k":
  		# masked image modeling
  		__magic_name__ :					Optional[Any]  			=  True
  		__magic_name__ :					Optional[Any]  			=  True
  		__magic_name__ :					Tuple  			=  True
  	elif checkpoint_url[-9:-4] == "ft22k":
  		# intermediate fine-tuning on ImageNet-22k
  		__magic_name__ :					int  			=  True
  		__magic_name__ :					Any  			=  2_1841
  		__magic_name__ :					Optional[int]  			=  'imagenet-22k-id2label.json'
  		__magic_name__ :					str  			=  json.load(open(hf_hub_download(lowerCAmelCase	,	lowerCAmelCase	,	repo_type='dataset'	)	,	'r'	)	)
  		__magic_name__ :					Dict  			=  {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[1_5027]
  		__magic_name__ :					Optional[Any]  			=  idalabel
  		__magic_name__ :					Dict  			=  {v: k for k, v in idalabel.items()}
  	elif checkpoint_url[-8:-4] == "to1k":
  		# fine-tuning on ImageNet-1k
  		__magic_name__ :					Tuple  			=  True
  		__magic_name__ :					Union[str, Any]  			=  1000
  		__magic_name__ :					int  			=  'imagenet-1k-id2label.json'
  		__magic_name__ :					List[str]  			=  json.load(open(hf_hub_download(lowerCAmelCase	,	lowerCAmelCase	,	repo_type='dataset'	)	,	'r'	)	)
  		__magic_name__ :					Any  			=  {int(lowerCAmelCase	): v for k, v in idalabel.items()}
  		__magic_name__ :					str  			=  idalabel
  		__magic_name__ :					Tuple  			=  {v: k for k, v in idalabel.items()}
  		if "384" in checkpoint_url:
  			__magic_name__ :					Optional[Any]  			=  384
  		if "512" in checkpoint_url:
  			__magic_name__ :					str  			=  512
  	elif "ade20k" in checkpoint_url:
  		# fine-tuning
  		__magic_name__ :					str  			=  True
  		__magic_name__ :					Tuple  			=  150
  		__magic_name__ :					Any  			=  'ade20k-id2label.json'
  		__magic_name__ :					Optional[int]  			=  json.load(open(hf_hub_download(lowerCAmelCase	,	lowerCAmelCase	,	repo_type='dataset'	)	,	'r'	)	)
  		__magic_name__ :					List[str]  			=  {int(lowerCAmelCase	): v for k, v in idalabel.items()}
  		__magic_name__ :					List[str]  			=  idalabel
  		__magic_name__ :					List[str]  			=  {v: k for k, v in idalabel.items()}
  		__magic_name__ :					str  			=  640
  		__magic_name__ :					Tuple  			=  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:
  		__magic_name__ :					Optional[int]  			=  1024
  		__magic_name__ :					Dict  			=  4096
  		__magic_name__ :					Optional[int]  			=  24
  		__magic_name__ :					Optional[int]  			=  16
  		if "ade20k" in checkpoint_url:
  			__magic_name__ :					Optional[Any]  			=  640
  			__magic_name__ :					Optional[int]  			=  [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
  	__magic_name__ :					Tuple  			=  torch.hub.load_state_dict_from_url(lowerCAmelCase	,	map_location='cpu'	,	check_hash=lowerCAmelCase	)
  	__magic_name__ :					Any  			=  state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']
  
  	__magic_name__ :					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():
  			__magic_name__ :					Optional[Any]  			=  state_dict.pop(lowerCAmelCase	)
  			if key.startswith('backbone.fpn'	):
  				__magic_name__ :					str  			=  key.replace('backbone.fpn'	,	'fpn'	)
  			__magic_name__ :					List[Any]  			=  val
  
      # load HuggingFace model
  	if checkpoint_url[-9:-4] == "pt22k":
  		__magic_name__ :					List[Any]  			=  BeitForMaskedImageModeling(lowerCAmelCase	)
  	elif "ade20k" in checkpoint_url:
  		__magic_name__ :					Dict  			=  BeitForSemanticSegmentation(lowerCAmelCase	)
  	else:
  		__magic_name__ :					str  			=  BeitForImageClassification(lowerCAmelCase	)
  	model.eval()
  	model.load_state_dict(lowerCAmelCase	)
  
