





import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)





def  __magic_name__  (				lowercase				,       lowercase				)				-> Optional[int]:

							"""simple docstring"""



							lowercase_	:   str  				=     checkpoint

							lowercase_	:   List[Any]  				=     {}

							lowercase_	:   Dict  				=     vae_state_dict["""encoder.conv_in.weight"""]
							lowercase_	:   Tuple  				=     vae_state_dict["""encoder.conv_in.bias"""]
							lowercase_	:   Union[str, Any]  				=     vae_state_dict["""encoder.conv_out.weight"""]
							lowercase_	:   Union[str, Any]  				=     vae_state_dict["""encoder.conv_out.bias"""]
							lowercase_	:   int  				=     vae_state_dict["""encoder.norm_out.weight"""]
							lowercase_	:   List[Any]  				=     vae_state_dict["""encoder.norm_out.bias"""]

							lowercase_	:   List[str]  				=     vae_state_dict["""decoder.conv_in.weight"""]
							lowercase_	:   Any  				=     vae_state_dict["""decoder.conv_in.bias"""]
							lowercase_	:   List[str]  				=     vae_state_dict["""decoder.conv_out.weight"""]
							lowercase_	:   Dict  				=     vae_state_dict["""decoder.conv_out.bias"""]
							lowercase_	:   Dict  				=     vae_state_dict["""decoder.norm_out.weight"""]
							lowercase_	:   Union[str, Any]  				=     vae_state_dict["""decoder.norm_out.bias"""]

							lowercase_	:   int  				=     vae_state_dict["""quant_conv.weight"""]
							lowercase_	:   Any  				=     vae_state_dict["""quant_conv.bias"""]
							lowercase_	:   str  				=     vae_state_dict["""post_quant_conv.weight"""]
							lowercase_	:   Dict  				=     vae_state_dict["""post_quant_conv.bias"""]

							# Retrieves the keys for the encoder down blocks only
							lowercase_	:   int  				=     len({""".""".join(layer.split("""."""				)[:3]				) for layer in vae_state_dict if """encoder.down""" in layer}				)
							lowercase_	:   Optional[int]  				=     {
							    layer_id: [key for key in vae_state_dict if f"""down.{layer_id}""" in key] for layer_id in range(lowercase				)
							}

							# Retrieves the keys for the decoder up blocks only
							lowercase_	:   Optional[Any]  				=     len({""".""".join(layer.split("""."""				)[:3]				) for layer in vae_state_dict if """decoder.up""" in layer}				)
							lowercase_	:   str  				=     {
							    layer_id: [key for key in vae_state_dict if f"""up.{layer_id}""" in key] for layer_id in range(lowercase				)
							}

							for i in range(lowercase				):
														lowercase_	:   Union[str, Any]  				=     [key for key in down_blocks[i] if f"""down.{i}""" in key and f"""down.{i}.downsample""" not in key]

														if f"""encoder.down.{i}.downsample.conv.weight""" in vae_state_dict:
																					lowercase_	:   Dict  				=     vae_state_dict.pop(
																					    f"""encoder.down.{i}.downsample.conv.weight"""				)
																					lowercase_	:   Union[str, Any]  				=     vae_state_dict.pop(
																					    f"""encoder.down.{i}.downsample.conv.bias"""				)

														lowercase_	:   Dict  				=     renew_vae_resnet_paths(lowercase				)
														lowercase_	:   str  				=     {"""old""": f"""down.{i}.block""", """new""": f"""down_blocks.{i}.resnets"""}
														assign_to_checkpoint(lowercase				,       lowercase				,       lowercase				,       additional_replacements=[meta_path]				,       config=lowercase				)

							lowercase_	:   Any  				=     [key for key in vae_state_dict if """encoder.mid.block""" in key]
							lowercase_	:   List[Any]  				=     2
							for i in range(1				,       num_mid_res_blocks + 1				):
														lowercase_	:   str  				=     [key for key in mid_resnets if f"""encoder.mid.block_{i}""" in key]

														lowercase_	:   Any  				=     renew_vae_resnet_paths(lowercase				)
														lowercase_	:   Union[str, Any]  				=     {"""old""": f"""mid.block_{i}""", """new""": f"""mid_block.resnets.{i - 1}"""}
														assign_to_checkpoint(lowercase				,       lowercase				,       lowercase				,       additional_replacements=[meta_path]				,       config=lowercase				)

