

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

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

def       __snake_case						( __UpperCamelCase	: Dict    ):



      """simple docstring"""

      A_  ,     A_						  =				image.size
      A_  ,     A_						  =				(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
      A_						  =				image.resize((w, h)				,resample=PIL_INTERPOLATION["lanczos"]    )
      A_						  =				np.array(__UpperCamelCase    ).astype(np.floataa    ) / 255.0
      A_						  =				image[None].transpose(0				,3				,1				,2    )
      A_						  =				torch.from_numpy(__UpperCamelCase    )
      return 2.0 * image - 1.0



class   _a					(			snake_case_				):




      """simple docstring"""

      def __init__(   self							:				Union[str, Any]						,			UpperCAmelCase							:				VQModel						,			UpperCAmelCase							:				UNetaDModel						,			UpperCAmelCase							:				Union[
              DDIMScheduler,
              PNDMScheduler,
              LMSDiscreteScheduler,
              EulerDiscreteScheduler,
              EulerAncestralDiscreteScheduler,
              DPMSolverMultistepScheduler,
          ]						,			):
            super().__init__()
            self.register_modules(vqvae=UpperCAmelCase						,			unet=UpperCAmelCase						,			scheduler=UpperCAmelCase	)







      @torch.no_grad()
      def __call__(   self							:				int						,			UpperCAmelCase							:				Union[torch.Tensor, PIL.Image.Image] = None						,			UpperCAmelCase							:				Optional[int] = 1						,			UpperCAmelCase							:				Optional[int] = 100						,			UpperCAmelCase							:				Optional[float] = 0.0						,			UpperCAmelCase							:				Optional[Union[torch.Generator, List[torch.Generator]]] = None						,			UpperCAmelCase							:				Optional[str] = "pil"						,			UpperCAmelCase							:				bool = True						,			):
            if isinstance(UpperCAmelCase						,			PIL.Image.Image	):
                  A_						  =				1
            elif isinstance(UpperCAmelCase						,			torch.Tensor	):
                  A_						  =				image.shape[0]
            else:
                  raise ValueError(f'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(UpperCAmelCase	)}'''	)

            if isinstance(UpperCAmelCase						,			PIL.Image.Image	):
                  A_						  =				preprocess(UpperCAmelCase	)

            A_  ,     A_						  =				image.shape[-2:]

            # in_channels should be 6: 3 for latents, 3 for low resolution image
            A_						  =				(batch_size, self.unet.config.in_channels // 2, height, width)
            A_						  =				next(self.unet.parameters()	).dtype

            A_						  =				randn_tensor(UpperCAmelCase						,			generator=UpperCAmelCase						,			device=self.device						,			dtype=UpperCAmelCase	)

            A_						  =				image.to(device=self.device						,			dtype=UpperCAmelCase	)

            # set timesteps and move to the correct device
            self.scheduler.set_timesteps(UpperCAmelCase						,			device=self.device	)
            A_						  =				self.scheduler.timesteps

            # scale the initial noise by the standard deviation required by the scheduler
            A_						  =				latents * self.scheduler.init_noise_sigma

            # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
            # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
            # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
            # and should be between [0, 1]
            A_						  =				"eta" in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
            A_						  =				{}
            if accepts_eta:
                  A_						  =				eta

            for t in self.progress_bar(UpperCAmelCase	):
                  # concat latents and low resolution image in the channel dimension.
                  A_						  =				torch.cat([latents, image]						,			dim=1	)
                  A_						  =				self.scheduler.scale_model_input(UpperCAmelCase						,			UpperCAmelCase	)
                  # predict the noise residual
                  A_						  =				self.unet(UpperCAmelCase						,			UpperCAmelCase	).sample
                  # compute the previous noisy sample x_t -> x_t-1
                  A_						  =				self.scheduler.step(UpperCAmelCase						,			UpperCAmelCase						,			UpperCAmelCase						,			**UpperCAmelCase	).prev_sample

