






import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


UpperCamelCase__						:      int								=					"""pcuenq/k-upscaler"""





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_,  *, snake_case_,  snake_case_  )   ->  List[Any]:


	"""simple docstring"""



	a		=			{
	    # norm1
	    f"""{diffusers_resnet_prefix}.norm1.linear.weight""": checkpoint[f"""{resnet_prefix}.main.0.mapper.weight"""],
	    f"""{diffusers_resnet_prefix}.norm1.linear.bias""": checkpoint[f"""{resnet_prefix}.main.0.mapper.bias"""],
	    # conv1
	    f"""{diffusers_resnet_prefix}.conv1.weight""": checkpoint[f"""{resnet_prefix}.main.2.weight"""],
	    f"""{diffusers_resnet_prefix}.conv1.bias""": checkpoint[f"""{resnet_prefix}.main.2.bias"""],
	    # norm2
	    f"""{diffusers_resnet_prefix}.norm2.linear.weight""": checkpoint[f"""{resnet_prefix}.main.4.mapper.weight"""],
	    f"""{diffusers_resnet_prefix}.norm2.linear.bias""": checkpoint[f"""{resnet_prefix}.main.4.mapper.bias"""],
	    # conv2
	    f"""{diffusers_resnet_prefix}.conv2.weight""": checkpoint[f"""{resnet_prefix}.main.6.weight"""],
	    f"""{diffusers_resnet_prefix}.conv2.bias""": checkpoint[f"""{resnet_prefix}.main.6.bias"""],
	}

	if resnet.conv_shortcut is not None:
		rv.update(
		    {
		        f"""{diffusers_resnet_prefix}.conv_shortcut.weight""": checkpoint[f"""{resnet_prefix}.skip.weight"""],
		    }  )

	return rv





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  *, snake_case_,  snake_case_  )   ->  Optional[int]:


	"""simple docstring"""



	a	, a	, a		=			checkpoint[f"""{attention_prefix}.qkv_proj.weight"""].chunk(3,  dim=0  )
	a	, a	, a		=			checkpoint[f"""{attention_prefix}.qkv_proj.bias"""].chunk(3,  dim=0  )
	a		=			{
	    # norm
	    f"""{diffusers_attention_prefix}.norm1.linear.weight""": checkpoint[f"""{attention_prefix}.norm_in.mapper.weight"""],
	    f"""{diffusers_attention_prefix}.norm1.linear.bias""": checkpoint[f"""{attention_prefix}.norm_in.mapper.bias"""],
	    # to_q
	    f"""{diffusers_attention_prefix}.attn1.to_q.weight""": weight_q.squeeze(-1  ).squeeze(-1  ),
	    f"""{diffusers_attention_prefix}.attn1.to_q.bias""": bias_q,
	    # to_k
	    f"""{diffusers_attention_prefix}.attn1.to_k.weight""": weight_k.squeeze(-1  ).squeeze(-1  ),
	    f"""{diffusers_attention_prefix}.attn1.to_k.bias""": bias_k,
	    # to_v
	    f"""{diffusers_attention_prefix}.attn1.to_v.weight""": weight_v.squeeze(-1  ).squeeze(-1  ),
	    f"""{diffusers_attention_prefix}.attn1.to_v.bias""": bias_v,
	    # to_out
	    f"""{diffusers_attention_prefix}.attn1.to_out.0.weight""": checkpoint[f"""{attention_prefix}.out_proj.weight"""]
	    .squeeze(-1  )
	    .squeeze(-1  ),
	    f"""{diffusers_attention_prefix}.attn1.to_out.0.bias""": checkpoint[f"""{attention_prefix}.out_proj.bias"""],
	}

	return rv





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  *, snake_case_,  snake_case_,  snake_case_  )   ->  Union[str, Any]:


	"""simple docstring"""



	a	, a		=			checkpoint[f"""{attention_prefix}.kv_proj.weight"""].chunk(2,  dim=0  )
	a	, a		=			checkpoint[f"""{attention_prefix}.kv_proj.bias"""].chunk(2,  dim=0  )

	a		=			{
	    # norm2 (ada groupnorm)
	    f"""{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight""": checkpoint[
	        f"""{attention_prefix}.norm_dec.mapper.weight"""
	    ],
	    f"""{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias""": checkpoint[
	        f"""{attention_prefix}.norm_dec.mapper.bias"""
	    ],
	    # layernorm on encoder_hidden_state
	    f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight""": checkpoint[
	        f"""{attention_prefix}.norm_enc.weight"""
	    ],
	    f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias""": checkpoint[
	        f"""{attention_prefix}.norm_enc.bias"""
	    ],
	    # to_q
	    f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight""": checkpoint[
	        f"""{attention_prefix}.q_proj.weight"""
	    ]
	    .squeeze(-1  )
	    .squeeze(-1  ),
	    f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias""": checkpoint[
	        f"""{attention_prefix}.q_proj.bias"""
	    ],
	    # to_k
	    f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight""": weight_k.squeeze(-1  ).squeeze(-1  ),
	    f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias""": bias_k,
	    # to_v
	    f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight""": weight_v.squeeze(-1  ).squeeze(-1  ),
	    f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias""": bias_v,
	    # to_out
	    f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight""": checkpoint[
	        f"""{attention_prefix}.out_proj.weight"""
	    ]
	    .squeeze(-1  )
	    .squeeze(-1  ),
	    f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias""": checkpoint[
	        f"""{attention_prefix}.out_proj.bias"""
	    ],
	}

	return rv





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_,  snake_case_,  snake_case_  )   ->  List[Any]:


