


import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase							:   Union[str, Any]						=						logging.get_logger()






@dataclass
class    _UpperCamelCase     :

				"""simple docstring"""







				lowerCAmelCase					=			42
				lowerCAmelCase					=			field(default_factory=__snake_case					)
				lowerCAmelCase					=			field(default_factory=__snake_case					)

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__ )						->					Dict:
					A             =					len(list(m.modules() ) ) == 1 or isinstance(a__					, nn.Convad ) or isinstance(a__					, nn.BatchNormad )
					if has_not_submodules:
						self.traced.append(a__ )

				def __call__(    self					, a__ )						->					List[Any]:
					for m in self.module.modules():
						self.handles.append(m.register_forward_hook(self._forward_hook ) )
					self.module(a__ )
					[x.remove() for x in self.handles]
					return self

				@property
				def   _UpperCAmelCase       (    self )						->					List[Any]:
					# check the len of the state_dict keys to see if we have learnable params
					return list(filter(lambda a__ : len(list(x.state_dict().keys() ) ) > 0					, self.traced ) )






@dataclass
class    _UpperCamelCase     :

				"""simple docstring"""







				lowerCAmelCase					=			42
				lowerCAmelCase					=			42
				lowerCAmelCase					=			1
				lowerCAmelCase					=			field(default_factory=__snake_case					)
				lowerCAmelCase					=			field(default_factory=__snake_case					)
				lowerCAmelCase					=			True

				def __call__(    self					, a__ )						->					Optional[Any]:
					A             =					Tracker(self.dest )(a__ ).parametrized
					A             =					Tracker(self.src )(a__ ).parametrized

					A             =					list(filter(lambda a__ : type(a__ ) not in self.src_skip					, a__ ) )
					A             =					list(filter(lambda a__ : type(a__ ) not in self.dest_skip					, a__ ) )

					if len(a__ ) != len(a__ ) and self.raise_if_mismatch:
						raise Exception(
						    f'Numbers of operations are different. Source module has {len(a__ )} operations while'
						    f' destination module has {len(a__ )}.' )

					for dest_m, src_m in zip(a__					, a__ ):
						dest_m.load_state_dict(src_m.state_dict() )
						if self.verbose == 1:
							print(f'Transfered from={src_m} to={dest_m}' )






class    _UpperCamelCase     (       nn.Module					):

				"""simple docstring"""







				def __init__(    self					, a__ )						->					int:
					super().__init__()

					A             =					[]
					# - get the stem
					feature_blocks.append(("""conv1""", model.stem) )
					# - get all the feature blocks
					for k, v in model.trunk_output.named_children():
						assert k.startswith("""block""" ), f'Unexpected layer name {k}'
						A             =					len(a__ ) + 1
						feature_blocks.append((f'res{block_index}', v) )

					A             =					nn.ModuleDict(a__ )

				def   _UpperCAmelCase       (    self					, a__ )						->					Tuple:
					return get_trunk_forward_outputs(
					    a__					, out_feat_keys=a__					, feature_blocks=self._feature_blocks					, )






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				def   _UpperCAmelCase       (    self					, a__ )						->					str:
					A             =					x.split("""-""" )
					return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )

				def __getitem__(    self					, a__ )						->					Callable[[], Tuple[nn.Module, Dict]]:
					# default to timm!
					if x not in self:
						A             =					self.convert_name_to_timm(a__ )
						A             =					partial(lambda: (timm.create_model(a__					, pretrained=a__ ).eval(), None) )

					else:
						A             =					super().__getitem__(a__ )

					return val






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				def __getitem__(    self					, a__ )						->					Callable[[], nn.Module]:
					if "seer" in x and "in1k" not in x:
						A             =					RegNetModel
					else:
						A             =					RegNetForImageClassification
					return val





def 	_lowerCAmelCase					( UpperCamelCase__:  List[str]				,       UpperCamelCase__:  Tuple				,       UpperCamelCase__:  List[Tuple[str, str]]    )			-> Tuple:





	"""simple docstring"""







	for from_key, to_key in keys:
		A             =					from_state_dict[from_key].clone()
		print(f'Copied key={from_key} to={to_key}'    )
	return to_state_dict





def 	_lowerCAmelCase					( UpperCamelCase__:  str				,       UpperCamelCase__:  Callable[[], nn.Module]				,       UpperCamelCase__:  Callable[[], nn.Module]				,       UpperCamelCase__:  RegNetConfig				,       UpperCamelCase__:  Path				,       UpperCamelCase__:  bool = True				,       )			-> Any:





