

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    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     Dict:
	__lowerCamelCase				:				List[Any]					       = SwinaSRConfig()

	if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
		__lowerCamelCase				:				Optional[int]					       = 4
	elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
		__lowerCamelCase				:				int					       = 4
		__lowerCamelCase				:				List[str]					       = 4_8
		__lowerCamelCase				:				Any					       = 'pixelshuffle_aux'
	elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
		__lowerCamelCase				:				str					       = [6, 6, 6, 6]
		__lowerCamelCase				:				Optional[int]					       = 6_0
		__lowerCamelCase				:				Union[str, Any]					       = [6, 6, 6, 6]
		__lowerCamelCase				:				List[str]					       = 'pixelshuffledirect'
	elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
		__lowerCamelCase				:				Optional[Any]					       = 4
		__lowerCamelCase				:				List[Any]					       = 'nearest+conv'
	elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
		__lowerCamelCase				:				Tuple					       = 1
		__lowerCamelCase				:				Union[str, Any]					       = 1
		__lowerCamelCase				:				List[str]					       = 1_2_6
		__lowerCamelCase				:				List[Any]					       = 7
		__lowerCamelCase				:				Tuple					       = 255.0
		__lowerCamelCase				:				Any					       = ''

	return config






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

	if name == "norm.weight":
		__lowerCamelCase				:				Dict					       = 'layernorm.weight'
	if name == "norm.bias":
		__lowerCamelCase				:				Dict					       = 'layernorm.bias'

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

	return name






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__			)  ->     Any:
	for key in orig_state_dict.copy().keys():
		__lowerCamelCase				:				int					       = orig_state_dict.pop(lowerCamelCase__			)

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

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

	return orig_state_dict






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__			)  ->     int:
	__lowerCamelCase				:				Optional[int]					       = get_config(lowerCamelCase__			)
	__lowerCamelCase				:				List[Any]					       = SwinaSRForImageSuperResolution(lowerCamelCase__			)
	model.eval()

	__lowerCamelCase				:				Optional[Any]					       = torch.hub.load_state_dict_from_url(lowerCamelCase__	, map_location='cpu'			)
	__lowerCamelCase				:				Tuple					       = convert_state_dict(lowerCamelCase__	, lowerCamelCase__			)
	__lowerCamelCase       , __lowerCamelCase				:				Optional[Any]					       = model.load_state_dict(lowerCamelCase__	, strict=lowerCamelCase__			)

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

	__lowerCamelCase				:				str					       = 1_2_6 if 'Jpeg' in checkpoint_url else 2_5_6
	__lowerCamelCase				:				List[str]					       = Compose(
	    [
	        Resize((image_size, image_size)			),
	        ToTensor(),
	        Normalize(mean=[0.485, 0.456, 0.406]	, std=[0.229, 0.224, 0.225]			),
	    ]			)
	__lowerCamelCase				:				Union[str, Any]					       = transforms(lowerCamelCase__			).unsqueeze(0			)

	if config.num_channels == 1:
		__lowerCamelCase				:				List[str]					       = pixel_values[:, 0, :, :].unsqueeze(1			)

	__lowerCamelCase				:				Tuple					       = model(lowerCamelCase__			)

	# assert values
	if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
		__lowerCamelCase				:				Tuple					       = torch.Size([1, 3, 5_1_2, 5_1_2]			)
		__lowerCamelCase				:				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:
		__lowerCamelCase				:				List[Any]					       = torch.Size([1, 3, 1_0_2_4, 1_0_2_4]			)
		__lowerCamelCase				:				Dict					       = 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
		__lowerCamelCase				:				Optional[int]					       = torch.Size([1, 3, 1_0_2_4, 1_0_2_4]			)
		__lowerCamelCase				:				Dict					       = 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:
		__lowerCamelCase				:				Optional[Any]					       = torch.Size([1, 3, 5_1_2, 5_1_2]			)
		__lowerCamelCase				:				Union[str, Any]					       = 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:
		__lowerCamelCase				:				Optional[Any]					       = torch.Size([1, 3, 1_0_2_4, 1_0_2_4]			)
		__lowerCamelCase				:				List[str]					       = 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]	, lowerCamelCase__	, atol=1e-3			)
	print('Looks ok!'			)

	__lowerCamelCase				:				Optional[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'
	    ),
	}
	__lowerCamelCase				:				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(lowerCamelCase__			)
		print(F"Saving image processor to {pytorch_dump_folder_path}"			)
		processor.save_pretrained(lowerCamelCase__			)

	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)





import math
import random




def    lowerCAmelCase_ (     __A,  __A = False							)     ->				float:







      '''simple docstring'''


      if deriv:
            return value * (1 - value)
      return 1 / (1 + math.exp(-value							))


# Initial Value
UpperCamelCase__											=					0.0_2




def    lowerCAmelCase_ (     __A,  __A							)     ->				float:







      '''simple docstring'''


      UpperCAmelCase__								=  float(2 * (random.randint(1,  100							)) - 1							)

      for _ in range(__A							):
            # Forward propagation
            UpperCAmelCase__								=  sigmoid_function(INITIAL_VALUE * weight							)
            # How much did we miss?
            UpperCAmelCase__								=  (expected / 100) - layer_a
            # Error delta
            UpperCAmelCase__								=  layer_1_error * sigmoid_function(__A,  __A							)
            # Update weight
            weight += INITIAL_VALUE * layer_1_delta

      return layer_a * 100


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      UpperCamelCase__											=					int(input('Expected value: '))
      UpperCamelCase__											=					int(input('Number of propagations: '))
      print(forward_propagation(expected, number_propagations))



