



'''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()
_lowerCamelCase      :			str       =      logging.get_logger(__name__)







def 							__lowerCamelCase							(  A__       ,		A__=False       )			->      Union[str, Any]:






   """simple docstring"""
   UpperCamelCase      							=    []
   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"
      UpperCamelCase      							=    [(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 							__lowerCamelCase							(  A__       ,		A__       ,		A__=False       )			->      Dict:






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







def 							__lowerCamelCase							(  A__       )			->      str:






   """simple docstring"""
   UpperCamelCase      							=    ['head.weight', 'head.bias']
   for k in ignore_keys:
      state_dict.pop(__a       ,		__a       )







def 							__lowerCamelCase							(  A__       ,		A__       ,		A__       )			->      List[Any]:






   """simple docstring"""
   UpperCamelCase      							=    dct.pop(__a       )
   UpperCamelCase      							=    val







def 							__lowerCamelCase							(  )			->      Tuple:






   """simple docstring"""
   UpperCamelCase      							=    'http://images.cocodataset.org/val2017/000000039769.jpg'
   UpperCamelCase      							=    Image.open(requests.get(__a       ,		stream=__a       ).raw       )
   return im







@torch.no_grad()
def 							__lowerCamelCase							(  A__       ,		A__       )			->      Union[str, Any]:






   """simple docstring"""
   UpperCamelCase      							=    ViTConfig()
   UpperCamelCase      							=    False
   # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
   if vit_name[-5:] == "in21k":
      UpperCamelCase      							=    True
      UpperCamelCase      							=    int(vit_name[-12:-10]       )
      UpperCamelCase      							=    int(vit_name[-9:-6]       )
   else:
      UpperCamelCase      							=    1_000
      UpperCamelCase      							=    'huggingface/label-files'
      UpperCamelCase      							=    'imagenet-1k-id2label.json'
      UpperCamelCase      							=    json.load(open(hf_hub_download(__a       ,		__a       ,		repo_type='dataset'       )       ,		'r'       )       )
      UpperCamelCase      							=    {int(__a       ): v for k, v in idalabel.items()}
      UpperCamelCase      							=    idalabel
      UpperCamelCase      							=    {v: k for k, v in idalabel.items()}
      UpperCamelCase      							=    int(vit_name[-6:-4]       )
      UpperCamelCase      							=    int(vit_name[-3:]       )
   # size of the architecture
   if "deit" in vit_name:
      if vit_name[9:].startswith('tiny'       ):
         UpperCamelCase      							=    192
         UpperCamelCase      							=    768
         UpperCamelCase      							=    12
         UpperCamelCase      							=    3
      elif vit_name[9:].startswith('small'       ):
         UpperCamelCase      							=    384
         UpperCamelCase      							=    1_536
         UpperCamelCase      							=    12
         UpperCamelCase      							=    6
      else:
         pass
   else:
      if vit_name[4:].startswith('small'       ):
         UpperCamelCase      							=    768
         UpperCamelCase      							=    2_304
         UpperCamelCase      							=    8
         UpperCamelCase      							=    8
      elif vit_name[4:].startswith('base'       ):
         pass
      elif vit_name[4:].startswith('large'       ):
         UpperCamelCase      							=    1_024
         UpperCamelCase      							=    4_096
         UpperCamelCase      							=    24
         UpperCamelCase      							=    16
      elif vit_name[4:].startswith('huge'       ):
         UpperCamelCase      							=    1_280
         UpperCamelCase      							=    5_120
         UpperCamelCase      							=    32
         UpperCamelCase      							=    16

    # load original model from timm
   UpperCamelCase      							=    timm.create_model(__a       ,		pretrained=__a       )
   timm_model.eval()

   # load state_dict of original model, remove and rename some keys
   UpperCamelCase      							=    timm_model.state_dict()
   if base_model:
      remove_classification_head_(__a       )
   UpperCamelCase      							=    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":
      UpperCamelCase      							=    ViTModel(__a       ).eval()
   else:
      UpperCamelCase      							=    ViTForImageClassification(__a       ).eval()
   model.load_state_dict(__a       )

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

   if base_model:
      UpperCamelCase      							=    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:
      UpperCamelCase      							=    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__":
   _lowerCamelCase      :			str       =      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."
   )

   _lowerCamelCase      :			Optional[int]       =      parser.parse_args()
   convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)





def   _lowercase(					__a				:						list[int]     ):
							a__      =len(__a     )
							for i in range(__a     ):
														for j in range(i + 1    ,     __a     ):
																					if numbers[j] < numbers[i]:
																												a__    ,     a__      =numbers[j], numbers[i]
							return numbers


if __name__ == "__main__":
	_lowerCAmelCase:						Tuple   				=       input('Enter numbers separated by a comma:\n').strip()
	_lowerCAmelCase:						int   				=       [int(item) for item in user_input.split(',')]
	print(exchange_sort(unsorted))