	"""simple docstring"""



	a		=			'''inner_model.u_net.u_blocks''' if block_type == '''up''' else '''inner_model.u_net.d_blocks'''
	a		=			f"""{block_prefix}.{block_idx}"""

	a		=			{}

	if not hasattr(snake_case_,  '''attentions'''  ):
		a		=			1  # resnet only
	elif not block.attentions[0].add_self_attention:
		a		=			2  # resnet -> cross-attention
	else:
		a		=			3  # resnet -> self-attention -> cross-attention)

	for resnet_idx, resnet in enumerate(block.resnets  ):
		# diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
		a		=			f"""{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"""
		a		=			n * resnet_idx if block_type == '''up''' else n * resnet_idx + 1
		a		=			f"""{block_prefix}.{idx}""" if block_type == '''up''' else f"""{block_prefix}.{idx}"""

		diffusers_checkpoint.update(
		    resnet_to_diffusers_checkpoint(
		        snake_case_,  snake_case_,  diffusers_resnet_prefix=snake_case_,  resnet_prefix=snake_case_  )  )

	if hasattr(snake_case_,  '''attentions'''  ):
		for attention_idx, attention in enumerate(block.attentions  ):
			a		=			f"""{block_type}_blocks.{block_idx}.attentions.{attention_idx}"""
			a		=			n * attention_idx + 1 if block_type == '''up''' else n * attention_idx + 2
			a		=			f"""{block_prefix}.{idx}"""
			a		=			f"""{block_prefix}.{idx }"""
			a		=			1 if not attention.add_self_attention else 2
			a		=			(
			    n * attention_idx + cross_attention_index
			    if block_type == '''up'''
			    else n * attention_idx + cross_attention_index + 1
			)
			a		=			f"""{block_prefix}.{idx }"""

			diffusers_checkpoint.update(
			    cross_attn_to_diffusers_checkpoint(
			        snake_case_,  diffusers_attention_prefix=snake_case_,  diffusers_attention_index=2,  attention_prefix=snake_case_,  )  )

			if attention.add_self_attention is True:
				diffusers_checkpoint.update(
				    self_attn_to_diffusers_checkpoint(
				        snake_case_,  diffusers_attention_prefix=snake_case_,  attention_prefix=snake_case_,  )  )

	return diffusers_checkpoint





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  Dict:


	"""simple docstring"""



	a		=			{}

	# pre-processing
	diffusers_checkpoint.update(
	    {
	        '''conv_in.weight''': checkpoint['''inner_model.proj_in.weight'''],
	        '''conv_in.bias''': checkpoint['''inner_model.proj_in.bias'''],
	    }  )

	# timestep and class embedding
	diffusers_checkpoint.update(
	    {
	        '''time_proj.weight''': checkpoint['''inner_model.timestep_embed.weight'''].squeeze(-1  ),
	        '''time_embedding.linear_1.weight''': checkpoint['''inner_model.mapping.0.weight'''],
	        '''time_embedding.linear_1.bias''': checkpoint['''inner_model.mapping.0.bias'''],
	        '''time_embedding.linear_2.weight''': checkpoint['''inner_model.mapping.2.weight'''],
	        '''time_embedding.linear_2.bias''': checkpoint['''inner_model.mapping.2.bias'''],
	        '''time_embedding.cond_proj.weight''': checkpoint['''inner_model.mapping_cond.weight'''],
	    }  )

	# down_blocks
	for down_block_idx, down_block in enumerate(model.down_blocks  ):
		diffusers_checkpoint.update(block_to_diffusers_checkpoint(snake_case_,  snake_case_,  snake_case_,  '''down'''  )  )

	# up_blocks
	for up_block_idx, up_block in enumerate(model.up_blocks  ):
		diffusers_checkpoint.update(block_to_diffusers_checkpoint(snake_case_,  snake_case_,  snake_case_,  '''up'''  )  )

	# post-processing
	diffusers_checkpoint.update(
	    {
	        '''conv_out.weight''': checkpoint['''inner_model.proj_out.weight'''],
	        '''conv_out.bias''': checkpoint['''inner_model.proj_out.bias'''],
	    }  )

	return diffusers_checkpoint





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  Dict:


