



'''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			):



    """simple docstring"""





    lowerCAmelCase__ :  str							      =					SwinaSRConfig()

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

    return config
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





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

    if name == "norm.weight":
        lowerCAmelCase__ :  Any							      =					"""layernorm.weight"""
    if name == "norm.bias":
        lowerCAmelCase__ :  Optional[Any]							      =					"""layernorm.bias"""

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

    return name
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    for key in orig_state_dict.copy().keys():
        lowerCAmelCase__ :  Optional[Any]							      =					orig_state_dict.pop(UpperCamelCase			)

        if "qkv" in key:
            lowerCAmelCase__ :  Dict							      =					key.split("""."""			)
            lowerCAmelCase__ :  Any							      =					int(key_split[1]			)
            lowerCAmelCase__ :  Dict							      =					int(key_split[4]			)
            lowerCAmelCase__ :  List[str]							      =					config.embed_dim

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

    return orig_state_dict
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  List[Any]							      =					get_config(UpperCamelCase			)
    lowerCAmelCase__ :  Any							      =					SwinaSRForImageSuperResolution(UpperCamelCase			)
    model.eval()

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

    lowerCAmelCase__ :  str							      =					126 if """Jpeg""" in checkpoint_url else 256
    lowerCAmelCase__ :  List[Any]							      =					Compose(
        [
            Resize((image_size, image_size)			),
            ToTensor(),
            Normalize(mean=[0.485, 0.456, 0.406]							,    std=[0.229, 0.224, 0.225]			),
        ]			)
    lowerCAmelCase__ :  Optional[int]							      =					transforms(UpperCamelCase			).unsqueeze(0			)

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

    lowerCAmelCase__ :  Optional[Any]							      =					model(UpperCamelCase			)

    # assert values
    if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
        lowerCAmelCase__ :  Tuple							      =					torch.Size([1, 3, 512, 512]			)
        lowerCAmelCase__ :  Optional[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:
        lowerCAmelCase__ :  Union[str, Any]							      =					torch.Size([1, 3, 1024, 1024]			)
        lowerCAmelCase__ :  str							      =					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__ :  str							      =					torch.Size([1, 3, 1024, 1024]			)
        lowerCAmelCase__ :  Dict							      =					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__ :  Tuple							      =					torch.Size([1, 3, 512, 512]			)
        lowerCAmelCase__ :  Dict							      =					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__ :  Tuple							      =					torch.Size([1, 3, 1024, 1024]			)
        lowerCAmelCase__ :  int							      =					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__ :  Optional[Any]							      =					{
        """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__ :  Optional[int]							      =					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__":
 _lowerCAmelCase      						=	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.''')

 _lowerCAmelCase      						=	parser.parse_args()
 convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''





import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

_lowerCAmelCase      						=	[
    # (stable-diffusion, HF Diffusers)
    ('''time_embed.0.weight''', '''time_embedding.linear_1.weight'''),
    ('''time_embed.0.bias''', '''time_embedding.linear_1.bias'''),
    ('''time_embed.2.weight''', '''time_embedding.linear_2.weight'''),
    ('''time_embed.2.bias''', '''time_embedding.linear_2.bias'''),
    ('''input_blocks.0.0.weight''', '''conv_in.weight'''),
    ('''input_blocks.0.0.bias''', '''conv_in.bias'''),
    ('''out.0.weight''', '''conv_norm_out.weight'''),
    ('''out.0.bias''', '''conv_norm_out.bias'''),
    ('''out.2.weight''', '''conv_out.weight'''),
    ('''out.2.bias''', '''conv_out.bias'''),
]

_lowerCAmelCase      						=	[
    # (stable-diffusion, HF Diffusers)
    ('''in_layers.0''', '''norm1'''),
    ('''in_layers.2''', '''conv1'''),
    ('''out_layers.0''', '''norm2'''),
    ('''out_layers.3''', '''conv2'''),
    ('''emb_layers.1''', '''time_emb_proj'''),
    ('''skip_connection''', '''conv_shortcut'''),
]

