

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):
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							SwinaSRConfig()

	if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
		SCREAMING_SNAKE_CASE :   List[str]					  =							4
	elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
		SCREAMING_SNAKE_CASE :   Optional[int]					  =							4
		SCREAMING_SNAKE_CASE :   Optional[Any]					  =							48
		SCREAMING_SNAKE_CASE :   int					  =							"""pixelshuffle_aux"""
	elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
		SCREAMING_SNAKE_CASE :   List[str]					  =							[6, 6, 6, 6]
		SCREAMING_SNAKE_CASE :   Any					  =							60
		SCREAMING_SNAKE_CASE :   Tuple					  =							[6, 6, 6, 6]
		SCREAMING_SNAKE_CASE :   Dict					  =							"""pixelshuffledirect"""
	elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
		SCREAMING_SNAKE_CASE :   Tuple					  =							4
		SCREAMING_SNAKE_CASE :   Any					  =							"""nearest+conv"""
	elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
		SCREAMING_SNAKE_CASE :   str					  =							1
		SCREAMING_SNAKE_CASE :   Optional[int]					  =							1
		SCREAMING_SNAKE_CASE :   Optional[int]					  =							126
		SCREAMING_SNAKE_CASE :   Any					  =							7
		SCREAMING_SNAKE_CASE :   int					  =							255.0
		SCREAMING_SNAKE_CASE :   List[Any]					  =							""""""

	return config

def 			lowerCamelCase__			(       _a				,      _a):






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

	if name == "norm.weight":
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							"""layernorm.weight"""
	if name == "norm.bias":
		SCREAMING_SNAKE_CASE :   List[str]					  =							"""layernorm.bias"""

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

	return name

def 			lowerCamelCase__			(       _a				,      _a):






	for key in orig_state_dict.copy().keys():
		SCREAMING_SNAKE_CASE :   str					  =							orig_state_dict.pop(lowercase_)

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

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

	return orig_state_dict

def 			lowerCamelCase__			(       _a				,      _a				,      _a):
	SCREAMING_SNAKE_CASE :   Dict					  =							get_config(lowercase_)
	SCREAMING_SNAKE_CASE :   Any					  =							SwinaSRForImageSuperResolution(lowercase_)
	model.eval()

	SCREAMING_SNAKE_CASE :   List[str]					  =							torch.hub.load_state_dict_from_url(lowercase_				,      map_location="cpu")
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							convert_state_dict(lowercase_				,      lowercase_)
	SCREAMING_SNAKE_CASE :   Dict					  =							model.load_state_dict(lowercase_				,      strict=lowercase_)

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

	SCREAMING_SNAKE_CASE :   Optional[int]					  =							126 if """Jpeg""" in checkpoint_url else 256
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							Compose(
	    [
	        Resize((image_size, image_size)),
	        ToTensor(),
	        Normalize(mean=[0.485, 0.456, 0.406]				,      std=[0.229, 0.224, 0.225]),
	    ])
	SCREAMING_SNAKE_CASE :   Dict					  =							transforms(lowercase_).unsqueeze(0)

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

	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							model(lowercase_)

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

	SCREAMING_SNAKE_CASE :   str					  =							{
	    """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 :   str					  =							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(lowercase_)
		print(f"Saving image processor to {pytorch_dump_folder_path}")
		processor.save_pretrained(lowercase_)

	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)

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						List[str]:


  '''simple docstring'''






  global f  # a global dp table for knapsack
  if f[i][j] < 0:
    if j < wt[i - 1]:
      lowercase__	: str     		=   mf_knapsack(i - 1			,			lowercase_			,			lowercase_			,			lowercase_ )
    else:
      lowercase__	: List[str]     		=   max(
          mf_knapsack(i - 1			,			lowercase_			,			lowercase_			,			lowercase_ )			,			mf_knapsack(i - 1			,			lowercase_			,			lowercase_			,			j - wt[i - 1] ) + val[i - 1]			,			)
    lowercase__	: List[Any]     		=   val
  return f[i][j]

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						str:


  '''simple docstring'''






  lowercase__	: Any     		=   [[0] * (w + 1) for _ in range(n + 1 )]

  for i in range(1			,			n + 1 ):
    for w_ in range(1			,			w + 1 ):
      if wt[i - 1] <= w_:
        lowercase__	: List[Any]     		=   max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]			,			dp[i - 1][w_] )
      else:
        lowercase__	: Tuple     		=   dp[i - 1][w_]

  return dp[n][w_], dp

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						Optional[Any]:


  '''simple docstring'''






  if not (isinstance(lowercase_			,			(list, tuple) ) and isinstance(lowercase_			,			(list, tuple) )):
    raise ValueError(
        """Both the weights and values vectors must be either lists or tuples""" )

  lowercase__	: str     		=   len(lowercase_ )
  if num_items != len(lowercase_ ):
    lowercase__	: Optional[int]     		=   (
        """The number of weights must be the same as the number of values.\n"""
        F'But got {num_items} weights and {len(lowercase_ )} values'
    )
    raise ValueError(lowercase_ )
  for i in range(lowercase_ ):
    if not isinstance(wt[i]			,			lowercase_ ):
      lowercase__	: int     		=   (
          """All weights must be integers but got weight of """
          F'type {type(wt[i] )} at index {i}'
      )
      raise TypeError(lowercase_ )

  lowercase__		,     lowercase__	: Tuple     		=   knapsack(lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )
  lowercase__	: set     		=   set()
  _construct_solution(lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )

  return optimal_val, example_optional_set

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						Any:


  '''simple docstring'''






  if i > 0 and j > 0:
    if dp[i - 1][j] == dp[i][j]:
      _construct_solution(lowercase_			,			lowercase_			,			i - 1			,			lowercase_			,			lowercase_ )
    else:
      optimal_set.add(lowercase_ )
      _construct_solution(lowercase_			,			lowercase_			,			i - 1			,			j - wt[i - 1]			,			lowercase_ )


if __name__ == "__main__":
     lowerCamelCase__     :		Dict        = [3, 2, 4, 4]
     lowerCamelCase__     :		List[Any]        = [4, 3, 2, 3]
     lowerCamelCase__     :		Optional[int]        = 4
     lowerCamelCase__     :		Dict        = 6
     lowerCamelCase__     :		Optional[int]        = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
     lowerCamelCase__   ,    lowerCamelCase__     :		int        = knapsack(w, wt, val, n)
     print(optimal_solution)
     print(mf_knapsack(n, wt, val, w))  # switched the n and w

     # testing the dynamic programming problem with example
     # the optimal subset for the above example are items 3 and 4
     lowerCamelCase__   ,    lowerCamelCase__     :		Optional[int]        = knapsack_with_example_solution(w, wt, val)
     assert optimal_solution == 8
     assert optimal_subset == {3, 4}
     print("""optimal_value = """, optimal_solution)
     print("""An optimal subset corresponding to the optimal value""", optimal_subset)






