



"""simple docstring"""

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 __lowercase			(							_a    ):
    snake_case_ :     Tuple				=	SwinaSRConfig()

    if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
        snake_case_ :     str				=	4
    elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
        snake_case_ :     str				=	4
        snake_case_ :     Optional[int]				=	48
        snake_case_ :     int				=	"""pixelshuffle_aux"""
    elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
        snake_case_ :     Union[str, Any]				=	[6, 6, 6, 6]
        snake_case_ :     Dict				=	60
        snake_case_ :     int				=	[6, 6, 6, 6]
        snake_case_ :     Dict				=	"""pixelshuffledirect"""
    elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
        snake_case_ :     Union[str, Any]				=	4
        snake_case_ :     Union[str, Any]				=	"""nearest+conv"""
    elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
        snake_case_ :     Tuple				=	1
        snake_case_ :     List[Any]				=	1
        snake_case_ :     List[Any]				=	126
        snake_case_ :     List[Any]				=	7
        snake_case_ :     List[Any]				=	255.0
        snake_case_ :     List[Any]				=	""""""

    return config




def __lowercase			(							_a		,			_a    ):
    if "patch_embed.proj" in name and "layers" not in name:
        snake_case_ :     str				=	name.replace('''patch_embed.proj'''		,			'''embeddings.patch_embeddings.projection'''    )
    if "patch_embed.norm" in name:
        snake_case_ :     str				=	name.replace('''patch_embed.norm'''		,			'''embeddings.patch_embeddings.layernorm'''    )
    if "layers" in name:
        snake_case_ :     Any				=	name.replace('''layers'''		,			'''encoder.stages'''    )
    if "residual_group.blocks" in name:
        snake_case_ :     Dict				=	name.replace('''residual_group.blocks'''		,			'''layers'''    )
    if "attn.proj" in name:
        snake_case_ :     Optional[Any]				=	name.replace('''attn.proj'''		,			'''attention.output.dense'''    )
    if "attn" in name:
        snake_case_ :     str				=	name.replace('''attn'''		,			'''attention.self'''    )
    if "norm1" in name:
        snake_case_ :     Union[str, Any]				=	name.replace('''norm1'''		,			'''layernorm_before'''    )
    if "norm2" in name:
        snake_case_ :     int				=	name.replace('''norm2'''		,			'''layernorm_after'''    )
    if "mlp.fc1" in name:
        snake_case_ :     List[str]				=	name.replace('''mlp.fc1'''		,			'''intermediate.dense'''    )
    if "mlp.fc2" in name:
        snake_case_ :     Dict				=	name.replace('''mlp.fc2'''		,			'''output.dense'''    )
    if "q_bias" in name:
        snake_case_ :     Union[str, Any]				=	name.replace('''q_bias'''		,			'''query.bias'''    )
    if "k_bias" in name:
        snake_case_ :     Any				=	name.replace('''k_bias'''		,			'''key.bias'''    )
    if "v_bias" in name:
        snake_case_ :     Dict				=	name.replace('''v_bias'''		,			'''value.bias'''    )
    if "cpb_mlp" in name:
        snake_case_ :     Dict				=	name.replace('''cpb_mlp'''		,			'''continuous_position_bias_mlp'''    )
    if "patch_embed.proj" in name:
        snake_case_ :     Union[str, Any]				=	name.replace('''patch_embed.proj'''		,			'''patch_embed.projection'''    )

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

    if "conv_first" in name:
        snake_case_ :     Union[str, Any]				=	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:
            snake_case_ :     Dict				=	name.replace('''conv_last'''		,			'''final_convolution'''    )
        if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
            if "conv_before_upsample.0" in name:
                snake_case_ :     Optional[int]				=	name.replace('''conv_before_upsample.0'''		,			'''conv_before_upsample'''    )
            if "upsample.0" in name:
                snake_case_ :     List[str]				=	name.replace('''upsample.0'''		,			'''upsample.convolution_0'''    )
            if "upsample.2" in name:
                snake_case_ :     Any				=	name.replace('''upsample.2'''		,			'''upsample.convolution_1'''    )
            snake_case_ :     List[Any]				=	"""upsample.""" + name
        elif config.upsampler == "pixelshuffledirect":
            snake_case_ :     Any				=	name.replace('''upsample.0.weight'''		,			'''upsample.conv.weight'''    )
            snake_case_ :     Tuple				=	name.replace('''upsample.0.bias'''		,			'''upsample.conv.bias'''    )
        else:
            pass
    else:
        snake_case_ :     List[str]				=	"""swin2sr.""" + name

    return name




def __lowercase			(							_a		,			_a    ):
    for key in orig_state_dict.copy().keys():
        snake_case_ :     Optional[Any]				=	orig_state_dict.pop(_UpperCAmelCase    )

        if "qkv" in key:
            snake_case_ :     int				=	key.split('''.'''    )
            snake_case_ :     List[Any]				=	int(key_split[1]    )
            snake_case_ :     Union[str, Any]				=	int(key_split[4]    )
            snake_case_ :     List[str]				=	config.embed_dim

