





import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int     ):
		SCREAMING_SNAKE_CASE__								=   SwinConfig()
		SCREAMING_SNAKE_CASE__								=   swin_name.split("""_"""     )

		SCREAMING_SNAKE_CASE__								=   name_split[1]
		SCREAMING_SNAKE_CASE__								=   int(name_split[4]     )
		SCREAMING_SNAKE_CASE__								=   int(name_split[3][-1]     )

		if model_size == "tiny":
				SCREAMING_SNAKE_CASE__								=   96
				SCREAMING_SNAKE_CASE__								=   (2, 2, 6, 2)
				SCREAMING_SNAKE_CASE__								=   (3, 6, 12, 24)
		elif model_size == "small":
				SCREAMING_SNAKE_CASE__								=   96
				SCREAMING_SNAKE_CASE__								=   (2, 2, 18, 2)
				SCREAMING_SNAKE_CASE__								=   (3, 6, 12, 24)
		elif model_size == "base":
				SCREAMING_SNAKE_CASE__								=   128
				SCREAMING_SNAKE_CASE__								=   (2, 2, 18, 2)
				SCREAMING_SNAKE_CASE__								=   (4, 8, 16, 32)
		else:
				SCREAMING_SNAKE_CASE__								=   192
				SCREAMING_SNAKE_CASE__								=   (2, 2, 18, 2)
				SCREAMING_SNAKE_CASE__								=   (6, 12, 24, 48)

		if "in22k" in swin_name:
				SCREAMING_SNAKE_CASE__								=   21_841
		else:
				SCREAMING_SNAKE_CASE__								=   1_000
				SCREAMING_SNAKE_CASE__								=   'huggingface/label-files'
				SCREAMING_SNAKE_CASE__								=   'imagenet-1k-id2label.json'
				SCREAMING_SNAKE_CASE__								=   json.load(open(hf_hub_download(_UpperCAmelCase       , _UpperCAmelCase       , repo_type="""dataset"""     )       , """r"""     )     )
				SCREAMING_SNAKE_CASE__								=   {int(_UpperCAmelCase     ): v for k, v in idalabel.items()}
				SCREAMING_SNAKE_CASE__								=   idalabel
				SCREAMING_SNAKE_CASE__								=   {v: k for k, v in idalabel.items()}

		SCREAMING_SNAKE_CASE__								=   img_size
		SCREAMING_SNAKE_CASE__								=   num_classes
		SCREAMING_SNAKE_CASE__								=   embed_dim
		SCREAMING_SNAKE_CASE__								=   depths
		SCREAMING_SNAKE_CASE__								=   num_heads
		SCREAMING_SNAKE_CASE__								=   window_size

		return config






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[Any]     ):
		if "patch_embed.proj" in name:
				SCREAMING_SNAKE_CASE__								=   name.replace("""patch_embed.proj"""       , """embeddings.patch_embeddings.projection"""     )
		if "patch_embed.norm" in name:
				SCREAMING_SNAKE_CASE__								=   name.replace("""patch_embed.norm"""       , """embeddings.norm"""     )
		if "layers" in name:
				SCREAMING_SNAKE_CASE__								=   'encoder.' + name
		if "attn.proj" in name:
				SCREAMING_SNAKE_CASE__								=   name.replace("""attn.proj"""       , """attention.output.dense"""     )
		if "attn" in name:
				SCREAMING_SNAKE_CASE__								=   name.replace("""attn"""       , """attention.self"""     )
		if "norm1" in name:
				SCREAMING_SNAKE_CASE__								=   name.replace("""norm1"""       , """layernorm_before"""     )
		if "norm2" in name:
				SCREAMING_SNAKE_CASE__								=   name.replace("""norm2"""       , """layernorm_after"""     )
		if "mlp.fc1" in name:
				SCREAMING_SNAKE_CASE__								=   name.replace("""mlp.fc1"""       , """intermediate.dense"""     )
		if "mlp.fc2" in name:
				SCREAMING_SNAKE_CASE__								=   name.replace("""mlp.fc2"""       , """output.dense"""     )

		if name == "norm.weight":
				SCREAMING_SNAKE_CASE__								=   'layernorm.weight'
		if name == "norm.bias":
				SCREAMING_SNAKE_CASE__								=   'layernorm.bias'

		if "head" in name:
				SCREAMING_SNAKE_CASE__								=   name.replace("""head"""       , """classifier"""     )
		else:
				SCREAMING_SNAKE_CASE__								=   'swin.' + name

