



"""simple docstring"""

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 __lowercase			(							_a    ):
    snake_case_ :     Optional[int]				=	SwinaSRConfig()

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

    return config




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

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

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

    return name




def __lowercase			(							_a		,			_a    ):
    for key in orig_state_dict.copy().keys():
        snake_case_ :     Union[str, Any]				=	orig_state_dict.pop(_a    )

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

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

    return orig_state_dict




def __lowercase			(							_a		,			_a		,			_a    ):
    snake_case_ :     Tuple				=	get_config(_a    )
    snake_case_ :     Any				=	SwinaSRForImageSuperResolution(_a    )
    model.eval()

    snake_case_ :     List[str]				=	torch.hub.load_state_dict_from_url(_a		,			map_location='''cpu'''    )
    snake_case_ :     Optional[Any]				=	convert_state_dict(_a		,			_a    )
    snake_case_,	snake_case_ :     List[str]				=	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
    snake_case_ :     int				=	'''https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'''
    snake_case_ :     Any				=	Image.open(requests.get(_a		,			stream=_a    ).raw    ).convert('''RGB'''    )
    snake_case_ :     List[str]				=	SwinaSRImageProcessor()
    # pixel_values = processor(image, return_tensors="pt").pixel_values

    snake_case_ :     Any				=	126 if '''Jpeg''' in checkpoint_url else 256
    snake_case_ :     str				=	Compose(
        [
            Resize((image_size, image_size)    ),
            ToTensor(),
            Normalize(mean=[0.485, 0.456, 0.406]		,			std=[0.229, 0.224, 0.225]    ),
        ]    )
    snake_case_ :     Dict				=	transforms(_a    ).unsqueeze(0    )

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

    snake_case_ :     Optional[Any]				=	model(_a    )

    # assert values
    if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
        snake_case_ :     Optional[int]				=	torch.Size([1, 3, 512, 512]    )
        snake_case_ :     Tuple				=	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:
        snake_case_ :     Any				=	torch.Size([1, 3, 1_024, 1_024]    )
        snake_case_ :     List[Any]				=	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
        snake_case_ :     Dict				=	torch.Size([1, 3, 1_024, 1_024]    )
        snake_case_ :     Union[str, Any]				=	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:
        snake_case_ :     List[Any]				=	torch.Size([1, 3, 512, 512]    )
        snake_case_ :     Dict				=	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:
        snake_case_ :     Optional[int]				=	torch.Size([1, 3, 1_024, 1_024]    )
        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]		,			_a		,			atol=1E-3    )
    print('''Looks ok!'''    )

    snake_case_ :     int				=	{
        '''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'''
        ),
    }
    snake_case_ :     Union[str, Any]				=	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__":
     lowercase__    :  List[str]							    =	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.''')

     lowercase__    :  Optional[Any]							    =	parser.parse_args()
     convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)










"""simple docstring"""

from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class        _UpperCAmelCase       (			lowerCAmelCase__):

 def __init__(     self		:       Any  ,     lowercase_		:       TransformeraDModel  ,     lowercase_		:       AutoencoderKL  ,     lowercase_		:       KarrasDiffusionSchedulers  ,     lowercase_		:       Optional[Dict[int, str]] = None  ,     ):
     super().__init__()
     self.register_modules(transformer=lowercase_  ,     vae=lowercase_  ,     scheduler=lowercase_				)

     # create a imagenet -> id dictionary for easier use
     snake_case_ :     Tuple				=	{}
     if idalabel is not None:
         for key, value in idalabel.items():
             for label in value.split(''','''				):
                 snake_case_ :     str				=	int(lowercase_				)
         snake_case_ :     Any				=	dict(sorted(self.labels.items()				)				)

 def 						_snake_case					(     self		:       List[Any]  ,     lowercase_		:       Union[str, List[str]]				):

     if not isinstance(lowercase_  ,     lowercase_				):
         snake_case_ :     Tuple				=	list(lowercase_				)

     for l in label:
         if l not in self.labels:
             raise ValueError(
                 f"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."				)

     return [self.labels[l] for l in label]