	"""simple docstring"""







	print(f'Converting {name}...'    )
	with torch.no_grad():
		A       ,      A             =					from_model_func()
		A             =					our_model_func(UpperCamelCase__    ).eval()
		A             =					ModuleTransfer(src=UpperCamelCase__				,       dest=UpperCamelCase__				,       raise_if_mismatch=UpperCamelCase__    )
		A             =					torch.randn((1, 3, 2_24, 2_24)    )
		module_transfer(UpperCamelCase__    )

	if from_state_dict is not None:
		A             =					[]
		# for seer - in1k finetuned we have to manually copy the head
		if "seer" in name and "in1k" in name:
			A             =					[("""0.clf.0.weight""", """classifier.1.weight"""), ("""0.clf.0.bias""", """classifier.1.bias""")]
		A             =					manually_copy_vissl_head(UpperCamelCase__				,       our_model.state_dict()				,       UpperCamelCase__    )
		our_model.load_state_dict(UpperCamelCase__    )

	A             =					our_model(UpperCamelCase__				,       output_hidden_states=UpperCamelCase__    )
	A             =					(
	    our_outputs.logits if isinstance(UpperCamelCase__				,       UpperCamelCase__    ) else our_outputs.last_hidden_state
	)

	A             =					from_model(UpperCamelCase__    )
	A             =					from_output[-1] if type(UpperCamelCase__    ) is list else from_output

	# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
	if "seer" in name and "in1k" in name:
		A             =					our_outputs.hidden_states[-1]

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

	if push_to_hub:
		our_model.push_to_hub(
		    repo_path_or_name=save_directory / name				,       commit_message="""Add model"""				,       use_temp_dir=UpperCamelCase__				,       )

		A             =					2_24 if """seer""" not in name else 3_84
		# we can use the convnext one
		A             =					AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""				,       size=UpperCamelCase__    )
		image_processor.push_to_hub(
		    repo_path_or_name=save_directory / name				,       commit_message="""Add image processor"""				,       use_temp_dir=UpperCamelCase__				,       )

		print(f'Pushed {name}'    )





def 	_lowerCAmelCase					( UpperCamelCase__:  Path				,       UpperCamelCase__:  str = None				,       UpperCamelCase__:  bool = True    )			-> Optional[int]:





	"""simple docstring"""







	A             =					"""imagenet-1k-id2label.json"""
	A             =					10_00
	A             =					(1, num_labels)

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

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

	A             =					partial(UpperCamelCase__				,       num_labels=UpperCamelCase__				,       idalabel=UpperCamelCase__				,       labelaid=UpperCamelCase__    )