							lowercase_	:   Optional[int]  				=     [key for key in vae_state_dict if """encoder.mid.attn""" in key]
							lowercase_	:   str  				=     renew_vae_attention_paths(lowercase				)
							lowercase_	:   Any  				=     {"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
							assign_to_checkpoint(lowercase				,       lowercase				,       lowercase				,       additional_replacements=[meta_path]				,       config=lowercase				)
							conv_attn_to_linear(lowercase				)

							for i in range(lowercase				):
														lowercase_	:   Any  				=     num_up_blocks - 1 - i
														lowercase_	:   List[str]  				=     [
														    key for key in up_blocks[block_id] if f"""up.{block_id}""" in key and f"""up.{block_id}.upsample""" not in key
														]

														if f"""decoder.up.{block_id}.upsample.conv.weight""" in vae_state_dict:
																					lowercase_	:   Tuple  				=     vae_state_dict[
																					    f"""decoder.up.{block_id}.upsample.conv.weight"""
																					]
																					lowercase_	:   List[str]  				=     vae_state_dict[
																					    f"""decoder.up.{block_id}.upsample.conv.bias"""
																					]

														lowercase_	:   Optional[Any]  				=     renew_vae_resnet_paths(lowercase				)
														lowercase_	:   str  				=     {"""old""": f"""up.{block_id}.block""", """new""": f"""up_blocks.{i}.resnets"""}
														assign_to_checkpoint(lowercase				,       lowercase				,       lowercase				,       additional_replacements=[meta_path]				,       config=lowercase				)

							lowercase_	:   Any  				=     [key for key in vae_state_dict if """decoder.mid.block""" in key]
							lowercase_	:   Optional[int]  				=     2
							for i in range(1				,       num_mid_res_blocks + 1				):
														lowercase_	:   Optional[int]  				=     [key for key in mid_resnets if f"""decoder.mid.block_{i}""" in key]

														lowercase_	:   Union[str, Any]  				=     renew_vae_resnet_paths(lowercase				)
														lowercase_	:   Tuple  				=     {"""old""": f"""mid.block_{i}""", """new""": f"""mid_block.resnets.{i - 1}"""}
														assign_to_checkpoint(lowercase				,       lowercase				,       lowercase				,       additional_replacements=[meta_path]				,       config=lowercase				)

							lowercase_	:   Dict  				=     [key for key in vae_state_dict if """decoder.mid.attn""" in key]
							lowercase_	:   Optional[int]  				=     renew_vae_attention_paths(lowercase				)
							lowercase_	:   Any  				=     {"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
							assign_to_checkpoint(lowercase				,       lowercase				,       lowercase				,       additional_replacements=[meta_path]				,       config=lowercase				)
							conv_attn_to_linear(lowercase				)
							return new_checkpoint





def  __magic_name__  (				lowercase				,       lowercase				,       )				-> Union[str, Any]:

							"""simple docstring"""



							lowercase_	:   Optional[int]  				=     requests.get(
							    """ https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"""				)
							lowercase_	:   int  				=     io.BytesIO(r.content				)

							lowercase_	:   int  				=     OmegaConf.load(lowercase				)
							lowercase_	:   int  				=     512
							lowercase_	:   Dict  				=     """cuda""" if torch.cuda.is_available() else """cpu"""
							if checkpoint_path.endswith("""safetensors"""				):
														from safetensors import safe_open

														lowercase_	:   str  				=     {}
														with safe_open(lowercase				,       framework="""pt"""				,       device="""cpu"""				) as f:
																					for key in f.keys():
																												lowercase_	:   Any  				=     f.get_tensor(lowercase				)
							else:
														lowercase_	:   Optional[int]  				=     torch.load(lowercase				,       map_location=lowercase				)["""state_dict"""]

							# Convert the VAE model.
							lowercase_	:   List[str]  				=     create_vae_diffusers_config(lowercase				,       image_size=lowercase				)
							lowercase_	:   str  				=     custom_convert_ldm_vae_checkpoint(lowercase				,       lowercase				)

							lowercase_	:   int  				=     AutoencoderKL(**lowercase				)
							vae.load_state_dict(lowercase				)
							vae.save_pretrained(lowercase				)


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

							parser.add_argument("""--vae_pt_path""", default=None, type=str, required=True, help="""Path to the VAE.pt to convert.""")
							parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the VAE.pt to convert.""")

