



'''simple docstring'''

import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

UpperCAmelCase     		=   [
    # (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'),
]

UpperCAmelCase     		=   [
    # (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'),
]

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

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

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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[int]							)      ->						Tuple:







	"""simple docstring"""
	# buyer beware: this is a *brittle* function,
	# and correct output requires that all of these pieces interact in
	# the exact order in which I have arranged them.
	lowerCAmelCase   =     {k: k for k in unet_state_dict.keys()}
	for sd_name, hf_name in unet_conversion_map:
		lowerCAmelCase   =     sd_name
	for k, v in mapping.items():
		if "resnets" in k:
			for sd_part, hf_part in unet_conversion_map_resnet:
				lowerCAmelCase   =     v.replace(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
			lowerCAmelCase   =     v
	for k, v in mapping.items():
		for sd_part, hf_part in unet_conversion_map_layer:
			lowerCAmelCase   =     v.replace(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
		lowerCAmelCase   =     v
	lowerCAmelCase   =     {v: unet_state_dict[k] for k, v in mapping.items()}
	return new_state_dict


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

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

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

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


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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[Any]							)      ->						Optional[Any]:







	"""simple docstring"""
	# convert HF linear weights to SD conv2d weights
	return w.reshape(*w.shape							,		1							,		1							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[Any]							)      ->						Tuple:







	"""simple docstring"""
	lowerCAmelCase   =     {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   =     v.replace(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
		lowerCAmelCase   =     v
	for k, v in mapping.items():
		if "attentions" in k:
			for sd_part, hf_part in vae_conversion_map_attn:
				lowerCAmelCase   =     v.replace(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
			lowerCAmelCase   =     v
	lowerCAmelCase   =     {v: vae_state_dict[k] for k, v in mapping.items()}
	lowerCAmelCase   =     ["""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   =     reshape_weight_for_sd(_SCREAMING_SNAKE_CASE							)
	return new_state_dict


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


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

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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						int							)      ->						Optional[int]:







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

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

	return new_state_dict






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[Any]							)      ->						Tuple:







	"""simple docstring"""
	return text_enc_dict


if __name__ == "__main__":
				UpperCAmelCase     		=   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.'
				)

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

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

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

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

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

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

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




'''simple docstring'''

def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list							)      ->						list:







	"""simple docstring"""

	if len(_SCREAMING_SNAKE_CASE							) < 2:
		return collection

	def circle_sort_util(_SCREAMING_SNAKE_CASE					:						list							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						int							) -> bool:
		lowerCAmelCase   =     False

		if low == high:
			return swapped

		lowerCAmelCase   =     low
		lowerCAmelCase   =     high

		while left < right:
			if collection[left] > collection[right]:
				lowerCAmelCase,       lowerCAmelCase   =     (
				    collection[right],
				    collection[left],
				)
				lowerCAmelCase   =     True

			left += 1
			right -= 1

		if left == right and collection[left] > collection[right + 1]:
			lowerCAmelCase,       lowerCAmelCase   =     (
			    collection[right + 1],
			    collection[left],
			)

			lowerCAmelCase   =     True

		lowerCAmelCase   =     low + int((high - low) / 2							)
		lowerCAmelCase   =     circle_sort_util(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
		lowerCAmelCase   =     circle_sort_util(_SCREAMING_SNAKE_CASE							,		mid + 1							,		_SCREAMING_SNAKE_CASE							)

		return swapped or left_swap or right_swap

	lowerCAmelCase   =     True

	while is_not_sorted is True:
		lowerCAmelCase   =     circle_sort_util(_SCREAMING_SNAKE_CASE							,		0							,		len(_SCREAMING_SNAKE_CASE							) - 1							)

	return collection


if __name__ == "__main__":
				UpperCAmelCase     		=   input('Enter numbers separated by a comma:\n').strip()
				UpperCAmelCase     		=   [int(item) for item in user_input.split(',')]
				print(circle_sort(unsorted))