	A             =					{
	    """regnet-x-002""": ImageNetPreTrainedConfig(
	        depths=[1, 1, 4, 7]				,       hidden_sizes=[24, 56, 1_52, 3_68]				,       groups_width=8				,       layer_type="""x"""    ),
	    """regnet-x-004""": ImageNetPreTrainedConfig(
	        depths=[1, 2, 7, 12]				,       hidden_sizes=[32, 64, 1_60, 3_84]				,       groups_width=16				,       layer_type="""x"""    ),
	    """regnet-x-006""": ImageNetPreTrainedConfig(
	        depths=[1, 3, 5, 7]				,       hidden_sizes=[48, 96, 2_40, 5_28]				,       groups_width=24				,       layer_type="""x"""    ),
	    """regnet-x-008""": ImageNetPreTrainedConfig(
	        depths=[1, 3, 7, 5]				,       hidden_sizes=[64, 1_28, 2_88, 6_72]				,       groups_width=16				,       layer_type="""x"""    ),
	    """regnet-x-016""": ImageNetPreTrainedConfig(
	        depths=[2, 4, 10, 2]				,       hidden_sizes=[72, 1_68, 4_08, 9_12]				,       groups_width=24				,       layer_type="""x"""    ),
	    """regnet-x-032""": ImageNetPreTrainedConfig(
	        depths=[2, 6, 15, 2]				,       hidden_sizes=[96, 1_92, 4_32, 10_08]				,       groups_width=48				,       layer_type="""x"""    ),
	    """regnet-x-040""": ImageNetPreTrainedConfig(
	        depths=[2, 5, 14, 2]				,       hidden_sizes=[80, 2_40, 5_60, 13_60]				,       groups_width=40				,       layer_type="""x"""    ),
	    """regnet-x-064""": ImageNetPreTrainedConfig(
	        depths=[2, 4, 10, 1]				,       hidden_sizes=[1_68, 3_92, 7_84, 16_24]				,       groups_width=56				,       layer_type="""x"""    ),
	    """regnet-x-080""": ImageNetPreTrainedConfig(
	        depths=[2, 5, 15, 1]				,       hidden_sizes=[80, 2_40, 7_20, 19_20]				,       groups_width=1_20				,       layer_type="""x"""    ),
	    """regnet-x-120""": ImageNetPreTrainedConfig(
	        depths=[2, 5, 11, 1]				,       hidden_sizes=[2_24, 4_48, 8_96, 22_40]				,       groups_width=1_12				,       layer_type="""x"""    ),
	    """regnet-x-160""": ImageNetPreTrainedConfig(
	        depths=[2, 6, 13, 1]				,       hidden_sizes=[2_56, 5_12, 8_96, 20_48]				,       groups_width=1_28				,       layer_type="""x"""    ),
	    """regnet-x-320""": ImageNetPreTrainedConfig(
	        depths=[2, 7, 13, 1]				,       hidden_sizes=[3_36, 6_72, 13_44, 25_20]				,       groups_width=1_68				,       layer_type="""x"""    ),
	    # y variant
	    """regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]				,       hidden_sizes=[24, 56, 1_52, 3_68]				,       groups_width=8    ),
	    """regnet-y-004""": ImageNetPreTrainedConfig(
	        depths=[1, 3, 6, 6]				,       hidden_sizes=[48, 1_04, 2_08, 4_40]				,       groups_width=8    ),
	    """regnet-y-006""": ImageNetPreTrainedConfig(
	        depths=[1, 3, 7, 4]				,       hidden_sizes=[48, 1_12, 2_56, 6_08]				,       groups_width=16    ),
	    """regnet-y-008""": ImageNetPreTrainedConfig(
	        depths=[1, 3, 8, 2]				,       hidden_sizes=[64, 1_28, 3_20, 7_68]				,       groups_width=16    ),
	    """regnet-y-016""": ImageNetPreTrainedConfig(
	        depths=[2, 6, 17, 2]				,       hidden_sizes=[48, 1_20, 3_36, 8_88]				,       groups_width=24    ),
	    """regnet-y-032""": ImageNetPreTrainedConfig(
	        depths=[2, 5, 13, 1]				,       hidden_sizes=[72, 2_16, 5_76, 15_12]				,       groups_width=24    ),
	    """regnet-y-040""": ImageNetPreTrainedConfig(
	        depths=[2, 6, 12, 2]				,       hidden_sizes=[1_28, 1_92, 5_12, 10_88]				,       groups_width=64    ),
	    """regnet-y-064""": ImageNetPreTrainedConfig(
	        depths=[2, 7, 14, 2]				,       hidden_sizes=[1_44, 2_88, 5_76, 12_96]				,       groups_width=72    ),
	    """regnet-y-080""": ImageNetPreTrainedConfig(
	        depths=[2, 4, 10, 1]				,       hidden_sizes=[1_68, 4_48, 8_96, 20_16]				,       groups_width=56    ),
	    """regnet-y-120""": ImageNetPreTrainedConfig(
	        depths=[2, 5, 11, 1]				,       hidden_sizes=[2_24, 4_48, 8_96, 22_40]				,       groups_width=1_12    ),
	    """regnet-y-160""": ImageNetPreTrainedConfig(
	        depths=[2, 4, 11, 1]				,       hidden_sizes=[2_24, 4_48, 12_32, 30_24]				,       groups_width=1_12    ),
	    """regnet-y-320""": ImageNetPreTrainedConfig(
	        depths=[2, 5, 12, 1]				,       hidden_sizes=[2_32, 6_96, 13_92, 37_12]				,       groups_width=2_32    ),
	    # models created by SEER -> https://arxiv.org/abs/2202.08360
	    """regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 12, 1]				,       hidden_sizes=[2_32, 6_96, 13_92, 37_12]				,       groups_width=2_32    ),
	    """regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 12, 1]				,       hidden_sizes=[3_28, 9_84, 19_68, 49_20]				,       groups_width=3_28    ),
	    """regnet-y-1280-seer""": RegNetConfig(
	        depths=[2, 7, 17, 1]				,       hidden_sizes=[5_28, 10_56, 29_04, 73_92]				,       groups_width=2_64    ),
	    """regnet-y-2560-seer""": RegNetConfig(
	        depths=[3, 7, 16, 1]				,       hidden_sizes=[6_40, 16_96, 25_44, 50_88]				,       groups_width=6_40    ),
	    """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
	        depths=[2, 7, 17, 1]				,       hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]				,       groups_width=10_10    ),
	    # finetuned on imagenet
	    """regnet-y-320-seer-in1k""": ImageNetPreTrainedConfig(
	        depths=[2, 5, 12, 1]				,       hidden_sizes=[2_32, 6_96, 13_92, 37_12]				,       groups_width=2_32    ),
	    """regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
	        depths=[2, 5, 12, 1]				,       hidden_sizes=[3_28, 9_84, 19_68, 49_20]				,       groups_width=3_28    ),
	    """regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
	        depths=[2, 7, 17, 1]				,       hidden_sizes=[5_28, 10_56, 29_04, 73_92]				,       groups_width=2_64    ),
	    """regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
	        depths=[3, 7, 16, 1]				,       hidden_sizes=[6_40, 16_96, 25_44, 50_88]				,       groups_width=6_40    ),
	    """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
	        depths=[2, 7, 17, 1]				,       hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]				,       groups_width=10_10    ),
	}

