


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_						(			__A :			Union[str, Any]				)	->		Union[str, Any]:

       """simple docstring"""

       a_					:		str							=  SwinaSRConfig()

       if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
              a_					:		Tuple							=  4
       elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
              a_					:		int							=  4
              a_					:		str							=  48
              a_					:		Union[str, Any]							=  'pixelshuffle_aux'
       elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
              a_					:		List[Any]							=  [6, 6, 6, 6]
              a_					:		Tuple							=  60
              a_					:		Union[str, Any]							=  [6, 6, 6, 6]
              a_					:		Optional[int]							=  'pixelshuffledirect'
       elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
              a_					:		Tuple							=  4
              a_					:		Optional[int]							=  'nearest+conv'
       elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
              a_					:		List[str]							=  1
              a_					:		Optional[int]							=  1
              a_					:		int							=  1_26
              a_					:		Optional[Any]							=  7
              a_					:		Optional[int]							=  255.0
              a_					:		Tuple							=  ''

       return config







def        SCREAMING_SNAKE_CASE_						(			__A :			Tuple  ,    __A :			Optional[int]				)	->		List[str]:

       """simple docstring"""

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

       if name == "norm.weight":
              a_					:		Union[str, Any]							=  'layernorm.weight'
       if name == "norm.bias":
              a_					:		Optional[int]							=  'layernorm.bias'

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

       return name







def        SCREAMING_SNAKE_CASE_						(			__A :			Union[str, Any]  ,    __A :			List[str]				)	->		Dict:

       """simple docstring"""

       for key in orig_state_dict.copy().keys():
              a_					:		List[Any]							=  orig_state_dict.pop(__A				)

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

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

       return orig_state_dict







def        SCREAMING_SNAKE_CASE_						(			__A :			Tuple  ,    __A :			Optional[int]  ,    __A :			List[Any]				)	->		Optional[int]:

       """simple docstring"""

       a_					:		Any							=  get_config(__A				)
       a_					:		Any							=  SwinaSRForImageSuperResolution(__A				)
       model.eval()

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

       a_					:		Any							=  1_26 if 'Jpeg' in checkpoint_url else 2_56
       a_					:		Optional[int]							=  Compose(
           [
               Resize((image_size, image_size)				),
               ToTensor(),
               Normalize(mean=[0.485, 0.456, 0.406]  ,    std=[0.229, 0.224, 0.225]				),
           ]				)
       a_					:		str							=  transforms(__A				).unsqueeze(0				)

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

       a_					:		int							=  model(__A				)

       # assert values
       if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
              a_					:		Optional[int]							=  torch.Size([1, 3, 5_12, 5_12]				)
              a_					:		str							=  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_					:		Dict							=  torch.Size([1, 3, 10_24, 10_24]				)
              a_					:		Union[str, 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
              a_					:		Union[str, Any]							=  torch.Size([1, 3, 10_24, 10_24]				)
              a_					:		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:
              a_					:		Union[str, Any]							=  torch.Size([1, 3, 5_12, 5_12]				)
              a_					:		Optional[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:
              a_					:		Any							=  torch.Size([1, 3, 10_24, 10_24]				)
              a_					:		Tuple							=  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!'				)

       a_					:		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'
           ),
       }
       a_					:		List[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(__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__":
   UpperCAmelCase_      : Tuple  					=       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_      : List[str]  					=       parser.parse_args()
   convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)










import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


_a      	=  get_logger(__name__)





class __lowerCamelCase (      enum.Enum):





	"""simple docstring"""






	UpperCamelCase__								=      "all_checks"
	UpperCamelCase__								=      "basic_checks"
	UpperCamelCase__								=      "no_checks"





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""











class __lowerCamelCase (      snake_case__):





	"""simple docstring"""











class __lowerCamelCase (      snake_case__):





	"""simple docstring"""











class __lowerCamelCase (      snake_case__):





	"""simple docstring"""









def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase=None	)->  str:





						"""simple docstring"""



						if expected_checksums is None:
												logger.info('Unable to verify checksums.'	)
												return
						if len(set(__lowerCAmelCase	) - set(__lowerCAmelCase	)	) > 0:
												raise ExpectedMoreDownloadedFiles(str(set(__lowerCAmelCase	) - set(__lowerCAmelCase	)	)	)
						if len(set(__lowerCAmelCase	) - set(__lowerCAmelCase	)	) > 0:
												raise UnexpectedDownloadedFile(str(set(__lowerCAmelCase	) - set(__lowerCAmelCase	)	)	)
						_UpperCAmelCase									=			[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
						_UpperCAmelCase									=			' for ' + verification_name if verification_name is not None else ''
						if len(__lowerCAmelCase	) > 0:
												raise NonMatchingChecksumError(
												    F"""Checksums didn't match{for_verification_name}:\n"""
												    F"""{bad_urls}\n"""
												    'Set `verification_mode=\'no_checks\'` to skip checksums verification and ignore this error'	)
						logger.info('All the checksums matched successfully' + for_verification_name	)





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""











class __lowerCamelCase (      snake_case__):





	"""simple docstring"""











class __lowerCamelCase (      snake_case__):





	"""simple docstring"""











class __lowerCamelCase (      snake_case__):





	"""simple docstring"""









def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  int:





						"""simple docstring"""



						if expected_splits is None:
												logger.info('Unable to verify splits sizes.'	)
												return
						if len(set(__lowerCAmelCase	) - set(__lowerCAmelCase	)	) > 0:
												raise ExpectedMoreSplits(str(set(__lowerCAmelCase	) - set(__lowerCAmelCase	)	)	)
						if len(set(__lowerCAmelCase	) - set(__lowerCAmelCase	)	) > 0:
												raise UnexpectedSplits(str(set(__lowerCAmelCase	) - set(__lowerCAmelCase	)	)	)
						_UpperCAmelCase									=			[
						    {'expected': expected_splits[name], 'recorded': recorded_splits[name]}
						    for name in expected_splits
						    if expected_splits[name].num_examples != recorded_splits[name].num_examples
						]
						if len(__lowerCAmelCase	) > 0:
												raise NonMatchingSplitsSizesError(str(__lowerCAmelCase	)	)
						logger.info('All the splits matched successfully.'	)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase = True	)->  dict:





						"""simple docstring"""



						if record_checksum:
												_UpperCAmelCase									=			shaaaa()
												with open(__lowerCAmelCase ,		'rb'	) as f:
																		for chunk in iter(lambda: f.read(1 << 20	) ,		b''	):
																								m.update(__lowerCAmelCase	)
																		_UpperCAmelCase									=			m.hexdigest()
						else:
												_UpperCAmelCase									=			None
						return {"num_bytes": os.path.getsize(__lowerCAmelCase	), "checksum": checksum}


def __A    (      __lowerCAmelCase	)->  List[str]:





						"""simple docstring"""



						if dataset_size and config.IN_MEMORY_MAX_SIZE:
												return dataset_size < config.IN_MEMORY_MAX_SIZE
						else:
												return False
