



from math import sqrt


def  __lowerCamelCase (       lowerCamelCase__						):

    """simple docstring"""


    assert isinstance(lowerCamelCase__					,			lowerCamelCase__						) and (
        number >= 0
    ), "'number' must been an int and positive"

    lowercase__					:   List[Any]													=					True

    # 0 and 1 are none primes.
    if number <= 1:
        lowercase__					:   str													=					False

    for divisor in range(2					,			int(round(sqrt(lowerCamelCase__						)						)						) + 1						):
        # if 'number' divisible by 'divisor' then sets 'status'
        # of false and break up the loop.
        if number % divisor == 0:
            lowercase__					:   Union[str, Any]													=					False
            break

    # precondition
    assert isinstance(lowerCamelCase__					,			lowerCamelCase__						), "'status' must been from type bool"

    return status


def  __lowerCamelCase (       lowerCamelCase__						):

    """simple docstring"""


    assert isinstance(lowerCamelCase__					,			lowerCamelCase__						) and (n > 2), "'N' must been an int and > 2"

    # beginList: contains all natural numbers from 2 up to N
    lowercase__					:   List[str]													=					list(range(2					,			n + 1						)						)

    lowercase__					:   str													=					[]  # this list will be returns.

    # actual sieve of erathostenes
    for i in range(len(lowerCamelCase__						)						):
        for j in range(i + 1					,			len(lowerCamelCase__						)						):
            if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
                lowercase__					:   Tuple													=					0

    # filters actual prime numbers.
    lowercase__					:   Tuple													=					[x for x in begin_list if x != 0]

    # precondition
    assert isinstance(lowerCamelCase__					,			lowerCamelCase__						), "'ans' must been from type list"

    return ans


def  __lowerCamelCase (       lowerCamelCase__						):

    """simple docstring"""


    assert isinstance(lowerCamelCase__					,			lowerCamelCase__						) and (n > 2), "'N' must been an int and > 2"

    lowercase__					:   List[str]													=					[]

    # iterates over all numbers between 2 up to N+1
    # if a number is prime then appends to list 'ans'
    for number in range(2					,			n + 1						):
        if is_prime(lowerCamelCase__						):
            ans.append(lowerCamelCase__						)

    # precondition
    assert isinstance(lowerCamelCase__					,			lowerCamelCase__						), "'ans' must been from type list"

    return ans


def  __lowerCamelCase (       lowerCamelCase__						):

    """simple docstring"""


    assert isinstance(lowerCamelCase__					,			lowerCamelCase__						) and number >= 0, "'number' must been an int and >= 0"

    lowercase__					:   List[Any]													=					[]  # this list will be returns of the function.

    # potential prime number factors.

    lowercase__					:   Optional[Any]													=					2

    lowercase__					:   Optional[Any]													=					number

    if number == 0 or number == 1:
        ans.append(lowerCamelCase__						)

    # if 'number' not prime then builds the prime factorization of 'number'
    elif not is_prime(lowerCamelCase__						):
        while quotient != 1:
            if is_prime(lowerCamelCase__						) and (quotient % factor == 0):
                ans.append(lowerCamelCase__						)
                quotient /= factor
            else:
                factor += 1

    else:
        ans.append(lowerCamelCase__						)

    # precondition
    assert isinstance(lowerCamelCase__					,			lowerCamelCase__						), "'ans' must been from type list"

    return ans


def  __lowerCamelCase (       lowerCamelCase__						):

    """simple docstring"""


    assert isinstance(lowerCamelCase__					,			lowerCamelCase__						) and (
        number >= 0
    ), "'number' bust been an int and >= 0"

    lowercase__					:   str													=					0

    # prime factorization of 'number'
    lowercase__					:   Any													=					prime_factorization(lowerCamelCase__						)

    lowercase__					:   Optional[int]													=					max(lowerCamelCase__						)

    # precondition
    assert isinstance(lowerCamelCase__					,			lowerCamelCase__						), "'ans' must been from type int"

    return ans


def  __lowerCamelCase (       lowerCamelCase__						):

