



"""simple docstring"""






import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


__UpperCAmelCase      :      Any   					=					'''pcuenq/k-upscaler'''
def      A      (			_A,     _A,     *, _A,     _A							):




					"""simple docstring"""
					snake_case_						:Tuple								=						{
					    # 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      A      (			_A,     *, _A,     _A							):




					"""simple docstring"""
					snake_case_						:List[Any]								=						checkpoint[F'''{attention_prefix}.qkv_proj.weight'''].chunk(3,     dim=0							)
					snake_case_						:Any								=						checkpoint[F'''{attention_prefix}.qkv_proj.bias'''].chunk(3,     dim=0							)
					snake_case_						:List[Any]								=						{
					    # 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      A      (			_A,     *, _A,     _A,     _A							):




					"""simple docstring"""
					snake_case_						:Dict								=						checkpoint[F'''{attention_prefix}.kv_proj.weight'''].chunk(2,     dim=0							)
					snake_case_						:Any								=						checkpoint[F'''{attention_prefix}.kv_proj.bias'''].chunk(2,     dim=0							)

					snake_case_						:Any								=						{
					    # 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      A      (			_A,     _A,     _A,     _A							):




					"""simple docstring"""
					snake_case_						:List[str]								=						'inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
					snake_case_						:Union[str, Any]								=						F'''{block_prefix}.{block_idx}'''

					snake_case_						:Dict								=						{}

					if not hasattr(_A,     "attentions"							):
										snake_case_						:Optional[Any]								=						1  # resnet only
					elif not block.attentions[0].add_self_attention:
										snake_case_						:Union[str, Any]								=						2  # resnet -> cross-attention
					else:
										snake_case_						:Dict								=						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}"
										snake_case_						:List[str]								=						F'''{block_type}_blocks.{block_idx}.resnets.{resnet_idx}'''
										snake_case_						:Any								=						n * resnet_idx if block_type == 'up' else n * resnet_idx + 1
										snake_case_						:Union[str, Any]								=						F'''{block_prefix}.{idx}''' if block_type == 'up' else F'''{block_prefix}.{idx}'''

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

					if hasattr(_A,     "attentions"							):
										for attention_idx, attention in enumerate(block.attentions							):
															snake_case_						:Optional[int]								=						F'''{block_type}_blocks.{block_idx}.attentions.{attention_idx}'''
															snake_case_						:Union[str, Any]								=						n * attention_idx + 1 if block_type == 'up' else n * attention_idx + 2
															snake_case_						:str								=						F'''{block_prefix}.{idx}'''
															snake_case_						:Union[str, Any]								=						F'''{block_prefix}.{idx }'''
															snake_case_						:Dict								=						1 if not attention.add_self_attention else 2
															snake_case_						:Tuple								=						(
															    n * attention_idx + cross_attention_index
															    if block_type == 'up'
															    else n * attention_idx + cross_attention_index + 1
															)
															snake_case_						:List[str]								=						F'''{block_prefix}.{idx }'''

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

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

					return diffusers_checkpoint
def      A      (			_A,     _A							):




					"""simple docstring"""
					snake_case_						:List[str]								=						{}

					# 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(_A,     _A,     _A,     "down"							)							)

					# up_blocks
					for up_block_idx, up_block in enumerate(model.up_blocks							):
										diffusers_checkpoint.update(block_to_diffusers_checkpoint(_A,     _A,     _A,     "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      A      (			_A							):




					"""simple docstring"""
					snake_case_						:List[str]								=						original_config['input_channels'] + original_config['unet_cond_dim']
					snake_case_						:Dict								=						original_config['input_channels'] + (1 if original_config['has_variance'] else 0)

					snake_case_						:Optional[int]								=						original_config['channels']

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

					snake_case_						:str								=						original_config['mapping_cond_dim']
					snake_case_						:Tuple								=						original_config['cross_cond_dim']

					snake_case_						:List[str]								=						[]
					snake_case_						:str								=						[]
					for s, c in zip(original_config["self_attn_depths"],     original_config["cross_attn_depths"]							):
										if s:
															snake_case_						:Union[str, Any]								=						'self'
															snake_case_						:Dict								=						'cross' if c else None
										elif c:
															snake_case_						:Tuple								=						'cross'
															snake_case_						:List[str]								=						None
										else:
															snake_case_						:Optional[int]								=						None
															snake_case_						:List[Any]								=						None
										attna_types.append(_A							)
										attna_types.append(_A							)

					snake_case_						:Dict								=						UNetaDConditionModel(
					    in_channels=_A,     out_channels=_A,     down_block_types=("KDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D"),     mid_block_type=_A,     up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D"),     block_out_channels=_A,     layers_per_block=_A,     act_fn="gelu",     norm_num_groups=_A,     cross_attention_dim=_A,     attention_head_dim=64,     time_cond_proj_dim=_A,     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      A      (			_A							):




