

"""simple docstring"""





import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

__UpperCamelCase  :      Union[str, 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'''),
]

__UpperCamelCase  :      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'''),
]

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

__UpperCamelCase  :      int							      =		'''mid_block.attentions.0.'''
__UpperCamelCase  :      Any							      =		'''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
							__UpperCamelCase  :      Tuple							      =		F'''mid_block.resnets.{j}.'''
							__UpperCamelCase  :      Optional[Any]							      =		F'''middle_block.{2*j}.'''
							unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))

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


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

__UpperCamelCase  :      Any							      =		[
    # (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  :      Optional[Any]							      =		F'''encoder.down_blocks.{i}.resnets.{j}.'''
														__UpperCamelCase  :      Tuple							      =		F'''encoder.down.{i}.block.{j}.'''
														vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

							if i < 3:
														__UpperCamelCase  :      Union[str, Any]							      =		F'''down_blocks.{i}.downsamplers.0.'''
														__UpperCamelCase  :      Any							      =		F'''down.{i}.downsample.'''
														vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

														__UpperCamelCase  :      Dict							      =		F'''up_blocks.{i}.upsamplers.0.'''
														__UpperCamelCase  :      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):
														__UpperCamelCase  :      List[str]							      =		F'''decoder.up_blocks.{i}.resnets.{j}.'''
														__UpperCamelCase  :      List[str]							      =		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  :      List[str]							      =		F'''mid_block.resnets.{i}.'''
							__UpperCamelCase  :      List[Any]							      =		F'''mid.block_{i+1}.'''
							vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	# convert HF linear weights to SD conv2d weights
	return w.reshape(*w.shape						,     1						,     1 )

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__	:						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:
			lowerCAmelCase__	:						List[Any]  				=	v.replace(A_						,     A_ )
		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__	:						int  				=	v.replace(A_						,     A_ )
			lowerCAmelCase__	:						Any  				=	v
	lowerCAmelCase__	:						int  				=	{v: vae_state_dict[k] for k, v in mapping.items()}
	lowerCAmelCase__	:						Dict  				=	['''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__	:						str  				=	reshape_weight_for_sd(A_ )
	return new_state_dict


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


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

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

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

		lowerCAmelCase__	:						Any  				=	textenc_pattern.sub(lambda A_ : protected[re.escape(m.group(0 ) )]						,     A_ )
		lowerCAmelCase__	:						int  				=	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__	:						Optional[Any]  				=	textenc_pattern.sub(lambda A_ : protected[re.escape(m.group(0 ) )]						,     A_ )
		lowerCAmelCase__	:						Tuple  				=	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''' )
		lowerCAmelCase__	:						Union[str, Any]  				=	textenc_pattern.sub(lambda A_ : protected[re.escape(m.group(0 ) )]						,     A_ )
		lowerCAmelCase__	:						Union[str, Any]  				=	torch.cat(A_ )

	return new_state_dict

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	return text_enc_dict


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

							__UpperCamelCase  :      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
							__UpperCamelCase  :      Optional[int]							      =		osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
							__UpperCamelCase  :      Dict							      =		osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
							__UpperCamelCase  :      Optional[int]							      =		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  :      str							      =		load_file(unet_path, device='''cpu''')
							else:
														__UpperCamelCase  :      Any							      =		osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
														__UpperCamelCase  :      List[Any]							      =		torch.load(unet_path, map_location='''cpu''')

							if osp.exists(vae_path):
														__UpperCamelCase  :      Dict							      =		load_file(vae_path, device='''cpu''')
							else:
														__UpperCamelCase  :      Union[str, Any]							      =		osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.bin''')
														__UpperCamelCase  :      Optional[int]							      =		torch.load(vae_path, map_location='''cpu''')

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

							# Convert the UNet model
							__UpperCamelCase  :      Optional[Any]							      =		convert_unet_state_dict(unet_state_dict)
							__UpperCamelCase  :      List[str]							      =		{'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}

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

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

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








"""simple docstring"""




__UpperCamelCase						=      0  # The first color of the flag.
__UpperCamelCase						=      1  # The second color of the flag.
__UpperCamelCase						=      2  # The third color of the flag.
__UpperCamelCase						=      (red, white, blue)


def     lowercase			(SCREAMING_SNAKE_CASE_		:				list  )   ->						list:
						if not sequence:
												return []
						if len(SCREAMING_SNAKE_CASE_  ) == 1:
												return list(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					0
						SCREAMING_SNAKE_CASE													=					len(SCREAMING_SNAKE_CASE_  ) - 1
						SCREAMING_SNAKE_CASE													=					0
						while mid <= high:
												if sequence[mid] == colors[0]:
																		SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					sequence[mid], sequence[low]
																		low += 1
																		mid += 1
												elif sequence[mid] == colors[1]:
																		mid += 1
												elif sequence[mid] == colors[2]:
																		SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					sequence[high], sequence[mid]
																		high -= 1
												else:
																		SCREAMING_SNAKE_CASE													=					F'The elements inside the sequence must contains only {colors} values'
																		raise ValueError(SCREAMING_SNAKE_CASE_  )
						return sequence


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	__UpperCamelCase						=      input('''Enter numbers separated by commas:\n''').strip()
	__UpperCamelCase						=      [int(item.strip()) for item in user_input.split(''',''')]
	print(f'''{dutch_national_flag_sort(unsorted)}''')


