






'''simple docstring'''



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()
SCREAMING_SNAKE_CASE						=	logging.get_logger()


def 							lowercase_				(    __A :       int					,      __A :       str					,      __A :       LevitConfig					,      __A :       Path					,      __A :       bool = True			)       ->      int:



 """simple docstring"""




 print(F'Converting {name}...'			)

 with torch.no_grad():
  if hidden_sizes == 1_2_8:
   if name[-1] == "S":
    lowercase					:   Tuple				  =timm.create_model('''levit_128s'''					,      pretrained=__A			)
   else:
    lowercase					:   int				  =timm.create_model('''levit_128'''					,      pretrained=__A			)
  if hidden_sizes == 1_9_2:
   lowercase					:   List[Any]				  =timm.create_model('''levit_192'''					,      pretrained=__A			)
  if hidden_sizes == 2_5_6:
   lowercase					:   str				  =timm.create_model('''levit_256'''					,      pretrained=__A			)
  if hidden_sizes == 3_8_4:
   lowercase					:   Optional[int]				  =timm.create_model('''levit_384'''					,      pretrained=__A			)

  from_model.eval()
  lowercase					:   List[str]				  =LevitForImageClassificationWithTeacher(__A			).eval()
  lowercase					:   Optional[Any]				  =OrderedDict()

  lowercase					:   Any				  =from_model.state_dict()
  lowercase					:   Union[str, Any]				  =list(from_model.state_dict().keys()			)
  lowercase					:   List[str]				  =list(our_model.state_dict().keys()			)
  print(len(__A			)					,      len(__A			)			)
  for i in range(len(__A			)			):
   lowercase					:   int				  =weights[og_keys[i]]
  our_model.load_state_dict(__A			)

  lowercase					:   Dict				  =torch.randn((2, 3, 2_2_4, 2_2_4)			)
  lowercase					:   Optional[int]				  =from_model(__A			)
  lowercase					:   Union[str, Any]				  =our_model(__A			).logits

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

 lowercase					:   List[Any]				  =name
 print(__A			)

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

  print(F'Pushed {checkpoint_name}'			)


def 							lowercase_				(    __A :       Path					,      __A :       str = None					,      __A :       bool = True			)       ->      str:



 """simple docstring"""




 lowercase					:   Tuple				  ='''imagenet-1k-id2label.json'''
 lowercase					:   List[Any]				  =1_0_0_0
 lowercase					:   List[str]				  =(1, num_labels)

 lowercase					:   int				  ='''huggingface/label-files'''
 lowercase					:   Any				  =num_labels
 lowercase					:   Optional[int]				  =json.load(open(hf_hub_download(__A					,      __A					,      repo_type='''dataset'''			)					,      '''r'''			)			)
 lowercase					:   Union[str, Any]				  ={int(__A			): v for k, v in idalabel.items()}

 lowercase					:   int				  =idalabel
 lowercase					:   Union[str, Any]				  ={v: k for k, v in idalabel.items()}

 lowercase					:   Optional[Any]				  =partial(__A					,      num_labels=__A					,      idalabel=__A					,      labelaid=__A			)

 lowercase					:   Dict				  ={
     '''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,
 }

 lowercase					:   Any				  ={
     '''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]					,      __A					,      names_to_config[model_name]					,      __A					,      __A			)
 else:
  for model_name, config in names_to_config.items():
   convert_weight_and_push(names_to_hidden_sizes[model_name]					,      __A					,      __A					,      __A					,      __A			)
 return config, expected_shape


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE						=	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',
      )

      SCREAMING_SNAKE_CASE						=	parser.parse_args()
      SCREAMING_SNAKE_CASE						=	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)











'''simple docstring'''



SCREAMING_SNAKE_CASE						=	'Alexander Joslin'

import operator as op

from .stack import Stack


def 							lowercase_				(    __A :       str			)       ->      int:



 """simple docstring"""




 lowercase					:   int				  ={'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}

 lowercase					:   Stack[int]				  =Stack()
 lowercase					:   Stack[str]				  =Stack()

 for i in equation:
  if i.isdigit():
   # RULE 1
   operand_stack.push(int(__A			)			)
  elif i in operators:
   # RULE 2
   operator_stack.push(__A			)
  elif i == ")":
   # RULE 4
   lowercase					:   Optional[Any]				  =operator_stack.peek()
   operator_stack.pop()
   lowercase					:   Optional[Any]				  =operand_stack.peek()
   operand_stack.pop()
   lowercase					:   Optional[Any]				  =operand_stack.peek()
   operand_stack.pop()

   lowercase					:   List[str]				  =operators[opr](__A					,      __A			)
   operand_stack.push(__A			)

    # RULE 5
 return operand_stack.peek()


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE						=	'(5 + ((4 * 2) * (2 + 3)))'
      # answer = 45
      print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")



