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							:			Optional[int]				=logging.get_logger(__name__)
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      int		, _lowerCamelCase      :      Optional[Any]=False		, _lowerCamelCase      :      List[Any]=False ):
      A__       =    "backbone." if is_semantic else ""
  
      A__       =    []
      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      :      Tuple		, _lowerCamelCase      :      List[str]		, _lowerCamelCase      :      str=False		, _lowerCamelCase      :      str=False ):
  
  
  
  
  
  
  
      for i in range(config.num_hidden_layers ):
          A__       =    "backbone." if is_semantic else ""
          # queries, keys and values
          A__       =    state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight" )
          A__       =    state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias" )
          A__       =    state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias" )
  
          A__       =    in_proj_weight[
              : config.hidden_size, :
          ]
          A__       =    q_bias
          A__       =    in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          A__       =    in_proj_weight[
              -config.hidden_size :, :
          ]
          A__       =    v_bias
  
          # gamma_1 and gamma_2
          # we call them lambda because otherwise they are renamed when using .from_pretrained
          A__       =    state_dict.pop(F"{prefix}blocks.{i}.gamma_1" )
          A__       =    state_dict.pop(F"{prefix}blocks.{i}.gamma_2" )
  
          A__       =    gamma_a
          A__       =    gamma_a
  
          # relative_position bias table + index
          if not has_lm_head:
              # each layer has its own relative position bias
              A__       =    state_dict.pop(F"{prefix}blocks.{i}.attn.relative_position_bias_table" )
              A__       =    state_dict.pop(F"{prefix}blocks.{i}.attn.relative_position_index" )
  
              A__       =    table
              A__       =    index
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      List[str]		, _lowerCamelCase      :      int		, _lowerCamelCase      :      Optional[int] ):
      A__       =    dct.pop(_lowerCamelCase )
      A__       =    val
  
  
  
  
  
  def 		UpperCamelCase       (							):
      A__       =    "http://images.cocodataset.org/val2017/000000039769.jpg"
      A__       =    Image.open(requests.get(_lowerCamelCase		, stream=_lowerCamelCase ).raw )
      return im
  
  
  
  
  
  @torch.no_grad()
  def 		UpperCamelCase       (							_lowerCamelCase      :      List[Any]		, _lowerCamelCase      :      int ):
      A__       =    BeitConfig()
      A__       =    False
      A__       =    False
      A__       =    "huggingface/label-files"
      # set config parameters based on URL
      if checkpoint_url[-9:-4] == "pt22k":
          # masked image modeling
          A__       =    True
          A__       =    True
          A__       =    True
      elif checkpoint_url[-9:-4] == "ft22k":
          # intermediate fine-tuning on ImageNet-22k
          A__       =    True
          A__       =    2_18_41
          A__       =    "imagenet-22k-id2label.json"
          A__       =    json.load(open(hf_hub_download(_lowerCamelCase		, _lowerCamelCase		, repo_type="dataset" )		, "r" ) )
          A__       =    {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[92_05]
          del idalabel[1_50_27]
          A__       =    idalabel
          A__       =    {v: k for k, v in idalabel.items()}
      elif checkpoint_url[-8:-4] == "to1k":
          # fine-tuning on ImageNet-1k
          A__       =    True
          A__       =    10_00
          A__       =    "imagenet-1k-id2label.json"
          A__       =    json.load(open(hf_hub_download(_lowerCamelCase		, _lowerCamelCase		, repo_type="dataset" )		, "r" ) )
          A__       =    {int(_lowerCamelCase ): v for k, v in idalabel.items()}
          A__       =    idalabel
          A__       =    {v: k for k, v in idalabel.items()}
          if "384" in checkpoint_url:
              A__       =    3_84
          if "512" in checkpoint_url:
              A__       =    5_12
      elif "ade20k" in checkpoint_url:
          # fine-tuning
          A__       =    True
          A__       =    1_50
          A__       =    "ade20k-id2label.json"
          A__       =    json.load(open(hf_hub_download(_lowerCamelCase		, _lowerCamelCase		, repo_type="dataset" )		, "r" ) )
          A__       =    {int(_lowerCamelCase ): v for k, v in idalabel.items()}
          A__       =    idalabel
          A__       =    {v: k for k, v in idalabel.items()}
          A__       =    6_40
          A__       =    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__       =    10_24
          A__       =    40_96
          A__       =    24
          A__       =    16
          if "ade20k" in checkpoint_url:
              A__       =    6_40
              A__       =    [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
      A__       =    torch.hub.load_state_dict_from_url(_lowerCamelCase		, map_location="cpu"		, check_hash=_lowerCamelCase )
      A__       =    state_dict["model"] if "ade20k" not in checkpoint_url else state_dict["state_dict"]
  