    """simple docstring"""


    assert isinstance(lowerCamelCase__					,			lowerCamelCase__						) and (
        number >= 0
    ), "'number' bust been an int and >= 0"

    lowercase__					:   Union[str, Any]													=					0

    # prime factorization of 'number'
    lowercase__					:   Any													=					prime_factorization(lowerCamelCase__						)

    lowercase__					:   Optional[Any]													=					min(lowerCamelCase__						)

    # precondition
    assert isinstance(lowerCamelCase__					,			lowerCamelCase__						), "'ans' must been from type int"

    return ans


def  __lowerCamelCase (       lowerCamelCase__						):

    """simple docstring"""


    assert isinstance(lowerCamelCase__					,			lowerCamelCase__						), "'number' must been an int"
    assert isinstance(number % 2 == 0					,			lowerCamelCase__						), "compare bust been from type bool"

    return number % 2 == 0


def  __lowerCamelCase (       lowerCamelCase__						):

    """simple docstring"""


    assert isinstance(lowerCamelCase__					,			lowerCamelCase__						), "'number' must been an int"
    assert isinstance(number % 2 != 0					,			lowerCamelCase__						), "compare bust been from type bool"

    return number % 2 != 0


def  __lowerCamelCase (       lowerCamelCase__						):

    """simple docstring"""


    assert (
        isinstance(lowerCamelCase__					,			lowerCamelCase__						) and (number > 2) and is_even(lowerCamelCase__						)
    ), "'number' must been an int, even and > 2"

    lowercase__					:   Dict													=					[]  # this list will returned

    # creates a list of prime numbers between 2 up to 'number'
    lowercase__					:   int													=					get_prime_numbers(lowerCamelCase__						)
    lowercase__					:   Tuple													=					len(lowerCamelCase__						)

    # run variable for while-loops.
    lowercase__					:   Union[str, Any]													=					0
    lowercase__					:   Tuple													=					None

    # exit variable. for break up the loops
    lowercase__					:   List[Any]													=					True

    while i < len_pn and loop:
        lowercase__					:   List[Any]													=					i + 1

        while j < len_pn and loop:
            if prime_numbers[i] + prime_numbers[j] == number:
                lowercase__					:   List[Any]													=					False
                ans.append(prime_numbers[i]						)
                ans.append(prime_numbers[j]						)

            j += 1

        i += 1

    # precondition
    assert (
        isinstance(lowerCamelCase__					,			lowerCamelCase__						)
        and (len(lowerCamelCase__						) == 2)
        and (ans[0] + ans[1] == number)
        and is_prime(ans[0]						)
        and is_prime(ans[1]						)
    ), "'ans' must contains two primes. And sum of elements must been eq 'number'"

    return ans


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__						):

    """simple docstring"""


    assert (
        isinstance(lowerCamelCase__					,			lowerCamelCase__						)
        and isinstance(lowerCamelCase__					,			lowerCamelCase__						)
        and (numbera >= 0)
        and (numbera >= 0)
    ), "'number1' and 'number2' must been positive integer."

    lowercase__					:   int													=					0

    while numbera != 0:
        lowercase__					:   int													=					numbera % numbera
        lowercase__					:   Dict													=					numbera
        lowercase__					:   Optional[Any]													=					rest

    # precondition
    assert isinstance(lowerCamelCase__					,			lowerCamelCase__						) and (
        numbera >= 0
    ), "'number' must been from type int and positive"

    return numbera


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__						):

    """simple docstring"""


    assert (
        isinstance(lowerCamelCase__					,			lowerCamelCase__						)
        and isinstance(lowerCamelCase__					,			lowerCamelCase__						)
        and (numbera >= 1)
        and (numbera >= 1)
    ), "'number1' and 'number2' must been positive integer."

    lowercase__					:   Any													=					1  # actual answer that will be return.