	"""simple docstring"""



	a		=			original_config['''input_channels'''] + original_config['''unet_cond_dim''']
	a		=			original_config['''input_channels'''] + (1 if original_config['''has_variance'''] else 0)

	a		=			original_config['''channels''']

	assert (
	    len(set(original_config['''depths''']  )  ) == 1
	), "UNet2DConditionModel currently do not support blocks with different number of layers"
	a		=			original_config['''depths'''][0]

	a		=			original_config['''mapping_cond_dim''']
	a		=			original_config['''cross_cond_dim''']

	a		=			[]
	a		=			[]
	for s, c in zip(original_config['''self_attn_depths'''],  original_config['''cross_attn_depths''']  ):
		if s:
			a		=			'''self'''
			a		=			'''cross''' if c else None
		elif c:
			a		=			'''cross'''
			a		=			None
		else:
			a		=			None
			a		=			None
		attna_types.append(snake_case_  )
		attna_types.append(snake_case_  )

	a		=			UNetaDConditionModel(
	    in_channels=snake_case_,  out_channels=snake_case_,  down_block_types=('''KDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D'''),  mid_block_type=snake_case_,  up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D'''),  block_out_channels=snake_case_,  layers_per_block=snake_case_,  act_fn='''gelu''',  norm_num_groups=snake_case_,  cross_attention_dim=snake_case_,  attention_head_dim=6_4,  time_cond_proj_dim=snake_case_,  resnet_time_scale_shift='''scale_shift''',  time_embedding_type='''fourier''',  timestep_post_act='''gelu''',  conv_in_kernel=1,  conv_out_kernel=1,  )

	return unet





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  List[str]:


	"""simple docstring"""



	a		=			torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''  )

	a		=			huggingface_hub.hf_hub_download(snake_case_,  '''config_laion_text_cond_latent_upscaler_2.json'''  )
	a		=			huggingface_hub.hf_hub_download(
	    snake_case_,  '''laion_text_cond_latent_upscaler_2_1_00470000_slim.pth'''  )
	print(f"""loading original model configuration from {orig_config_path}"""  )
	print(f"""loading original model checkpoint from {orig_weights_path}"""  )

	print('''converting to diffusers unet'''  )
	a		=			K.config.load_config(open(snake_case_  )  )['''model''']
	a		=			unet_model_from_original_config(snake_case_  )

	a		=			torch.load(snake_case_,  map_location=snake_case_  )['''model_ema''']
	a		=			unet_to_diffusers_checkpoint(snake_case_,  snake_case_  )

	model.load_state_dict(snake_case_,  strict=snake_case_  )
	model.save_pretrained(args.dump_path  )
	print(f"""saving converted unet model in {args.dump_path}"""  )


if __name__ == "__main__":
				UpperCamelCase__						:      str								=					argparse.ArgumentParser()

				parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
				UpperCamelCase__						:      Union[str, Any]								=					parser.parse_args()

				main(args)













import random
import sys

UpperCamelCase__						:      List[str]								=					"""ABCDEFGHIJKLMNOPQRSTUVWXYZ"""





def   SCREAMING_SNAKE_CASE__						(   )   ->  None:


	"""simple docstring"""



	a		=			input('''Enter message: '''  )
	a		=			'''LFWOAYUISVKMNXPBDCRJTQEGHZ'''
	a		=			input('''Encrypt/Decrypt [e/d]: '''  )

	check_valid_key(snake_case_  )

	if resp.lower().startswith('''e'''  ):
		a		=			'''encrypt'''
		a		=			encrypt_message(snake_case_,  snake_case_  )
	elif resp.lower().startswith('''d'''  ):
		a		=			'''decrypt'''
		a		=			decrypt_message(snake_case_,  snake_case_  )

	print(f"""\n{mode.title()}ion: \n{translated}"""  )





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  None:


	"""simple docstring"""



	a		=			list(snake_case_  )
	a		=			list(snake_case_  )
	key_list.sort()
	letters_list.sort()

	if key_list != letters_list:
		sys.exit('''Error in the key or symbol set.'''  )





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  str:


	"""simple docstring"""



	return translate_message(snake_case_,  snake_case_,  '''encrypt'''  )





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  str:


	"""simple docstring"""



	return translate_message(snake_case_,  snake_case_,  '''decrypt'''  )





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_,  snake_case_  )   ->  str:


	"""simple docstring"""



	a		=			''''''
	a		=			LETTERS
	a		=			key

	if mode == "decrypt":
		a	, a		=			chars_b, chars_a

	for symbol in message:
		if symbol.upper() in chars_a:
			a		=			chars_a.find(symbol.upper()  )
			if symbol.isupper():
				translated += chars_b[sym_index].upper()
			else:
				translated += chars_b[sym_index].lower()
		else:
			translated += symbol

	return translated





def   SCREAMING_SNAKE_CASE__						(   )   ->  str:


	"""simple docstring"""



	a		=			list(snake_case_  )
	random.shuffle(snake_case_  )
	return "".join(snake_case_  )


if __name__ == "__main__":
				main()