_lowerCAmelCase      						=	[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
 # loop over downblocks/upblocks

 for j in range(2):
  # loop over resnets/attentions for downblocks
  _lowerCAmelCase      						=	F"""down_blocks.{i}.resnets.{j}."""
  _lowerCAmelCase      						=	F"""input_blocks.{3*i + j + 1}.0."""
  unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

  if i < 3:
   # no attention layers in down_blocks.3
   _lowerCAmelCase      						=	F"""down_blocks.{i}.attentions.{j}."""
   _lowerCAmelCase      						=	F"""input_blocks.{3*i + j + 1}.1."""
   unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

 for j in range(3):
  # loop over resnets/attentions for upblocks
  _lowerCAmelCase      						=	F"""up_blocks.{i}.resnets.{j}."""
  _lowerCAmelCase      						=	F"""output_blocks.{3*i + j}.0."""
  unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

  if i > 0:
   # no attention layers in up_blocks.0
   _lowerCAmelCase      						=	F"""up_blocks.{i}.attentions.{j}."""
   _lowerCAmelCase      						=	F"""output_blocks.{3*i + j}.1."""
   unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

 if i < 3:
  # no downsample in down_blocks.3
  _lowerCAmelCase      						=	F"""down_blocks.{i}.downsamplers.0.conv."""
  _lowerCAmelCase      						=	F"""input_blocks.{3*(i+1)}.0.op."""
  unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

  # no upsample in up_blocks.3
  _lowerCAmelCase      						=	F"""up_blocks.{i}.upsamplers.0."""
  _lowerCAmelCase      						=	F"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
  unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

_lowerCAmelCase      						=	'''mid_block.attentions.0.'''
_lowerCAmelCase      						=	'''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
 _lowerCAmelCase      						=	F"""mid_block.resnets.{j}."""
 _lowerCAmelCase      						=	F"""middle_block.{2*j}."""
 unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Any							      =					{k: k for k in unet_state_dict.keys()}
    for sd_name, hf_name in unet_conversion_map:
        lowerCAmelCase__ :  Optional[int]							      =					sd_name
    for k, v in mapping.items():
        if "resnets" in k:
            for sd_part, hf_part in unet_conversion_map_resnet:
                lowerCAmelCase__ :  Any							      =					v.replace(UpperCamelCase							,    UpperCamelCase			)
            lowerCAmelCase__ :  List[Any]							      =					v
    for k, v in mapping.items():
        for sd_part, hf_part in unet_conversion_map_layer:
            lowerCAmelCase__ :  List[Any]							      =					v.replace(UpperCamelCase							,    UpperCamelCase			)
        lowerCAmelCase__ :  Optional[Any]							      =					v
    lowerCAmelCase__ :  Tuple							      =					{v: unet_state_dict[k] for k, v in mapping.items()}
    return new_state_dict


# ================#
# VAE Conversion #
# ================#

_lowerCAmelCase      						=	[
    # (stable-diffusion, HF Diffusers)
    ('''nin_shortcut''', '''conv_shortcut'''),
    ('''norm_out''', '''conv_norm_out'''),
    ('''mid.attn_1.''', '''mid_block.attentions.0.'''),
]

for i in range(4):
 # down_blocks have two resnets
 for j in range(2):
  _lowerCAmelCase      						=	F"""encoder.down_blocks.{i}.resnets.{j}."""
  _lowerCAmelCase      						=	F"""encoder.down.{i}.block.{j}."""
  vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

 if i < 3:
  _lowerCAmelCase      						=	F"""down_blocks.{i}.downsamplers.0."""
  _lowerCAmelCase      						=	F"""down.{i}.downsample."""
  vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

  _lowerCAmelCase      						=	F"""up_blocks.{i}.upsamplers.0."""
  _lowerCAmelCase      						=	F"""up.{3-i}.upsample."""
  vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

 # up_blocks have three resnets
 # also, up blocks in hf are numbered in reverse from sd
 for j in range(3):
  _lowerCAmelCase      						=	F"""decoder.up_blocks.{i}.resnets.{j}."""
  _lowerCAmelCase      						=	F"""decoder.up.{3-i}.block.{j}."""
  vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
 _lowerCAmelCase      						=	F"""mid_block.resnets.{i}."""
 _lowerCAmelCase      						=	F"""mid.block_{i+1}."""
 vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


