




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      :       List[str]				  =   logging.get_logger()



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]			,				__lowercase   :			List[Any]			,				__lowercase   :			Optional[Any]			,				__lowercase   :			List[Any]			,				__lowercase   :			Tuple = True    )						->      Optional[Any]:





    """simple docstring"""

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

    with torch.no_grad():
        if hidden_sizes == 1_2_8:
            if name[-1] == "S":
                __A         =	timm.create_model("""levit_128s"""			,				pretrained=__lowercase    )
            else:
                __A         =	timm.create_model("""levit_128"""			,				pretrained=__lowercase    )
        if hidden_sizes == 1_9_2:
            __A         =	timm.create_model("""levit_192"""			,				pretrained=__lowercase    )
        if hidden_sizes == 2_5_6:
            __A         =	timm.create_model("""levit_256"""			,				pretrained=__lowercase    )
        if hidden_sizes == 3_8_4:
            __A         =	timm.create_model("""levit_384"""			,				pretrained=__lowercase    )

        from_model.eval()
        __A         =	LevitForImageClassificationWithTeacher(__lowercase    ).eval()
        __A         =	OrderedDict()

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

        __A         =	torch.randn((2, 3, 2_2_4, 2_2_4)    )
        __A         =	from_model(__lowercase    )
        __A         =	our_model(__lowercase    ).logits

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

    __A         =	name
    print(__lowercase    )

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

        print(f"Pushed {checkpoint_name}"    )



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str			,				__lowercase   :			Any = None			,				__lowercase   :			List[str] = True    )						->      Tuple:





    """simple docstring"""

    __A         =	"""imagenet-1k-id2label.json"""
    __A         =	1_0_0_0
    __A         =	(1, num_labels)

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

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

    __A         =	partial(__lowercase			,				num_labels=__lowercase			,				idalabel=__lowercase			,				labelaid=__lowercase    )

    __A         =	{
        """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,
    }

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


if __name__ == "__main__":
  __a      :       List[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",
  )

  __a      :       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)












from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				["image_processor", "tokenizer"]
     SCREAMING_SNAKE_CASE   						=				"AutoImageProcessor"
     SCREAMING_SNAKE_CASE   						=				"AutoTokenizer"




     def __init__(				self       :			str ,	UpperCamelCase_       :			Any ,	UpperCamelCase_       :			str				):


         """simple docstring"""



         super().__init__(UpperCamelCase_ ,	UpperCamelCase_				)
         __A         =	self.image_processor




     def __call__(				self       :			Optional[Any] ,	UpperCamelCase_       :			Optional[Any]=None ,	UpperCamelCase_       :			Any=None ,	UpperCamelCase_       :			List[Any]=None ,	**UpperCamelCase_       :			int				):


         """simple docstring"""



         if text is None and images is None:
             raise ValueError("""You have to specify either text or images. Both cannot be none."""				)

         if text is not None:
             __A         =	self.tokenizer(UpperCamelCase_ ,	return_tensors=UpperCamelCase_ ,	**UpperCamelCase_				)

         if images is not None:
             __A         =	self.image_processor(UpperCamelCase_ ,	return_tensors=UpperCamelCase_ ,	**UpperCamelCase_				)

         if text is not None and images is not None:
             __A         =	image_features.pixel_values
             return encoding
         elif text is not None:
             return encoding
         else:
             return BatchEncoding(data=dict(**UpperCamelCase_				) ,	tensor_type=UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			List[str] ,	*UpperCamelCase_       :			Tuple ,	**UpperCamelCase_       :			List[str]				):


         """simple docstring"""



         return self.tokenizer.batch_decode(*UpperCamelCase_ ,	**UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Any ,	*UpperCamelCase_       :			int ,	**UpperCamelCase_       :			Tuple				):


         """simple docstring"""



         return self.tokenizer.decode(*UpperCamelCase_ ,	**UpperCamelCase_				)





     @property
     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         return ["input_ids", "attention_mask", "pixel_values"]