    # for kgV (x,1)
    if numbera > 1 and numbera > 1:
        # builds the prime factorization of 'number1' and 'number2'
        lowercase__					:   Any													=					prime_factorization(lowerCamelCase__						)
        lowercase__					:   str													=					prime_factorization(lowerCamelCase__						)

    elif numbera == 1 or numbera == 1:
        lowercase__					:   Any													=					[]
        lowercase__					:   Optional[Any]													=					[]
        lowercase__					:   Dict													=					max(lowerCamelCase__					,			lowerCamelCase__						)

    lowercase__					:   Optional[Any]													=					0
    lowercase__					:   str													=					0

    lowercase__					:   Optional[Any]													=					[]  # captured numbers int both 'primeFac1' and 'primeFac2'

    # iterates through primeFac1
    for n in prime_fac_a:
        if n not in done:
            if n in prime_fac_a:
                lowercase__					:   int													=					prime_fac_a.count(lowerCamelCase__						)
                lowercase__					:   List[Any]													=					prime_fac_a.count(lowerCamelCase__						)

                for _ in range(max(lowerCamelCase__					,			lowerCamelCase__						)						):
                    ans *= n

            else:
                lowercase__					:   Any													=					prime_fac_a.count(lowerCamelCase__						)

                for _ in range(lowerCamelCase__						):
                    ans *= n

            done.append(lowerCamelCase__						)

    # iterates through primeFac2
    for n in prime_fac_a:
        if n not in done:
            lowercase__					:   Optional[Any]													=					prime_fac_a.count(lowerCamelCase__						)

            for _ in range(lowerCamelCase__						):
                ans *= n

            done.append(lowerCamelCase__						)

    # precondition
    assert isinstance(lowerCamelCase__					,			lowerCamelCase__						) and (
        ans >= 0
    ), "'ans' must been from type int and positive"

    return ans


def  __lowerCamelCase (       lowerCamelCase__						):

    """simple docstring"""


    assert isinstance(lowerCamelCase__					,			lowerCamelCase__						) and (n >= 0), "'number' must been a positive int"

    lowercase__					:   Any													=					0
    lowercase__					:   Optional[int]													=					2  # this variable holds the answer

    while index < n:
        index += 1

        ans += 1  # counts to the next number

        # if ans not prime then
        # runs to the next prime number.
        while not is_prime(lowerCamelCase__						):
            ans += 1

    # precondition
    assert isinstance(lowerCamelCase__					,			lowerCamelCase__						) and is_prime(
        lowerCamelCase__						), "'ans' must been a prime number and from type int"

    return ans


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__						):

    """simple docstring"""


    assert (
        is_prime(lowerCamelCase__						) and is_prime(lowerCamelCase__						) and (p_number_a < p_number_a)
    ), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"

    lowercase__					:   Any													=					p_number_a + 1  # jump to the next number

    lowercase__					:   Tuple													=					[]  # this list will be returns.

    # if number is not prime then
    # fetch the next prime number.
    while not is_prime(lowerCamelCase__						):
        number += 1

    while number < p_number_a:
        ans.append(lowerCamelCase__						)

        number += 1

        # fetch the next prime number.
        while not is_prime(lowerCamelCase__						):
            number += 1

    # precondition
    assert (
        isinstance(lowerCamelCase__					,			lowerCamelCase__						)
        and ans[0] != p_number_a
        and ans[len(lowerCamelCase__						) - 1] != p_number_a
    ), "'ans' must been a list without the arguments"

    # 'ans' contains not 'pNumber1' and 'pNumber2' !
    return ans


def  __lowerCamelCase (       lowerCamelCase__						):

    """simple docstring"""


    assert isinstance(lowerCamelCase__					,			lowerCamelCase__						) and (n >= 1), "'n' must been int and >= 1"

    lowercase__					:   int													=					[]  # will be returned.

    for divisor in range(1					,			n + 1						):
        if n % divisor == 0:
            ans.append(lowerCamelCase__						)

    # precondition
    assert ans[0] == 1 and ans[len(lowerCamelCase__						) - 1] == n, "Error in function getDivisiors(...)"

    return ans


def  __lowerCamelCase (       lowerCamelCase__						):

    """simple docstring"""


    assert isinstance(lowerCamelCase__					,			lowerCamelCase__						) and (
        number > 1
    ), "'number' must been an int and >= 1"

    lowercase__					:   Optional[int]													=					get_divisors(lowerCamelCase__						)

    # precondition
    assert (
        isinstance(lowerCamelCase__					,			lowerCamelCase__						)
        and (divisors[0] == 1)
        and (divisors[len(lowerCamelCase__						) - 1] == number)
    ), "Error in help-function getDivisiors(...)"

