
'''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      A (       A_							:					Optional[int]       ):
						snake_case       :     Dict =    SwinaSRConfig()

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

						return config

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

						if name == "norm.weight":
												snake_case       :     int =    '''layernorm.weight'''
						if name == "norm.bias":
												snake_case       :     str =    '''layernorm.bias'''

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

						return name

def      A (       A_							:					Any    ,							A_							:					Optional[Any]       ):
						for key in orig_state_dict.copy().keys():
												snake_case       :     Union[str, Any] =    orig_state_dict.pop(A_       )

												if "qkv" in key:
																		snake_case       :     str =    key.split('''.'''       )
																		snake_case       :     Dict =    int(key_split[1]       )
																		snake_case       :     Union[str, Any] =    int(key_split[4]       )
																		snake_case       :     Tuple =    config.embed_dim

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

						return orig_state_dict

def      A (       A_							:					Tuple    ,							A_							:					Any    ,							A_							:					Optional[Any]       ):
						snake_case       :     str =    get_config(A_       )
						snake_case       :     str =    SwinaSRForImageSuperResolution(A_       )
						model.eval()

						snake_case       :     Tuple =    torch.hub.load_state_dict_from_url(A_    ,							map_location='''cpu'''       )
						snake_case       :     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       :     Any =    '''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       :     Any =    SwinaSRImageProcessor()
						# pixel_values = processor(image, return_tensors="pt").pixel_values

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

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

						snake_case       :     Optional[int] =    model(A_       )

						# assert values
						if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
												snake_case       :     List[Any] =    torch.Size([1, 3, 512, 512]       )
												snake_case       :     Union[str, Any] =    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       :     List[str] =    torch.Size([1, 3, 1024, 1024]       )
												snake_case       :     Optional[int] =    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       :     str =    torch.Size([1, 3, 1024, 1024]       )
												snake_case       :     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:
												snake_case       :     Dict =    torch.Size([1, 3, 512, 512]       )
												snake_case       :     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:
												snake_case       :     Union[str, Any] =    torch.Size([1, 3, 1024, 1024]       )
												snake_case       :     Any =    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       :     Optional[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       :     Dict =    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    					=   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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase    					=   {
    "configuration_swiftformer": [
        "SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SwiftFormerConfig",
        "SwiftFormerOnnxConfig",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   [
						    "SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "SwiftFormerForImageClassification",
						    "SwiftFormerModel",
						    "SwiftFormerPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_swiftformer import (
						    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    SwiftFormerConfig,
						    SwiftFormerOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_swiftformer import (
												    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SwiftFormerForImageClassification,
												    SwiftFormerModel,
												    SwiftFormerPreTrainedModel,
												)

else:
						import sys

						UpperCAmelCase    					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

