






"""simple docstring"""






import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


SCREAMING_SNAKE_CASE    :    Tuple        =							{
    '''sample_size''': 32,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1_000,
    '''block_out_channels''': [32, 64],
    '''attention_head_dim''': 8,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

SCREAMING_SNAKE_CASE    :    Union[str, Any]        =							{
    '''sample_size''': 64,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1_000,
    '''block_out_channels''': [192, 192 * 2, 192 * 3, 192 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

SCREAMING_SNAKE_CASE    :    Tuple        =							{
    '''sample_size''': 256,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''default''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

SCREAMING_SNAKE_CASE    :    Tuple        =							{
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

SCREAMING_SNAKE_CASE    :    Union[str, Any]        =							{
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

SCREAMING_SNAKE_CASE    :    Tuple        =							{
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}



def 			__lowerCamelCase       (       lowerCAmelCase__					):






				if isinstance(_a							,_a					):
								return v
				if v.lower() in ("yes", "true", "t", "y", "1"):
								return True
				elif v.lower() in ("no", "false", "f", "n", "0"):
								return False
				else:
								raise argparse.ArgumentTypeError('boolean value expected'					)



def 			__lowerCamelCase       (       lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__=False					):
				A__							=							checkpoint[f'''{old_prefix}.in_layers.0.weight''']
				A__							=							checkpoint[f'''{old_prefix}.in_layers.0.bias''']
				A__							=							checkpoint[f'''{old_prefix}.in_layers.2.weight''']
				A__							=							checkpoint[f'''{old_prefix}.in_layers.2.bias''']
				A__							=							checkpoint[f'''{old_prefix}.emb_layers.1.weight''']
				A__							=							checkpoint[f'''{old_prefix}.emb_layers.1.bias''']
				A__							=							checkpoint[f'''{old_prefix}.out_layers.0.weight''']
				A__							=							checkpoint[f'''{old_prefix}.out_layers.0.bias''']
				A__							=							checkpoint[f'''{old_prefix}.out_layers.3.weight''']
				A__							=							checkpoint[f'''{old_prefix}.out_layers.3.bias''']

				if has_skip:
								A__							=							checkpoint[f'''{old_prefix}.skip_connection.weight''']
								A__							=							checkpoint[f'''{old_prefix}.skip_connection.bias''']

				return new_checkpoint



def 			__lowerCamelCase       (       lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__=None					):
				A__			,      A__			,      A__							=							checkpoint[f'''{old_prefix}.qkv.weight'''].chunk(3							,dim=0					)
				A__			,      A__			,      A__							=							checkpoint[f'''{old_prefix}.qkv.bias'''].chunk(3							,dim=0					)

				A__							=							checkpoint[f'''{old_prefix}.norm.weight''']
				A__							=							checkpoint[f'''{old_prefix}.norm.bias''']

				A__							=							weight_q.squeeze(-1					).squeeze(-1					)
				A__							=							bias_q.squeeze(-1					).squeeze(-1					)
				A__							=							weight_k.squeeze(-1					).squeeze(-1					)
				A__							=							bias_k.squeeze(-1					).squeeze(-1					)
				A__							=							weight_v.squeeze(-1					).squeeze(-1					)
				A__							=							bias_v.squeeze(-1					).squeeze(-1					)

				A__							=							(
				    checkpoint[f'''{old_prefix}.proj_out.weight'''].squeeze(-1					).squeeze(-1					)
				)
				A__							=							checkpoint[f'''{old_prefix}.proj_out.bias'''].squeeze(-1					).squeeze(-1					)

				return new_checkpoint



def 			__lowerCamelCase       (       lowerCAmelCase__							,lowerCAmelCase__					):
				A__							=							torch.load(_a							,map_location='cpu'					)
				A__							=							{}

				A__							=							checkpoint['time_embed.0.weight']
				A__							=							checkpoint['time_embed.0.bias']
				A__							=							checkpoint['time_embed.2.weight']
				A__							=							checkpoint['time_embed.2.bias']

				if unet_config["num_class_embeds"] is not None:
								A__							=							checkpoint['label_emb.weight']

				A__							=							checkpoint['input_blocks.0.0.weight']
				A__							=							checkpoint['input_blocks.0.0.bias']

				A__							=							unet_config['down_block_types']
				A__							=							unet_config['layers_per_block']
				A__							=							unet_config['attention_head_dim']
				A__							=							unet_config['block_out_channels']
				A__							=							1
				A__							=							channels_list[0]

				for i, layer_type in enumerate(_a					):
								A__							=							channels_list[i]
								A__							=							current_channels != prev_channels
								if layer_type == "ResnetDownsampleBlock2D":
												for j in range(_a					):
																A__							=							f'''down_blocks.{i}.resnets.{j}'''
																A__							=							f'''input_blocks.{current_layer}.0'''
																A__							=							True if j == 0 and downsample_block_has_skip else False
																A__							=							convert_resnet(_a							,_a							,_a							,_a							,has_skip=_a					)
																current_layer += 1

								elif layer_type == "AttnDownBlock2D":
												for j in range(_a					):
																A__							=							f'''down_blocks.{i}.resnets.{j}'''
																A__							=							f'''input_blocks.{current_layer}.0'''
																A__							=							True if j == 0 and downsample_block_has_skip else False
																A__							=							convert_resnet(_a							,_a							,_a							,_a							,has_skip=_a					)
																A__							=							f'''down_blocks.{i}.attentions.{j}'''
																A__							=							f'''input_blocks.{current_layer}.1'''
																A__							=							convert_attention(
																    _a							,_a							,_a							,_a							,_a					)
																current_layer += 1

								if i != len(_a					) - 1:
												A__							=							f'''down_blocks.{i}.downsamplers.0'''
												A__							=							f'''input_blocks.{current_layer}.0'''
												A__							=							convert_resnet(_a							,_a							,_a							,_a					)
												current_layer += 1

