



import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

lowercase_										=  [
    # (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"),
]

lowercase_										=  [
    # (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"),
]

lowercase_										=  []
# 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
														lowercase_										=  F'down_blocks.{i}.resnets.{j}.'
														lowercase_										=  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
																					lowercase_										=  F'down_blocks.{i}.attentions.{j}.'
																					lowercase_										=  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
														lowercase_										=  F'up_blocks.{i}.resnets.{j}.'
														lowercase_										=  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
																					lowercase_										=  F'up_blocks.{i}.attentions.{j}.'
																					lowercase_										=  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
														lowercase_										=  F'down_blocks.{i}.downsamplers.0.conv.'
														lowercase_										=  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
														lowercase_										=  F'up_blocks.{i}.upsamplers.0.'
														lowercase_										=  F'output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'
														unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

lowercase_										=  "mid_block.attentions.0."
lowercase_										=  "middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
							lowercase_										=  F'mid_block.resnets.{j}.'
							lowercase_										=  F'middle_block.{2*j}.'
							unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_	):
			lowercase__       	=					{k: k for k in unet_state_dict.keys()}
			for sd_name, hf_name in unet_conversion_map:
						lowercase__       	=					sd_name
			for k, v in mapping.items():
						if "resnets" in k:
									for sd_part, hf_part in unet_conversion_map_resnet:
												lowercase__       	=					v.replace(snake_case__    ,	snake_case__	)
									lowercase__       	=					v
			for k, v in mapping.items():
						for sd_part, hf_part in unet_conversion_map_layer:
									lowercase__       	=					v.replace(snake_case__    ,	snake_case__	)
						lowercase__       	=					v
			lowercase__       	=					{v: unet_state_dict[k] for k, v in mapping.items()}
			return new_state_dict


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

lowercase_										=  [
    # (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):
														lowercase_										=  F'encoder.down_blocks.{i}.resnets.{j}.'
														lowercase_										=  F'encoder.down.{i}.block.{j}.'
														vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

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

														lowercase_										=  F'up_blocks.{i}.upsamplers.0.'
														lowercase_										=  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):
														lowercase_										=  F'decoder.up_blocks.{i}.resnets.{j}.'
														lowercase_										=  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):
							lowercase_										=  F'mid_block.resnets.{i}.'
							lowercase_										=  F'mid.block_{i+1}.'
							vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_	):
			return w.reshape(*w.shape    ,	1    ,	1	)



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_	):
			lowercase__       	=					{k: k for k in vae_state_dict.keys()}
			for k, v in mapping.items():
						for sd_part, hf_part in vae_conversion_map:
									lowercase__       	=					v.replace(snake_case__    ,	snake_case__	)
						lowercase__       	=					v
			for k, v in mapping.items():
						if "attentions" in k:
									for sd_part, hf_part in vae_conversion_map_attn:
												lowercase__       	=					v.replace(snake_case__    ,	snake_case__	)
									lowercase__       	=					v
			lowercase__       	=					{v: vae_state_dict[k] for k, v in mapping.items()}
			lowercase__       	=					["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'''	)
												lowercase__       	=					reshape_weight_for_sd(snake_case__	)
			return new_state_dict


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


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

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



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_	):
			lowercase__       	=					{}
			lowercase__       	=					{}
			lowercase__       	=					{}
			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"	)
						):
									lowercase__       	=					k[: -len(".q_proj.weight"	)]
									lowercase__       	=					k[-len("q_proj.weight"	)]
									if k_pre not in capture_qkv_weight:
												lowercase__       	=					[None, None, None]
									lowercase__       	=					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"	)
						):
									lowercase__       	=					k[: -len(".q_proj.bias"	)]
									lowercase__       	=					k[-len("q_proj.bias"	)]
									if k_pre not in capture_qkv_bias:
												lowercase__       	=					[None, None, None]
									lowercase__       	=					v
									continue

						lowercase__       	=					textenc_pattern.sub(lambda SCREAMING_SNAKE_CASE_	: protected[re.escape(m.group(0	)	)]    ,	snake_case__	)
						lowercase__       	=					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"	)
						lowercase__       	=					textenc_pattern.sub(lambda SCREAMING_SNAKE_CASE_	: protected[re.escape(m.group(0	)	)]    ,	snake_case__	)
						lowercase__       	=					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"	)
						lowercase__       	=					textenc_pattern.sub(lambda SCREAMING_SNAKE_CASE_	: protected[re.escape(m.group(0	)	)]    ,	snake_case__	)
						lowercase__       	=					torch.cat(snake_case__	)

			return new_state_dict



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_	):
			return text_enc_dict


if __name__ == "__main__":
							lowercase_										=  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."""
							)

							lowercase_										=  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
							lowercase_										=  osp.join(args.model_path, """unet""", """diffusion_pytorch_model.safetensors""")
							lowercase_										=  osp.join(args.model_path, """vae""", """diffusion_pytorch_model.safetensors""")
							lowercase_										=  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):
														lowercase_										=  load_file(unet_path, device="""cpu""")
							else:
														lowercase_										=  osp.join(args.model_path, """unet""", """diffusion_pytorch_model.bin""")
														lowercase_										=  torch.load(unet_path, map_location="""cpu""")

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

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

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

							# Convert the VAE model
							lowercase_										=  convert_vae_state_dict(vae_state_dict)
							lowercase_										=  {"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
							lowercase_										=  "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
														lowercase_										=  {"transformer." + k: v for k, v in text_enc_dict.items()}
														lowercase_										=  convert_text_enc_state_dict_vaa(text_enc_dict)
														lowercase_										=  {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
							else:
														lowercase_										=  convert_text_enc_state_dict(text_enc_dict)
														lowercase_										=  {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}

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

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









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCAmelCase	:   Any    =   {
    "configuration_chinese_clip": [
        "CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ChineseCLIPConfig",
        "ChineseCLIPOnnxConfig",
        "ChineseCLIPTextConfig",
        "ChineseCLIPVisionConfig",
    ],
    "processing_chinese_clip": ["ChineseCLIPProcessor"],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase	:   str    =   ["ChineseCLIPFeatureExtractor"]
	_lowerCAmelCase	:   Any    =   ["ChineseCLIPImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase	:   Optional[int]    =   [
	    "CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "ChineseCLIPModel",
	    "ChineseCLIPPreTrainedModel",
	    "ChineseCLIPTextModel",
	    "ChineseCLIPVisionModel",
	]

if TYPE_CHECKING:
	from .configuration_chinese_clip import (
	    CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    ChineseCLIPConfig,
	    ChineseCLIPOnnxConfig,
	    ChineseCLIPTextConfig,
	    ChineseCLIPVisionConfig,
	)
	from .processing_chinese_clip import ChineseCLIPProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_chinese_clip import (
		    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ChineseCLIPModel,
		    ChineseCLIPPreTrainedModel,
		    ChineseCLIPTextModel,
		    ChineseCLIPVisionModel,
		)

else:
	import sys

	_lowerCAmelCase	:   Any    =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