_lowerCAmelCase      						=	[
    # (stable-diffusion, HF Diffusers)
    ('''norm.''', '''group_norm.'''),
    ('''q.''', '''query.'''),
    ('''k.''', '''key.'''),
    ('''v.''', '''value.'''),
    ('''proj_out.''', '''proj_attn.'''),
]
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    return w.reshape(*w.shape							,    1							,    1			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Optional[int]							      =					{k: k for k in vae_state_dict.keys()}
    for k, v in mapping.items():
        for sd_part, hf_part in vae_conversion_map:
            lowerCAmelCase__ :  str							      =					v.replace(UpperCamelCase							,    UpperCamelCase			)
        lowerCAmelCase__ :  Optional[Any]							      =					v
    for k, v in mapping.items():
        if "attentions" in k:
            for sd_part, hf_part in vae_conversion_map_attn:
                lowerCAmelCase__ :  Dict							      =					v.replace(UpperCamelCase							,    UpperCamelCase			)
            lowerCAmelCase__ :  List[Any]							      =					v
    lowerCAmelCase__ :  Union[str, Any]							      =					{v: vae_state_dict[k] for k, v in mapping.items()}
    lowerCAmelCase__ :  Tuple							      =					["""q""", """k""", """v""", """proj_out"""]
    for k, v in new_state_dict.items():
        for weight_name in weights_to_convert:
            if f"""mid.attn_1.{weight_name}.weight""" in k:
                print(f"""Reshaping {k} for SD format"""			)
                lowerCAmelCase__ :  Optional[int]							      =					reshape_weight_for_sd(UpperCamelCase			)
    return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


_lowerCAmelCase      						=	[
    # (stable-diffusion, HF Diffusers)
    ('''resblocks.''', '''text_model.encoder.layers.'''),
    ('''ln_1''', '''layer_norm1'''),
    ('''ln_2''', '''layer_norm2'''),
    ('''.c_fc.''', '''.fc1.'''),
    ('''.c_proj.''', '''.fc2.'''),
    ('''.attn''', '''.self_attn'''),
    ('''ln_final.''', '''transformer.text_model.final_layer_norm.'''),
    ('''token_embedding.weight''', '''transformer.text_model.embeddings.token_embedding.weight'''),
    ('''positional_embedding''', '''transformer.text_model.embeddings.position_embedding.weight'''),
]
_lowerCAmelCase      						=	{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
_lowerCAmelCase      						=	re.compile('''|'''.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
_lowerCAmelCase      						=	{'''q''': 0, '''k''': 1, '''v''': 2}
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Optional[Any]							      =					{}
    lowerCAmelCase__ :  int							      =					{}
    lowerCAmelCase__ :  List[Any]							      =					{}
    for k, v in text_enc_dict.items():
        if (
            k.endswith(""".self_attn.q_proj.weight"""			)
            or k.endswith(""".self_attn.k_proj.weight"""			)
            or k.endswith(""".self_attn.v_proj.weight"""			)
        ):
            lowerCAmelCase__ :  Optional[int]							      =					k[: -len(""".q_proj.weight"""			)]
            lowerCAmelCase__ :  Tuple							      =					k[-len("""q_proj.weight"""			)]
            if k_pre not in capture_qkv_weight:
                lowerCAmelCase__ :  List[Any]							      =					[None, None, None]
            lowerCAmelCase__ :  Dict							      =					v
            continue

        if (
            k.endswith(""".self_attn.q_proj.bias"""			)
            or k.endswith(""".self_attn.k_proj.bias"""			)
            or k.endswith(""".self_attn.v_proj.bias"""			)
        ):
            lowerCAmelCase__ :  str							      =					k[: -len(""".q_proj.bias"""			)]
            lowerCAmelCase__ :  List[str]							      =					k[-len("""q_proj.bias"""			)]
            if k_pre not in capture_qkv_bias:
                lowerCAmelCase__ :  Union[str, Any]							      =					[None, None, None]
            lowerCAmelCase__ :  Any							      =					v
            continue

        lowerCAmelCase__ :  Dict							      =					textenc_pattern.sub(lambda UpperCamelCase			: protected[re.escape(m.group(0			)			)]							,    UpperCamelCase			)
        lowerCAmelCase__ :  Union[str, Any]							      =					v