  	# Check outputs on an image
  	if is_semantic:
  		__magic_name__ :					str  			=  BeitImageProcessor(size=config.image_size	,	do_center_crop=lowerCAmelCase	)
  		__magic_name__ :					Union[str, Any]  			=  load_dataset('hf-internal-testing/fixtures_ade20k'	,	split='test'	)
  		__magic_name__ :					Dict  			=  Image.open(ds[0]['file']	)
  	else:
  		__magic_name__ :					Dict  			=  BeitImageProcessor(
  		    size=config.image_size	,	resample=PILImageResampling.BILINEAR	,	do_center_crop=lowerCAmelCase	)
  		__magic_name__ :					Union[str, Any]  			=  prepare_img()
  
  	__magic_name__ :					Optional[int]  			=  image_processor(images=lowerCAmelCase	,	return_tensors='pt'	)
  	__magic_name__ :					Tuple  			=  encoding['pixel_values']
  
  	__magic_name__ :					List[Any]  			=  model(lowerCAmelCase	)
  	__magic_name__ :					str  			=  outputs.logits
  
  	# verify logits
  	__magic_name__ :					Union[str, Any]  			=  torch.Size([1, 1000]	)
  	if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'	):
  		__magic_name__ :					Tuple  			=  torch.Size([1, 196, 8192]	)
  	elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'	):
  		__magic_name__ :					Tuple  			=  torch.Size([1, 196, 8192]	)
  	elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'	):
  		__magic_name__ :					Tuple  			=  torch.Size([1, 2_1841]	)
  		__magic_name__ :					Optional[int]  			=  torch.tensor([2.2288, 2.4671, 0.7395]	)
  		__magic_name__ :					Optional[Any]  			=  2397
  	elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'	):
  		__magic_name__ :					str  			=  torch.Size([1, 2_1841]	)
  		__magic_name__ :					str  			=  torch.tensor([1.6881, -0.2787, 0.5901]	)
  		__magic_name__ :					Tuple  			=  2396
  	elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'	):
  		__magic_name__ :					Any  			=  torch.tensor([0.1241, 0.0798, -0.6569]	)
  		__magic_name__ :					Tuple  			=  285
  	elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'	):
  		__magic_name__ :					Any  			=  torch.tensor([-1.2385, -1.0987, -1.0108]	)
  		__magic_name__ :					str  			=  281
  	elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'	):
  		__magic_name__ :					List[str]  			=  torch.tensor([-1.5303, -0.9484, -0.3147]	)
  		__magic_name__ :					Any  			=  761
  	elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'	):
  		__magic_name__ :					Optional[int]  			=  torch.tensor([0.4610, -0.0928, 0.2086]	)
  		__magic_name__ :					Dict  			=  761
  	elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'	):
  		__magic_name__ :					int  			=  torch.tensor([-0.4804, 0.6257, -0.1837]	)
  		__magic_name__ :					Optional[int]  			=  761
  	elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'	):
  		__magic_name__ :					Any  			=  torch.tensor([[-0.5122, 0.5117, -0.2113]]	)
  		__magic_name__ :					Tuple  			=  761
  	elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'	):
  		__magic_name__ :					Optional[Any]  			=  torch.tensor([-0.3062, 0.7261, 0.4852]	)
  		__magic_name__ :					List[Any]  			=  761
  	elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'	):
  		__magic_name__ :					Dict  			=  (1, 150, 160, 160)
  		__magic_name__ :					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'	):
  		__magic_name__ :					Optional[int]  			=  (1, 150, 160, 160)
  		__magic_name__ :					List[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]	,	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__":
  							lowerCAmelCase   :Optional[Any]								=    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.'''
  							)
  							lowerCAmelCase   :int								=    parser.parse_args()
  							convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
style_context: 
  
  
  '''simple docstring'''
  
  from __future__ import annotations
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			list[int | float]	,	lowerCAmelCase       :			int	,	lowerCAmelCase       :			int	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if len(lowerCAmelCase	) == 0:
  		raise ValueError('find_min() arg is an empty sequence'	)
  	if (
  	    left >= len(lowerCAmelCase	)
  	    or left < -len(lowerCAmelCase	)
  	    or right >= len(lowerCAmelCase	)
  	    or right < -len(lowerCAmelCase	)
  	):
  		raise IndexError('list index out of range'	)
  	if left == right:
  		return nums[left]
  	__magic_name__ :					Tuple  			=  (left + right) >> 1  # the middle
  	__magic_name__ :					str  			=  find_min(lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	)  # find min in range[left, mid]
  	__magic_name__ :					Optional[Any]  			=  find_min(lowerCAmelCase	,	mid + 1	,	lowerCAmelCase	)  # find min in range[mid + 1, right]
  
  	return left_min if left_min <= right_min else right_min
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod(verbose=True)