      A__       =    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__       =    state_dict.pop(_lowerCamelCase )
              if key.startswith("backbone.fpn" ):
                  A__       =    key.replace("backbone.fpn"		, "fpn" )
              A__       =    val
  
      # load HuggingFace model
      if checkpoint_url[-9:-4] == "pt22k":
          A__       =    BeitForMaskedImageModeling(_lowerCamelCase )
      elif "ade20k" in checkpoint_url:
          A__       =    BeitForSemanticSegmentation(_lowerCamelCase )
      else:
          A__       =    BeitForImageClassification(_lowerCamelCase )
      model.eval()
      model.load_state_dict(_lowerCamelCase )
  
      # Check outputs on an image
      if is_semantic:
          A__       =    BeitImageProcessor(size=config.image_size		, do_center_crop=_lowerCamelCase )
          A__       =    load_dataset("hf-internal-testing/fixtures_ade20k"		, split="test" )
          A__       =    Image.open(ds[0]["file"] )
      else:
          A__       =    BeitImageProcessor(
              size=config.image_size		, resample=PILImageResampling.BILINEAR		, do_center_crop=_lowerCamelCase )
          A__       =    prepare_img()
  
      A__       =    image_processor(images=_lowerCamelCase		, return_tensors="pt" )
      A__       =    encoding["pixel_values"]
  
      A__       =    model(_lowerCamelCase )
      A__       =    outputs.logits
  
      # verify logits
      A__       =    torch.Size([1, 10_00] )
      if checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k" ):
          A__       =    torch.Size([1, 1_96, 81_92] )
      elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k" ):
          A__       =    torch.Size([1, 1_96, 81_92] )
      elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22k" ):
          A__       =    torch.Size([1, 2_18_41] )
          A__       =    torch.tensor([2.2_2_8_8, 2.4_6_7_1, 0.7_3_9_5] )
          A__       =    23_97
      elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22k" ):
          A__       =    torch.Size([1, 2_18_41] )
          A__       =    torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1] )
          A__       =    23_96
      elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft1k" ):
          A__       =    torch.tensor([0.1_2_4_1, 0.0_7_9_8, -0.6_5_6_9] )
          A__       =    2_85
      elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22kto1k" ):
          A__       =    torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8] )
          A__       =    2_81
      elif checkpoint_url[:-4].endswith("beit_base_patch16_384_pt22k_ft22kto1k" ):
          A__       =    torch.tensor([-1.5_3_0_3, -0.9_4_8_4, -0.3_1_4_7] )
          A__       =    7_61
      elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft1k" ):
          A__       =    torch.tensor([0.4_6_1_0, -0.0_9_2_8, 0.2_0_8_6] )
          A__       =    7_61
      elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22kto1k" ):
          A__       =    torch.tensor([-0.4_8_0_4, 0.6_2_5_7, -0.1_8_3_7] )
          A__       =    7_61
      elif checkpoint_url[:-4].endswith("beit_large_patch16_384_pt22k_ft22kto1k" ):
          A__       =    torch.tensor([[-0.5_1_2_2, 0.5_1_1_7, -0.2_1_1_3]] )
          A__       =    7_61
      elif checkpoint_url[:-4].endswith("beit_large_patch16_512_pt22k_ft22kto1k" ):
          A__       =    torch.tensor([-0.3_0_6_2, 0.7_2_6_1, 0.4_8_5_2] )
          A__       =    7_61
      elif checkpoint_url[:-4].endswith("beit_base_patch16_640_pt22k_ft22ktoade20k" ):
          A__       =    (1, 1_50, 1_60, 1_60)
          A__       =    torch.tensor(
              [
                  [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
                  [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
                  [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
              ] )
      elif checkpoint_url[:-4].endswith("beit_large_patch16_640_pt22k_ft22ktoade20k" ):
          A__       =    (1, 1_50, 1_60, 1_60)
          A__       =    torch.tensor(
              [
                  [[-4.3_3_0_5, -2.3_0_4_9, -3.0_1_6_1], [-2.9_5_9_1, -1.5_3_0_5, -2.2_2_5_1], [-3.4_1_9_8, -1.8_0_0_4, -2.9_0_6_2]],
                  [[-5.8_9_2_2, -3.7_4_3_5, -4.3_9_7_8], [-4.2_0_6_3, -2.7_8_7_2, -3.4_7_5_5], [-4.2_7_9_1, -3.1_8_7_4, -4.1_6_8_1]],
                  [[0.9_8_9_5, 4.3_4_6_7, 4.7_6_6_3], [4.2_4_7_6, 5.6_8_3_0, 6.1_5_1_8], [4.5_5_5_0, 6.2_4_9_5, 6.5_1_5_4]],
              ] )
      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[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."
    )
    __lowerCAmelCase							:			Dict				=parser.parse_args()
    convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import torch
  