								A__							=							current_channels

				# hardcoded the mid-block for now
				A__							=							'mid_block.resnets.0'
				A__							=							'middle_block.0'
				A__							=							convert_resnet(_a							,_a							,_a							,_a					)
				A__							=							'mid_block.attentions.0'
				A__							=							'middle_block.1'
				A__							=							convert_attention(_a							,_a							,_a							,_a							,_a					)
				A__							=							'mid_block.resnets.1'
				A__							=							'middle_block.2'
				A__							=							convert_resnet(_a							,_a							,_a							,_a					)

				A__							=							0
				A__							=							unet_config['up_block_types']

				for i, layer_type in enumerate(_a					):
								if layer_type == "ResnetUpsampleBlock2D":
												for j in range(layers_per_block + 1					):
																A__							=							f'''up_blocks.{i}.resnets.{j}'''
																A__							=							f'''output_blocks.{current_layer}.0'''
																A__							=							convert_resnet(_a							,_a							,_a							,_a							,has_skip=_a					)
																current_layer += 1

												if i != len(_a					) - 1:
																A__							=							f'''up_blocks.{i}.upsamplers.0'''
																A__							=							f'''output_blocks.{current_layer-1}.1'''
																A__							=							convert_resnet(_a							,_a							,_a							,_a					)
								elif layer_type == "AttnUpBlock2D":
												for j in range(layers_per_block + 1					):
																A__							=							f'''up_blocks.{i}.resnets.{j}'''
																A__							=							f'''output_blocks.{current_layer}.0'''
																A__							=							convert_resnet(_a							,_a							,_a							,_a							,has_skip=_a					)
																A__							=							f'''up_blocks.{i}.attentions.{j}'''
																A__							=							f'''output_blocks.{current_layer}.1'''
																A__							=							convert_attention(
																    _a							,_a							,_a							,_a							,_a					)
																current_layer += 1

												if i != len(_a					) - 1:
																A__							=							f'''up_blocks.{i}.upsamplers.0'''
																A__							=							f'''output_blocks.{current_layer-1}.2'''
																A__							=							convert_resnet(_a							,_a							,_a							,_a					)

				A__							=							checkpoint['out.0.weight']
				A__							=							checkpoint['out.0.bias']
				A__							=							checkpoint['out.2.weight']
				A__							=							checkpoint['out.2.bias']

				return new_checkpoint


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE    :    List[Any]        =							argparse.ArgumentParser()

					parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
					parser.add_argument(
					    '''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
					)
					parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')

					SCREAMING_SNAKE_CASE    :    List[Any]        =							parser.parse_args()
					SCREAMING_SNAKE_CASE    :    Optional[int]        =							strabool(args.class_cond)

					SCREAMING_SNAKE_CASE    :    Union[str, Any]        =							os.path.basename(args.unet_path)
					print(f'''Checkpoint: {ckpt_name}''')

					# Get U-Net config
					if "imagenet64" in ckpt_name:
										SCREAMING_SNAKE_CASE    :    List[str]        =							IMAGENET_64_UNET_CONFIG
					elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
										SCREAMING_SNAKE_CASE    :    Union[str, Any]        =							LSUN_256_UNET_CONFIG
					elif "test" in ckpt_name:
										SCREAMING_SNAKE_CASE    :    str        =							TEST_UNET_CONFIG
					else:
										raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

					if not args.class_cond:
										SCREAMING_SNAKE_CASE    :    int        =							None

					SCREAMING_SNAKE_CASE    :    List[str]        =							con_pt_to_diffuser(args.unet_path, unet_config)

					SCREAMING_SNAKE_CASE    :    int        =							UNetaDModel(**unet_config)
					image_unet.load_state_dict(converted_unet_ckpt)

					# Get scheduler config
					if "cd" in ckpt_name or "test" in ckpt_name:
										SCREAMING_SNAKE_CASE    :    List[Any]        =							CD_SCHEDULER_CONFIG
					elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
										SCREAMING_SNAKE_CASE    :    Optional[int]        =							CT_IMAGENET_64_SCHEDULER_CONFIG
					elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
										SCREAMING_SNAKE_CASE    :    Optional[Any]        =							CT_LSUN_256_SCHEDULER_CONFIG
					else:
										raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

					SCREAMING_SNAKE_CASE    :    List[str]        =							CMStochasticIterativeScheduler(**scheduler_config)

					SCREAMING_SNAKE_CASE    :    Union[str, Any]        =							ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
					consistency_model.save_pretrained(args.dump_path)





"""simple docstring"""


import random





class lowerCamelCase__   :



    '''simple docstring'''




    @staticmethod
    def UpperCamelCase__		( lowerCamelCase_    )    ->      tuple[list[int], list[int]]:
          A				  =		[ord(lowerCamelCase_    ) for i in text]
          A				  =		[]
          A				  =		[]
          for i in plain:
                A				  =		random.randint(1 ,3_0_0    )
                A				  =		(i + k) * k
                cipher.append(lowerCamelCase_    )
                key.append(lowerCamelCase_    )
          return cipher, key




    @staticmethod
    def UpperCamelCase__		( lowerCamelCase_ ,lowerCamelCase_    )    ->      str:
          A				  =		[]
          for i in range(len(lowerCamelCase_    )    ):
                A				  =		int((cipher[i] - (key[i]) ** 2) / key[i]    )
                plain.append(chr(lowerCamelCase_    )    )
          return "".join(lowerCamelCase_    )


if __name__ == "__main__":
  UpperCAmelCase							,			UpperCAmelCase													=Onepad().encrypt("Hello")
  print(c, k)
  print(Onepad().decrypt(c, k))