					"""simple docstring"""
					snake_case_						:str								=						torch.device("cuda" if torch.cuda.is_available() else "cpu"							)

					snake_case_						:Any								=						huggingface_hub.hf_hub_download(_A,     "config_laion_text_cond_latent_upscaler_2.json"							)
					snake_case_						:List[str]								=						huggingface_hub.hf_hub_download(
					    _A,     "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"							)
					snake_case_						:List[Any]								=						K.config.load_config(open(_A							)							)['model']
					snake_case_						:Optional[Any]								=						unet_model_from_original_config(_A							)

					snake_case_						:str								=						torch.load(_A,     map_location=_A							)['model_ema']
					snake_case_						:Dict								=						unet_to_diffusers_checkpoint(_A,     _A							)

					model.load_state_dict(_A,     strict=_A							)
					model.save_pretrained(args.dump_path							)
					print(F'''saving converted unet model in {args.dump_path}'''							)


if __name__ == "__main__":
				__UpperCAmelCase      :      Any   					=					argparse.ArgumentParser()

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

				main(args)










"""simple docstring"""






def      A      (			_A,     _A							):




					"""simple docstring"""
					if not isinstance(_A,     _A							):
										raise TypeError("plaintext must be a string"							)
					if not isinstance(_A,     _A							):
										raise TypeError("key must be a string"							)

					if not plaintext:
										raise ValueError("plaintext is empty"							)
					if not key:
										raise ValueError("key is empty"							)

					key += plaintext
					snake_case_						:Optional[int]								=						plaintext.lower()
					snake_case_						:Union[str, Any]								=						key.lower()
					snake_case_						:Any								=						0
					snake_case_						:str								=						0
					snake_case_						:Union[str, Any]								=						""
					while plaintext_iterator < len(_A							):
										if (
										    ord(plaintext[plaintext_iterator]							) < 97
										    or ord(plaintext[plaintext_iterator]							) > 122
										):
															ciphertext += plaintext[plaintext_iterator]
															plaintext_iterator += 1
										elif ord(key[key_iterator]							) < 97 or ord(key[key_iterator]							) > 122:
															key_iterator += 1
										else:
															ciphertext += chr(
															    (
															        (ord(plaintext[plaintext_iterator]							) - 97 + ord(key[key_iterator]							))
															        - 97
															    )
															    % 26
															    + 97							)
															key_iterator += 1
															plaintext_iterator += 1
					return ciphertext
def      A      (			_A,     _A							):




					"""simple docstring"""
					if not isinstance(_A,     _A							):
										raise TypeError("ciphertext must be a string"							)
					if not isinstance(_A,     _A							):
										raise TypeError("key must be a string"							)

					if not ciphertext:
										raise ValueError("ciphertext is empty"							)
					if not key:
										raise ValueError("key is empty"							)

					snake_case_						:Tuple								=						key.lower()
					snake_case_						:Tuple								=						0
					snake_case_						:int								=						0
					snake_case_						:int								=						""
					while ciphertext_iterator < len(_A							):
										if (
										    ord(ciphertext[ciphertext_iterator]							) < 97
										    or ord(ciphertext[ciphertext_iterator]							) > 122
										):
															plaintext += ciphertext[ciphertext_iterator]
										else:
															plaintext += chr(
															    (ord(ciphertext[ciphertext_iterator]							) - ord(key[key_iterator]							)) % 26
															    + 97							)
															key += chr(
															    (ord(ciphertext[ciphertext_iterator]							) - ord(key[key_iterator]							)) % 26
															    + 97							)
															key_iterator += 1
										ciphertext_iterator += 1
					return plaintext


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				__UpperCAmelCase      :      Dict   					=					int(input('Type 1 to encrypt or 2 to decrypt:'))
				if operation == 1:
								__UpperCAmelCase      :      List[str]   					=					input('Typeplaintext to be encrypted:\n')
								__UpperCAmelCase      :      Optional[int]   					=					input('Type the key:\n')
								print(encrypt(plaintext, key))
				elif operation == 2:
								__UpperCAmelCase      :      List[Any]   					=					input('Type the ciphertext to be decrypted:\n')
								__UpperCAmelCase      :      Dict   					=					input('Type the key:\n')
								print(decrypt(ciphertext, key))
				decrypt('jsqqs avvwo', 'coffee')

