





'''simple docstring'''







from binascii import hexlify
from hashlib import shaaaa
from os import urandom

# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526

lowerCAmelCase_					:    Any      	=			{
    # 1536-bit
    5: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
            + '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
            + 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
            + 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
            + 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
            + 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
            + '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
    # 2048-bit
    14: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
            + '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
            + 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
            + 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
            + 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
            + 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
            + '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
            + 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
            + 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
            + '15728E5A8AACAA68FFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
    # 3072-bit
    15: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
            + '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
            + 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
            + 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
            + 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
            + 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
            + '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
            + 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
            + 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
            + '15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'
            + 'ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'
            + 'ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'
            + 'F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
            + 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'
            + '43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
    # 4096-bit
    16: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
            + '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
            + 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
            + 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
            + 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
            + 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
            + '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
            + 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
            + 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
            + '15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'
            + 'ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'
            + 'ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'
            + 'F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
            + 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'
            + '43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'
            + '88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'
            + '2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'
            + '287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'
            + '1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'
            + '93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199'
            + 'FFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
    # 6144-bit
    17: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08'
            + '8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B'
            + '302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9'
            + 'A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6'
            + '49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8'
            + 'FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C'
            + '180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718'
            + '3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D'
            + '04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D'
            + 'B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226'
            + '1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
            + 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC'
            + 'E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26'
            + '99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB'
            + '04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2'
            + '233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127'
            + 'D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'
            + '36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406'
            + 'AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918'
            + 'DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151'
            + '2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03'
            + 'F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F'
            + 'BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'
            + 'CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B'
            + 'B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632'
            + '387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E'
            + '6DCC4024FFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
    # 8192-bit
    18: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
            + '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
            + 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
            + 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
            + 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
            + 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
            + '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
            + 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
            + 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
            + '15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'
            + 'ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'
            + 'ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'
            + 'F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
            + 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'
            + '43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'
            + '88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'
            + '2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'
            + '287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'
            + '1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'
            + '93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'
            + '36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD'
            + 'F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831'
            + '179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B'
            + 'DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF'
            + '5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6'
            + 'D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3'
            + '23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'
            + 'CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328'
            + '06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C'
            + 'DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE'
            + '12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4'
            + '38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300'
            + '741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568'
            + '3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9'
            + '22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B'
            + '4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A'
            + '062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36'
            + '4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1'
            + 'B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92'
            + '4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47'
            + '9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71'
            + '60C980DD98EDD3DFFFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
}
class 				__SCREAMING_SNAKE_CASE  :






				"""simple docstring"""







				def __init__(  self     :					Any					,				__a     :					int = 14					):
								if group not in primes:
												raise ValueError("Unsupported Group"					)
								_a								= primes[group]["prime"]
								_a								= primes[group]["generator"]

								_a								= int(hexlify(urandom(32					)					)					,				base=16					)




				def   UpperCamelCase__   (  self     :					Dict					):
								return hex(self.__private_key					)[2:]




				def   UpperCamelCase__   (  self     :					Any					):
								_a								= pow(self.generator					,				self.__private_key					,				self.prime					)
								return hex(__a					)[2:]




				def   UpperCamelCase__   (  self     :					Optional[Any]					,				__a     :					int					):
								# check if the other public key is valid based on NIST SP800-56
								return (
								    2 <= key <= self.prime - 2
								    and pow(__a					,				(self.prime - 1) // 2					,				self.prime					) == 1
								)




				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					str					):
								_a								= int(__a					,				base=16					)
								if not self.is_valid_public_key(__a					):
												raise ValueError("Invalid public key"					)
								_a								= pow(__a					,				self.__private_key					,				self.prime					)
								return shaaaa(str(__a					).encode()					).hexdigest()




				@staticmethod
				def   UpperCamelCase__   (  __a     :					int					,				__a     :					int					):
								# check if the other public key is valid based on NIST SP800-56
								return (
								    2 <= remote_public_key_str <= prime - 2
								    and pow(__a					,				(prime - 1) // 2					,				__a					) == 1
								)




				@staticmethod
				def   UpperCamelCase__   (  __a     :					str					,				__a     :					str					,				__a     :					int = 14					):
								_a								= int(__a					,				base=16					)
								_a								= int(__a					,				base=16					)
								_a								= primes[group]["prime"]
								if not DiffieHellman.is_valid_public_key_static(__a					,				__a					):
												raise ValueError("Invalid public key"					)
								_a								= pow(__a					,				__a					,				__a					)
								return shaaaa(str(__a					).encode()					).hexdigest()


if __name__ == "__main__":
					import doctest

					doctest.testmod()







