




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_									=	logging.get_logger()




def 					lowerCamelCase__       (						A__       :					int  ,			A__       :					str  ,			A__       :					LevitConfig  ,			A__       :					Path  ,			A__       :					bool = True	):





       '''simple docstring'''



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

       with torch.no_grad():
              if hidden_sizes == 128:
                     if name[-1] == "S":
                            __lowerCamelCase   =						timm.create_model("""levit_128s"""  ,			pretrained=A__	)
                     else:
                            __lowerCamelCase   =						timm.create_model("""levit_128"""  ,			pretrained=A__	)
              if hidden_sizes == 192:
                     __lowerCamelCase   =						timm.create_model("""levit_192"""  ,			pretrained=A__	)
              if hidden_sizes == 256:
                     __lowerCamelCase   =						timm.create_model("""levit_256"""  ,			pretrained=A__	)
              if hidden_sizes == 384:
                     __lowerCamelCase   =						timm.create_model("""levit_384"""  ,			pretrained=A__	)

              from_model.eval()
              __lowerCamelCase   =						LevitForImageClassificationWithTeacher(A__	).eval()
              __lowerCamelCase   =						OrderedDict()

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

              __lowerCamelCase   =						torch.randn((2, 3, 224, 224)	)
              __lowerCamelCase   =						from_model(A__	)
              __lowerCamelCase   =						our_model(A__	).logits

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

       __lowerCamelCase   =						name
       print(A__	)

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

              print(f'Pushed {checkpoint_name}'	)




def 					lowerCamelCase__       (						A__       :					Path  ,			A__       :					str = None  ,			A__       :					bool = True	):





       '''simple docstring'''



       __lowerCamelCase   =						"""imagenet-1k-id2label.json"""
       __lowerCamelCase   =						1000
       __lowerCamelCase   =						(1, num_labels)

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

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

       __lowerCamelCase   =						partial(A__  ,			num_labels=A__  ,			idalabel=A__  ,			labelaid=A__	)

       __lowerCamelCase   =						{
           """levit-128S""": 128,
           """levit-128""": 128,
           """levit-192""": 192,
           """levit-256""": 256,
           """levit-384""": 384,
       }

       __lowerCamelCase   =						{
           """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]  ,			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__":
       UpperCAmelCase_									=	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_									=	parser.parse_args()
       UpperCAmelCase_									=	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_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase_									=	{
    'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase_									=	['VisionEncoderDecoderModel']

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase_									=	['TFVisionEncoderDecoderModel']

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase_									=	['FlaxVisionEncoderDecoderModel']

if TYPE_CHECKING:
       from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
       import sys

       UpperCAmelCase_									=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