            if "weight" in key:
                snake_case_ :     Tuple				=	val[:dim, :]
                snake_case_ :     Any				=	val[dim : dim * 2, :]
                snake_case_ :     int				=	val[-dim:, :]
            else:
                snake_case_ :     Any				=	val[:dim]
                snake_case_ :     Optional[Any]				=	val[dim : dim * 2]
                snake_case_ :     Optional[int]				=	val[-dim:]
            pass
        else:
            snake_case_ :     Union[str, Any]				=	val

    return orig_state_dict




def __lowercase			(							_a		,			_a		,			_a    ):
    snake_case_ :     int				=	get_config(_UpperCAmelCase    )
    snake_case_ :     Dict				=	SwinaSRForImageSuperResolution(_UpperCAmelCase    )
    model.eval()

    snake_case_ :     Any				=	torch.hub.load_state_dict_from_url(_UpperCAmelCase		,			map_location='''cpu'''    )
    snake_case_ :     List[Any]				=	convert_state_dict(_UpperCAmelCase		,			_UpperCAmelCase    )
    snake_case_ :     List[Any]				=	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
    snake_case_ :     str				=	"""https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"""
    snake_case_ :     Any				=	Image.open(requests.get(_UpperCAmelCase		,			stream=_UpperCAmelCase    ).raw    ).convert('''RGB'''    )
    snake_case_ :     Optional[int]				=	SwinaSRImageProcessor()
    # pixel_values = processor(image, return_tensors="pt").pixel_values

    snake_case_ :     List[str]				=	126 if """Jpeg""" in checkpoint_url else 256
    snake_case_ :     Dict				=	Compose(
        [
            Resize((image_size, image_size)    ),
            ToTensor(),
            Normalize(mean=[0.485, 0.456, 0.406]		,			std=[0.229, 0.224, 0.225]    ),
        ]    )
    snake_case_ :     Optional[int]				=	transforms(_UpperCAmelCase    ).unsqueeze(0    )

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

    snake_case_ :     List[str]				=	model(_UpperCAmelCase    )

    # assert values
    if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
        snake_case_ :     List[str]				=	torch.Size([1, 3, 512, 512]    )
        snake_case_ :     Optional[Any]				=	torch.tensor(
            [[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]]    )
    elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
        snake_case_ :     Tuple				=	torch.Size([1, 3, 1_024, 1_024]    )
        snake_case_ :     Union[str, Any]				=	torch.tensor(
            [[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]]    )
    elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
        # TODO values didn't match exactly here
        snake_case_ :     Any				=	torch.Size([1, 3, 1_024, 1_024]    )
        snake_case_ :     List[str]				=	torch.tensor(
            [[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]]    )
    elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
        snake_case_ :     Optional[Any]				=	torch.Size([1, 3, 512, 512]    )
        snake_case_ :     Union[str, Any]				=	torch.tensor(
            [[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]]    )
    elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
        snake_case_ :     int				=	torch.Size([1, 3, 1_024, 1_024]    )
        snake_case_ :     Tuple				=	torch.tensor(
            [[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]]    )

    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!'''    )

    snake_case_ :     Tuple				=	{
        """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"""
        ),
    }
    snake_case_ :     List[Any]				=	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__    :  Optional[int]							    =	parser.parse_args()
     convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''







class A__       :






      def __init__( self			:List[str]	)       ->					List[Any]:




             '''simple docstring'''
             _a    : Tuple     					=0
             _a    : Any     					=0
             _a    : int     					={}






      def __UpperCAmelCase       ( self			:Any     ,						SCREAMING_SNAKE_CASE			:List[str]	)       ->					Optional[int]:




             '''simple docstring'''
             if vertex not in self.adjacency:
                    _a    : Dict     					={}
                    self.num_vertices += 1






      def __UpperCAmelCase       ( self			:Optional[Any]     ,						SCREAMING_SNAKE_CASE			:str     ,						SCREAMING_SNAKE_CASE			:str     ,						SCREAMING_SNAKE_CASE			:Any	)       ->					List[str]:




             '''simple docstring'''
             self.add_vertex(SCREAMING_SNAKE_CASE	)
             self.add_vertex(SCREAMING_SNAKE_CASE	)

             if head == tail:
                    return

             _a    : Any     					=weight
             _a    : Tuple     					=weight






      def __UpperCAmelCase       ( self			:Dict	)       ->					Optional[int]:




             '''simple docstring'''
             _a    : Union[str, Any]     					=self.get_edges()
             for edge in edges:
                    _a					,    _a					,    _a    : List[str]     					=edge
                    edges.remove((tail, head, weight)	)
             for i in range(len(SCREAMING_SNAKE_CASE	)	):
                    _a    : str     					=list(edges[i]	)

             edges.sort(key=lambda SCREAMING_SNAKE_CASE	: e[2]	)
             for i in range(len(SCREAMING_SNAKE_CASE	) - 1	):
                    if edges[i][2] >= edges[i + 1][2]:
                           _a    : Union[str, Any]     					=edges[i][2] + 1
             for edge in edges:
                    _a					,    _a					,    _a    : Tuple     					=edge
                    _a    : Tuple     					=weight
                    _a    : List[Any]     					=weight






      def __str__( self			:int	)       ->					str:




