import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs




@pytest.mark.parametrize(
    "kwargs, expected"						,	[
        ({"num_shards": 0, "max_num_jobs": 1}, []),
        ({"num_shards": 10, "max_num_jobs": 1}, [range(10					)]),
        ({"num_shards": 10, "max_num_jobs": 10}, [range(A_						,	i + 1					) for i in range(10					)]),
        ({"num_shards": 1, "max_num_jobs": 10}, [range(1					)]),
        ({"num_shards": 10, "max_num_jobs": 3}, [range(0						,	4					), range(4						,	7					), range(7						,	10					)]),
        ({"num_shards": 3, "max_num_jobs": 10}, [range(0						,	1					), range(1						,	2					), range(2						,	3					)]),
    ]						,	)
def       __lowerCAmelCase      (						A_   : Optional[Any]						,	A_   : Dict					)    ->  Dict:
  __UpperCAmelCase						    =       _distribute_shards(**A_					)
  assert out == expected




@pytest.mark.parametrize(
    "gen_kwargs, max_num_jobs, expected"						,	[
        ({"foo": 0}, 10, [{"foo": 0}]),
        ({"shards": [0, 1, 2, 3]}, 1, [{"shards": [0, 1, 2, 3]}]),
        ({"shards": [0, 1, 2, 3]}, 4, [{"shards": [0]}, {"shards": [1]}, {"shards": [2]}, {"shards": [3]}]),
        ({"shards": [0, 1]}, 4, [{"shards": [0]}, {"shards": [1]}]),
        ({"shards": [0, 1, 2, 3]}, 2, [{"shards": [0, 1]}, {"shards": [2, 3]}]),
    ]						,	)
def       __lowerCAmelCase      (						A_   : List[Any]						,	A_   : Optional[Any]						,	A_   : int					)    ->  Any:
  __UpperCAmelCase						    =       _split_gen_kwargs(A_						,	A_					)
  assert out == expected





@pytest.mark.parametrize(
    "gen_kwargs, expected"						,	[
        ({"foo": 0}, 1),
        ({"shards": [0]}, 1),
        ({"shards": [0, 1, 2, 3]}, 4),
        ({"shards": [0, 1, 2, 3], "foo": 0}, 4),
        ({"shards": [0, 1, 2, 3], "other": (0, 1)}, 4),
        ({"shards": [0, 1, 2, 3], "shards2": [0, 1]}, RuntimeError),
    ]						,	)
def       __lowerCAmelCase      (						A_   : Tuple						,	A_   : Tuple					)    ->  Union[str, Any]:
  if expected is RuntimeError:
    with pytest.raises(A_					):
      _number_of_shards_in_gen_kwargs(A_					)
  else:
    __UpperCAmelCase						    =       _number_of_shards_in_gen_kwargs(A_					)
    assert out == expected





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)