	A             =					NameToOurModelFuncMap()
	A             =					NameToFromModelFuncMap()
	# add seer weights logic

	def load_using_classy_vision(UpperCamelCase__:  str				,       UpperCamelCase__:  Callable[[], nn.Module]    ) -> Tuple[nn.Module, Dict]:
		A             =					torch.hub.load_state_dict_from_url(UpperCamelCase__				,       model_dir=str(UpperCamelCase__    )				,       map_location="""cpu"""    )
		A             =					model_func()
		# check if we have a head, if yes add it
		A             =					files["""classy_state_dict"""]["""base_model"""]["""model"""]
		A             =					model_state_dict["""trunk"""]
		model.load_state_dict(UpperCamelCase__    )
		return model.eval(), model_state_dict["heads"]

	# pretrained
	A             =					partial(
	    UpperCamelCase__				,       """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"""				,       lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )				,       )

	A             =					partial(
	    UpperCamelCase__				,       """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"""				,       lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )				,       )

	A             =					partial(
	    UpperCamelCase__				,       """https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"""				,       lambda: FakeRegNetVisslWrapper(RegNetYaaagf()    )				,       )

	A             =					partial(
	    UpperCamelCase__				,       """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""				,       lambda: FakeRegNetVisslWrapper(
	        RegNet(RegNetParams(depth=27				,       group_width=10_10				,       w_a=17_44				,       w_a=6_20.83				,       w_m=2.52    )    )    )				,       )

	# IN1K finetuned
	A             =					partial(
	    UpperCamelCase__				,       """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"""				,       lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )				,       )

	A             =					partial(
	    UpperCamelCase__				,       """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"""				,       lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )				,       )

	A             =					partial(
	    UpperCamelCase__				,       """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"""				,       lambda: FakeRegNetVisslWrapper(RegNetYaaagf()    )				,       )

	A             =					partial(
	    UpperCamelCase__				,       """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""				,       lambda: FakeRegNetVisslWrapper(
	        RegNet(RegNetParams(depth=27				,       group_width=10_10				,       w_a=17_44				,       w_a=6_20.83				,       w_m=2.52    )    )    )				,       )

	if model_name:
		convert_weight_and_push(
		    UpperCamelCase__				,       names_to_from_model_map[model_name]				,       names_to_ours_model_map[model_name]				,       names_to_config[model_name]				,       UpperCamelCase__				,       UpperCamelCase__				,       )
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(
			    UpperCamelCase__				,       names_to_from_model_map[model_name]				,       names_to_ours_model_map[model_name]				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       )
	return config, expected_shape


if __name__ == "__main__":
				_lowercase							:   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 regnet* architecture,"
				        " currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
				    ),
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path",
				    default=None,
				    type=Path,
				    required=True,
				    help="Path to the output PyTorch model directory.",
				)
				parser.add_argument(
				    "--push_to_hub",
				    default=True,
				    type=bool,
				    required=False,
				    help="If True, push model and image processor to the hub.",
				)

				_lowercase							:   Union[str, Any]						=						parser.parse_args()

				_lowercase							:   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

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor





def 	_lowerCAmelCase					( UpperCamelCase__:  Any    )			-> Optional[int]:





	"""simple docstring"""