    # summed all divisors up to 'number' (exclusive), hence [:-1]
    return sum(divisors[:-1]						) == number


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__						):

    """simple docstring"""


    assert (
        isinstance(lowerCamelCase__					,			lowerCamelCase__						)
        and isinstance(lowerCamelCase__					,			lowerCamelCase__						)
        and (denominator != 0)
    ), "The arguments must been from type int and 'denominator' != 0"

    # build the greatest common divisor of numerator and denominator.
    lowercase__					:   Union[str, Any]													=					gcd(abs(lowerCamelCase__						)					,			abs(lowerCamelCase__						)						)

    # precondition
    assert (
        isinstance(lowerCamelCase__					,			lowerCamelCase__						)
        and (numerator % gcd_of_fraction == 0)
        and (denominator % gcd_of_fraction == 0)
    ), "Error in function gcd(...,...)"

    return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)


def  __lowerCamelCase (       lowerCamelCase__						):

    """simple docstring"""


    assert isinstance(lowerCamelCase__					,			lowerCamelCase__						) and (n >= 0), "'n' must been a int and >= 0"

    lowercase__					:   Any													=					1  # this will be return.

    for factor in range(1					,			n + 1						):
        ans *= factor

    return ans


def  __lowerCamelCase (       lowerCamelCase__						):

    """simple docstring"""


    assert isinstance(lowerCamelCase__					,			lowerCamelCase__						) and (n >= 0), "'n' must been an int and >= 0"

    lowercase__					:   List[Any]													=					0
    lowercase__					:   int													=					1
    lowercase__					:   Union[str, Any]													=					1  # this will be return

    for _ in range(n - 1						):
        lowercase__					:   int													=					ans
        ans += fiba
        lowercase__					:   Optional[int]													=					tmp

    return ans









import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__=1						):

				"""simple docstring"""


				if n_shave_prefix_segments >= 0:
								return ".".join(path.split("."						)[n_shave_prefix_segments:]						)
				else:
								return ".".join(path.split("."						)[:n_shave_prefix_segments]						)


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__=0						):

				"""simple docstring"""


				lowercase__					:   List[str]													=					[]
				for old_item in old_list:
								lowercase__					:   Optional[Any]													=					old_item.replace("in_layers.0"					,			"norm1"						)
								lowercase__					:   Union[str, Any]													=					new_item.replace("in_layers.2"					,			"conv1"						)

								lowercase__					:   Optional[Any]													=					new_item.replace("out_layers.0"					,			"norm2"						)
								lowercase__					:   Union[str, Any]													=					new_item.replace("out_layers.3"					,			"conv2"						)

								lowercase__					:   Dict													=					new_item.replace("emb_layers.1"					,			"time_emb_proj"						)
								lowercase__					:   int													=					new_item.replace("skip_connection"					,			"conv_shortcut"						)

								lowercase__					:   Tuple													=					shave_segments(lowerCamelCase__					,			n_shave_prefix_segments=lowerCamelCase__						)

								mapping.append({"old": old_item, "new": new_item}						)

				return mapping


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__=0						):

