






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			:					Dict         =	logging.get_logger(__name__)





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





def       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase								=					["head.weight", "head.bias"]
     for k in ignore_keys:
          state_dict.pop(__snake_case					, __snake_case       )





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case       ):
     __lowerCAmelCase								=					dct.pop(__snake_case       )
     __lowerCAmelCase								=					val





def       __lowerCAmelCase      (						):
     __lowerCAmelCase								=					"http://images.cocodataset.org/val2017/000000039769.jpg"
     __lowerCAmelCase								=					Image.open(requests.get(__snake_case					, stream=__snake_case       ).raw       )
     return im





@torch.no_grad()
def       __lowerCAmelCase      (						__snake_case					, __snake_case       ):
     __lowerCAmelCase								=					ViTConfig()
     __lowerCAmelCase								=					False
     # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
     if vit_name[-5:] == "in21k":
          __lowerCAmelCase								=					True
          __lowerCAmelCase								=					int(vit_name[-12:-10]       )
          __lowerCAmelCase								=					int(vit_name[-9:-6]       )
     else:
          __lowerCAmelCase								=					1000
          __lowerCAmelCase								=					"huggingface/label-files"
          __lowerCAmelCase								=					"imagenet-1k-id2label.json"
          __lowerCAmelCase								=					json.load(open(hf_hub_download(__snake_case					, __snake_case					, repo_type="dataset"       )					, "r"       )       )
          __lowerCAmelCase								=					{int(__snake_case       ): v for k, v in idalabel.items()}
          __lowerCAmelCase								=					idalabel
          __lowerCAmelCase								=					{v: k for k, v in idalabel.items()}
          __lowerCAmelCase								=					int(vit_name[-6:-4]       )
          __lowerCAmelCase								=					int(vit_name[-3:]       )
     # size of the architecture
     if "deit" in vit_name:
          if vit_name[9:].startswith("tiny"       ):
               __lowerCAmelCase								=					192
               __lowerCAmelCase								=					768
               __lowerCAmelCase								=					12
               __lowerCAmelCase								=					3
          elif vit_name[9:].startswith("small"       ):
               __lowerCAmelCase								=					384
               __lowerCAmelCase								=					1536
               __lowerCAmelCase								=					12
               __lowerCAmelCase								=					6
          else:
               pass
     else:
          if vit_name[4:].startswith("small"       ):
               __lowerCAmelCase								=					768
               __lowerCAmelCase								=					2304
               __lowerCAmelCase								=					8
               __lowerCAmelCase								=					8
          elif vit_name[4:].startswith("base"       ):
               pass
          elif vit_name[4:].startswith("large"       ):
               __lowerCAmelCase								=					1024
               __lowerCAmelCase								=					4096
               __lowerCAmelCase								=					24
               __lowerCAmelCase								=					16
          elif vit_name[4:].startswith("huge"       ):
               __lowerCAmelCase								=					1280
               __lowerCAmelCase								=					5120
               __lowerCAmelCase								=					32
               __lowerCAmelCase								=					16

    # load original model from timm
     __lowerCAmelCase								=					timm.create_model(__snake_case					, pretrained=__snake_case       )
     timm_model.eval()

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

     # load HuggingFace model
     if vit_name[-5:] == "in21k":
          __lowerCAmelCase								=					ViTModel(__snake_case       ).eval()
     else:
          __lowerCAmelCase								=					ViTForImageClassification(__snake_case       ).eval()
     model.load_state_dict(__snake_case       )

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

     if base_model:
          __lowerCAmelCase								=					timm_model.forward_features(__snake_case       )
          assert timm_pooled_output.shape == outputs.pooler_output.shape
          assert torch.allclose(__snake_case					, outputs.pooler_output					, atol=1E-3       )
     else:
          __lowerCAmelCase								=					timm_model(__snake_case       )
          assert timm_logits.shape == outputs.logits.shape
          assert torch.allclose(__snake_case					, outputs.logits					, atol=1E-3       )

     Path(__snake_case       ).mkdir(exist_ok=__snake_case       )
     print(F"""Saving model {vit_name} to {pytorch_dump_folder_path}"""       )
     model.save_pretrained(__snake_case       )
     print(F"""Saving image processor to {pytorch_dump_folder_path}"""       )
     image_processor.save_pretrained(__snake_case       )


if __name__ == "__main__":
 lowerCamelCase			:					List[Any]         =	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			:					int         =	parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)














def       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase								=					len(__snake_case       )
     while cur > 1:
          # Find the maximum number in arr
          __lowerCAmelCase								=					arr.index(max(arr[0:cur]       )       )
          # Reverse from 0 to mi
          __lowerCAmelCase								=					arr[mi::-1] + arr[mi + 1 : len(__snake_case       )]
          # Reverse whole list
          __lowerCAmelCase								=					arr[cur - 1 :: -1] + arr[cur : len(__snake_case       )]
          cur -= 1
     return arr


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