	A             =					SwinaSRConfig()

	if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
		A             =					4
	elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
		A             =					4
		A             =					48
		A             =					"""pixelshuffle_aux"""
	elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
		A             =					[6, 6, 6, 6]
		A             =					60
		A             =					[6, 6, 6, 6]
		A             =					"""pixelshuffledirect"""
	elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
		A             =					4
		A             =					"""nearest+conv"""
	elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
		A             =					1
		A             =					1
		A             =					1_26
		A             =					7
		A             =					2_55.0
		A             =					""""""

	return config





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[Any]				,       UpperCamelCase__:  Dict    )			-> Optional[Any]:





	"""simple docstring"""







	if "patch_embed.proj" in name and "layers" not in name:
		A             =					name.replace("""patch_embed.proj"""				,       """embeddings.patch_embeddings.projection"""    )
	if "patch_embed.norm" in name:
		A             =					name.replace("""patch_embed.norm"""				,       """embeddings.patch_embeddings.layernorm"""    )
	if "layers" in name:
		A             =					name.replace("""layers"""				,       """encoder.stages"""    )
	if "residual_group.blocks" in name:
		A             =					name.replace("""residual_group.blocks"""				,       """layers"""    )
	if "attn.proj" in name:
		A             =					name.replace("""attn.proj"""				,       """attention.output.dense"""    )
	if "attn" in name:
		A             =					name.replace("""attn"""				,       """attention.self"""    )
	if "norm1" in name:
		A             =					name.replace("""norm1"""				,       """layernorm_before"""    )
	if "norm2" in name:
		A             =					name.replace("""norm2"""				,       """layernorm_after"""    )
	if "mlp.fc1" in name:
		A             =					name.replace("""mlp.fc1"""				,       """intermediate.dense"""    )
	if "mlp.fc2" in name:
		A             =					name.replace("""mlp.fc2"""				,       """output.dense"""    )
	if "q_bias" in name:
		A             =					name.replace("""q_bias"""				,       """query.bias"""    )
	if "k_bias" in name:
		A             =					name.replace("""k_bias"""				,       """key.bias"""    )
	if "v_bias" in name:
		A             =					name.replace("""v_bias"""				,       """value.bias"""    )
	if "cpb_mlp" in name:
		A             =					name.replace("""cpb_mlp"""				,       """continuous_position_bias_mlp"""    )
	if "patch_embed.proj" in name:
		A             =					name.replace("""patch_embed.proj"""				,       """patch_embed.projection"""    )

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

	if "conv_first" in name:
		A             =					name.replace("""conv_first"""				,       """first_convolution"""    )

	if (
	    "upsample" in name
	    or "conv_before_upsample" in name
	    or "conv_bicubic" in name
	    or "conv_up" in name
	    or "conv_hr" in name
	    or "conv_last" in name
	    or "aux" in name
	):
		# heads
		if "conv_last" in name:
			A             =					name.replace("""conv_last"""				,       """final_convolution"""    )
		if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
			if "conv_before_upsample.0" in name:
				A             =					name.replace("""conv_before_upsample.0"""				,       """conv_before_upsample"""    )
			if "upsample.0" in name:
				A             =					name.replace("""upsample.0"""				,       """upsample.convolution_0"""    )
			if "upsample.2" in name:
				A             =					name.replace("""upsample.2"""				,       """upsample.convolution_1"""    )
			A             =					"""upsample.""" + name
		elif config.upsampler == "pixelshuffledirect":
			A             =					name.replace("""upsample.0.weight"""				,       """upsample.conv.weight"""    )
			A             =					name.replace("""upsample.0.bias"""				,       """upsample.conv.bias"""    )
		else:
			pass
	else:
		A             =					"""swin2sr.""" + name

	return name





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[int]				,       UpperCamelCase__:  Union[str, Any]    )			-> Tuple:





	"""simple docstring"""







	for key in orig_state_dict.copy().keys():
		A             =					orig_state_dict.pop(UpperCamelCase__    )

		if "qkv" in key:
			A             =					key.split("""."""    )
			A             =					int(key_split[1]    )
			A             =					int(key_split[4]    )
			A             =					config.embed_dim

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

	return orig_state_dict





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[Any]				,       UpperCamelCase__:  Any				,       UpperCamelCase__:  List[Any]    )			-> str:





	"""simple docstring"""







	A             =					get_config(UpperCamelCase__    )
	A             =					SwinaSRForImageSuperResolution(UpperCamelCase__    )
	model.eval()

