






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

SCREAMING_SNAKE_CASE__      :			List[Any]							=		[
    # (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"""),
]

SCREAMING_SNAKE_CASE__      :			int							=		[
    # (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"""),
]

SCREAMING_SNAKE_CASE__      :			int							=		[]
# 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
          SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		f"down_blocks.{i}.resnets.{j}."
          SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		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
               SCREAMING_SNAKE_CASE__      :			Optional[int]							=		f"down_blocks.{i}.attentions.{j}."
               SCREAMING_SNAKE_CASE__      :			List[Any]							=		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
          SCREAMING_SNAKE_CASE__      :			Dict							=		f"up_blocks.{i}.resnets.{j}."
          SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		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
               SCREAMING_SNAKE_CASE__      :			List[Any]							=		f"up_blocks.{i}.attentions.{j}."
               SCREAMING_SNAKE_CASE__      :			Optional[int]							=		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
          SCREAMING_SNAKE_CASE__      :			Optional[int]							=		f"down_blocks.{i}.downsamplers.0.conv."
          SCREAMING_SNAKE_CASE__      :			List[Any]							=		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
          SCREAMING_SNAKE_CASE__      :			Tuple							=		f"up_blocks.{i}.upsamplers.0."
          SCREAMING_SNAKE_CASE__      :			List[Any]							=		f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
          unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

SCREAMING_SNAKE_CASE__      :			Dict							=		"""mid_block.attentions.0."""
SCREAMING_SNAKE_CASE__      :			str							=		"""middle_block.1."""
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
     SCREAMING_SNAKE_CASE__      :			Optional[int]							=		f"mid_block.resnets.{j}."
     SCREAMING_SNAKE_CASE__      :			Dict							=		f"middle_block.{2*j}."
     unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))

def      __lowercase					( snake_case ):







    """simple docstring"""






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


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

SCREAMING_SNAKE_CASE__      :			List[str]							=		[
    # (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):
          SCREAMING_SNAKE_CASE__      :			Any							=		f"encoder.down_blocks.{i}.resnets.{j}."
          SCREAMING_SNAKE_CASE__      :			int							=		f"encoder.down.{i}.block.{j}."
          vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

     if i < 3:
          SCREAMING_SNAKE_CASE__      :			Dict							=		f"down_blocks.{i}.downsamplers.0."
          SCREAMING_SNAKE_CASE__      :			Optional[int]							=		f"down.{i}.downsample."
          vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

          SCREAMING_SNAKE_CASE__      :			int							=		f"up_blocks.{i}.upsamplers.0."
          SCREAMING_SNAKE_CASE__      :			List[str]							=		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):
          SCREAMING_SNAKE_CASE__      :			Tuple							=		f"decoder.up_blocks.{i}.resnets.{j}."
          SCREAMING_SNAKE_CASE__      :			Optional[int]							=		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):
     SCREAMING_SNAKE_CASE__      :			str							=		f"mid_block.resnets.{i}."
     SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		f"mid.block_{i+1}."
     vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


SCREAMING_SNAKE_CASE__      :			int							=		[
    # (stable-diffusion, HF Diffusers)
    ("""norm.""", """group_norm."""),
    ("""q.""", """query."""),
    ("""k.""", """key."""),
    ("""v.""", """value."""),
    ("""proj_out.""", """proj_attn."""),
]

def      __lowercase					( snake_case ):







    """simple docstring"""






    return w.reshape(*w.shape,  1,  1 )

def      __lowercase					( snake_case ):







    """simple docstring"""






    __magic_name__		:Union[str, Any]        =      {k: k for k in vae_state_dict.keys()}
    for k, v in mapping.items():
        for sd_part, hf_part in vae_conversion_map:
            __magic_name__		:Tuple        =      v.replace(__snake_case,  __snake_case )
        __magic_name__		:Tuple        =      v
    for k, v in mapping.items():
        if "attentions" in k:
            for sd_part, hf_part in vae_conversion_map_attn:
                __magic_name__		:Optional[int]        =      v.replace(__snake_case,  __snake_case )
            __magic_name__		:Union[str, Any]        =      v
    __magic_name__		:List[Any]        =      {v: vae_state_dict[k] for k, v in mapping.items()}
    __magic_name__		:List[Any]        =      ['''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''' )
                __magic_name__		:List[Any]        =      reshape_weight_for_sd(__snake_case )
    return new_state_dict


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


SCREAMING_SNAKE_CASE__      :			str							=		[
    # (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"""),
]
SCREAMING_SNAKE_CASE__      :			Optional[int]							=		{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
SCREAMING_SNAKE_CASE__      :			str							=		re.compile("""|""".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		{"""q""": 0, """k""": 1, """v""": 2}

def      __lowercase					( snake_case ):







    """simple docstring"""






