



'''simple docstring'''




import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

lowerCamelCase							: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'''),
]

lowerCamelCase							:List[str]       = [
    # (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'''),
]

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

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

for j in range(2):
						lowerCamelCase							:List[str]       = F"mid_block.resnets.{j}."
						lowerCamelCase							:Any       = F"middle_block.{2*j}."
						unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





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


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

lowerCamelCase							:Optional[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):
												lowerCamelCase							:Optional[Any]       = F"encoder.down_blocks.{i}.resnets.{j}."
												lowerCamelCase							:Optional[int]       = F"encoder.down.{i}.block.{j}."
												vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

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

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


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



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





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



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





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


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


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

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



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





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

						A_     :						Optional[Any]     =					textenc_pattern.sub(lambda lowerCamelCase__   : protected[re.escape(m.group(0   )   )]						,				lowerCamelCase__   )
						A_     :						Optional[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"""   )
						A_     :						Tuple     =					textenc_pattern.sub(lambda lowerCamelCase__   : protected[re.escape(m.group(0   )   )]						,				lowerCamelCase__   )
						A_     :						Dict     =					torch.cat(lowerCamelCase__   )

			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"""   )
						A_     :						Any     =					textenc_pattern.sub(lambda lowerCamelCase__   : protected[re.escape(m.group(0   )   )]						,				lowerCamelCase__   )
						A_     :						Union[str, Any]     =					torch.cat(lowerCamelCase__   )

			return new_state_dict



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			return text_enc_dict


if __name__ == "__main__":
						lowerCamelCase							:Tuple       = 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.'''
						)

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

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

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

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

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

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

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




'''simple docstring'''




lowerCamelCase							:Any       = {
    '''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
    '''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',
    '''O''': '''---''', '''P''': '''.--.''', '''Q''': '''--.-''', '''R''': '''.-.''', '''S''': '''...''', '''T''': '''-''', '''U''': '''..-''',
    '''V''': '''...-''', '''W''': '''.--''', '''X''': '''-..-''', '''Y''': '''-.--''', '''Z''': '''--..''', '''1''': '''.----''',
    '''2''': '''..---''', '''3''': '''...--''', '''4''': '''....-''', '''5''': '''.....''', '''6''': '''-....''', '''7''': '''--...''',
    '''8''': '''---..''', '''9''': '''----.''', '''0''': '''-----''', '''&''': '''.-...''', '''@''': '''.--.-.''',
    ''':''': '''---...''', ''',''': '''--..--''', '''.''': '''.-.-.-''', '''\'''': '''.----.''', '''"''': '''.-..-.''',
    '''?''': '''..--..''', '''/''': '''-..-.''', '''=''': '''-...-''', '''+''': '''.-.-.''', '''-''': '''-....-''',
    '''(''': '''-.--.''', ''')''': '''-.--.-''', '''!''': '''-.-.--''', ''' ''': '''/'''
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
lowerCamelCase							:Any       = {value: key for key, value in MORSE_CODE_DICT.items()}



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			return " ".join(MORSE_CODE_DICT[char] for char in message.upper()   )



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			return "".join(REVERSE_DICT[char] for char in message.split()   )



def      a      (			):
			'''simple docstring'''





			A_     :						List[str]     =					"""Morse code here!"""
			print(lowerCamelCase__   )
			A_     :						str     =					encrypt(lowerCamelCase__   )
			print(lowerCamelCase__   )
			A_     :						Dict     =					decrypt(lowerCamelCase__   )
			print(lowerCamelCase__   )


if __name__ == "__main__":
						main()