'''simple docstring'''

import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__lowerCAmelCase							:	List[str]			={
    "sample_size": 32,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 1000,
    "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",
}

__lowerCAmelCase							:	Dict			={
    "sample_size": 64,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 1000,
    "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",
}

__lowerCAmelCase							:	Union[str, Any]			={
    "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",
}

__lowerCAmelCase							:	str			={
    "num_train_timesteps": 40,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

__lowerCAmelCase							:	Tuple			={
    "num_train_timesteps": 201,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

__lowerCAmelCase							:	Dict			={
    "num_train_timesteps": 151,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}





def 						UpperCamelCase				(							_lowerCamelCase					:					Tuple       ):
    if isinstance(_lowerCamelCase						,       _lowerCamelCase       ):
        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 						UpperCamelCase				(							_lowerCamelCase					:					List[str]						,       _lowerCamelCase					:					List[str]						,       _lowerCamelCase					:					Optional[int]						,       _lowerCamelCase					:					Dict						,       _lowerCamelCase					:					Optional[int]=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 						UpperCamelCase				(							_lowerCamelCase					:					str						,       _lowerCamelCase					:					Any						,       _lowerCamelCase					:					Tuple						,       _lowerCamelCase					:					Optional[int]						,       _lowerCamelCase					:					List[Any]=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 						UpperCamelCase				(							_lowerCamelCase					:					str						,       _lowerCamelCase					:					List[str]       ):
    A__				=			torch.load(_lowerCamelCase						,       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(_lowerCamelCase       ):
        A__				=			channels_list[i]
        A__				=			current_channels != prev_channels
        if layer_type == "ResnetDownsampleBlock2D":
            for j in range(_lowerCamelCase       ):
                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(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       has_skip=_lowerCamelCase       )
                current_layer += 1

        elif layer_type == "AttnDownBlock2D":
            for j in range(_lowerCamelCase       ):
                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(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       has_skip=_lowerCamelCase       )
                A__				=			F"down_blocks.{i}.attentions.{j}"
                A__				=			F"input_blocks.{current_layer}.1"
                A__				=			convert_attention(
                    _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )
                current_layer += 1

        if i != len(_lowerCamelCase       ) - 1:
            A__				=			F"down_blocks.{i}.downsamplers.0"
            A__				=			F"input_blocks.{current_layer}.0"
            A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )
            current_layer += 1

        A__				=			current_channels

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

    A__				=			0
    A__				=			unet_config["up_block_types"]

    for i, layer_type in enumerate(_lowerCamelCase       ):
        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(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       has_skip=_lowerCamelCase       )
                current_layer += 1

            if i != len(_lowerCamelCase       ) - 1:
                A__				=			F"up_blocks.{i}.upsamplers.0"
                A__				=			F"output_blocks.{current_layer-1}.1"
                A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )
        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(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       has_skip=_lowerCamelCase       )
                A__				=			F"up_blocks.{i}.attentions.{j}"
                A__				=			F"output_blocks.{current_layer}.1"
                A__				=			convert_attention(
                    _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )
                current_layer += 1

            if i != len(_lowerCamelCase       ) - 1:
                A__				=			F"up_blocks.{i}.upsamplers.0"
                A__				=			F"output_blocks.{current_layer-1}.2"
                A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )

    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__":
  __lowerCAmelCase							:	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.")

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

  __lowerCAmelCase							:	List[str]			=os.path.basename(args.unet_path)
  print(f"""Checkpoint: {ckpt_name}""")

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

  if not args.class_cond:
    __lowerCAmelCase							:	Dict			=None

  __lowerCAmelCase							:	Optional[int]			=con_pt_to_diffuser(args.unet_path, unet_config)

  __lowerCAmelCase							:	Dict			=UNetaDModel(**unet_config)
  image_unet.load_state_dict(converted_unet_ckpt)

  # Get scheduler config
  if "cd" in ckpt_name or "test" in ckpt_name:
    __lowerCAmelCase							:	List[str]			=CD_SCHEDULER_CONFIG
  elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
    __lowerCAmelCase							:	Dict			=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)):
    __lowerCAmelCase							:	Dict			=CT_LSUN_256_SCHEDULER_CONFIG
  else:
    raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

  __lowerCAmelCase							:	Dict			=CMStochasticIterativeScheduler(**scheduler_config)

  __lowerCAmelCase							:	str			=ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
  consistency_model.save_pretrained(args.dump_path)