    __magic_name__		:Tuple        =      {}
    __magic_name__		:Any        =      {}
    __magic_name__		:str        =      {}
    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''' )
        ):
            __magic_name__		:List[str]        =      k[: -len('''.q_proj.weight''' )]
            __magic_name__		:int        =      k[-len('''q_proj.weight''' )]
            if k_pre not in capture_qkv_weight:
                __magic_name__		:List[str]        =      [None, None, None]
            __magic_name__		:List[str]        =      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''' )
        ):
            __magic_name__		:Dict        =      k[: -len('''.q_proj.bias''' )]
            __magic_name__		:Optional[Any]        =      k[-len('''q_proj.bias''' )]
            if k_pre not in capture_qkv_bias:
                __magic_name__		:Union[str, Any]        =      [None, None, None]
            __magic_name__		:str        =      v
            continue

        __magic_name__		:Any        =      textenc_pattern.sub(lambda snake_case : protected[re.escape(m.group(0 ) )],  __snake_case )
        __magic_name__		:List[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''' )
        __magic_name__		:Union[str, Any]        =      textenc_pattern.sub(lambda snake_case : protected[re.escape(m.group(0 ) )],  __snake_case )
        __magic_name__		:Dict        =      torch.cat(__snake_case )

    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''' )
        __magic_name__		:Any        =      textenc_pattern.sub(lambda snake_case : protected[re.escape(m.group(0 ) )],  __snake_case )
        __magic_name__		:Any        =      torch.cat(__snake_case )

    return new_state_dict

def      __lowercase					( snake_case ):







    """simple docstring"""






    return text_enc_dict


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__      :			List[str]							=		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."""
     )

     SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		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
     SCREAMING_SNAKE_CASE__      :			List[Any]							=		osp.join(args.model_path, """unet""", """diffusion_pytorch_model.safetensors""")
     SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		osp.join(args.model_path, """vae""", """diffusion_pytorch_model.safetensors""")
     SCREAMING_SNAKE_CASE__      :			List[str]							=		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):
          SCREAMING_SNAKE_CASE__      :			Dict							=		load_file(unet_path, device="""cpu""")
     else:
          SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		osp.join(args.model_path, """unet""", """diffusion_pytorch_model.bin""")
          SCREAMING_SNAKE_CASE__      :			Tuple							=		torch.load(unet_path, map_location="""cpu""")

     if osp.exists(vae_path):
          SCREAMING_SNAKE_CASE__      :			str							=		load_file(vae_path, device="""cpu""")
     else:
          SCREAMING_SNAKE_CASE__      :			Dict							=		osp.join(args.model_path, """vae""", """diffusion_pytorch_model.bin""")
          SCREAMING_SNAKE_CASE__      :			List[str]							=		torch.load(vae_path, map_location="""cpu""")

     if osp.exists(text_enc_path):
          SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		load_file(text_enc_path, device="""cpu""")
     else:
          SCREAMING_SNAKE_CASE__      :			int							=		osp.join(args.model_path, """text_encoder""", """pytorch_model.bin""")
          SCREAMING_SNAKE_CASE__      :			List[Any]							=		torch.load(text_enc_path, map_location="""cpu""")

     # Convert the UNet model
     SCREAMING_SNAKE_CASE__      :			Optional[int]							=		convert_unet_state_dict(unet_state_dict)
     SCREAMING_SNAKE_CASE__      :			Tuple							=		{"""model.diffusion_model.""" + k: v for k, v in unet_state_dict.items()}

     # Convert the VAE model
     SCREAMING_SNAKE_CASE__      :			Any							=		convert_vae_state_dict(vae_state_dict)
     SCREAMING_SNAKE_CASE__      :			Tuple							=		{"""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
     SCREAMING_SNAKE_CASE__      :			List[Any]							=		"""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
          SCREAMING_SNAKE_CASE__      :			int							=		{"""transformer.""" + k: v for k, v in text_enc_dict.items()}
          SCREAMING_SNAKE_CASE__      :			Dict							=		convert_text_enc_state_dict_vaa(text_enc_dict)
          SCREAMING_SNAKE_CASE__      :			int							=		{"""cond_stage_model.model.""" + k: v for k, v in text_enc_dict.items()}
     else:
          SCREAMING_SNAKE_CASE__      :			Optional[int]							=		convert_text_enc_state_dict(text_enc_dict)
          SCREAMING_SNAKE_CASE__      :			str							=		{"""cond_stage_model.transformer.""" + k: v for k, v in text_enc_dict.items()}

     # Put together new checkpoint
     SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		{**unet_state_dict, **vae_state_dict, **text_enc_dict}
     if args.half:
          SCREAMING_SNAKE_CASE__      :			str							=		{k: v.half() for k, v in state_dict.items()}

     if args.use_safetensors:
          save_file(state_dict, args.checkpoint_path)
     else:
          SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		{"""state_dict""": state_dict}
          torch.save(state_dict, args.checkpoint_path)














from urllib.parse import quote

import pytest

from datasets.utils.hub import hf_hub_url

@pytest.mark.parametrize('''repo_id''',  ['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''',  ['''filename.csv''', '''filename with blanks.csv'''] )
@pytest.mark.parametrize('''revision''',  [None, '''v2'''] )
def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Tuple        =      hf_hub_url(repo_id=snake_case,  path=snake_case,  revision=snake_case )
				assert url == f'''https://huggingface.co/datasets/{repo_id}/resolve/{revision or "main"}/{quote(snake_case )}'''






