

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        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					SwinaSRConfig()

     if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
          SCREAMING_SNAKE_CASE__					=					4
     elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
          SCREAMING_SNAKE_CASE__					=					4
          SCREAMING_SNAKE_CASE__					=					48
          SCREAMING_SNAKE_CASE__					=					'''pixelshuffle_aux'''
     elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
          SCREAMING_SNAKE_CASE__					=					[6, 6, 6, 6]
          SCREAMING_SNAKE_CASE__					=					60
          SCREAMING_SNAKE_CASE__					=					[6, 6, 6, 6]
          SCREAMING_SNAKE_CASE__					=					'''pixelshuffledirect'''
     elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
          SCREAMING_SNAKE_CASE__					=					4
          SCREAMING_SNAKE_CASE__					=					'''nearest+conv'''
     elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
          SCREAMING_SNAKE_CASE__					=					1
          SCREAMING_SNAKE_CASE__					=					1
          SCREAMING_SNAKE_CASE__					=					1_26
          SCREAMING_SNAKE_CASE__					=					7
          SCREAMING_SNAKE_CASE__					=					2_5_5.0
          SCREAMING_SNAKE_CASE__					=					''''''

     return config
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




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

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

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

     return name
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     for key in orig_state_dict.copy().keys():
          SCREAMING_SNAKE_CASE__					=					orig_state_dict.pop(_A       )

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

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

     return orig_state_dict
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					get_config(_A       )
     SCREAMING_SNAKE_CASE__					=					SwinaSRForImageSuperResolution(_A       )
     model.eval()

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

     SCREAMING_SNAKE_CASE__					=					1_26 if '''Jpeg''' in checkpoint_url else 2_56
     SCREAMING_SNAKE_CASE__					=					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]       ),
         ]       )
     SCREAMING_SNAKE_CASE__					=					transforms(_A       ).unsqueeze(0       )

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

     SCREAMING_SNAKE_CASE__					=					model(_A       )

     # assert values
     if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 3, 5_12, 5_12]       )
          SCREAMING_SNAKE_CASE__					=					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:
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 3, 10_24, 10_24]       )
          SCREAMING_SNAKE_CASE__					=					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
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 3, 10_24, 10_24]       )
          SCREAMING_SNAKE_CASE__					=					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:
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 3, 5_12, 5_12]       )
          SCREAMING_SNAKE_CASE__					=					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:
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 3, 10_24, 10_24]       )
          SCREAMING_SNAKE_CASE__					=					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]   ,	_A   ,	atol=1e-3       )
     print('''Looks ok!'''       )

     SCREAMING_SNAKE_CASE__					=					{
         '''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'''
         ),
     }
     SCREAMING_SNAKE_CASE__					=					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__":
       _SCREAMING_SNAKE_CASE  :						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.''')

       _SCREAMING_SNAKE_CASE  :						List[str]         =    parser.parse_args()
       convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





from collections.abc import Callable

import numpy as np
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					int(np.ceil((x_end - xa) / step_size       )       )
     SCREAMING_SNAKE_CASE__					=					np.zeros((n + 1,)       )
     SCREAMING_SNAKE_CASE__					=					ya
     SCREAMING_SNAKE_CASE__					=					xa

     for k in range(_A       ):
          SCREAMING_SNAKE_CASE__					=					y[k] + step_size * ode_func(_A   ,	y[k]       )
          SCREAMING_SNAKE_CASE__					=					y[k] + (
              (step_size / 2) * (ode_func(_A   ,	y[k]       ) + ode_func(x + step_size   ,	_A       ))
          )
          x += step_size

     return y


if __name__ == "__main__":
       import doctest

       doctest.testmod()


