






'''simple docstring'''







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      )      ->       List[str]:
							lowerCamelCase__					:	Optional[int]         =       SwinConfig()
							lowerCamelCase__					:	Any         =       swin_name.split("""_"""      )

							lowerCamelCase__					:	str         =       name_split[1]
							lowerCamelCase__					:	int         =       int(name_split[4]      )
							lowerCamelCase__					:	Tuple         =       int(name_split[3][-1]      )

							if model_size == "tiny":
														lowerCamelCase__					:	Any         =       96
														lowerCamelCase__					:	Optional[Any]         =       (2, 2, 6, 2)
														lowerCamelCase__					:	Optional[Any]         =       (3, 6, 12, 24)
							elif model_size == "small":
														lowerCamelCase__					:	List[Any]         =       96
														lowerCamelCase__					:	Dict         =       (2, 2, 18, 2)
														lowerCamelCase__					:	Optional[int]         =       (3, 6, 12, 24)
							elif model_size == "base":
														lowerCamelCase__					:	Tuple         =       128
														lowerCamelCase__					:	Union[str, Any]         =       (2, 2, 18, 2)
														lowerCamelCase__					:	Optional[int]         =       (4, 8, 16, 32)
							else:
														lowerCamelCase__					:	int         =       192
														lowerCamelCase__					:	List[str]         =       (2, 2, 18, 2)
														lowerCamelCase__					:	Any         =       (6, 12, 24, 48)

							if "in22k" in swin_name:
														lowerCamelCase__					:	int         =       21841
							else:
														lowerCamelCase__					:	List[str]         =       1000
														lowerCamelCase__					:	Tuple         =       """huggingface/label-files"""
														lowerCamelCase__					:	Dict         =       """imagenet-1k-id2label.json"""
														lowerCamelCase__					:	Tuple         =       json.load(open(hf_hub_download(UpperCamelCase				,			UpperCamelCase				,			repo_type="""dataset"""      )				,			"""r"""      )      )
														lowerCamelCase__					:	Optional[int]         =       {int(UpperCamelCase      ): v for k, v in idalabel.items()}
														lowerCamelCase__					:	List[str]         =       idalabel
														lowerCamelCase__					:	List[Any]         =       {v: k for k, v in idalabel.items()}

							lowerCamelCase__					:	str         =       img_size
							lowerCamelCase__					:	Any         =       num_classes
							lowerCamelCase__					:	List[Any]         =       embed_dim
							lowerCamelCase__					:	Union[str, Any]         =       depths
							lowerCamelCase__					:	Any         =       num_heads
							lowerCamelCase__					:	Dict         =       window_size

							return config





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       List[Any]:
							if "patch_embed.proj" in name:
														lowerCamelCase__					:	int         =       name.replace("""patch_embed.proj"""				,			"""embeddings.patch_embeddings.projection"""      )
							if "patch_embed.norm" in name:
														lowerCamelCase__					:	str         =       name.replace("""patch_embed.norm"""				,			"""embeddings.norm"""      )
							if "layers" in name:
														lowerCamelCase__					:	str         =       """encoder.""" + name
							if "attn.proj" in name:
														lowerCamelCase__					:	List[str]         =       name.replace("""attn.proj"""				,			"""attention.output.dense"""      )
							if "attn" in name:
														lowerCamelCase__					:	Tuple         =       name.replace("""attn"""				,			"""attention.self"""      )
							if "norm1" in name:
														lowerCamelCase__					:	List[str]         =       name.replace("""norm1"""				,			"""layernorm_before"""      )
							if "norm2" in name:
														lowerCamelCase__					:	Optional[Any]         =       name.replace("""norm2"""				,			"""layernorm_after"""      )
							if "mlp.fc1" in name:
														lowerCamelCase__					:	Optional[int]         =       name.replace("""mlp.fc1"""				,			"""intermediate.dense"""      )
							if "mlp.fc2" in name:
														lowerCamelCase__					:	List[str]         =       name.replace("""mlp.fc2"""				,			"""output.dense"""      )

							if name == "norm.weight":
														lowerCamelCase__					:	Union[str, Any]         =       """layernorm.weight"""
							if name == "norm.bias":
														lowerCamelCase__					:	Tuple         =       """layernorm.bias"""

							if "head" in name:
														lowerCamelCase__					:	Optional[Any]         =       name.replace("""head"""				,			"""classifier"""      )
							else:
														lowerCamelCase__					:	List[str]         =       """swin.""" + name

							return name





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       Optional[int]:
							for key in orig_state_dict.copy().keys():
														lowerCamelCase__					:	Optional[Any]         =       orig_state_dict.pop(UpperCamelCase      )

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

																					if "weight" in key:
																												lowerCamelCase__					:	Any         =       val[:dim, :]
																												lowerCamelCase__					:	Tuple         =       val[
																												    dim : dim * 2, :
																												]
																												lowerCamelCase__					:	Tuple         =       val[-dim:, :]
																					else:
																												lowerCamelCase__					:	int         =       val[
																												    :dim
																												]
																												lowerCamelCase__					:	Any         =       val[
																												    dim : dim * 2
																												]
																												lowerCamelCase__					:	Any         =       val[
																												    -dim:
																												]
														else:
																					lowerCamelCase__					:	Tuple         =       val

							return orig_state_dict





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       Tuple:
							lowerCamelCase__					:	Union[str, Any]         =       timm.create_model(UpperCamelCase				,			pretrained=UpperCamelCase      )
							timm_model.eval()

							lowerCamelCase__					:	Union[str, Any]         =       get_swin_config(UpperCamelCase      )
							lowerCamelCase__					:	Union[str, Any]         =       SwinForImageClassification(UpperCamelCase      )
							model.eval()

							lowerCamelCase__					:	Optional[int]         =       convert_state_dict(timm_model.state_dict()				,			UpperCamelCase      )
							model.load_state_dict(UpperCamelCase      )

							lowerCamelCase__					:	Dict         =       """http://images.cocodataset.org/val2017/000000039769.jpg"""

							lowerCamelCase__					:	Tuple         =       AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_"""				,			"""-"""      )      )      )
							lowerCamelCase__					:	Union[str, Any]         =       Image.open(requests.get(UpperCamelCase				,			stream=UpperCamelCase      ).raw      )
							lowerCamelCase__					:	Tuple         =       image_processor(images=UpperCamelCase				,			return_tensors="""pt"""      )

							lowerCamelCase__					:	List[str]         =       timm_model(inputs["""pixel_values"""]      )
							lowerCamelCase__					:	Union[str, Any]         =       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__":
			_A	:			List[str]         =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.'''
			)

			_A	:			int         =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	:			Dict         ={
    '''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A	:			List[Any]         =[
			    '''SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''Swinv2ForImageClassification''',
			    '''Swinv2ForMaskedImageModeling''',
			    '''Swinv2Model''',
			    '''Swinv2PreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_swinva import (
						    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SwinvaForImageClassification,
						    SwinvaForMaskedImageModeling,
						    SwinvaModel,
						    SwinvaPreTrainedModel,
						)


else:
			import sys

			_A	:			Optional[Any]         =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