							UpperCAmelCase_							=				parser.parse_args()

							vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)






from __future__ import annotations

from math import gcd





def  __magic_name__  (				lowercase				,       lowercase = 2				,       lowercase = 1				,       lowercase = 3				,       )				-> int | None:

							"""simple docstring"""



							if num < 2:
														raise ValueError("""The input value cannot be less than 2"""				)

							# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
							# algorithm struggles to find factors that are divisible by two.
							# As a workaround, we specifically check for two and even inputs.
							#   See: https://math.stackexchange.com/a/2856214/165820
							if num > 2 and num % 2 == 0:
														return 2

							# Pollard's Rho algorithm requires a function that returns pseudorandom
							# values between 0 <= X < ``num``.  It doesn't need to be random in the
							# sense that the output value is cryptographically secure or difficult
							# to calculate, it only needs to be random in the sense that all output
							# values should be equally likely to appear.
							# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
							# However, the success of Pollard's algorithm isn't guaranteed and is
							# determined in part by the initial seed and the chosen random function.
							# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
							# where ``C`` is a value that we can modify between each attempt.
							def rand_fn(lowercase				,       lowercase				,       lowercase				) -> int:
														return (pow(lowercase				,       2				) + step) % modulus

							for _ in range(lowercase				):
														# These track the position within the cycle detection logic.
														lowercase_	:   List[Any]  				=     seed
														lowercase_	:   Any  				=     seed

														while True:
																					# At each iteration, the tortoise moves one step and the hare moves two.
																					lowercase_	:   List[str]  				=     rand_fn(lowercase				,       lowercase				,       lowercase				)
																					lowercase_	:   str  				=     rand_fn(lowercase				,       lowercase				,       lowercase				)
																					lowercase_	:   Any  				=     rand_fn(lowercase				,       lowercase				,       lowercase				)

																					# At some point both the tortoise and the hare will enter a cycle whose
																					# length ``p`` is a divisor of ``num``.  Once in that cycle, at some point
																					# the tortoise and hare will end up on the same value modulo ``p``.
																					# We can detect when this happens because the position difference between
																					# the tortoise and the hare will share a common divisor with ``num``.
																					lowercase_	:   Tuple  				=     gcd(hare - tortoise				,       lowercase				)

																					if divisor == 1:
																												# No common divisor yet, just keep searching.
																												continue
																					else:
																												# We found a common divisor!
																												if divisor == num:
																																			# Unfortunately, the divisor is ``num`` itself and is useless.
																																			break
																												else:
																																			# The divisor is a nontrivial factor of ``num``!
																																			return divisor

        # If we made it here, then this attempt failed.
        # We need to pick a new starting seed for the tortoise and hare
        # in addition to a new step value for the random function.
        # To keep this example implementation deterministic, the
        # new values will be generated based on currently available
        # values instead of using something like ``random.randint``.

        # We can use the hare's position as the new seed.
        # This is actually what Richard Brent's the "optimized" variant does.
														lowercase_	:   int  				=     hare

														# The new step value for the random function can just be incremented.
														# At first the results will be similar to what the old function would
														# have produced, but the value will quickly diverge after a bit.
														step += 1

							# We haven't found a divisor within the requested number of attempts.
							# We were unlucky or ``num`` itself is actually prime.
							return None


if __name__ == "__main__":
							import argparse

							UpperCAmelCase_							=				argparse.ArgumentParser()
							parser.add_argument(
							    """num""",
							    type=int,
							    help="""The value to find a divisor of""",
							)
							parser.add_argument(
							    """--attempts""",
							    type=int,
							    default=3,
							    help="""The number of attempts before giving up""",
							)
							UpperCAmelCase_							=				parser.parse_args()

							UpperCAmelCase_							=				pollard_rho(args.num, attempts=args.attempts)
							if divisor is None:
														print(F'''{args.num} is probably prime''')
							else:
														UpperCAmelCase_							=				args.num // divisor
														print(F'''{args.num} = {divisor} * {quotient}''')