


'''simple docstring'''






def     UpperCamelCase_      (   A__ :       Tuple ):
  '''simple docstring'''





  lowerCAmelCase_,							lowerCAmelCase_      :				int      =       [], []
  while len(A__ ) > 1:
    lowerCAmelCase_,							lowerCAmelCase_      :				List[str]      =       min(A__ ), max(A__ )
    start.append(A__ )
    end.append(A__ )
    collection.remove(A__ )
    collection.remove(A__ )
  end.reverse()
  return start + collection + end


if __name__ == "__main__":
  __A :  str    							=				input("Enter numbers separated by a comma:\n").strip()
  __A :  int    							=				[int(item) for item in user_input.split(",")]
  print(*merge_sort(unsorted), sep=",")







'''simple docstring'''






import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
__A :  List[str]    							=				logging.get_logger(__name__)







def     UpperCamelCase_      (   A__ :       Union[str, Any]							,   A__ :       Tuple=False ):
  '''simple docstring'''





  lowerCAmelCase_      :				Tuple      =       []
  for i in range(config.num_hidden_layers ):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((f'blocks.{i}.norm1.weight', f'vit.encoder.layer.{i}.layernorm_before.weight') )
    rename_keys.append((f'blocks.{i}.norm1.bias', f'vit.encoder.layer.{i}.layernorm_before.bias') )
    rename_keys.append((f'blocks.{i}.attn.proj.weight', f'vit.encoder.layer.{i}.attention.output.dense.weight') )
    rename_keys.append((f'blocks.{i}.attn.proj.bias', f'vit.encoder.layer.{i}.attention.output.dense.bias') )
    rename_keys.append((f'blocks.{i}.norm2.weight', f'vit.encoder.layer.{i}.layernorm_after.weight') )
    rename_keys.append((f'blocks.{i}.norm2.bias', f'vit.encoder.layer.{i}.layernorm_after.bias') )
    rename_keys.append((f'blocks.{i}.mlp.fc1.weight', f'vit.encoder.layer.{i}.intermediate.dense.weight') )
    rename_keys.append((f'blocks.{i}.mlp.fc1.bias', f'vit.encoder.layer.{i}.intermediate.dense.bias') )
    rename_keys.append((f'blocks.{i}.mlp.fc2.weight', f'vit.encoder.layer.{i}.output.dense.weight') )
    rename_keys.append((f'blocks.{i}.mlp.fc2.bias', f'vit.encoder.layer.{i}.output.dense.bias') )

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ("""cls_token""", """vit.embeddings.cls_token"""),
          ("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
          ("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
          ("""pos_embed""", """vit.embeddings.position_embeddings"""),
      ] )

  if base_model:
    # layernorm + pooler
    rename_keys.extend(
        [
            ("""norm.weight""", """layernorm.weight"""),
            ("""norm.bias""", """layernorm.bias"""),
            ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
            ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
        ] )

    # if just the base model, we should remove "vit" from all keys that start with "vit"
    lowerCAmelCase_      :				List[str]      =       [(pair[0], pair[1][4:]) if pair[1].startswith("""vit""" ) else pair for pair in rename_keys]
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ("""norm.weight""", """vit.layernorm.weight"""),
            ("""norm.bias""", """vit.layernorm.bias"""),
            ("""head.weight""", """classifier.weight"""),
            ("""head.bias""", """classifier.bias"""),
        ] )

  return rename_keys







def     UpperCamelCase_      (   A__ :       Any							,   A__ :       Any							,   A__ :       Tuple=False ):
  '''simple docstring'''





  for i in range(config.num_hidden_layers ):
    if base_model:
      lowerCAmelCase_      :				Optional[Any]      =       """"""
    else:
      lowerCAmelCase_      :				Optional[Any]      =       """vit."""
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    lowerCAmelCase_      :				List[Any]      =       state_dict.pop(f'blocks.{i}.attn.qkv.weight' )
    lowerCAmelCase_      :				Union[str, Any]      =       state_dict.pop(f'blocks.{i}.attn.qkv.bias' )
    # next, add query, keys and values (in that order) to the state dict
    lowerCAmelCase_      :				Dict      =       in_proj_weight[
        : config.hidden_size, :
    ]
    lowerCAmelCase_      :				List[Any]      =       in_proj_bias[: config.hidden_size]
    lowerCAmelCase_      :				Union[str, Any]      =       in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    lowerCAmelCase_      :				List[str]      =       in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    lowerCAmelCase_      :				Any      =       in_proj_weight[
        -config.hidden_size :, :
    ]
    lowerCAmelCase_      :				Union[str, Any]      =       in_proj_bias[-config.hidden_size :]







def     UpperCamelCase_      (   A__ :       str ):
  '''simple docstring'''





  lowerCAmelCase_      :				Dict      =       ["""head.weight""", """head.bias"""]
  for k in ignore_keys:
    state_dict.pop(A__							,   A__ )







def     UpperCamelCase_      (   A__ :       List[Any]							,   A__ :       Optional[Any]							,   A__ :       Dict ):
  '''simple docstring'''





  lowerCAmelCase_      :				Tuple      =       dct.pop(A__ )
  lowerCAmelCase_      :				Tuple      =       val







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				str      =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
  lowerCAmelCase_      :				Optional[int]      =       Image.open(requests.get(A__							,   stream=A__ ).raw )
  return im







