




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        lowerCAmelCase_			(  snake_case_  ):
      _A							:  str    =		SwinaSRConfig()

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

      return config







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      if "patch_embed.proj" in name and "layers" not in name:
            _A							:  Optional[int]    =		name.replace("""patch_embed.proj""","""embeddings.patch_embeddings.projection"""  )
      if "patch_embed.norm" in name:
            _A							:  List[Any]    =		name.replace("""patch_embed.norm""","""embeddings.patch_embeddings.layernorm"""  )
      if "layers" in name:
            _A							:  int    =		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							:  int    =		name.replace("""attn.proj""","""attention.output.dense"""  )
      if "attn" in name:
            _A							:  List[str]    =		name.replace("""attn""","""attention.self"""  )
      if "norm1" in name:
            _A							:  int    =		name.replace("""norm1""","""layernorm_before"""  )
      if "norm2" in name:
            _A							:  Tuple    =		name.replace("""norm2""","""layernorm_after"""  )
      if "mlp.fc1" in name:
            _A							:  Tuple    =		name.replace("""mlp.fc1""","""intermediate.dense"""  )
      if "mlp.fc2" in name:
            _A							:  str    =		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							:  List[str]    =		name.replace("""k_bias""","""key.bias"""  )
      if "v_bias" in name:
            _A							:  str    =		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							:  Dict    =		name.replace("""patch_embed.proj""","""patch_embed.projection"""  )

      if name == "norm.weight":
            _A							:  int    =		"""layernorm.weight"""
      if name == "norm.bias":
            _A							:  List[Any]    =		"""layernorm.bias"""

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

      return name







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      for key in orig_state_dict.copy().keys():
            _A							:  str    =		orig_state_dict.pop(snake_case_  )

            if "qkv" in key:
                  _A							:  Dict    =		key.split("""."""  )
                  _A							:  Any    =		int(key_split[1]  )
                  _A							:  Any    =		int(key_split[4]  )
                  _A							:  Optional[Any]    =		config.embed_dim

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

      return orig_state_dict







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_  ):
      _A							:  Tuple    =		get_config(snake_case_  )
      _A							:  Optional[int]    =		SwinaSRForImageSuperResolution(snake_case_  )
      model.eval()

      _A							:  Optional[int]    =		torch.hub.load_state_dict_from_url(snake_case_,map_location="""cpu"""  )
      _A							:  str    =		convert_state_dict(snake_case_,snake_case_  )
      _A    ,     _A							:  Union[str, Any]    =		model.load_state_dict(snake_case_,strict=snake_case_  )

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

      _A							:  str    =		126 if """Jpeg""" in checkpoint_url else 256
      _A							:  List[str]    =		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							:  List[str]    =		transforms(snake_case_  ).unsqueeze(0  )

      if config.num_channels == 1:
            _A							:  Optional[Any]    =		pixel_values[:, 0, :, :].unsqueeze(1  )

      _A							:  int    =		model(snake_case_  )

      # assert values
      if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
            _A							:  int    =		torch.Size([1, 3, 512, 512]  )
            _A							:  Union[str, 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							:  Any    =		torch.Size([1, 3, 1024, 1024]  )
            _A							:  str    =		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, 1024, 1024]  )
            _A							:  List[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							:  str    =		torch.Size([1, 3, 512, 512]  )
            _A							:  List[Any]    =		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							:  Union[str, Any]    =		torch.Size([1, 3, 1024, 1024]  )
            _A							:  Optional[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],snake_case_,atol=1e-3  )
      print("""Looks ok!"""  )

      _A							:  Union[str, 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							:  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(snake_case_  )
            print(f'''Saving image processor to {pytorch_dump_folder_path}'''  )
            processor.save_pretrained(snake_case_  )

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


if __name__ == "__main__":
  _snake_case            = 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.")

  _snake_case            = parser.parse_args()
  convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset







class 		lowercase							(     UpperCamelCase__	):

     def       a__			(						self       )		->							Optional[int]:
           return [
               {"col_1": 3, "col_2": "a"},
               {"col_1": 2, "col_2": "b"},
               {"col_1": 1, "col_2": "c"},
               {"col_1": 0, "col_2": "d"},
           ]

     def       a__			(						self       )		->							Optional[int]:
           _A							:  Union[str, Any]    =		{"""col_1""": [3, 2, 1, 0], """col_2""": ["""a""", """b""", """c""", """d"""]}
           return Dataset.from_dict(_a       )

     def       a__			(						self       )		->							Any:
           _A							:  str    =		self._create_example_records()
           _A							:  List[Any]    =		Dataset.from_list(_a       )
           self.assertListEqual(dset.column_names	,  ["""col_1""", """col_2"""]       )
           for i, r in enumerate(_a       ):
                 self.assertDictEqual(_a	,  example_records[i]       )

     def       a__			(						self       )		->							List[str]:
           _A							:  Dict    =		self._create_example_records()
           _A							:  List[str]    =		Dataset.from_list(_a       )
           _A							:  str    =		Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}       )
           self.assertEqual(dset.info	,  dset_from_dict.info       )

     def       a__			(						self       )		->							str:  # checks what happens with missing columns
           _A							:  List[str]    =		[{"""col_1""": 1}, {"""col_2""": """x"""}]
           _A							:  List[str]    =		Dataset.from_list(_a       )
           self.assertDictEqual(dset[0]	,  {"""col_1""": 1}       )
           self.assertDictEqual(dset[1]	,  {"""col_1""": None}       )  # NB: first record is used for columns

     def       a__			(						self       )		->							Dict:  # checks if the type can be inferred from the second record
           _A							:  List[Any]    =		[{"""col_1""": []}, {"""col_1""": [1, 2]}]
           _A							:  str    =		Dataset.from_list(_a       )
           self.assertEqual(dset.info.features["""col_1"""]	,  Sequence(Value("""int64"""       )       )       )



     def       a__			(						self       )		->							Dict:
           _A							:  List[str]    =		Dataset.from_list([]       )
           self.assertEqual(len(_a       )	,  0       )
           self.assertListEqual(dset.column_names	,  []       )