            # decode the image latents with the VQVAE
            A_						  =				self.vqvae.decode(UpperCAmelCase	).sample
            A_						  =				torch.clamp(UpperCAmelCase						,			-1.0						,			1.0	)
            A_						  =				image / 2 + 0.5
            A_						  =				image.cpu().permute(0						,			2						,			3						,			1	).numpy()

            if output_type == "pil":
                  A_						  =				self.numpy_to_pil(UpperCAmelCase	)

            if not return_dict:
                  return (image,)

            return ImagePipelineOutput(images=UpperCAmelCase	)






"""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 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Any			):
 lowerCAmelCase    				= SwinaSRConfig()

 if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
  lowerCAmelCase    				= 4
 elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
  lowerCAmelCase    				= 4
  lowerCAmelCase    				= 48
  lowerCAmelCase    				= 'pixelshuffle_aux'
 elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
  lowerCAmelCase    				= [6, 6, 6, 6]
  lowerCAmelCase    				= 60
  lowerCAmelCase    				= [6, 6, 6, 6]
  lowerCAmelCase    				= 'pixelshuffledirect'
 elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
  lowerCAmelCase    				= 4
  lowerCAmelCase    				= 'nearest+conv'
 elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
  lowerCAmelCase    				= 1
  lowerCAmelCase    				= 1
  lowerCAmelCase    				= 126
  lowerCAmelCase    				= 7
  lowerCAmelCase    				= 255.0
  lowerCAmelCase    				= ''

 return config

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

 if name == "norm.weight":
  lowerCAmelCase    				= 'layernorm.weight'
 if name == "norm.bias":
  lowerCAmelCase    				= 'layernorm.bias'

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

 return name

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Any  ,     _UpperCAmelCase					:    Dict			):
 for key in orig_state_dict.copy().keys():
  lowerCAmelCase    				= orig_state_dict.pop(_UpperCAmelCase			)

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

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

 return orig_state_dict

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Tuple  ,     _UpperCAmelCase					:    Optional[Any]  ,     _UpperCAmelCase					:    Tuple			):
 lowerCAmelCase    				= get_config(_UpperCAmelCase			)
 lowerCAmelCase    				= SwinaSRForImageSuperResolution(_UpperCAmelCase			)
 model.eval()

 lowerCAmelCase    				= torch.hub.load_state_dict_from_url(_UpperCAmelCase  ,     map_location='cpu'			)
 lowerCAmelCase    				= convert_state_dict(_UpperCAmelCase  ,     _UpperCAmelCase			)
 lowerCAmelCase				,lowerCAmelCase    				= model.load_state_dict(_UpperCAmelCase  ,     strict=_UpperCAmelCase			)

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

 lowerCAmelCase    				= 126 if 'Jpeg' in checkpoint_url else 256
 lowerCAmelCase    				= Compose(
     [
         Resize((image_size, image_size)			),
         ToTensor(),
         Normalize(mean=[0.485, 0.456, 0.406]  ,     std=[0.229, 0.224, 0.225]			),
     ]			)
 lowerCAmelCase    				= transforms(_UpperCAmelCase			).unsqueeze(0			)

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

 lowerCAmelCase    				= model(_UpperCAmelCase			)

 # assert values
 if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
  lowerCAmelCase    				= torch.Size([1, 3, 512, 512]			)
  lowerCAmelCase    				= 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    				= torch.Size([1, 3, 1024, 1024]			)
  lowerCAmelCase    				= 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    				= torch.Size([1, 3, 1024, 1024]			)
  lowerCAmelCase    				= 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    				= torch.Size([1, 3, 512, 512]			)
  lowerCAmelCase    				= 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    				= torch.Size([1, 3, 1024, 1024]			)
  lowerCAmelCase    				= 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]  ,     _UpperCAmelCase  ,     atol=1e-3			)
 print('Looks ok!'			)

 lowerCAmelCase    				= {
     '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    				= 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(_UpperCAmelCase			)
  print(F'Saving image processor to {pytorch_dump_folder_path}'			)
  processor.save_pretrained(_UpperCAmelCase			)

 if push_to_hub:
  model.push_to_hub(F'caidas/{model_name}'			)
  processor.push_to_hub(F'caidas/{model_name}'			)


if __name__ == "__main__":
       __UpperCamelCase     :						Any						       =  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.''')

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





