


import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
							from .testing_utils import (
							    floats_tensor,
							    load_hf_numpy,
							    load_image,
							    load_numpy,
							    load_pt,
							    nightly,
							    parse_flag_from_env,
							    print_tensor_test,
							    require_torch_a,
							    require_torch_gpu,
							    skip_mps,
							    slow,
							    torch_all_close,
							    torch_device,
							)
							from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


snake_case			:      Optional[Any] 							=   get_logger(__name__)


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				if version.parse(UpperCamelCase_							) < version.parse(UpperCamelCase_							):
								if "dev" in min_version:
												_SCREAMING_SNAKE_CASE      = (
												    'This example requires a source install from HuggingFace diffusers (see '
												    '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
												)
								else:
												_SCREAMING_SNAKE_CASE      = f'''This example requires a minimum version of {min_version},'''
								error_message += f''' but the version found is {__version__}.\n'''
								raise ImportError(UpperCamelCase_							)








from __future__ import annotations

from collections import Counter


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = [True] * n
				_SCREAMING_SNAKE_CASE      = False
				_SCREAMING_SNAKE_CASE      = False
				_SCREAMING_SNAKE_CASE      = True

				for i in range(3   ,int(n**0.5 + 1							)   ,2							):
								_SCREAMING_SNAKE_CASE      = i * 2
								while index < n:
												_SCREAMING_SNAKE_CASE      = False
												_SCREAMING_SNAKE_CASE      = index + i

				_SCREAMING_SNAKE_CASE      = [2]

				for i in range(3   ,UpperCAmelCase__   ,2							):
								if is_prime[i]:
												primes.append(UpperCAmelCase__							)

				return primes


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = str(UpperCAmelCase__							)
				_SCREAMING_SNAKE_CASE      = []
				_SCREAMING_SNAKE_CASE      = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']

				for duplicate in Counter(UpperCAmelCase__							) - Counter(set(UpperCAmelCase__							)							):
								_SCREAMING_SNAKE_CASE      = [int(number_str.replace(UpperCAmelCase__   ,UpperCAmelCase__							)							) for digit in digits]
								replacements.append(UpperCAmelCase__							)

				return replacements


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__ = 8							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = set()

				# Filter primes with less than 3 replaceable digits
				_SCREAMING_SNAKE_CASE      = {
				    x for x in set(prime_sieve(100_0000							)							) if len(str(UpperCAmelCase__							)							) - len(set(str(UpperCAmelCase__							)							)							) >= 3
				}

				for prime in primes:
								if prime in numbers_checked:
												continue

								_SCREAMING_SNAKE_CASE      = digit_replacements(UpperCAmelCase__							)

								for family in replacements:
												numbers_checked.update(UpperCAmelCase__							)
												_SCREAMING_SNAKE_CASE      = primes.intersection(UpperCAmelCase__							)

												if len(UpperCAmelCase__							) != family_length:
																continue

												return min(UpperCAmelCase__							)

				return -1


if __name__ == "__main__":
							print(solution())
