





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)












import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
	lowerCAmelCase_    =				argparse.ArgumentParser()

	parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

	parser.add_argument(
	    '''--txt2img_unclip''',
	    default='''kakaobrain/karlo-v1-alpha''',
	    type=str,
	    required=False,
	    help='''The pretrained txt2img unclip.''',
	)

	lowerCAmelCase_    =				parser.parse_args()

	lowerCAmelCase_    =				UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

	lowerCAmelCase_    =				CLIPImageProcessor()
	lowerCAmelCase_    =				CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

	lowerCAmelCase_    =				UnCLIPImageVariationPipeline(
	    decoder=txtaimg.decoder,
	    text_encoder=txtaimg.text_encoder,
	    tokenizer=txtaimg.tokenizer,
	    text_proj=txtaimg.text_proj,
	    feature_extractor=feature_extractor,
	    image_encoder=image_encoder,
	    super_res_first=txtaimg.super_res_first,
	    super_res_last=txtaimg.super_res_last,
	    decoder_scheduler=txtaimg.decoder_scheduler,
	    super_res_scheduler=txtaimg.super_res_scheduler,
	)

	imgaimg.save_pretrained(args.dump_path)