@torch.no_grad()
def     UpperCamelCase_      (   A__ :       Union[str, Any]							,   A__ :       List[Any] ):
  '''simple docstring'''





  lowerCAmelCase_      :				Optional[Any]      =       ViTConfig()
  lowerCAmelCase_      :				Any      =       False
  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  if vit_name[-5:] == "in21k":
    lowerCAmelCase_      :				int      =       True
    lowerCAmelCase_      :				Tuple      =       int(vit_name[-12:-10] )
    lowerCAmelCase_      :				Optional[int]      =       int(vit_name[-9:-6] )
  else:
    lowerCAmelCase_      :				Optional[int]      =       10_00
    lowerCAmelCase_      :				Tuple      =       """huggingface/label-files"""
    lowerCAmelCase_      :				Any      =       """imagenet-1k-id2label.json"""
    lowerCAmelCase_      :				Dict      =       json.load(open(hf_hub_download(A__							,   A__							,   repo_type="""dataset""" )							,   """r""" ) )
    lowerCAmelCase_      :				Union[str, Any]      =       {int(A__ ): v for k, v in idalabel.items()}
    lowerCAmelCase_      :				Union[str, Any]      =       idalabel
    lowerCAmelCase_      :				Union[str, Any]      =       {v: k for k, v in idalabel.items()}
    lowerCAmelCase_      :				Optional[int]      =       int(vit_name[-6:-4] )
    lowerCAmelCase_      :				Dict      =       int(vit_name[-3:] )
  # size of the architecture
  if "deit" in vit_name:
    if vit_name[9:].startswith("""tiny""" ):
      lowerCAmelCase_      :				int      =       1_92
      lowerCAmelCase_      :				List[str]      =       7_68
      lowerCAmelCase_      :				List[str]      =       12
      lowerCAmelCase_      :				int      =       3
    elif vit_name[9:].startswith("""small""" ):
      lowerCAmelCase_      :				Optional[Any]      =       3_84
      lowerCAmelCase_      :				Optional[int]      =       15_36
      lowerCAmelCase_      :				Dict      =       12
      lowerCAmelCase_      :				str      =       6
    else:
      pass
  else:
    if vit_name[4:].startswith("""small""" ):
      lowerCAmelCase_      :				Tuple      =       7_68
      lowerCAmelCase_      :				Any      =       23_04
      lowerCAmelCase_      :				List[str]      =       8
      lowerCAmelCase_      :				List[str]      =       8
    elif vit_name[4:].startswith("""base""" ):
      pass
    elif vit_name[4:].startswith("""large""" ):
      lowerCAmelCase_      :				Dict      =       10_24
      lowerCAmelCase_      :				List[Any]      =       40_96
      lowerCAmelCase_      :				Any      =       24
      lowerCAmelCase_      :				List[str]      =       16
    elif vit_name[4:].startswith("""huge""" ):
      lowerCAmelCase_      :				Optional[int]      =       12_80
      lowerCAmelCase_      :				Dict      =       51_20
      lowerCAmelCase_      :				Union[str, Any]      =       32
      lowerCAmelCase_      :				Optional[int]      =       16

    # load original model from timm
  lowerCAmelCase_      :				Union[str, Any]      =       timm.create_model(A__							,   pretrained=A__ )
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  lowerCAmelCase_      :				int      =       timm_model.state_dict()
  if base_model:
    remove_classification_head_(A__ )
  lowerCAmelCase_      :				str      =       create_rename_keys(A__							,   A__ )
  for src, dest in rename_keys:
    rename_key(A__							,   A__							,   A__ )
  read_in_q_k_v(A__							,   A__							,   A__ )

  # load HuggingFace model
  if vit_name[-5:] == "in21k":
    lowerCAmelCase_      :				int      =       ViTModel(A__ ).eval()
  else:
    lowerCAmelCase_      :				Optional[int]      =       ViTForImageClassification(A__ ).eval()
  model.load_state_dict(A__ )

  # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
  if "deit" in vit_name:
    lowerCAmelCase_      :				Any      =       DeiTImageProcessor(size=config.image_size )
  else:
    lowerCAmelCase_      :				Any      =       ViTImageProcessor(size=config.image_size )
  lowerCAmelCase_      :				Tuple      =       image_processor(images=prepare_img()							,   return_tensors="""pt""" )
  lowerCAmelCase_      :				int      =       encoding["""pixel_values"""]
  lowerCAmelCase_      :				int      =       model(A__ )

  if base_model:
    lowerCAmelCase_      :				Union[str, Any]      =       timm_model.forward_features(A__ )
    assert timm_pooled_output.shape == outputs.pooler_output.shape
    assert torch.allclose(A__							,   outputs.pooler_output							,   atol=1E-3 )
  else:
    lowerCAmelCase_      :				Union[str, Any]      =       timm_model(A__ )
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(A__							,   outputs.logits							,   atol=1E-3 )

  Path(A__ ).mkdir(exist_ok=A__ )
  print(f'Saving model {vit_name} 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__":
  __A :  Optional[int]    							=				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--vit_name",
      default="vit_base_patch16_224",
      type=str,
      help="Name of the ViT timm model you'd like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
  )

  __A :  Union[str, Any]    							=				parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)