				"""simple docstring"""


				lowercase__					:   str													=					[]
				for old_item in old_list:
								lowercase__					:   Optional[int]													=					old_item

								lowercase__					:   Dict													=					new_item.replace("norm.weight"					,			"group_norm.weight"						)
								lowercase__					:   Optional[int]													=					new_item.replace("norm.bias"					,			"group_norm.bias"						)

								lowercase__					:   Tuple													=					new_item.replace("proj_out.weight"					,			"proj_attn.weight"						)
								lowercase__					:   List[Any]													=					new_item.replace("proj_out.bias"					,			"proj_attn.bias"						)

								lowercase__					:   Optional[Any]													=					shave_segments(lowerCamelCase__					,			n_shave_prefix_segments=lowerCamelCase__						)

								mapping.append({"old": old_item, "new": new_item}						)

				return mapping


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__=None					,			lowerCamelCase__=None					,			lowerCamelCase__=None						):

				"""simple docstring"""


				assert isinstance(lowerCamelCase__					,			lowerCamelCase__						), "Paths should be a list of dicts containing 'old' and 'new' keys."

				# Splits the attention layers into three variables.
				if attention_paths_to_split is not None:
								for path, path_map in attention_paths_to_split.items():
												lowercase__					:   List[str]													=					old_checkpoint[path]
												lowercase__					:   str													=					old_tensor.shape[0] // 3

												lowercase__					:   List[str]													=					(-1, channels) if len(old_tensor.shape						) == 3 else (-1)

												lowercase__					:   Union[str, Any]													=					old_tensor.shape[0] // config["num_head_channels"] // 3

												lowercase__					:   Union[str, Any]													=					old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]						)
												lowercase__							,							lowercase__							,							lowercase__					:   Optional[Any]													=					old_tensor.split(channels // num_heads					,			dim=1						)

												lowercase__					:   Dict													=					query.reshape(lowerCamelCase__						)
												lowercase__					:   Dict													=					key.reshape(lowerCamelCase__						)
												lowercase__					:   int													=					value.reshape(lowerCamelCase__						)

				for path in paths:
								lowercase__					:   Union[str, Any]													=					path["new"]

								# These have already been assigned
								if attention_paths_to_split is not None and new_path in attention_paths_to_split:
												continue

								# Global renaming happens here
								lowercase__					:   List[Any]													=					new_path.replace("middle_block.0"					,			"mid_block.resnets.0"						)
								lowercase__					:   Optional[Any]													=					new_path.replace("middle_block.1"					,			"mid_block.attentions.0"						)
								lowercase__					:   List[str]													=					new_path.replace("middle_block.2"					,			"mid_block.resnets.1"						)

								if additional_replacements is not None:
												for replacement in additional_replacements:
																lowercase__					:   Tuple													=					new_path.replace(replacement["old"]					,			replacement["new"]						)

        # proj_attn.weight has to be converted from conv 1D to linear
								if "proj_attn.weight" in new_path:
												lowercase__					:   List[Any]													=					old_checkpoint[path["old"]][:, :, 0]
								else:
												lowercase__					:   List[Any]													=					old_checkpoint[path["old"]]


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Union[str, Any]													=					{}

				lowercase__					:   Optional[Any]													=					checkpoint["time_embed.0.weight"]
				lowercase__					:   Tuple													=					checkpoint["time_embed.0.bias"]
				lowercase__					:   Dict													=					checkpoint["time_embed.2.weight"]
				lowercase__					:   Optional[Any]													=					checkpoint["time_embed.2.bias"]

				lowercase__					:   Optional[int]													=					checkpoint["input_blocks.0.0.weight"]
				lowercase__					:   List[Any]													=					checkpoint["input_blocks.0.0.bias"]

				lowercase__					:   Tuple													=					checkpoint["out.0.weight"]
				lowercase__					:   List[Any]													=					checkpoint["out.0.bias"]
				lowercase__					:   Tuple													=					checkpoint["out.2.weight"]
				lowercase__					:   Optional[Any]													=					checkpoint["out.2.bias"]

				# Retrieves the keys for the input blocks only
				lowercase__					:   Dict													=					len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "input_blocks" in layer}						)
				lowercase__					:   str													=					{
				    layer_id: [key for key in checkpoint if F"""input_blocks.{layer_id}""" in key]
				    for layer_id in range(lowerCamelCase__						)
				}

