


'''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 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	List[Any]  )  ->			Any:
       _a    : Tuple     					=SwinaSRConfig()

       if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
              _a    : str     					=4
       elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
              _a    : str     					=4
              _a    : Optional[int]     					=48
              _a    : int     					="""pixelshuffle_aux"""
       elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
              _a    : Union[str, Any]     					=[6, 6, 6, 6]
              _a    : Dict     					=60
              _a    : int     					=[6, 6, 6, 6]
              _a    : Dict     					="""pixelshuffledirect"""
       elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
              _a    : Union[str, Any]     					=4
              _a    : Union[str, Any]     					="""nearest+conv"""
       elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
              _a    : Tuple     					=1
              _a    : List[Any]     					=1
              _a    : List[Any]     					=126
              _a    : List[Any]     					=7
              _a    : List[Any]     					=2_5_5.0
              _a    : List[Any]     					=""""""

       return config


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

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

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

       return name


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

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

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

       return orig_state_dict


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str    ,_UpperCAmelCase	:	List[Any]    ,_UpperCAmelCase	:	Dict  )  ->			List[Any]:
       _a    : int     					=get_config(_UpperCAmelCase  )
       _a    : Dict     					=SwinaSRForImageSuperResolution(_UpperCAmelCase  )
       model.eval()

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

       _a    : List[str]     					=126 if """Jpeg""" in checkpoint_url else 256
       _a    : Dict     					=Compose(
           [
               Resize((image_size, image_size)  ),
               ToTensor(),
               Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]    ,std=[0.2_2_9, 0.2_2_4, 0.2_2_5]  ),
           ]  )
       _a    : Optional[int]     					=transforms(_UpperCAmelCase  ).unsqueeze(0  )

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

       _a    : List[str]     					=model(_UpperCAmelCase  )

       # assert values
       if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
              _a    : List[str]     					=torch.Size([1, 3, 512, 512]  )
              _a    : Optional[Any]     					=torch.tensor(
                  [[-0.7_0_8_7, -0.7_1_3_8, -0.6_7_2_1], [-0.8_3_4_0, -0.8_0_9_5, -0.7_2_9_8], [-0.9_1_4_9, -0.8_4_1_4, -0.7_9_4_0]]  )
       elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
              _a    : Tuple     					=torch.Size([1, 3, 1024, 1024]  )
              _a    : Union[str, Any]     					=torch.tensor(
                  [[-0.7_7_7_5, -0.8_1_0_5, -0.8_9_3_3], [-0.7_7_6_4, -0.8_3_5_6, -0.9_2_2_5], [-0.7_9_7_6, -0.8_6_8_6, -0.9_5_7_9]]  )
       elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
              # TODO values didn't match exactly here
              _a    : Any     					=torch.Size([1, 3, 1024, 1024]  )
              _a    : List[str]     					=torch.tensor(
                  [[-0.8_0_3_5, -0.7_5_0_4, -0.7_4_9_1], [-0.8_5_3_8, -0.8_1_2_4, -0.7_7_8_2], [-0.8_8_0_4, -0.8_6_5_1, -0.8_4_9_3]]  )
       elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
              _a    : Optional[Any]     					=torch.Size([1, 3, 512, 512]  )
              _a    : Union[str, Any]     					=torch.tensor(
                  [[-0.7_6_6_9, -0.8_6_6_2, -0.8_7_6_7], [-0.8_8_1_0, -0.9_9_6_2, -0.9_8_2_0], [-0.9_3_4_0, -1.0_3_2_2, -1.1_1_4_9]]  )
       elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
              _a    : int     					=torch.Size([1, 3, 1024, 1024]  )
              _a    : Tuple     					=torch.tensor(
                  [[-0.5_2_3_8, -0.5_5_5_7, -0.6_3_2_1], [-0.6_0_1_6, -0.5_9_0_3, -0.6_3_9_1], [-0.6_2_4_4, -0.6_3_3_4, -0.6_8_8_9]]  )

       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    : 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"""
           ),
       }
       _a    : 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__":
   A__:    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.''')