  from transformers import AutoTokenizer
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   def __init__(       self   :Any		, lowercase_   :Dict							)->							Any:
       A__       =    AutoTokenizer.from_pretrained(lowercase_							)
   def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :int							)->							Optional[int]:
  
       if isinstance(lowercase_		, lowercase_							) and all(isinstance(_x		, lowercase_							) for _x in x							):
           A__       =    None
           for l in x:
               A__       =    self.tokenizer(
                   lowercase_		, padding="max_length"		, max_length=3_84		, truncation=lowercase_		, return_tensors="pt"		, )
               A__       =    torch.tensor([len(lowercase_							)]							)
               if d is not None:
                   for k in d.keys():
                       A__       =    torch.cat((d[k], t[k])		, 0							)
               else:
                   A__       =    t
  
           A__       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"							)							)
           A__       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"							)							)
  
       elif isinstance(lowercase_		, lowercase_							) and all(isinstance(_x		, lowercase_							) for _x in x							):
           A__       =    self.tokenizer(
               lowercase_		, padding="max_length"		, max_length=3_84		, truncation=lowercase_		, return_tensors="pt"		, )
  
       else:
           raise Exception(
               "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"
               " strings` for supports are supported."							)
  
       return d
  
  
  
   def   UpperCAmelCase_							(       self   :Union[str, Any]		, lowercase_   :List[str]		, lowercase_   :str		, lowercase_   :str		, lowercase_   :List[Any]		, lowercase_   :Dict=0.7_0							)->							int:
       A__       =    []
       for idx in range(len(W_query["input_ids"]							)							):
           A__       =    A__       =    range(p_start.shape[1]							)
  
           A__       =    []
           for start_id in start_indexes:
               for end_id in end_indexes:
                   if start_id < end_id:
                       output.append(
                           (
                               start_id,
                               end_id,
                               p_start[idx][start_id].item(),
                               p_end[idx][end_id].item(),
                           )							)
  
           output.sort(key=lambda lowercase_							: (tup[2] * tup[3])		, reverse=lowercase_							)
           A__       =    []
           for k in range(len(lowercase_							)							):
               if output[k][2] * output[k][3] >= thresh:
                   A__,			A__       =    output[k][0], output[k][1]
                   A__       =    self.tokenizer.decode(W_query["input_ids"][idx][c_start_pos:c_end_pos]							)
                   temp.append((decoded, output[k][2] * output[k][3])							)
  
           final_outputs.append(lowercase_							)
  
       return final_outputs
  
  
  
  