


"""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 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				Dict:
							snake_case_           =		SwinConfig()
							snake_case_           =		swin_name.split("""_"""				)

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

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

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

							snake_case_           =		img_size
							snake_case_           =		num_classes
							snake_case_           =		embed_dim
							snake_case_           =		depths
							snake_case_           =		num_heads
							snake_case_           =		window_size

							return config







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				Optional[Any]:


							if "patch_embed.proj" in name:
														snake_case_           =		name.replace("""patch_embed.proj"""				, """embeddings.patch_embeddings.projection"""				)
							if "patch_embed.norm" in name:
														snake_case_           =		name.replace("""patch_embed.norm"""				, """embeddings.norm"""				)
							if "layers" in name:
														snake_case_           =		"""encoder.""" + name
							if "attn.proj" in name:
														snake_case_           =		name.replace("""attn.proj"""				, """attention.output.dense"""				)
							if "attn" in name:
														snake_case_           =		name.replace("""attn"""				, """attention.self"""				)
							if "norm1" in name:
														snake_case_           =		name.replace("""norm1"""				, """layernorm_before"""				)
							if "norm2" in name:
														snake_case_           =		name.replace("""norm2"""				, """layernorm_after"""				)
							if "mlp.fc1" in name:
														snake_case_           =		name.replace("""mlp.fc1"""				, """intermediate.dense"""				)
							if "mlp.fc2" in name:
														snake_case_           =		name.replace("""mlp.fc2"""				, """output.dense"""				)

							if name == "norm.weight":
														snake_case_           =		"""layernorm.weight"""
							if name == "norm.bias":
														snake_case_           =		"""layernorm.bias"""

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

							return name







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				List[Any]:


							for key in orig_state_dict.copy().keys():
														snake_case_           =		orig_state_dict.pop(_SCREAMING_SNAKE_CASE				)

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

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

							return orig_state_dict







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Any:
							snake_case_           =		timm.create_model(_SCREAMING_SNAKE_CASE				, pretrained=_SCREAMING_SNAKE_CASE				)
							timm_model.eval()

							snake_case_           =		get_swin_config(_SCREAMING_SNAKE_CASE				)
							snake_case_           =		SwinForImageClassification(_SCREAMING_SNAKE_CASE				)
							model.eval()

							snake_case_           =		convert_state_dict(timm_model.state_dict()				, _SCREAMING_SNAKE_CASE				)
							model.load_state_dict(_SCREAMING_SNAKE_CASE				)

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

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

							snake_case_           =		timm_model(inputs["""pixel_values"""]				)
							snake_case_           =		model(**_SCREAMING_SNAKE_CASE				).logits

							assert torch.allclose(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, atol=1E-3				)

							print(f"""Saving model {swin_name} to {pytorch_dump_folder_path}"""				)
							model.save_pretrained(_SCREAMING_SNAKE_CASE				)

							print(f"""Saving image processor to {pytorch_dump_folder_path}"""				)
							image_processor.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:    int		=    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.'''
				)

				__SCREAMING_SNAKE_CASE							:    int		=    parser.parse_args()
				convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)







"""simple docstring"""
from functools import reduce

__SCREAMING_SNAKE_CASE							:    Tuple		=    (
    '73167176531330624919225119674426574742355349194934'
    '96983520312774506326239578318016984801869478851843'
    '85861560789112949495459501737958331952853208805511'
    '12540698747158523863050715693290963295227443043557'
    '66896648950445244523161731856403098711121722383113'
    '62229893423380308135336276614282806444486645238749'
    '30358907296290491560440772390713810515859307960866'
    '70172427121883998797908792274921901699720888093776'
    '65727333001053367881220235421809751254540594752243'
    '52584907711670556013604839586446706324415722155397'
    '53697817977846174064955149290862569321978468622482'
    '83972241375657056057490261407972968652414535100474'
    '82166370484403199890008895243450658541227588666881'
    '16427171479924442928230863465674813919123162824586'
    '17866458359124566529476545682848912883142607690042'
    '24219022671055626321111109370544217506941658960408'
    '07198403850962455444362981230987879927244284909188'
    '84580156166097919133875499200524063689912560717606'
    '05886116467109405077541002256983155200055935729725'
    '71636269561882670428252483600823257530420752963450'
)







def 						_a				(      _SCREAMING_SNAKE_CASE = N				)    ->				int:
							return max(
							    # mypy cannot properly interpret reduce
							    int(reduce(lambda _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				: str(int(_SCREAMING_SNAKE_CASE				) * int(_SCREAMING_SNAKE_CASE				)				)				, n[i : i + 13]				)				)
							    for i in range(len(_SCREAMING_SNAKE_CASE				) - 12				)				)


if __name__ == "__main__":
				print(f"""{solution() = }""")