	A             =					torch.hub.load_state_dict_from_url(UpperCamelCase__				,       map_location="""cpu"""    )
	A             =					convert_state_dict(UpperCamelCase__				,       UpperCamelCase__    )
	A       ,      A             =					model.load_state_dict(UpperCamelCase__				,       strict=UpperCamelCase__    )

	if len(UpperCamelCase__    ) > 0:
		raise ValueError("""Missing keys when converting: {}""".format(UpperCamelCase__    )    )
	for key in unexpected_keys:
		if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
			raise ValueError(f'Unexpected key {key} in state_dict'    )

    # verify values
	A             =					"""https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"""
	A             =					Image.open(requests.get(UpperCamelCase__				,       stream=UpperCamelCase__    ).raw    ).convert("""RGB"""    )
	A             =					SwinaSRImageProcessor()
	# pixel_values = processor(image, return_tensors="pt").pixel_values

	A             =					1_26 if """Jpeg""" in checkpoint_url else 2_56
	A             =					Compose(
	    [
	        Resize((image_size, image_size)    ),
	        ToTensor(),
	        Normalize(mean=[0.4_85, 0.4_56, 0.4_06]				,       std=[0.2_29, 0.2_24, 0.2_25]    ),
	    ]    )
	A             =					transforms(UpperCamelCase__    ).unsqueeze(0    )

	if config.num_channels == 1:
		A             =					pixel_values[:, 0, :, :].unsqueeze(1    )

	A             =					model(UpperCamelCase__    )

	# assert values
	if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
		A             =					torch.Size([1, 3, 5_12, 5_12]    )
		A             =					torch.tensor(
		    [[-0.70_87, -0.71_38, -0.67_21], [-0.83_40, -0.80_95, -0.72_98], [-0.91_49, -0.84_14, -0.79_40]]    )
	elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
		A             =					torch.Size([1, 3, 10_24, 10_24]    )
		A             =					torch.tensor(
		    [[-0.77_75, -0.81_05, -0.89_33], [-0.77_64, -0.83_56, -0.92_25], [-0.79_76, -0.86_86, -0.95_79]]    )
	elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
		# TODO values didn't match exactly here
		A             =					torch.Size([1, 3, 10_24, 10_24]    )
		A             =					torch.tensor(
		    [[-0.80_35, -0.75_04, -0.74_91], [-0.85_38, -0.81_24, -0.77_82], [-0.88_04, -0.86_51, -0.84_93]]    )
	elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
		A             =					torch.Size([1, 3, 5_12, 5_12]    )
		A             =					torch.tensor(
		    [[-0.76_69, -0.86_62, -0.87_67], [-0.88_10, -0.99_62, -0.98_20], [-0.93_40, -1.03_22, -1.11_49]]    )
	elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
		A             =					torch.Size([1, 3, 10_24, 10_24]    )
		A             =					torch.tensor(
		    [[-0.52_38, -0.55_57, -0.63_21], [-0.60_16, -0.59_03, -0.63_91], [-0.62_44, -0.63_34, -0.68_89]]    )

	assert (
	    outputs.reconstruction.shape == expected_shape
	), f'Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}'
	assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]				,       UpperCamelCase__				,       atol=1e-3    )
	print("""Looks ok!"""    )

	A             =					{
	    """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth""": (
	        """swin2SR-classical-sr-x2-64"""
	    ),
	    """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth""": (
	        """swin2SR-classical-sr-x4-64"""
	    ),
	    """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth""": (
	        """swin2SR-compressed-sr-x4-48"""
	    ),
	    """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth""": (
	        """swin2SR-lightweight-x2-64"""
	    ),
	    """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth""": (
	        """swin2SR-realworld-sr-x4-64-bsrgan-psnr"""
	    ),
	}
	A             =					url_to_name[checkpoint_url]

	if pytorch_dump_folder_path is not None:
		print(f'Saving model {model_name} to {pytorch_dump_folder_path}'    )
		model.save_pretrained(UpperCamelCase__    )
		print(f'Saving image processor to {pytorch_dump_folder_path}'    )
		processor.save_pretrained(UpperCamelCase__    )

	if push_to_hub:
		model.push_to_hub(f'caidas/{model_name}'    )
		processor.push_to_hub(f'caidas/{model_name}'    )


if __name__ == "__main__":
				_lowercase							:   List[str]						=						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--checkpoint_url",
				    default="https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth",
				    type=str,
				    help="URL of the original Swin2SR checkpoint you'd like to convert.",
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
				)
				parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the converted model to the hub.")

				_lowercase							:   Tuple						=						parser.parse_args()
				convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)



