





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()
_UpperCAmelCase				:       Tuple	   =				logging.get_logger()


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase = True )			->     int:
    print(F"""Converting {name}...""" )

    with torch.no_grad():
        if hidden_sizes == 128:
            if name[-1] == "S":
                lowerCamelCase__    :      Union[str, Any]					 =		timm.create_model('levit_128s'						,	pretrained=_UpperCAmelCase )
            else:
                lowerCamelCase__    :      int					 =		timm.create_model('levit_128'						,	pretrained=_UpperCAmelCase )
        if hidden_sizes == 192:
            lowerCamelCase__    :      List[str]					 =		timm.create_model('levit_192'						,	pretrained=_UpperCAmelCase )
        if hidden_sizes == 256:
            lowerCamelCase__    :      List[Any]					 =		timm.create_model('levit_256'						,	pretrained=_UpperCAmelCase )
        if hidden_sizes == 384:
            lowerCamelCase__    :      int					 =		timm.create_model('levit_384'						,	pretrained=_UpperCAmelCase )

        from_model.eval()
        lowerCamelCase__    :      Optional[Any]					 =		LevitForImageClassificationWithTeacher(_UpperCAmelCase ).eval()
        lowerCamelCase__    :      List[str]					 =		OrderedDict()

        lowerCamelCase__    :      str					 =		from_model.state_dict()
        lowerCamelCase__    :      Optional[Any]					 =		list(from_model.state_dict().keys() )
        lowerCamelCase__    :      str					 =		list(our_model.state_dict().keys() )
        print(len(_UpperCAmelCase )						,	len(_UpperCAmelCase ) )
        for i in range(len(_UpperCAmelCase ) ):
            lowerCamelCase__    :      List[Any]					 =		weights[og_keys[i]]
        our_model.load_state_dict(_UpperCAmelCase )

        lowerCamelCase__    :      int					 =		torch.randn((2, 3, 224, 224) )
        lowerCamelCase__    :      Optional[int]					 =		from_model(_UpperCAmelCase )
        lowerCamelCase__    :      Any					 =		our_model(_UpperCAmelCase ).logits

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

    lowerCamelCase__    :      Optional[Any]					 =		name
    print(_UpperCAmelCase )

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

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


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase = None						,	_UpperCAmelCase = True )			->     List[str]:
    lowerCamelCase__    :      Optional[int]					 =		'imagenet-1k-id2label.json'
    lowerCamelCase__    :      Tuple					 =		1000
    lowerCamelCase__    :      Union[str, Any]					 =		(1, num_labels)

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

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

    lowerCamelCase__    :      Union[str, Any]					 =		partial(_UpperCAmelCase						,	num_labels=_UpperCAmelCase						,	idalabel=_UpperCAmelCase						,	labelaid=_UpperCAmelCase )

    lowerCamelCase__    :      int					 =		{
        'levit-128S': 128,
        'levit-128': 128,
        'levit-192': 192,
        'levit-256': 256,
        'levit-384': 384,
    }

    lowerCamelCase__    :      int					 =		{
        'levit-128S': ImageNetPreTrainedConfig(
            hidden_sizes=[128, 256, 384]						,	num_attention_heads=[4, 6, 8]						,	depths=[2, 3, 4]						,	key_dim=[16, 16, 16]						,	drop_path_rate=0						,	),
        'levit-128': ImageNetPreTrainedConfig(
            hidden_sizes=[128, 256, 384]						,	num_attention_heads=[4, 8, 12]						,	depths=[4, 4, 4]						,	key_dim=[16, 16, 16]						,	drop_path_rate=0						,	),
        'levit-192': ImageNetPreTrainedConfig(
            hidden_sizes=[192, 288, 384]						,	num_attention_heads=[3, 5, 6]						,	depths=[4, 4, 4]						,	key_dim=[32, 32, 32]						,	drop_path_rate=0						,	),
        'levit-256': ImageNetPreTrainedConfig(
            hidden_sizes=[256, 384, 512]						,	num_attention_heads=[4, 6, 8]						,	depths=[4, 4, 4]						,	key_dim=[32, 32, 32]						,	drop_path_rate=0						,	),
        'levit-384': ImageNetPreTrainedConfig(
            hidden_sizes=[384, 512, 768]						,	num_attention_heads=[6, 9, 12]						,	depths=[4, 4, 4]						,	key_dim=[32, 32, 32]						,	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__":
    _UpperCAmelCase				:       Union[str, 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""",
    )

    _UpperCAmelCase				:       Optional[Any]	   =				parser.parse_args()
    _UpperCAmelCase				:       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)









# using dfs for finding eulerian path traversal




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Any		,			SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      Optional[int]=None     ):
       '''simple docstring'''





       _lowerCamelCase       :		Optional[Any]		=(path or []) + [u]
       for v in graph[u]:
              if visited_edge[u][v] is False:
                     _lowerCamelCase	,     _lowerCamelCase       :		Dict		=True, True
                     _lowerCamelCase       :		Optional[Any]		=dfs(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       return path




def 	a_      (    SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      Optional[Any]     ):
       '''simple docstring'''





       _lowerCamelCase       :		Union[str, Any]		=0
       _lowerCamelCase       :		Union[str, Any]		=-1
       for i in range(SCREAMING_SNAKE_CASE__     ):
              if i not in graph.keys():
                     continue
              if len(graph[i]     ) % 2 == 1:
                     odd_degree_nodes += 1
                     _lowerCamelCase       :		Tuple		=i
       if odd_degree_nodes == 0:
              return 1, odd_node
       if odd_degree_nodes == 2:
              return 2, odd_node
       return 3, odd_node




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Union[str, Any]		,			SCREAMING_SNAKE_CASE__					:      Any     ):
       '''simple docstring'''





       _lowerCamelCase       :		Tuple		=[[False for _ in range(max_node + 1     )] for _ in range(max_node + 1     )]
       _lowerCamelCase	,     _lowerCamelCase       :		Optional[int]		=check_circuit_or_path(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       if check == 3:
              print('graph is not Eulerian'     )
              print('no path'     )
              return
       _lowerCamelCase       :		Any		=1
       if check == 2:
              _lowerCamelCase       :		Tuple		=odd_node
              print('graph has a Euler path'     )
       if check == 1:
              print('graph has a Euler cycle'     )
       _lowerCamelCase       :		int		=dfs(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       print(SCREAMING_SNAKE_CASE__     )




def 	a_      (    ):
       '''simple docstring'''





       _lowerCamelCase       :		Union[str, Any]		={1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
       _lowerCamelCase       :		str		={1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
       _lowerCamelCase       :		List[Any]		={1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
       _lowerCamelCase       :		Any		={1: [2, 3], 2: [1, 3], 3: [1, 2]}
       _lowerCamelCase       :		Dict		={
           1: [],
           2: []
           # all degree is zero
       }
       _lowerCamelCase       :		str		=10
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 main()


