





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					:				Any				=			logging.get_logger()


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ = True	)				->						Dict:
    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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ = None ,							lowerCAmelCase_ = True	)				->						Any:
    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[int]				=			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					:				Optional[Any]				=			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)







'''simple docstring'''




a__					:			Optional[Any]          =     '''Alexander Joslin'''

import operator as op

from .stack import Stack




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->int:
	snake_case__	       =   {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

	snake_case__	       =   Stack()
	snake_case__	       =   Stack()

	for i in equation:
		if i.isdigit():
			# RULE 1
			operand_stack.push(int(UpperCAmelCase_							)							)
		elif i in operators:
			# RULE 2
			operator_stack.push(UpperCAmelCase_							)
		elif i == ")":
			# RULE 4
			snake_case__	       =   operator_stack.peek()
			operator_stack.pop()
			snake_case__	       =   operand_stack.peek()
			operand_stack.pop()
			snake_case__	       =   operand_stack.peek()
			operand_stack.pop()

			snake_case__	       =   operators[opr](UpperCAmelCase_					,				UpperCAmelCase_							)
			operand_stack.push(UpperCAmelCase_							)

    # RULE 5
	return operand_stack.peek()


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