				# Retrieves the keys for the middle blocks only
				lowercase__					:   Tuple													=					len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "middle_block" in layer}						)
				lowercase__					:   Union[str, Any]													=					{
				    layer_id: [key for key in checkpoint if F"""middle_block.{layer_id}""" in key]
				    for layer_id in range(lowerCamelCase__						)
				}

				# Retrieves the keys for the output blocks only
				lowercase__					:   Tuple													=					len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "output_blocks" in layer}						)
				lowercase__					:   Tuple													=					{
				    layer_id: [key for key in checkpoint if F"""output_blocks.{layer_id}""" in key]
				    for layer_id in range(lowerCamelCase__						)
				}

				for i in range(1					,			lowerCamelCase__						):
								lowercase__					:   Tuple													=					(i - 1) // (config["num_res_blocks"] + 1)
								lowercase__					:   Optional[int]													=					(i - 1) % (config["num_res_blocks"] + 1)

								lowercase__					:   List[Any]													=					[key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key]
								lowercase__					:   Dict													=					[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]

								if F"""input_blocks.{i}.0.op.weight""" in checkpoint:
												lowercase__					:   int													=					checkpoint[
												    F"""input_blocks.{i}.0.op.weight"""
												]
												lowercase__					:   List[str]													=					checkpoint[
												    F"""input_blocks.{i}.0.op.bias"""
												]
												continue

								lowercase__					:   Union[str, Any]													=					renew_resnet_paths(lowerCamelCase__						)
								lowercase__					:   Optional[int]													=					{"old": F"""input_blocks.{i}.0""", "new": F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
								lowercase__					:   Optional[int]													=					{"old": "resnets.2.op", "new": "downsamplers.0.op"}
								assign_to_checkpoint(
								    lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			additional_replacements=[meta_path, resnet_op]					,			config=lowerCamelCase__						)

								if len(lowerCamelCase__						):
												lowercase__					:   Tuple													=					renew_attention_paths(lowerCamelCase__						)
												lowercase__					:   str													=					{
												    "old": F"""input_blocks.{i}.1""",
												    "new": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
												}
												lowercase__					:   List[str]													=					{
												    F"""input_blocks.{i}.1.qkv.bias""": {
												        "key": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
												        "query": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
												        "value": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
												    },
												    F"""input_blocks.{i}.1.qkv.weight""": {
												        "key": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
												        "query": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
												        "value": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
												    },
												}
												assign_to_checkpoint(
												    lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			additional_replacements=[meta_path]					,			attention_paths_to_split=lowerCamelCase__					,			config=lowerCamelCase__					,			)

				lowercase__					:   int													=					middle_blocks[0]
				lowercase__					:   Dict													=					middle_blocks[1]
				lowercase__					:   Dict													=					middle_blocks[2]

				lowercase__					:   Any													=					renew_resnet_paths(lowerCamelCase__						)
				assign_to_checkpoint(lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			config=lowerCamelCase__						)

				lowercase__					:   List[Any]													=					renew_resnet_paths(lowerCamelCase__						)
				assign_to_checkpoint(lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			config=lowerCamelCase__						)

				lowercase__					:   Optional[int]													=					renew_attention_paths(lowerCamelCase__						)
				lowercase__					:   Optional[int]													=					{
				    "middle_block.1.qkv.bias": {
				        "key": "mid_block.attentions.0.key.bias",
				        "query": "mid_block.attentions.0.query.bias",
				        "value": "mid_block.attentions.0.value.bias",
				    },
				    "middle_block.1.qkv.weight": {
				        "key": "mid_block.attentions.0.key.weight",
				        "query": "mid_block.attentions.0.query.weight",
				        "value": "mid_block.attentions.0.value.weight",
				    },
				}
				assign_to_checkpoint(
				    lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			attention_paths_to_split=lowerCamelCase__					,			config=lowerCamelCase__						)

				for i in range(lowerCamelCase__						):
								lowercase__					:   List[Any]													=					i // (config["num_res_blocks"] + 1)
								lowercase__					:   Optional[int]													=					i % (config["num_res_blocks"] + 1)
								lowercase__					:   List[Any]													=					[shave_segments(lowerCamelCase__					,			2						) for name in output_blocks[i]]
								lowercase__					:   Optional[Any]													=					{}

