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  snake_case  (       snake_case__			:str)						->   str:
       _A        =  SwinaSRConfig()

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

       return config






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

       if name == "norm.weight":
              _A        =  """layernorm.weight"""
       if name == "norm.bias":
              _A        =  """layernorm.bias"""

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

       return name






def  snake_case  (       snake_case__			:Optional[Any]   ,	snake_case__			:Union[str, Any])						->   int:
       for key in orig_state_dict.copy().keys():
              _A        =  orig_state_dict.pop(snake_case__)

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

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

       return orig_state_dict






def  snake_case  (       snake_case__			:Optional[Any]   ,	snake_case__			:Any   ,	snake_case__			:Union[str, Any])						->   Optional[int]:
       _A        =  get_config(snake_case__)
       _A        =  SwinaSRForImageSuperResolution(snake_case__)
       model.eval()

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

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

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

       _A        =  model(snake_case__)

       # assert values
       if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
              _A        =  torch.Size([1, 3, 512, 512])
              _A        =  torch.tensor(
                  [[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]])
       elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
              _A        =  torch.Size([1, 3, 1_024, 1_024])
              _A        =  torch.tensor(
                  [[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]])
       elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
              # TODO values didn't match exactly here
              _A        =  torch.Size([1, 3, 1_024, 1_024])
              _A        =  torch.tensor(
                  [[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]])
       elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
              _A        =  torch.Size([1, 3, 512, 512])
              _A        =  torch.tensor(
                  [[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]])
       elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
              _A        =  torch.Size([1, 3, 1_024, 1_024])
              _A        =  torch.tensor(
                  [[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]])

       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        =  {
           """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        =  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__":
    _SCREAMING_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.')

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





def  snake_case  (       snake_case__			:str = "The quick brown fox jumps over the lazy dog"   ,	)						->   bool:
       _A        =  set()

       # Replace all the whitespace in our sentence
       _A        =  input_str.replace(""" """   ,	"""""")
       for alpha in input_str:
              if "a" <= alpha.lower() <= "z":
                     frequency.add(alpha.lower())
       return len(snake_case__) == 26






def  snake_case  (       snake_case__			:str = "The quick brown fox jumps over the lazy dog"   ,	)						->   bool:
       _A        =  [False] * 26
       for char in input_str:
              if char.islower():
                     _A        =  True
              elif char.isupper():
                     _A        =  True
       return all(snake_case__)






def  snake_case  (       snake_case__			:str = "The quick brown fox jumps over the lazy dog"   ,	)						->   bool:
       return len({char for char in input_str.lower() if char.isalpha()}) == 26






def  snake_case  (       )						->   None:
       from timeit import timeit

       _A        =  """from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"""
       print(timeit("""is_pangram()"""   ,	setup=snake_case__))
       print(timeit("""is_pangram_faster()"""   ,	setup=snake_case__))
       print(timeit("""is_pangram_fastest()"""   ,	setup=snake_case__))
       # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
       # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()