    for k_pre, tensors in capture_qkv_weight.items():
        if None in tensors:
            raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""			)
        lowerCAmelCase__ :  Any							      =					textenc_pattern.sub(lambda UpperCamelCase			: protected[re.escape(m.group(0			)			)]							,    UpperCamelCase			)
        lowerCAmelCase__ :  Tuple							      =					torch.cat(UpperCamelCase			)

    for k_pre, tensors in capture_qkv_bias.items():
        if None in tensors:
            raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""			)
        lowerCAmelCase__ :  str							      =					textenc_pattern.sub(lambda UpperCamelCase			: protected[re.escape(m.group(0			)			)]							,    UpperCamelCase			)
        lowerCAmelCase__ :  List[Any]							      =					torch.cat(UpperCamelCase			)

    return new_state_dict
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    return text_enc_dict


if __name__ == "__main__":
 _lowerCAmelCase      						=	argparse.ArgumentParser()

 parser.add_argument('''--model_path''', default=None, type=str, required=True, help='''Path to the model to convert.''')
 parser.add_argument('''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output model.''')
 parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
 parser.add_argument(
     '''--use_safetensors''', action='''store_true''', help='''Save weights use safetensors, default is ckpt.'''
 )

 _lowerCAmelCase      						=	parser.parse_args()

 assert args.model_path is not None, "Must provide a model path!"

 assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

 # Path for safetensors
 _lowerCAmelCase      						=	osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
 _lowerCAmelCase      						=	osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
 _lowerCAmelCase      						=	osp.join(args.model_path, '''text_encoder''', '''model.safetensors''')

 # Load models from safetensors if it exists, if it doesn't pytorch
 if osp.exists(unet_path):
  _lowerCAmelCase      						=	load_file(unet_path, device='''cpu''')
 else:
  _lowerCAmelCase      						=	osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
  _lowerCAmelCase      						=	torch.load(unet_path, map_location='''cpu''')

 if osp.exists(vae_path):
  _lowerCAmelCase      						=	load_file(vae_path, device='''cpu''')
 else:
  _lowerCAmelCase      						=	osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.bin''')
  _lowerCAmelCase      						=	torch.load(vae_path, map_location='''cpu''')

 if osp.exists(text_enc_path):
  _lowerCAmelCase      						=	load_file(text_enc_path, device='''cpu''')
 else:
  _lowerCAmelCase      						=	osp.join(args.model_path, '''text_encoder''', '''pytorch_model.bin''')
  _lowerCAmelCase      						=	torch.load(text_enc_path, map_location='''cpu''')

 # Convert the UNet model
 _lowerCAmelCase      						=	convert_unet_state_dict(unet_state_dict)
 _lowerCAmelCase      						=	{'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}

 # Convert the VAE model
 _lowerCAmelCase      						=	convert_vae_state_dict(vae_state_dict)
 _lowerCAmelCase      						=	{'''first_stage_model.''' + k: v for k, v in vae_state_dict.items()}

 # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
 _lowerCAmelCase      						=	'''text_model.encoder.layers.22.layer_norm2.bias''' in text_enc_dict

 if is_vaa_model:
  # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
  _lowerCAmelCase      						=	{'''transformer.''' + k: v for k, v in text_enc_dict.items()}
  _lowerCAmelCase      						=	convert_text_enc_state_dict_vaa(text_enc_dict)
  _lowerCAmelCase      						=	{'''cond_stage_model.model.''' + k: v for k, v in text_enc_dict.items()}
 else:
  _lowerCAmelCase      						=	convert_text_enc_state_dict(text_enc_dict)
  _lowerCAmelCase      						=	{'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}

 # Put together new checkpoint
 _lowerCAmelCase      						=	{**unet_state_dict, **vae_state_dict, **text_enc_dict}
 if args.half:
  _lowerCAmelCase      						=	{k: v.half() for k, v in state_dict.items()}

 if args.use_safetensors:
  save_file(state_dict, args.checkpoint_path)
 else:
  _lowerCAmelCase      						=	{'''state_dict''': state_dict}
  torch.save(state_dict, args.checkpoint_path)


