

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()
_lowerCAmelCase  :      int			 =logging.get_logger()



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE = True      ):
	print(f"Converting {name}..."      )

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

		from_model.eval()
		UpperCAmelCase__:	str									= LevitForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE      ).eval()
		UpperCAmelCase__:	int									= OrderedDict()

		UpperCAmelCase__:	Dict									= from_model.state_dict()
		UpperCAmelCase__:	List[str]									= list(from_model.state_dict().keys()      )
		UpperCAmelCase__:	Dict									= list(our_model.state_dict().keys()      )
		print(len(SCREAMING_SNAKE_CASE      )	,len(SCREAMING_SNAKE_CASE      )      )
		for i in range(len(SCREAMING_SNAKE_CASE      )      ):
			UpperCAmelCase__:	List[str]									= weights[og_keys[i]]
		our_model.load_state_dict(SCREAMING_SNAKE_CASE      )

		UpperCAmelCase__:	Optional[Any]									= torch.randn((2, 3, 2_2_4, 2_2_4)      )
		UpperCAmelCase__:	List[str]									= from_model(SCREAMING_SNAKE_CASE      )
		UpperCAmelCase__:	Union[str, Any]									= our_model(SCREAMING_SNAKE_CASE      ).logits

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

	UpperCAmelCase__:	Optional[int]									= name
	print(SCREAMING_SNAKE_CASE      )

	if push_to_hub:
		our_model.save_pretrained(save_directory / checkpoint_name      )
		UpperCAmelCase__:	List[str]									= LevitImageProcessor()
		image_processor.save_pretrained(save_directory / checkpoint_name      )

		print(f"Pushed {checkpoint_name}"      )



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE = None	,SCREAMING_SNAKE_CASE = True      ):
	UpperCAmelCase__:	Optional[Any]									= "imagenet-1k-id2label.json"
	UpperCAmelCase__:	List[str]									= 1_0_0_0
	UpperCAmelCase__:	List[str]									= (1, num_labels)

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

	UpperCAmelCase__:	int									= idalabel
	UpperCAmelCase__:	Optional[int]									= {v: k for k, v in idalabel.items()}

	UpperCAmelCase__:	List[str]									= partial(SCREAMING_SNAKE_CASE	,num_labels=SCREAMING_SNAKE_CASE	,idalabel=SCREAMING_SNAKE_CASE	,labelaid=SCREAMING_SNAKE_CASE      )

	UpperCAmelCase__:	Any									= {
	    "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,
	}

	UpperCAmelCase__:	List[str]									= {
	    "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]	,SCREAMING_SNAKE_CASE	,names_to_config[model_name]	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(names_to_hidden_sizes[model_name]	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
	return config, expected_shape


if __name__ == "__main__":
					_lowerCAmelCase  :      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""",
					)

					_lowerCAmelCase  :      List[str]			 =parser.parse_args()
					_lowerCAmelCase  :      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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCAmelCase  :      Tuple			 ={
    """configuration_mobilenet_v2""": [
        """MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV2Config""",
        """MobileNetV2OnnxConfig""",
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      Tuple			 =["""MobileNetV2FeatureExtractor"""]
					_lowerCAmelCase  :      Optional[int]			 =["""MobileNetV2ImageProcessor"""]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      List[str]			 =[
					    """MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MobileNetV2ForImageClassification""",
					    """MobileNetV2ForSemanticSegmentation""",
					    """MobileNetV2Model""",
					    """MobileNetV2PreTrainedModel""",
					    """load_tf_weights_in_mobilenet_v2""",
					]


if TYPE_CHECKING:
					from .configuration_mobilenet_va import (
					    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    MobileNetVaConfig,
					    MobileNetVaOnnxConfig,
					)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
										from .image_processing_mobilenet_va import MobileNetVaImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mobilenet_va import (
										    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MobileNetVaForImageClassification,
										    MobileNetVaForSemanticSegmentation,
										    MobileNetVaModel,
										    MobileNetVaPreTrainedModel,
										    load_tf_weights_in_mobilenet_va,
										)


else:
					import sys

					_lowerCAmelCase  :      Any			 =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)