




from __future__ import annotations

from typing import TypedDict
class 		UpperCAmelCase   (				UpperCAmelCase__			):







						'''simple docstring'''





						lowerCAmelCase_     		=   42
						lowerCAmelCase_     		=   42







def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	if not isinstance(_A     ,	_A							):
		raise TypeError("The parameter s type must be str."							)

	return [s[i:] + s[:i] for i in range(len(_A							)							)]


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	if not isinstance(_A     ,	_A							):
		raise TypeError("The parameter s type must be str."							)
	if not s:
		raise ValueError("The parameter s must not be empty."							)

	snake_case_		    =     all_rotations(_A							)
	rotations.sort()  # sort the list of rotations in alphabetically order
	# make a string composed of the last char of each rotation
	snake_case_		    =     {
	    "bwt_string": "".join([word[-1] for word in rotations]							),
	    "idx_original_string": rotations.index(_A							),
	}
	return response


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	if not isinstance(_A     ,	_A							):
		raise TypeError("The parameter bwt_string type must be str."							)
	if not bwt_string:
		raise ValueError("The parameter bwt_string must not be empty."							)
	try:
		snake_case_		    =     int(_A							)
	except ValueError:
		raise TypeError(
		    "The parameter idx_original_string type must be int or passive"
		    " of cast to int."							)
	if idx_original_string < 0:
		raise ValueError("The parameter idx_original_string must not be lower than 0."							)
	if idx_original_string >= len(_A							):
		raise ValueError(
		    "The parameter idx_original_string must be lower than" " len(bwt_string)."							)

	snake_case_		    =     [""] * len(_A							)
	for _ in range(len(_A							)							):
		for i in range(len(_A							)							):
			snake_case_		    =     bwt_string[i] + ordered_rotations[i]
		ordered_rotations.sort()
	return ordered_rotations[idx_original_string]


if __name__ == "__main__":
		lowercase__				:     Optional[Any] 		=						"Provide a string that I will generate its BWT transform: "
		lowercase__				:     Union[str, Any] 		=						input(entry_msg).strip()
		lowercase__				:     Tuple 		=						bwt_transform(s)
		print(
		    f'''Burrows Wheeler transform for string \'{s}\' results '''
		    f'''in \'{result['bwt_string']}\''''
		)
		lowercase__				:     Dict 		=						reverse_bwt(result["bwt_string"], result["idx_original_string"])
		print(
		    f'''Reversing Burrows Wheeler transform for entry \'{result['bwt_string']}\' '''
		    f'''we get original string \'{original_string}\''''
		)






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

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

lowercase__				:     Union[str, Any] 		=						"mid_block.attentions.0."
lowercase__				:     List[Any] 		=						"middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
		lowercase__				:     Tuple 		=						f'''mid_block.resnets.{j}.'''
		lowercase__				:     List[str] 		=						f'''middle_block.{2*j}.'''
		unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





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


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

lowercase__				:     Dict 		=						[
    # (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__				:     Any 		=						f'''encoder.down_blocks.{i}.resnets.{j}.'''
				lowercase__				:     List[Any] 		=						f'''encoder.down.{i}.block.{j}.'''
				vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

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

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


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


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





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


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     {k: k for k in vae_state_dict.keys()}
	for k, v in mapping.items():
		for sd_part, hf_part in vae_conversion_map:
			snake_case_		    =     v.replace(_A     ,	_A							)
		snake_case_		    =     v
	for k, v in mapping.items():
		if "attentions" in k:
			for sd_part, hf_part in vae_conversion_map_attn:
				snake_case_		    =     v.replace(_A     ,	_A							)
			snake_case_		    =     v
	snake_case_		    =     {v: vae_state_dict[k] for k, v in mapping.items()}
	snake_case_		    =     ["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"							)
				snake_case_		    =     reshape_weight_for_sd(_A							)
	return new_state_dict


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


lowercase__				:     int 		=						[
    # (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__				:     Dict 		=						{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowercase__				:     Tuple 		=						re.compile("|".join(protected.keys()))

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


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





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

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

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

	return new_state_dict


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	return text_enc_dict


if __name__ == "__main__":
		lowercase__				:     int 		=						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__				:     Dict 		=						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__				:     Tuple 		=						osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
		lowercase__				:     int 		=						osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
		lowercase__				:     Any 		=						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__				:     str 		=						load_file(unet_path, device="cpu")
		else:
				lowercase__				:     Optional[Any] 		=						osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
				lowercase__				:     Any 		=						torch.load(unet_path, map_location="cpu")

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

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

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

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

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

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