


'''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      (A       )       ->       Tuple:






       """simple docstring"""







       lowercase__      			= SwinaSRConfig()

       if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
              lowercase__      			= 4
       elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
              lowercase__      			= 4
              lowercase__      			= 48
              lowercase__      			= '''pixelshuffle_aux'''
       elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
              lowercase__      			= [6, 6, 6, 6]
              lowercase__      			= 60
              lowercase__      			= [6, 6, 6, 6]
              lowercase__      			= '''pixelshuffledirect'''
       elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
              lowercase__      			= 4
              lowercase__      			= '''nearest+conv'''
       elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
              lowercase__      			= 1
              lowercase__      			= 1
              lowercase__      			= 126
              lowercase__      			= 7
              lowercase__      			= 255.0
              lowercase__      			= ''''''

       return config

def _SCREAMING_SNAKE_CASE      (A  , A       )       ->       Dict:






       """simple docstring"""







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

       if name == "norm.weight":
              lowercase__      			= '''layernorm.weight'''
       if name == "norm.bias":
              lowercase__      			= '''layernorm.bias'''

       if "conv_first" in name:
              lowercase__      			= 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:
                     lowercase__      			= name.replace('''conv_last'''  , '''final_convolution'''       )
              if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
                     if "conv_before_upsample.0" in name:
                            lowercase__      			= name.replace('''conv_before_upsample.0'''  , '''conv_before_upsample'''       )
                     if "upsample.0" in name:
                            lowercase__      			= name.replace('''upsample.0'''  , '''upsample.convolution_0'''       )
                     if "upsample.2" in name:
                            lowercase__      			= name.replace('''upsample.2'''  , '''upsample.convolution_1'''       )
                     lowercase__      			= '''upsample.''' + name
              elif config.upsampler == "pixelshuffledirect":
                     lowercase__      			= name.replace('''upsample.0.weight'''  , '''upsample.conv.weight'''       )
                     lowercase__      			= name.replace('''upsample.0.bias'''  , '''upsample.conv.bias'''       )
              else:
                     pass
       else:
              lowercase__      			= '''swin2sr.''' + name

       return name

def _SCREAMING_SNAKE_CASE      (A  , A       )       ->       Dict:






       """simple docstring"""







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

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

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

       return orig_state_dict

def _SCREAMING_SNAKE_CASE      (A  , A  , A       )       ->       Tuple:






       """simple docstring"""







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

       lowercase__      			= torch.hub.load_state_dict_from_url(A  , map_location='''cpu'''       )
       lowercase__      			= convert_state_dict(A  , A       )
       lowercase__			,lowercase__      			= model.load_state_dict(A  , strict=A       )

       if len(A       ) > 0:
              raise ValueError('''Missing keys when converting: {}'''.format(A       )       )
       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
       lowercase__      			= '''https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'''
       lowercase__      			= Image.open(requests.get(A  , stream=A       ).raw       ).convert('''RGB'''       )
       lowercase__      			= SwinaSRImageProcessor()
       # pixel_values = processor(image, return_tensors="pt").pixel_values

       lowercase__      			= 126 if '''Jpeg''' in checkpoint_url else 256
       lowercase__      			= Compose(
           [
               Resize((image_size, image_size)       ),
               ToTensor(),
               Normalize(mean=[0.485, 0.456, 0.406]  , std=[0.229, 0.224, 0.225]       ),
           ]       )
       lowercase__      			= transforms(A       ).unsqueeze(0       )

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

       lowercase__      			= model(A       )

       # assert values
       if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
              lowercase__      			= torch.Size([1, 3, 512, 512]       )
              lowercase__      			= torch.tensor(
                  [[-0.7_087, -0.7_138, -0.6_721], [-0.8_340, -0.8_095, -0.7_298], [-0.9_149, -0.8_414, -0.7_940]]       )
       elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
              lowercase__      			= torch.Size([1, 3, 1_024, 1_024]       )
              lowercase__      			= torch.tensor(
                  [[-0.7_775, -0.8_105, -0.8_933], [-0.7_764, -0.8_356, -0.9_225], [-0.7_976, -0.8_686, -0.9_579]]       )
       elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
              # TODO values didn't match exactly here
              lowercase__      			= torch.Size([1, 3, 1_024, 1_024]       )
              lowercase__      			= torch.tensor(
                  [[-0.8_035, -0.7_504, -0.7_491], [-0.8_538, -0.8_124, -0.7_782], [-0.8_804, -0.8_651, -0.8_493]]       )
       elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
              lowercase__      			= torch.Size([1, 3, 512, 512]       )
              lowercase__      			= torch.tensor(
                  [[-0.7_669, -0.8_662, -0.8_767], [-0.8_810, -0.9_962, -0.9_820], [-0.9_340, -1.0_322, -1.1_149]]       )
       elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
              lowercase__      			= torch.Size([1, 3, 1_024, 1_024]       )
              lowercase__      			= torch.tensor(
                  [[-0.5_238, -0.5_557, -0.6_321], [-0.6_016, -0.5_903, -0.6_391], [-0.6_244, -0.6_334, -0.6_889]]       )

       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]  , A  , atol=1E-3       )
       print('''Looks ok!'''       )

       lowercase__      			= {
           '''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'''
           ),
       }
       lowercase__      			= 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(A       )
              print(f"Saving image processor to {pytorch_dump_folder_path}"       )
              processor.save_pretrained(A       )

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


if __name__ == "__main__":
 lowerCamelCase							:   Dict  						=	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.')

 lowerCamelCase							:   Dict  						=	parser.parse_args()
 convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)











def A_	(		A__				)  ->		int:
       stooge(A__		,      0		,      len(A__				) - 1				)
       return arr



def A_	(		A__		,      A__		,      A__				)  ->		List[Any]:
       if i >= h:
              return

       # If first element is smaller than the last then swap them
       if arr[i] > arr[h]:
              a__ ,							a__						:	List[str] =				arr[h], arr[i]

       # If there are more than 2 elements in the array
       if h - i + 1 > 2:
              a__						:	Dict =				(int)((h - i + 1) / 3				)

              # Recursively sort first 2/3 elements
              stooge(A__		,      A__		,      (h - t)				)

              # Recursively sort last 2/3 elements
              stooge(A__		,      i + t		,      (A__)				)

              # Recursively sort first 2/3 elements
              stooge(A__		,      A__		,      (h - t)				)


if __name__ == "__main__":
    lowercase      :       Dict									=  input("""Enter numbers separated by a comma:\n""").strip()
    lowercase      :       Dict									=  [int(item) for item in user_input.split(""",""")]
    print(stooge_sort(unsorted))