   A__:    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[Any]	)       ->					None:




             '''simple docstring'''
             _a    : dict[str, TrieNode]     					={}  # Mapping from char to TrieNode
             _a    : List[str]     					=False






      def __UpperCAmelCase       ( self			:Tuple     ,						SCREAMING_SNAKE_CASE			:list[str]	)       ->					None:




             '''simple docstring'''
             for word in words:
                    self.insert(SCREAMING_SNAKE_CASE	)






      def __UpperCAmelCase       ( self			:Union[str, Any]     ,						SCREAMING_SNAKE_CASE			:str	)       ->					None:




             '''simple docstring'''
             _a    : str     					=self
             for char in word:
                    if char not in curr.nodes:
                           _a    : Dict     					=TrieNode()
                    _a    : List[Any]     					=curr.nodes[char]
             _a    : int     					=True






      def __UpperCAmelCase       ( self			:Union[str, Any]     ,						SCREAMING_SNAKE_CASE			:str	)       ->					bool:




             '''simple docstring'''
             _a    : int     					=self
             for char in word:
                    if char not in curr.nodes:
                           return False
                    _a    : List[Any]     					=curr.nodes[char]
             return curr.is_leaf






      def __UpperCAmelCase       ( self			:Dict     ,						SCREAMING_SNAKE_CASE			:str	)       ->					None:




             '''simple docstring'''

             def _delete(SCREAMING_SNAKE_CASE			:TrieNode     ,						SCREAMING_SNAKE_CASE			:str     ,						SCREAMING_SNAKE_CASE			:int	) -> bool:
                    if index == len(SCREAMING_SNAKE_CASE	):
                           # If word does not exist
                           if not curr.is_leaf:
                                  return False
                           _a    : Any     					=False
                           return len(curr.nodes	) == 0
                    _a    : int     					=word[index]
                    _a    : int     					=curr.nodes.get(SCREAMING_SNAKE_CASE	)
                    # If char not in current trie node
                    if not char_node:
                           return False
                    # Flag to check if node can be deleted
                    _a    : List[Any]     					=_delete(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						index + 1	)
                    if delete_curr:
                           del curr.nodes[char]
                           return len(curr.nodes	) == 0
                    return delete_curr

             _delete(self     ,						SCREAMING_SNAKE_CASE     ,						0	)


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	TrieNode    ,_UpperCAmelCase	:	str  )  ->			None:
       if node.is_leaf:
              print(_UpperCAmelCase    ,end=""" """  )

       for key, value in node.nodes.items():
              print_words(_UpperCAmelCase    ,word + key  )


def 	SCREAMING_SNAKE_CASE_					(  )  ->			bool:
       _a    : List[str]     					="""banana bananas bandana band apple all beast""".split()
       _a    : List[Any]     					=TrieNode()
       root.insert_many(_UpperCAmelCase  )
       # print_words(root, "")
       assert all(root.find(_UpperCAmelCase  ) for word in words  )
       assert root.find("""banana"""  )
       assert not root.find("""bandanas"""  )
       assert not root.find("""apps"""  )
       assert root.find("""apple"""  )
       assert root.find("""all"""  )
       root.delete("""all"""  )
       assert not root.find("""all"""  )
       root.delete("""banana"""  )
       assert not root.find("""banana"""  )
       assert root.find("""bananas"""  )
       return True


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str    ,_UpperCAmelCase	:	bool  )  ->			None:
       print(str(_UpperCAmelCase  )    ,"""works!""" if passes else """doesn't work :("""  )


def 	SCREAMING_SNAKE_CASE_					(  )  ->			None:
       assert test_trie()


def 	SCREAMING_SNAKE_CASE_					(  )  ->			None:
       print_results("""Testing trie functionality"""    ,test_trie()  )


if __name__ == "__main__":
   main()