 @torch.no_grad()
 def __call__(     self		:       Optional[int]  ,     lowercase_		:       List[int]  ,     lowercase_		:       float = 4.0  ,     lowercase_		:       Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,     lowercase_		:       int = 50  ,     lowercase_		:       Optional[str] = "pil"  ,     lowercase_		:       bool = True  ,     ):
     snake_case_ :     Any				=	len(lowercase_				)
     snake_case_ :     List[str]				=	self.transformer.config.sample_size
     snake_case_ :     Union[str, Any]				=	self.transformer.config.in_channels

     snake_case_ :     str				=	randn_tensor(
         shape=(batch_size, latent_channels, latent_size, latent_size)  ,     generator=lowercase_  ,     device=self.device  ,     dtype=self.transformer.dtype  ,     )
     snake_case_ :     Optional[Any]				=	torch.cat([latents] * 2				) if guidance_scale > 1 else latents

     snake_case_ :     Optional[int]				=	torch.tensor(lowercase_  ,     device=self.device				).reshape(-1				)
     snake_case_ :     Dict				=	torch.tensor([1000] * batch_size  ,     device=self.device				)
     snake_case_ :     Tuple				=	torch.cat([class_labels, class_null]  ,     0				) if guidance_scale > 1 else class_labels

     # set step values
     self.scheduler.set_timesteps(lowercase_				)

     for t in self.progress_bar(self.scheduler.timesteps				):
         if guidance_scale > 1:
             snake_case_ :     List[Any]				=	latent_model_input[: len(lowercase_				) // 2]
             snake_case_ :     Union[str, Any]				=	torch.cat([half, half]  ,     dim=0				)
         snake_case_ :     Optional[Any]				=	self.scheduler.scale_model_input(lowercase_  ,     lowercase_				)

         snake_case_ :     int				=	t
         if not torch.is_tensor(lowercase_				):
             # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
             # This would be a good case for the `match` statement (Python 3.10+)
             snake_case_ :     Tuple				=	latent_model_input.device.type == '''mps'''
             if isinstance(lowercase_  ,     lowercase_				):
                 snake_case_ :     List[str]				=	torch.floataa if is_mps else torch.floataa
             else:
                 snake_case_ :     Optional[int]				=	torch.intaa if is_mps else torch.intaa
             snake_case_ :     List[Any]				=	torch.tensor([timesteps]  ,     dtype=lowercase_  ,     device=latent_model_input.device				)
         elif len(timesteps.shape				) == 0:
             snake_case_ :     str				=	timesteps[None].to(latent_model_input.device				)
         # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
         snake_case_ :     Tuple				=	timesteps.expand(latent_model_input.shape[0]				)
         # predict noise model_output
         snake_case_ :     List[Any]				=	self.transformer(
             lowercase_  ,     timestep=lowercase_  ,     class_labels=lowercase_				).sample

         # perform guidance
         if guidance_scale > 1:
             snake_case_,	snake_case_ :     Dict				=	noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
             snake_case_,	snake_case_ :     Any				=	torch.split(lowercase_  ,     len(lowercase_				) // 2  ,     dim=0				)

             snake_case_ :     int				=	uncond_eps + guidance_scale * (cond_eps - uncond_eps)
             snake_case_ :     str				=	torch.cat([half_eps, half_eps]  ,     dim=0				)

             snake_case_ :     List[Any]				=	torch.cat([eps, rest]  ,     dim=1				)

         # learned sigma
         if self.transformer.config.out_channels // 2 == latent_channels:
             snake_case_,	snake_case_ :     Optional[Any]				=	torch.split(lowercase_  ,     lowercase_  ,     dim=1				)
         else:
             snake_case_ :     List[str]				=	noise_pred

         # compute previous image: x_t -> x_t-1
         snake_case_ :     int				=	self.scheduler.step(lowercase_  ,     lowercase_  ,     lowercase_				).prev_sample

     if guidance_scale > 1:
         snake_case_,	snake_case_ :     Optional[Any]				=	latent_model_input.chunk(2  ,     dim=0				)
     else:
         snake_case_ :     Dict				=	latent_model_input

     snake_case_ :     Union[str, Any]				=	1 / self.vae.config.scaling_factor * latents
     snake_case_ :     Tuple				=	self.vae.decode(lowercase_				).sample

     snake_case_ :     str				=	(samples / 2 + 0.5).clamp(0  ,     1				)

     # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
     snake_case_ :     Union[str, Any]				=	samples.cpu().permute(0  ,     2  ,     3  ,     1				).float().numpy()

     if output_type == "pil":
         snake_case_ :     Union[str, Any]				=	self.numpy_to_pil(lowercase_				)

     if not return_dict:
         return (samples,)

     return ImagePipelineOutput(images=lowercase_				)





