






'''simple docstring'''






import argparse
import json
from pathlib import Path

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

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


logging.set_verbosity_info()
__A    	=   logging.get_logger(__name__)




def        _SCREAMING_SNAKE_CASE  (				A  :       Optional[Any]  ,		A  :       Optional[Any]=False  )    -> Optional[Any]:







   """simple docstring"""
   __snake_case				:      str     							= []
   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'),
          ]  )

      # if just the base model, we should remove "vit" from all keys that start with "vit"
      __snake_case				:      int     							= [(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        _SCREAMING_SNAKE_CASE  (				A  :       str  ,		A  :       Dict  ,		A  :       Optional[Any]=False  )    -> int:







   """simple docstring"""
   for i in range(config.num_hidden_layers  ):
      if base_model:
         __snake_case				:      Optional[int]     							= ''
      else:
         __snake_case				:      List[str]     							= 'vit.'
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      __snake_case				:      List[str]     							= state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""  )
      __snake_case				:      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
      __snake_case				:      Any     							= in_proj_weight[
          : config.hidden_size, :
      ]
      __snake_case				:      Tuple     							= in_proj_bias[: config.hidden_size]
      __snake_case				:      Optional[int]     							= in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      __snake_case				:      Optional[Any]     							= in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      __snake_case				:      List[Any]     							= in_proj_weight[
          -config.hidden_size :, :
      ]
      __snake_case				:      Optional[int]     							= in_proj_bias[-config.hidden_size :]




def        _SCREAMING_SNAKE_CASE  (				A  :       str  )    -> Tuple:







   """simple docstring"""
   __snake_case				:      Optional[int]     							= ['head.weight', 'head.bias']
   for k in ignore_keys:
      state_dict.pop(A  ,		A  )




def        _SCREAMING_SNAKE_CASE  (				A  :       Tuple  ,		A  :       str  ,		A  :       Optional[Any]  )    -> List[Any]:







   """simple docstring"""
   __snake_case				:      List[str]     							= dct.pop(A  )
   __snake_case				:      List[str]     							= val




def        _SCREAMING_SNAKE_CASE  (				)    -> Union[str, Any]:







   """simple docstring"""
   __snake_case				:      Tuple     							= 'http://images.cocodataset.org/val2017/000000039769.jpg'
   __snake_case				:      Optional[Any]     							= Image.open(requests.get(A  ,		stream=A  ).raw  )
   return im




@torch.no_grad()
def        _SCREAMING_SNAKE_CASE  (				A  :       Tuple  ,		A  :       Union[str, Any]  ,		A  :       Dict=True  )    -> int:







   """simple docstring"""
   __snake_case				:      Optional[int]     							= ViTConfig()
   # patch_size
   if model_name[-1] == "8":
      __snake_case				:      Optional[int]     							= 8
   # set labels if required
   if not base_model:
      __snake_case				:      Any     							= 10_00
      __snake_case				:      Tuple     							= 'huggingface/label-files'
      __snake_case				:      str     							= 'imagenet-1k-id2label.json'
      __snake_case				:      int     							= json.load(open(hf_hub_download(A  ,		A  ,		repo_type='dataset'  )  ,		'r'  )  )
      __snake_case				:      Any     							= {int(A  ): v for k, v in idalabel.items()}
      __snake_case				:      Tuple     							= idalabel
      __snake_case				:      Optional[Any]     							= {v: k for k, v in idalabel.items()}
   # size of the architecture
   if model_name in ["dino_vits8", "dino_vits16"]:
      __snake_case				:      str     							= 3_84
      __snake_case				:      Dict     							= 15_36
      __snake_case				:      Tuple     							= 12
      __snake_case				:      Dict     							= 6

   # load original model from torch hub
   __snake_case				:      List[str]     							= torch.hub.load('facebookresearch/dino:main'  ,		A  )
   original_model.eval()

   # load state_dict of original model, remove and rename some keys
   __snake_case				:      List[Any]     							= original_model.state_dict()
   if base_model:
      remove_classification_head_(A  )
   __snake_case				:      Any     							= create_rename_keys(A  ,		base_model=A  )
   for src, dest in rename_keys:
      rename_key(A  ,		A  ,		A  )
   read_in_q_k_v(A  ,		A  ,		A  )

   # load HuggingFace model
   if base_model:
      __snake_case				:      Union[str, Any]     							= ViTModel(A  ,		add_pooling_layer=A  ).eval()
   else:
      __snake_case				:      Optional[Any]     							= ViTForImageClassification(A  ).eval()
   model.load_state_dict(A  )

   # Check outputs on an image, prepared by ViTImageProcessor
   __snake_case				:      Tuple     							= ViTImageProcessor()
   __snake_case				:      List[Any]     							= image_processor(images=prepare_img()  ,		return_tensors='pt'  )
   __snake_case				:      List[Any]     							= encoding['pixel_values']
   __snake_case				:      Optional[Any]     							= model(A  )

   if base_model:
      __snake_case				:      List[Any]     							= original_model(A  )
      assert torch.allclose(A  ,		outputs.last_hidden_state[:, 0, :]  ,		atol=1e-1  )
   else:
      __snake_case				:      str     							= original_model(A  )
      assert logits.shape == outputs.logits.shape
      assert torch.allclose(A  ,		outputs.logits  ,		atol=1e-3  )

   Path(A  ).mkdir(exist_ok=A  )
   print(F"""Saving model {model_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    	=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--model_name''',
          default='''dino_vitb16''',
          type=str,
          help='''Name of the model trained with DINO you\'d like to convert.''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
      )
      parser.add_argument(
          '''--base_model''',
          action='''store_true''',
          help='''Whether to only convert the base model (no projection head weights).''',
      )

      parser.set_defaults(base_model=True)
      __A    	=   parser.parse_args()
      convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)







'''simple docstring'''






def        _SCREAMING_SNAKE_CASE  (				A  :       list  )    -> list:







   """simple docstring"""
   __snake_case				:      Tuple     							= False
   while is_sorted is False:  # Until all the indices are traversed keep looping
      __snake_case				:      Optional[Any]     							= True
      for i in range(0  ,		len(A  ) - 1  ,		2  ):  # iterating over all even indices
         if input_list[i] > input_list[i + 1]:
            __snake_case							,__snake_case				:      int     							= input_list[i + 1], input_list[i]
            # swapping if elements not in order
            __snake_case				:      List[Any]     							= False

      for i in range(1  ,		len(A  ) - 1  ,		2  ):  # iterating over all odd indices
         if input_list[i] > input_list[i + 1]:
            __snake_case							,__snake_case				:      Tuple     							= input_list[i + 1], input_list[i]
            # swapping if elements not in order
            __snake_case				:      Any     							= False
   return input_list


if __name__ == "__main__":
      print('''Enter list to be sorted''')
      __A    	=   [int(x) for x in input().split()]
      # inputing elements of the list in one line
      __A    	=   odd_even_sort(input_list)
      print('''The sorted list is''')
      print(sorted_list)