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 A__  (			lowercase:     Optional[Any]					)				->		List[str]:
       A						:     Dict         =SwinaSRConfig()

       if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
              A						:     List[Any]         =4
       elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
              A						:     Dict         =4
              A						:     Any         =48
              A						:     Optional[int]         ='pixelshuffle_aux'
       elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
              A						:     List[str]         =[6, 6, 6, 6]
              A						:     Tuple         =60
              A						:     Tuple         =[6, 6, 6, 6]
              A						:     Optional[Any]         ='pixelshuffledirect'
       elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
              A						:     List[str]         =4
              A						:     List[str]         ='nearest+conv'
       elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
              A						:     Optional[Any]         =1
              A						:     Optional[Any]         =1
              A						:     Any         =126
              A						:     Any         =7
              A						:     Union[str, Any]         =2_55.0
              A						:     Dict         =''

       return config



def A__  (			lowercase:     List[Any],		lowercase:     Any					)				->		str:
       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						:     Optional[Any]         =name.replace('layers',		'encoder.stages'					)
       if "residual_group.blocks" in name:
              A						:     List[Any]         =name.replace('residual_group.blocks',		'layers'					)
       if "attn.proj" in name:
              A						:     str         =name.replace('attn.proj',		'attention.output.dense'					)
       if "attn" in name:
              A						:     Union[str, Any]         =name.replace('attn',		'attention.self'					)
       if "norm1" in name:
              A						:     Optional[int]         =name.replace('norm1',		'layernorm_before'					)
       if "norm2" in name:
              A						:     int         =name.replace('norm2',		'layernorm_after'					)
       if "mlp.fc1" in name:
              A						:     List[Any]         =name.replace('mlp.fc1',		'intermediate.dense'					)
       if "mlp.fc2" in name:
              A						:     List[Any]         =name.replace('mlp.fc2',		'output.dense'					)
       if "q_bias" in name:
              A						:     Dict         =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						:     int         =name.replace('v_bias',		'value.bias'					)
       if "cpb_mlp" in name:
              A						:     Optional[Any]         =name.replace('cpb_mlp',		'continuous_position_bias_mlp'					)
       if "patch_embed.proj" in name:
              A						:     List[str]         =name.replace('patch_embed.proj',		'patch_embed.projection'					)

       if name == "norm.weight":
              A						:     Union[str, Any]         ='layernorm.weight'
       if name == "norm.bias":
              A						:     Union[str, Any]         ='layernorm.bias'

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

       return name



def A__  (			lowercase:     Optional[Any],		lowercase:     Optional[int]					)				->		List[Any]:
       for key in orig_state_dict.copy().keys():
              A						:     Dict         =orig_state_dict.pop(lowercase					)

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

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

       return orig_state_dict



def A__  (			lowercase:     Optional[Any],		lowercase:     Optional[Any],		lowercase:     Optional[Any]					)				->		Tuple:
       A						:     Optional[Any]         =get_config(lowercase					)
       A						:     Any         =SwinaSRForImageSuperResolution(lowercase					)
       model.eval()

       A						:     List[str]         =torch.hub.load_state_dict_from_url(lowercase,		map_location='cpu'					)
       A						:     Tuple         =convert_state_dict(lowercase,		lowercase					)
       A  ,  A						:     List[Any]         =model.load_state_dict(lowercase,		strict=lowercase					)

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

       A						:     Optional[int]         =126 if 'Jpeg' in checkpoint_url else 256
       A						:     Optional[int]         =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						:     Optional[int]         =transforms(lowercase					).unsqueeze(0					)

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

       A						:     List[str]         =model(lowercase					)

       # assert values
       if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
              A						:     List[Any]         =torch.Size([1, 3, 512, 512]					)
              A						:     List[Any]         =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						:     Tuple         =torch.Size([1, 3, 1_024, 1_024]					)
              A						:     List[Any]         =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						:     int         =torch.Size([1, 3, 1_024, 1_024]					)
              A						:     Optional[Any]         =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						:     Optional[int]         =torch.Size([1, 3, 512, 512]					)
              A						:     str         =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						:     List[Any]         =torch.Size([1, 3, 1_024, 1_024]					)
              A						:     int         =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],		lowercase,		atol=1e-3					)
       print('Looks ok!'					)

       A						:     Optional[Any]         ={
           '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						:     Union[str, 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(lowercase					)
              print(F'Saving image processor to {pytorch_dump_folder_path}'					)
              processor.save_pretrained(lowercase					)

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


if __name__ == "__main__":
    _lowercase							:      Any      							=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[Any]      							=parser.parse_args()
    convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







import math



def A__  (			lowercase:     int					)				->		list:
       A						:     Optional[Any]         =[True] * n
       A						:     Tuple         =False
       A						:     List[Any]         =False
       A						:     Dict         =True

       for i in range(3,		int(n**0.5 + 1					),		2					):
              A						:     Dict         =i * 2
              while index < n:
                     A						:     Dict         =False
                     A						:     Dict         =index + i

       A						:     Tuple         =[2]

       for i in range(3,		lowercase,		2					):
              if is_prime[i]:
                     primes.append(lowercase					)

       return primes



def A__  (			lowercase:     int = 999_966_663_333					)				->		int:
       A						:     Optional[int]         =math.floor(math.sqrt(lowercase					)					) + 100
       A						:     Optional[int]         =prime_sieve(lowercase					)

       A						:     Optional[Any]         =0
       A						:     List[Any]         =0
       A						:     Union[str, Any]         =primes[prime_index]

       while (last_prime**2) <= limit:
              A						:     Tuple         =primes[prime_index + 1]

              A						:     Optional[int]         =last_prime**2
              A						:     Tuple         =next_prime**2

              # Get numbers divisible by lps(current)
              A						:     int         =lower_bound + last_prime
              while upper_bound > current <= limit:
                     matches_sum += current
                     current += last_prime

              # Reset the upper_bound
              while (upper_bound - next_prime) > limit:
                     upper_bound -= next_prime

              # Add the numbers divisible by ups(current)
              A						:     List[Any]         =upper_bound - next_prime
              while current > lower_bound:
                     matches_sum += current
                     current -= next_prime

              # Remove the numbers divisible by both ups and lps
              A						:     Any         =0
              while upper_bound > current <= limit:
                     if current <= lower_bound:
                            # Increment the current number
                            current += last_prime * next_prime
                            continue

                     if current > limit:
                            break

                     # Remove twice since it was added by both ups and lps
                     matches_sum -= current * 2

                     # Increment the current number
                     current += last_prime * next_prime

              # Setup for next pair
              A						:     List[str]         =next_prime
              prime_index += 1

       return matches_sum


if __name__ == "__main__":
    print(solution())






