






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()
lowerCamelCase__							=     logging.get_logger(__name__)






def   UpperCamelCase  (       snake_case__     :   Optional[int]	,snake_case__     :   Optional[Any]=False  ):




     '''simple docstring'''






     __snake_case			:Optional[int]        =						[]
     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   UpperCamelCase  (       snake_case__     :   Tuple	,snake_case__     :   Union[str, Any]	,snake_case__     :   Optional[Any]=False  ):




     '''simple docstring'''






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






def   UpperCamelCase  (       snake_case__     :   List[str]  ):




     '''simple docstring'''






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






def   UpperCamelCase  (       snake_case__     :   Optional[Any]	,snake_case__     :   List[Any]	,snake_case__     :   Union[str, Any]  ):




     '''simple docstring'''






     __snake_case			:Dict        =						dct.pop(a__  )
     __snake_case			:int        =						val






def   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case			:List[str]        =						"""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   UpperCamelCase  (       snake_case__     :   Tuple	,snake_case__     :   Optional[Any]	,snake_case__     :   int=True  ):




     '''simple docstring'''






     __snake_case			:int        =						ViTConfig()
     # patch_size
     if model_name[-1] == "8":
          __snake_case			:Dict        =						8
     # set labels if required
     if not base_model:
          __snake_case			:Optional[Any]        =						1000
          __snake_case			:List[str]        =						"""huggingface/label-files"""
          __snake_case			:List[Any]        =						"""imagenet-1k-id2label.json"""
          __snake_case			:int        =						json.load(open(hf_hub_download(a__	,a__	,repo_type="""dataset"""  )	,"""r"""  )  )
          __snake_case			:Optional[int]        =						{int(a__  ): v for k, v in idalabel.items()}
          __snake_case			:List[Any]        =						idalabel
          __snake_case			:str        =						{v: k for k, v in idalabel.items()}
     # size of the architecture
     if model_name in ["dino_vits8", "dino_vits16"]:
          __snake_case			:Union[str, Any]        =						384
          __snake_case			:str        =						1536
          __snake_case			:str        =						12
          __snake_case			:Optional[int]        =						6

     # load original model from torch hub
     __snake_case			:Optional[int]        =						torch.hub.load("""facebookresearch/dino:main"""	,a__  )
     original_model.eval()

     # load state_dict of original model, remove and rename some keys
     __snake_case			:Optional[Any]        =						original_model.state_dict()
     if base_model:
          remove_classification_head_(a__  )
     __snake_case			:int        =						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			:Optional[Any]        =						ViTModel(a__	,add_pooling_layer=a__  ).eval()
     else:
          __snake_case			:Dict        =						ViTForImageClassification(a__  ).eval()
     model.load_state_dict(a__  )

     # Check outputs on an image, prepared by ViTImageProcessor
     __snake_case			:Dict        =						ViTImageProcessor()
     __snake_case			:Dict        =						image_processor(images=prepare_img()	,return_tensors="""pt"""  )
     __snake_case			:int        =						encoding["""pixel_values"""]
     __snake_case			:List[str]        =						model(a__  )

     if base_model:
          __snake_case			:Tuple        =						original_model(a__  )
          assert torch.allclose(a__	,outputs.last_hidden_state[:, 0, :]	,atol=1e-1  )
     else:
          __snake_case			:List[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__":
     lowerCamelCase__							=     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)
     lowerCamelCase__							=     parser.parse_args()
     convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)










import os






def   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case			:List[str]          =						os.path.dirname(os.path.realpath(snake_case__  )  )
     __snake_case			:Union[str, Any]          =						os.path.join(snake_case__	,"""triangle.txt"""  )

     with open(snake_case__  ) as f:
          __snake_case			:int          =						f.readlines()

     __snake_case			:int          =						[]
     for line in triangle:
          __snake_case			:List[Any]          =						[]
          for number in line.strip().split(""" """  ):
               numbers_from_line.append(int(snake_case__  )  )
          a.append(snake_case__  )

     for i in range(1	,len(snake_case__  )  ):
          for j in range(len(a[i]  )  ):
               __snake_case			:Union[str, Any]          =						a[i - 1][j] if j != len(a[i - 1]  ) else 0
               __snake_case			:Dict          =						a[i - 1][j - 1] if j > 0 else 0
               a[i][j] += max(snake_case__	,snake_case__  )
     return max(a[-1]  )


if __name__ == "__main__":
     print(solution())