             '''simple docstring'''
             _a    : int     					=""""""
             for tail in self.adjacency:
                    for head in self.adjacency[tail]:
                           _a    : str     					=self.adjacency[head][tail]
                           string += f"{head} -> {tail} == {weight}\n"
             return string.rstrip("""\n"""	)






      def __UpperCAmelCase       ( self			:Optional[int]	)       ->					Optional[Any]:




             '''simple docstring'''
             _a    : Union[str, Any]     					=[]
             for tail in self.adjacency:
                    for head in self.adjacency[tail]:
                           output.append((tail, head, self.adjacency[head][tail])	)
             return output






      def __UpperCAmelCase       ( self			:List[Any]	)       ->					List[Any]:




             '''simple docstring'''
             return self.adjacency.keys()






      @staticmethod
      def __UpperCAmelCase       ( SCREAMING_SNAKE_CASE			:Dict=None     ,						SCREAMING_SNAKE_CASE			:List[Any]=None	)       ->					Optional[int]:




             '''simple docstring'''
             _a    : str     					=Graph()
             if vertices is None:
                    _a    : Union[str, Any]     					=[]
             if edges is None:
                    _a    : List[Any]     					=[]
             for vertex in vertices:
                    g.add_vertex(SCREAMING_SNAKE_CASE	)
             for edge in edges:
                    g.add_edge(*SCREAMING_SNAKE_CASE	)
             return g







      class A__       :






            def __init__( self			:Optional[int]	)       ->					Union[str, Any]:




                   '''simple docstring'''
                   _a    : Optional[int]     					={}
                   _a    : List[str]     					={}






            def __len__( self			:List[Any]	)       ->					List[Any]:




                   '''simple docstring'''
                   return len(self.parent	)






            def __UpperCAmelCase       ( self			:Tuple     ,						SCREAMING_SNAKE_CASE			:Tuple	)       ->					Dict:




                   '''simple docstring'''
                   if item in self.parent:
                          return self.find(SCREAMING_SNAKE_CASE	)

                   _a    : Optional[Any]     					=item
                   _a    : List[str]     					=0
                   return item






            def __UpperCAmelCase       ( self			:int     ,						SCREAMING_SNAKE_CASE			:Dict	)       ->					List[str]:




                   '''simple docstring'''
                   if item not in self.parent:
                          return self.make_set(SCREAMING_SNAKE_CASE	)
                   if item != self.parent[item]:
                          _a    : str     					=self.find(self.parent[item]	)
                   return self.parent[item]






            def __UpperCAmelCase       ( self			:Optional[int]     ,						SCREAMING_SNAKE_CASE			:Tuple     ,						SCREAMING_SNAKE_CASE			:List[Any]	)       ->					Optional[Any]:




                   '''simple docstring'''
                   _a    : Optional[int]     					=self.find(SCREAMING_SNAKE_CASE	)
                   _a    : Dict     					=self.find(SCREAMING_SNAKE_CASE	)

                   if roota == roota:
                          return roota

                   if self.rank[roota] > self.rank[roota]:
                          _a    : Any     					=roota
                          return roota

                   if self.rank[roota] < self.rank[roota]:
                          _a    : List[str]     					=roota
                          return roota

                   if self.rank[roota] == self.rank[roota]:
                          self.rank[roota] += 1
                          _a    : List[Any]     					=roota
                          return roota
                   return None






      @staticmethod
      def __UpperCAmelCase       ( SCREAMING_SNAKE_CASE			:Dict	)       ->					Union[str, Any]:




             '''simple docstring'''
             _a    : Any     					=graph.num_vertices

             _a    : Union[str, Any]     					=Graph.UnionFind()
             _a    : Optional[int]     					=[]
             while num_components > 1:
                    _a    : str     					={}
                    for vertex in graph.get_vertices():
                           _a    : List[str]     					=-1

                    _a    : Any     					=graph.get_edges()
                    for edge in edges:
                           _a					,    _a					,    _a    : Tuple     					=edge
                           edges.remove((tail, head, weight)	)
                    for edge in edges:
                           _a					,    _a					,    _a    : Any     					=edge
                           _a    : Any     					=union_find.find(SCREAMING_SNAKE_CASE	)
                           _a    : List[Any]     					=union_find.find(SCREAMING_SNAKE_CASE	)
                           if seta != seta:
                                  if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
                                         _a    : Optional[int]     					=[head, tail, weight]

                                  if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
                                         _a    : List[Any]     					=[head, tail, weight]
                    for vertex in cheap_edge:
                           if cheap_edge[vertex] != -1:
                                  _a					,    _a					,    _a    : Optional[Any]     					=cheap_edge[vertex]
                                  if union_find.find(SCREAMING_SNAKE_CASE	) != union_find.find(SCREAMING_SNAKE_CASE	):
                                         union_find.union(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)
                                         mst_edges.append(cheap_edge[vertex]	)
                                         _a    : str     					=num_components - 1
             _a    : str     					=Graph.build(edges=SCREAMING_SNAKE_CASE	)
             return mst


