import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 			UpperCAmelCase__    (		unittest.TestCase					):



  """simple docstring"""






  @property
  def 	_UpperCAmelCase				(				self:			Union[str, Any]   ) -> Tuple:




    '''simple docstring'''




    torch.manual_seed(0   )
    __UpperCAmelCase						    =       UNetaDModel(
        block_out_channels=(32, 64)				,				layers_per_block=2				,				sample_size=32				,				in_channels=3				,				out_channels=3				,				down_block_types=("DownBlock2D", "AttnDownBlock2D")				,				up_block_types=("AttnUpBlock2D", "UpBlock2D")				,				)
    return model






  def 	_UpperCAmelCase				(				self:			Union[str, Any]   ) -> str:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.dummy_uncond_unet
    __UpperCAmelCase						    =       PNDMScheduler()

    __UpperCAmelCase						    =       PNDMPipeline(unet=__lowerCAmelCase				,				scheduler=__lowerCAmelCase   )
    pndm.to(__lowerCAmelCase   )
    pndm.set_progress_bar_config(disable=__lowerCAmelCase   )

    __UpperCAmelCase						    =       torch.manual_seed(0   )
    __UpperCAmelCase						    =       pndm(generator=__lowerCAmelCase				,				num_inference_steps=20				,				output_type="numpy"   ).images

    __UpperCAmelCase						    =       torch.manual_seed(0   )
    __UpperCAmelCase						    =       pndm(generator=__lowerCAmelCase				,				num_inference_steps=20				,				output_type="numpy"				,				return_dict=__lowerCAmelCase   )[0]

    __UpperCAmelCase						    =       image[0, -3:, -3:, -1]
    __UpperCAmelCase						    =       image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __UpperCAmelCase						    =       np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

    assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2






@slow
@require_torch
class 			UpperCAmelCase__    (		unittest.TestCase					):



  """simple docstring"""






  def 	_UpperCAmelCase				(				self:			List[Any]   ) -> List[str]:




    '''simple docstring'''




    __UpperCAmelCase						    =       "google/ddpm-cifar10-32"

    __UpperCAmelCase						    =       UNetaDModel.from_pretrained(__lowerCAmelCase   )
    __UpperCAmelCase						    =       PNDMScheduler()

    __UpperCAmelCase						    =       PNDMPipeline(unet=__lowerCAmelCase				,				scheduler=__lowerCAmelCase   )
    pndm.to(__lowerCAmelCase   )
    pndm.set_progress_bar_config(disable=__lowerCAmelCase   )
    __UpperCAmelCase						    =       torch.manual_seed(0   )
    __UpperCAmelCase						    =       pndm(generator=__lowerCAmelCase				,				output_type="numpy"   ).images

    __UpperCAmelCase						    =       image[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __UpperCAmelCase						    =       np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]   )

    assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2





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      (						A_   : int					)    ->  Optional[Any]:
  __UpperCAmelCase						    =       SwinaSRConfig()

  if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
    __UpperCAmelCase						    =       4
  elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
    __UpperCAmelCase						    =       4
    __UpperCAmelCase						    =       48
    __UpperCAmelCase						    =       "pixelshuffle_aux"
  elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
    __UpperCAmelCase						    =       [6, 6, 6, 6]
    __UpperCAmelCase						    =       60
    __UpperCAmelCase						    =       [6, 6, 6, 6]
    __UpperCAmelCase						    =       "pixelshuffledirect"
  elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
    __UpperCAmelCase						    =       4
    __UpperCAmelCase						    =       "nearest+conv"
  elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
    __UpperCAmelCase						    =       1
    __UpperCAmelCase						    =       1
    __UpperCAmelCase						    =       1_26
    __UpperCAmelCase						    =       7
    __UpperCAmelCase						    =       2_55.0
    __UpperCAmelCase						    =       ""

  return config




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

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

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

  return name




def       __lowerCAmelCase      (						A_   : Dict						,	A_   : Tuple					)    ->  Optional[Any]:
  for key in orig_state_dict.copy().keys():
    __UpperCAmelCase						    =       orig_state_dict.pop(A_					)

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

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

  return orig_state_dict




def       __lowerCAmelCase      (						A_   : Any						,	A_   : str						,	A_   : Optional[int]					)    ->  Optional[Any]:
  __UpperCAmelCase						    =       get_config(A_					)
  __UpperCAmelCase						    =       SwinaSRForImageSuperResolution(A_					)
  model.eval()

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

  __UpperCAmelCase						    =       1_26 if "Jpeg" in checkpoint_url else 2_56
  __UpperCAmelCase						    =       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]					),
      ]					)
  __UpperCAmelCase						    =       transforms(A_					).unsqueeze(0					)

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

  __UpperCAmelCase						    =       model(A_					)

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

  __UpperCAmelCase						    =       {
      "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"
      ),
  }
  __UpperCAmelCase						    =       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__":
       a_							=      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.""")

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