		return name






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Dict       , UpperCamelCase__:				int     ):
		for key in orig_state_dict.copy().keys():
				SCREAMING_SNAKE_CASE__								=   orig_state_dict.pop(_UpperCAmelCase     )

				if "mask" in key:
						continue
				elif "qkv" in key:
						SCREAMING_SNAKE_CASE__								=   key.split("""."""     )
						SCREAMING_SNAKE_CASE__								=   int(key_split[1]     )
						SCREAMING_SNAKE_CASE__								=   int(key_split[3]     )
						SCREAMING_SNAKE_CASE__								=   model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

						if "weight" in key:
								SCREAMING_SNAKE_CASE__								=   val[:dim, :]
								SCREAMING_SNAKE_CASE__								=   val[
								    dim : dim * 2, :
								]
								SCREAMING_SNAKE_CASE__								=   val[-dim:, :]
						else:
								SCREAMING_SNAKE_CASE__								=   val[
								    :dim
								]
								SCREAMING_SNAKE_CASE__								=   val[
								    dim : dim * 2
								]
								SCREAMING_SNAKE_CASE__								=   val[
								    -dim:
								]
				else:
						SCREAMING_SNAKE_CASE__								=   val

		return orig_state_dict






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[str]       , UpperCamelCase__:				Union[str, Any]     ):
		SCREAMING_SNAKE_CASE__								=   timm.create_model(_UpperCAmelCase       , pretrained=_UpperCAmelCase     )
		timm_model.eval()

		SCREAMING_SNAKE_CASE__								=   get_swin_config(_UpperCAmelCase     )
		SCREAMING_SNAKE_CASE__								=   SwinForImageClassification(_UpperCAmelCase     )
		model.eval()

		SCREAMING_SNAKE_CASE__								=   convert_state_dict(timm_model.state_dict()       , _UpperCAmelCase     )
		model.load_state_dict(_UpperCAmelCase     )

		SCREAMING_SNAKE_CASE__								=   'http://images.cocodataset.org/val2017/000000039769.jpg'

		SCREAMING_SNAKE_CASE__								=   AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_"""       , """-"""     )     )     )
		SCREAMING_SNAKE_CASE__								=   Image.open(requests.get(_UpperCAmelCase       , stream=_UpperCAmelCase     ).raw     )
		SCREAMING_SNAKE_CASE__								=   image_processor(images=_UpperCAmelCase       , return_tensors="""pt"""     )

		SCREAMING_SNAKE_CASE__								=   timm_model(inputs["""pixel_values"""]     )
		SCREAMING_SNAKE_CASE__								=   model(**_UpperCAmelCase     ).logits

		assert torch.allclose(_UpperCAmelCase       , _UpperCAmelCase       , atol=1e-3     )

		print(f'''Saving model {swin_name} to {pytorch_dump_folder_path}'''     )
		model.save_pretrained(_UpperCAmelCase     )

		print(f'''Saving image processor to {pytorch_dump_folder_path}'''     )
		image_processor.save_pretrained(_UpperCAmelCase     )


if __name__ == "__main__":
	_lowerCamelCase 					=      argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--swin_name',
	    default='swin_tiny_patch4_window7_224',
	    type=str,
	    help='Name of the Swin timm model you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
	)

	_lowerCamelCase 					=      parser.parse_args()
	convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)




'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__    :   List[Any]           =					{
    '''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
    '''tokenization_luke''': ['''LukeTokenizer'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__    :   Union[str, Any]           =					[
        '''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''LukeForEntityClassification''',
        '''LukeForEntityPairClassification''',
        '''LukeForEntitySpanClassification''',
        '''LukeForMultipleChoice''',
        '''LukeForQuestionAnswering''',
        '''LukeForSequenceClassification''',
        '''LukeForTokenClassification''',
        '''LukeForMaskedLM''',
        '''LukeModel''',
        '''LukePreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
    from .tokenization_luke import LukeTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_luke import (
            LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
            LukeForEntityClassification,
            LukeForEntityPairClassification,
            LukeForEntitySpanClassification,
            LukeForMaskedLM,
            LukeForMultipleChoice,
            LukeForQuestionAnswering,
            LukeForSequenceClassification,
            LukeForTokenClassification,
            LukeModel,
            LukePreTrainedModel,
        )

else:
    import sys

    A__    :   List[str]           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



