



import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A     :  Tuple					     =       logging.get_logger()


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase = True				):
      print(F'''Converting {name}...'''				)

      with torch.no_grad():
            if hidden_sizes == 1_2_8:
                  if name[-1] == "S":
                        SCREAMING_SNAKE_CASE_          =  timm.create_model("levit_128s"     ,      pretrained=__UpperCamelCase				)
                  else:
                        SCREAMING_SNAKE_CASE_          =  timm.create_model("levit_128"     ,      pretrained=__UpperCamelCase				)
            if hidden_sizes == 1_9_2:
                  SCREAMING_SNAKE_CASE_          =  timm.create_model("levit_192"     ,      pretrained=__UpperCamelCase				)
            if hidden_sizes == 2_5_6:
                  SCREAMING_SNAKE_CASE_          =  timm.create_model("levit_256"     ,      pretrained=__UpperCamelCase				)
            if hidden_sizes == 3_8_4:
                  SCREAMING_SNAKE_CASE_          =  timm.create_model("levit_384"     ,      pretrained=__UpperCamelCase				)

            from_model.eval()
            SCREAMING_SNAKE_CASE_          =  LevitForImageClassificationWithTeacher(__UpperCamelCase				).eval()
            SCREAMING_SNAKE_CASE_          =  OrderedDict()

            SCREAMING_SNAKE_CASE_          =  from_model.state_dict()
            SCREAMING_SNAKE_CASE_          =  list(from_model.state_dict().keys()				)
            SCREAMING_SNAKE_CASE_          =  list(our_model.state_dict().keys()				)
            print(len(__UpperCamelCase				)     ,      len(__UpperCamelCase				)				)
            for i in range(len(__UpperCamelCase				)				):
                  SCREAMING_SNAKE_CASE_          =  weights[og_keys[i]]
            our_model.load_state_dict(__UpperCamelCase				)

            SCREAMING_SNAKE_CASE_          =  torch.randn((2, 3, 2_2_4, 2_2_4)				)
            SCREAMING_SNAKE_CASE_          =  from_model(__UpperCamelCase				)
            SCREAMING_SNAKE_CASE_          =  our_model(__UpperCamelCase				).logits

      assert torch.allclose(__UpperCamelCase     ,      __UpperCamelCase				), "The model logits don't match the original one."

      SCREAMING_SNAKE_CASE_          =  name
      print(__UpperCamelCase				)

      if push_to_hub:
            our_model.save_pretrained(save_directory / checkpoint_name				)
            SCREAMING_SNAKE_CASE_          =  LevitImageProcessor()
            image_processor.save_pretrained(save_directory / checkpoint_name				)

            print(F'''Pushed {checkpoint_name}'''				)


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase = None     ,      __UpperCamelCase = True				):
      SCREAMING_SNAKE_CASE_          =  "imagenet-1k-id2label.json"
      SCREAMING_SNAKE_CASE_          =  1_0_0_0
      SCREAMING_SNAKE_CASE_          =  (1, num_labels)

      SCREAMING_SNAKE_CASE_          =  "huggingface/label-files"
      SCREAMING_SNAKE_CASE_          =  num_labels
      SCREAMING_SNAKE_CASE_          =  json.load(open(hf_hub_download(__UpperCamelCase     ,      __UpperCamelCase     ,      repo_type="dataset"				)     ,      "r"				)				)
      SCREAMING_SNAKE_CASE_          =  {int(__UpperCamelCase				): v for k, v in idalabel.items()}

      SCREAMING_SNAKE_CASE_          =  idalabel
      SCREAMING_SNAKE_CASE_          =  {v: k for k, v in idalabel.items()}

      SCREAMING_SNAKE_CASE_          =  partial(__UpperCamelCase     ,      num_labels=__UpperCamelCase     ,      idalabel=__UpperCamelCase     ,      labelaid=__UpperCamelCase				)

      SCREAMING_SNAKE_CASE_          =  {
          "levit-128S": 1_2_8,
          "levit-128": 1_2_8,
          "levit-192": 1_9_2,
          "levit-256": 2_5_6,
          "levit-384": 3_8_4,
      }

      SCREAMING_SNAKE_CASE_          =  {
          "levit-128S": ImageNetPreTrainedConfig(
              hidden_sizes=[1_2_8, 2_5_6, 3_8_4]     ,      num_attention_heads=[4, 6, 8]     ,      depths=[2, 3, 4]     ,      key_dim=[1_6, 1_6, 1_6]     ,      drop_path_rate=0     ,      ),
          "levit-128": ImageNetPreTrainedConfig(
              hidden_sizes=[1_2_8, 2_5_6, 3_8_4]     ,      num_attention_heads=[4, 8, 1_2]     ,      depths=[4, 4, 4]     ,      key_dim=[1_6, 1_6, 1_6]     ,      drop_path_rate=0     ,      ),
          "levit-192": ImageNetPreTrainedConfig(
              hidden_sizes=[1_9_2, 2_8_8, 3_8_4]     ,      num_attention_heads=[3, 5, 6]     ,      depths=[4, 4, 4]     ,      key_dim=[3_2, 3_2, 3_2]     ,      drop_path_rate=0     ,      ),
          "levit-256": ImageNetPreTrainedConfig(
              hidden_sizes=[2_5_6, 3_8_4, 5_1_2]     ,      num_attention_heads=[4, 6, 8]     ,      depths=[4, 4, 4]     ,      key_dim=[3_2, 3_2, 3_2]     ,      drop_path_rate=0     ,      ),
          "levit-384": ImageNetPreTrainedConfig(
              hidden_sizes=[3_8_4, 5_1_2, 7_6_8]     ,      num_attention_heads=[6, 9, 1_2]     ,      depths=[4, 4, 4]     ,      key_dim=[3_2, 3_2, 3_2]     ,      drop_path_rate=0.1     ,      ),
      }

      if model_name:
            convert_weight_and_push(
                names_to_hidden_sizes[model_name]     ,      __UpperCamelCase     ,      names_to_config[model_name]     ,      __UpperCamelCase     ,      __UpperCamelCase				)
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(names_to_hidden_sizes[model_name]     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				)
      return config, expected_shape


if __name__ == "__main__":
      A     :  Optional[Any]					     =       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--model_name",
          default=None,
          type=str,
          help="The name of the model you wish to convert, it must be one of the supported Levit* architecture,",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path",
          default="levit-dump-folder/",
          type=Path,
          required=False,
          help="Path to the output PyTorch model directory.",
      )
      parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
      parser.add_argument(
          "--no-push_to_hub",
          dest="push_to_hub",
          action="store_false",
          help="Do not push model and image processor to the hub",
      )

      A     :  List[str]					     =       parser.parse_args()
      A     :  Path					     =       args.pytorch_dump_folder_path
      pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
      convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)



from __future__ import annotations


def      a__ (		__UpperCamelCase				):

      # preprocessing the first row
      for i in range(1     ,      len(matrix[0]				)				):
            matrix[0][i] += matrix[0][i - 1]

      # preprocessing the first column
      for i in range(1     ,      len(__UpperCamelCase				)				):
            matrix[i][0] += matrix[i - 1][0]

      # updating the path cost for current position
      for i in range(1     ,      len(__UpperCamelCase				)				):
            for j in range(1     ,      len(matrix[0]				)				):
                  matrix[i][j] += min(matrix[i - 1][j]     ,      matrix[i][j - 1]				)

      return matrix[-1][-1]


if __name__ == "__main__":
      import doctest

      doctest.testmod()