								for layer in output_block_layers:
												lowercase__							,							lowercase__					:   str													=					layer.split("."						)[0], shave_segments(lowerCamelCase__					,			1						)
												if layer_id in output_block_list:
																output_block_list[layer_id].append(lowerCamelCase__						)
												else:
																lowercase__					:   Tuple													=					[layer_name]

								if len(lowerCamelCase__						) > 1:
												lowercase__					:   Dict													=					[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
												lowercase__					:   Dict													=					[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]

												lowercase__					:   Optional[Any]													=					renew_resnet_paths(lowerCamelCase__						)
												lowercase__					:   Optional[Any]													=					renew_resnet_paths(lowerCamelCase__						)

												lowercase__					:   Tuple													=					{"old": F"""output_blocks.{i}.0""", "new": F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
												assign_to_checkpoint(lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			additional_replacements=[meta_path]					,			config=lowerCamelCase__						)

												if ["conv.weight", "conv.bias"] in output_block_list.values():
																lowercase__					:   List[str]													=					list(output_block_list.values()						).index(["conv.weight", "conv.bias"]						)
																lowercase__					:   Tuple													=					checkpoint[
																    F"""output_blocks.{i}.{index}.conv.weight"""
																]
																lowercase__					:   Optional[Any]													=					checkpoint[
																    F"""output_blocks.{i}.{index}.conv.bias"""
																]

																# Clear attentions as they have been attributed above.
																if len(lowerCamelCase__						) == 2:
																				lowercase__					:   int													=					[]

												if len(lowerCamelCase__						):
																lowercase__					:   Tuple													=					renew_attention_paths(lowerCamelCase__						)
																lowercase__					:   str													=					{
																    "old": F"""output_blocks.{i}.1""",
																    "new": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
																}
																lowercase__					:   Union[str, Any]													=					{
																    F"""output_blocks.{i}.1.qkv.bias""": {
																        "key": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
																        "query": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
																        "value": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
																    },
																    F"""output_blocks.{i}.1.qkv.weight""": {
																        "key": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
																        "query": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
																        "value": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
																    },
																}
																assign_to_checkpoint(
																    lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			additional_replacements=[meta_path]					,			attention_paths_to_split=to_split if any("qkv" in key for key in attentions						) else None					,			config=lowerCamelCase__					,			)
								else:
												lowercase__					:   int													=					renew_resnet_paths(lowerCamelCase__					,			n_shave_prefix_segments=1						)
												for path in resnet_0_paths:
																lowercase__					:   List[Any]													=					".".join(["output_blocks", str(lowerCamelCase__						), path["old"]]						)
																lowercase__					:   Any													=					".".join(["up_blocks", str(lowerCamelCase__						), "resnets", str(lowerCamelCase__						), path["new"]]						)

																lowercase__					:   List[Any]													=					checkpoint[old_path]

				return new_checkpoint


if __name__ == "__main__":
				lowerCAmelCase__					  =  argparse.ArgumentParser()

				parser.add_argument(
				    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
				)

				parser.add_argument(
				    '''--config_file''',
				    default=None,
				    type=str,
				    required=True,
				    help='''The config json file corresponding to the architecture.''',
				)

				parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

				lowerCAmelCase__					  =  parser.parse_args()

				lowerCAmelCase__					  =  torch.load(args.checkpoint_path)

				with open(args.config_file) as f:
								lowerCAmelCase__					  =  json.loads(f.read())

				lowerCAmelCase__					  =  convert_ldm_checkpoint(checkpoint, config)

				if "ldm" in config:
								del config["ldm"]

				lowerCAmelCase__					  =  UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

				try:
								lowerCAmelCase__					  =  DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
								lowerCAmelCase__					  =  VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

								lowerCAmelCase__					  =  LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
								pipe.save_pretrained(args.dump_path)
				except:  # noqa: E722
								model.save_pretrained(args.dump_path)




