






"""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 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  __A									= SwinaSRConfig()

  if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
    __A									= 4
  elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
    __A									= 4
    __A									= 4_8
    __A									= '''pixelshuffle_aux'''
  elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
    __A									= [6, 6, 6, 6]
    __A									= 6_0
    __A									= [6, 6, 6, 6]
    __A									= '''pixelshuffledirect'''
  elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
    __A									= 4
    __A									= '''nearest+conv'''
  elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
    __A									= 1
    __A									= 1
    __A									= 1_2_6
    __A									= 7
    __A									= 255.0
    __A									= ''''''

  return config



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







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

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

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

  return name



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  for key in orig_state_dict.copy().keys():
    __A									= orig_state_dict.pop(__UpperCamelCase )

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

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

  return orig_state_dict



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  __A									= get_config(__UpperCamelCase )
  __A									= SwinaSRForImageSuperResolution(__UpperCamelCase )
  model.eval()

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

  __A									= 1_2_6 if '''Jpeg''' in checkpoint_url else 2_5_6
  __A									= Compose(
      [
          Resize((image_size, image_size) ),
          ToTensor(),
          Normalize(mean=[0.485, 0.456, 0.406]		,       std=[0.229, 0.224, 0.225] ),
      ] )
  __A									= transforms(__UpperCamelCase ).unsqueeze(0 )

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

  __A									= model(__UpperCamelCase )

  # assert values
  if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
    __A									= torch.Size([1, 3, 5_1_2, 5_1_2] )
    __A									= 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:
    __A									= torch.Size([1, 3, 1_0_2_4, 1_0_2_4] )
    __A									= 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
    __A									= torch.Size([1, 3, 1_0_2_4, 1_0_2_4] )
    __A									= 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:
    __A									= torch.Size([1, 3, 5_1_2, 5_1_2] )
    __A									= 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:
    __A									= torch.Size([1, 3, 1_0_2_4, 1_0_2_4] )
    __A									= 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!''' )

  __A									= {
      '''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'''
      ),
  }
  __A									= 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__":
       lowercase_				     =     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_				     =     parser.parse_args()
       convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)












"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor


class 					snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   def __init__(	self :     List[str],       _lowerCamelCase :     Optional[Any],       _lowerCamelCase :     Union[str, Any]=13,       _lowerCamelCase :     Any=3,       _lowerCamelCase :     Optional[int]=2_24,       _lowerCamelCase :     str=30,       _lowerCamelCase :     Dict=4_00,       _lowerCamelCase :     Union[str, Any]=True,       _lowerCamelCase :     Any=None,       _lowerCamelCase :     Optional[Any]=True,       _lowerCamelCase :     Any=[0.5, 0.5, 0.5],       _lowerCamelCase :     List[str]=[0.5, 0.5, 0.5],       ):

     '''simple docstring'''
     __A									= size if size is not None else {'''height''': 18, '''width''': 18}
     __A									= parent
     __A									= batch_size
     __A									= num_channels
     __A									= image_size
     __A									= min_resolution
     __A									= max_resolution
     __A									= do_resize
     __A									= size
     __A									= do_normalize
     __A									= image_mean
     __A									= image_std






   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     return {
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_normalize": self.do_normalize,
         "do_resize": self.do_resize,
         "size": self.size,
     }





@require_torch
@require_vision
class 					snake_case		(							_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	str						 = ViTImageProcessor if is_vision_available() else None


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= EfficientFormerImageProcessorTester(self  )


   @property
   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     return self.image_proc_tester.prepare_image_processor_dict()


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= self.image_processing_class(**self.image_processor_dict  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''image_mean'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''image_std'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''do_normalize'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''do_resize'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''size'''  )  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     pass


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     # Initialize image_processor
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random PIL images
     __A									= prepare_image_inputs(self.image_proc_tester,       equal_resolution=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,       Image.Image  )

     # Test not batched input
     __A									= image_processor(image_inputs[0],       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             1,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )

     # Test batched
     __A									= image_processor(_lowerCamelCase,       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             self.image_proc_tester.batch_size,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     # Initialize image_processor
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random numpy tensors
     __A									= prepare_image_inputs(self.image_proc_tester,       equal_resolution=_lowerCamelCase,       numpify=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,       np.ndarray  )

     # Test not batched input
     __A									= image_processor(image_inputs[0],       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             1,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )

     # Test batched
     __A									= image_processor(_lowerCamelCase,       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             self.image_proc_tester.batch_size,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )






   def 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     # Initialize image_processor
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random PyTorch tensors
     __A									= prepare_image_inputs(self.image_proc_tester,       equal_resolution=_lowerCamelCase,       torchify=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,       torch.Tensor  )

     # Test not batched input
     __A									= image_processor(image_inputs[0],       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             1,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )

     # Test batched
     __A									= image_processor(_lowerCamelCase,       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             self.image_proc_tester.batch_size,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )




