
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       A__      (    __lowerCamelCase		):
   SCREAMING_SNAKE_CASE_     =	SwinaSRConfig()

   if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
      SCREAMING_SNAKE_CASE_     =	4
   elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
      SCREAMING_SNAKE_CASE_     =	4
      SCREAMING_SNAKE_CASE_     =	48
      SCREAMING_SNAKE_CASE_     =	"""pixelshuffle_aux"""
   elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
      SCREAMING_SNAKE_CASE_     =	[6, 6, 6, 6]
      SCREAMING_SNAKE_CASE_     =	60
      SCREAMING_SNAKE_CASE_     =	[6, 6, 6, 6]
      SCREAMING_SNAKE_CASE_     =	"""pixelshuffledirect"""
   elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
      SCREAMING_SNAKE_CASE_     =	4
      SCREAMING_SNAKE_CASE_     =	"""nearest+conv"""
   elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
      SCREAMING_SNAKE_CASE_     =	1
      SCREAMING_SNAKE_CASE_     =	1
      SCREAMING_SNAKE_CASE_     =	1_26
      SCREAMING_SNAKE_CASE_     =	7
      SCREAMING_SNAKE_CASE_     =	2_55.0
      SCREAMING_SNAKE_CASE_     =	""""""

   return config


def       A__      (    __lowerCamelCase,   __lowerCamelCase		):





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

   if name == "norm.weight":
      SCREAMING_SNAKE_CASE_     =	"""layernorm.weight"""
   if name == "norm.bias":
      SCREAMING_SNAKE_CASE_     =	"""layernorm.bias"""

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

   return name


def       A__      (    __lowerCamelCase,   __lowerCamelCase		):





   for key in orig_state_dict.copy().keys():
      SCREAMING_SNAKE_CASE_     =	orig_state_dict.pop(__lowerCamelCase		)

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

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

   return orig_state_dict


def       A__      (    __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase		):
   SCREAMING_SNAKE_CASE_     =	get_config(__lowerCamelCase		)
   SCREAMING_SNAKE_CASE_     =	SwinaSRForImageSuperResolution(__lowerCamelCase		)
   model.eval()

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

   SCREAMING_SNAKE_CASE_     =	1_26 if """Jpeg""" in checkpoint_url else 2_56
   SCREAMING_SNAKE_CASE_     =	Compose(
       [
           Resize((image_size, image_size)		),
           ToTensor(),
           Normalize(mean=[0.4_85, 0.4_56, 0.4_06],   std=[0.2_29, 0.2_24, 0.2_25]		),
       ]		)
   SCREAMING_SNAKE_CASE_     =	transforms(__lowerCamelCase		).unsqueeze(0		)

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

   SCREAMING_SNAKE_CASE_     =	model(__lowerCamelCase		)

   # assert values
   if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
      SCREAMING_SNAKE_CASE_     =	torch.Size([1, 3, 5_12, 5_12]		)
      SCREAMING_SNAKE_CASE_     =	torch.tensor(
          [[-0.70_87, -0.71_38, -0.67_21], [-0.83_40, -0.80_95, -0.72_98], [-0.91_49, -0.84_14, -0.79_40]]		)
   elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
      SCREAMING_SNAKE_CASE_     =	torch.Size([1, 3, 10_24, 10_24]		)
      SCREAMING_SNAKE_CASE_     =	torch.tensor(
          [[-0.77_75, -0.81_05, -0.89_33], [-0.77_64, -0.83_56, -0.92_25], [-0.79_76, -0.86_86, -0.95_79]]		)
   elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
      # TODO values didn't match exactly here
      SCREAMING_SNAKE_CASE_     =	torch.Size([1, 3, 10_24, 10_24]		)
      SCREAMING_SNAKE_CASE_     =	torch.tensor(
          [[-0.80_35, -0.75_04, -0.74_91], [-0.85_38, -0.81_24, -0.77_82], [-0.88_04, -0.86_51, -0.84_93]]		)
   elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
      SCREAMING_SNAKE_CASE_     =	torch.Size([1, 3, 5_12, 5_12]		)
      SCREAMING_SNAKE_CASE_     =	torch.tensor(
          [[-0.76_69, -0.86_62, -0.87_67], [-0.88_10, -0.99_62, -0.98_20], [-0.93_40, -1.03_22, -1.11_49]]		)
   elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
      SCREAMING_SNAKE_CASE_     =	torch.Size([1, 3, 10_24, 10_24]		)
      SCREAMING_SNAKE_CASE_     =	torch.tensor(
          [[-0.52_38, -0.55_57, -0.63_21], [-0.60_16, -0.59_03, -0.63_91], [-0.62_44, -0.63_34, -0.68_89]]		)

   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!'''		)

   SCREAMING_SNAKE_CASE_     =	{
       """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"""
       ),
   }
   SCREAMING_SNAKE_CASE_     =	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__":
   __UpperCAmelCase							=			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							=			parser.parse_args()
   convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''





from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


_lowerCAmelCase      						=	logging.get_logger(__name__)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    try:
        with open(UpperCamelCase							,    """rb"""			) as flax_state_f:
            lowerCAmelCase__ :  Union[str, Any]							      =					from_bytes(UpperCamelCase							,    flax_state_f.read()			)
    except UnpicklingError as e:
        try:
            with open(UpperCamelCase			) as f:
                if f.read().startswith("""version"""			):
                    raise OSError(
                        """You seem to have cloned a repository without having git-lfs installed. Please"""
                        """ install git-lfs and run `git lfs install` followed by `git lfs pull` in the"""
                        """ folder you cloned."""			)
                else:
                    raise ValueError from e
        except (UnicodeDecodeError, ValueError):
            raise EnvironmentError(f"""Unable to convert {model_file} to Flax deserializable object. """			)

    return load_flax_weights_in_pytorch_model(UpperCamelCase							,    UpperCamelCase			)




def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            """Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"""
            """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
            """ instructions."""			)
        raise

    # check if we have bf16 weights
    lowerCAmelCase__ :  str							      =					flatten_dict(jax.tree_util.tree_map(lambda UpperCamelCase			: x.dtype == jnp.bfloataa							,    UpperCamelCase			)			).values()
    if any(UpperCamelCase			):
        # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

        # and bf16 is not fully supported in PT yet.
        logger.warning(
            """Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
            """before loading those in PyTorch model."""			)
        lowerCAmelCase__ :  Dict							      =					jax.tree_util.tree_map(
            lambda UpperCamelCase			: params.astype(np.floataa			) if params.dtype == jnp.bfloataa else params							,    UpperCamelCase			)

    lowerCAmelCase__ :  Any							      =					""""""

    lowerCAmelCase__ :  Any							      =					flatten_dict(UpperCamelCase							,    sep="""."""			)
    lowerCAmelCase__ :  Optional[int]							      =					pt_model.state_dict()

    # keep track of unexpected & missing keys
    lowerCAmelCase__ :  Optional[Any]							      =					[]
    lowerCAmelCase__ :  int							      =					set(pt_model_dict.keys()			)

    for flax_key_tuple, flax_tensor in flax_state_dict.items():
        lowerCAmelCase__ :  Union[str, Any]							      =					flax_key_tuple.split("""."""			)

        if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
            lowerCAmelCase__ :  Optional[int]							      =					flax_key_tuple_array[:-1] + ["""weight"""]
            lowerCAmelCase__ :  Any							      =					jnp.transpose(UpperCamelCase							,    (3, 2, 0, 1)			)
        elif flax_key_tuple_array[-1] == "kernel":
            lowerCAmelCase__ :  str							      =					flax_key_tuple_array[:-1] + ["""weight"""]
            lowerCAmelCase__ :  Any							      =					flax_tensor.T
        elif flax_key_tuple_array[-1] == "scale":
            lowerCAmelCase__ :  int							      =					flax_key_tuple_array[:-1] + ["""weight"""]

        if "time_embedding" not in flax_key_tuple_array:
            for i, flax_key_tuple_string in enumerate(UpperCamelCase			):
                lowerCAmelCase__ :  List[str]							      =					(
                    flax_key_tuple_string.replace("""_0"""							,    """.0"""			)
                    .replace("""_1"""							,    """.1"""			)
                    .replace("""_2"""							,    """.2"""			)
                    .replace("""_3"""							,    """.3"""			)
                    .replace("""_4"""							,    """.4"""			)
                    .replace("""_5"""							,    """.5"""			)
                    .replace("""_6"""							,    """.6"""			)
                    .replace("""_7"""							,    """.7"""			)
                    .replace("""_8"""							,    """.8"""			)
                    .replace("""_9"""							,    """.9"""			)
                )

        lowerCAmelCase__ :  Union[str, Any]							      =					""".""".join(UpperCamelCase			)

        if flax_key in pt_model_dict:
            if flax_tensor.shape != pt_model_dict[flax_key].shape:
                raise ValueError(
                    f"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
                    f"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."""			)
            else:
                # add weight to pytorch dict
                lowerCAmelCase__ :  int							      =					np.asarray(UpperCamelCase			) if not isinstance(UpperCamelCase							,    np.ndarray			) else flax_tensor
                lowerCAmelCase__ :  int							      =					torch.from_numpy(UpperCamelCase			)
                # remove from missing keys
                missing_keys.remove(UpperCamelCase			)
        else:
            # weight is not expected by PyTorch model
            unexpected_keys.append(UpperCamelCase			)

    pt_model.load_state_dict(UpperCamelCase			)

    # re-transform missing_keys to list
    lowerCAmelCase__ :  Optional[int]							      =					list(UpperCamelCase			)

    if len(UpperCamelCase			) > 0:
        logger.warning(
            """Some weights of the Flax model were not used when initializing the PyTorch model"""
            f""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
            f""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
            """ (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
            f""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
            """ to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
            """ FlaxBertForSequenceClassification model)."""			)
    if len(UpperCamelCase			) > 0:
        logger.warning(
            f"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
            f""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
            """ use it for predictions and inference."""			)

    return pt_model


