






from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


UpperCAmelCase_ :						Any      =      logging.get_logger(__name__)  # pylint: disable=invalid-name

class     _SCREAMING_SNAKE_CASE       (			_a      ):





	def __init__(		self				:					Optional[int] ,  __lowerCamelCase				:					CLIPSegForImageSegmentation ,  __lowerCamelCase				:					CLIPSegProcessor ,  __lowerCamelCase				:					AutoencoderKL ,  __lowerCamelCase				:					CLIPTextModel ,  __lowerCamelCase				:					CLIPTokenizer ,  __lowerCamelCase				:					UNetaDConditionModel ,  __lowerCamelCase				:					Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] ,  __lowerCamelCase				:					StableDiffusionSafetyChecker ,  __lowerCamelCase				:					CLIPImageProcessor ,  ):
		super().__init__()

		if hasattr(scheduler.config ,  """steps_offset"""	) and scheduler.config.steps_offset != 1:
			UpperCamelCase     :str          =	(
			    F"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
			    F""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
			    """to update the config accordingly as leaving `steps_offset` might led to incorrect results"""
			    """ in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"""
			    """ it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"""
			    """ file"""
			)
			deprecate("""steps_offset!=1""" ,  """1.0.0""" ,  __lowerCamelCase ,  standard_warn=__lowerCamelCase	)
			UpperCamelCase     :Optional[Any]          =	dict(scheduler.config	)
			UpperCamelCase     :Tuple          =	1
			UpperCamelCase     :Dict          =	FrozenDict(__lowerCamelCase	)

		if hasattr(scheduler.config ,  """skip_prk_steps"""	) and scheduler.config.skip_prk_steps is False:
			UpperCamelCase     :Any          =	(
			    F"""The configuration file of this scheduler: {scheduler} has not set the configuration"""
			    """ `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"""
			    """ sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"""
			    """ incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"""
			    """ Hub, it would be very nice if you could open a Pull request for the"""
			    """ `scheduler/scheduler_config.json` file"""
			)
			deprecate("""skip_prk_steps not set""" ,  """1.0.0""" ,  __lowerCamelCase ,  standard_warn=__lowerCamelCase	)
			UpperCamelCase     :Tuple          =	dict(scheduler.config	)
			UpperCamelCase     :Any          =	True
			UpperCamelCase     :List[str]          =	FrozenDict(__lowerCamelCase	)

		if safety_checker is None:
			logger.warning(
			    F"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
			    """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
			    """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
			    """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
			    """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
			    """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""	)

		self.register_modules(
		    segmentation_model=__lowerCamelCase ,  segmentation_processor=__lowerCamelCase ,  vae=__lowerCamelCase ,  text_encoder=__lowerCamelCase ,  tokenizer=__lowerCamelCase ,  unet=__lowerCamelCase ,  scheduler=__lowerCamelCase ,  safety_checker=__lowerCamelCase ,  feature_extractor=__lowerCamelCase ,  )





	def 						_A (		self				:					Dict ,  __lowerCamelCase				:					Optional[Union[str, int]] = "auto"	):
		if slice_size == "auto":
			# half the attention head size is usually a good trade-off between
			# speed and memory
			UpperCamelCase     :Optional[Any]          =	self.unet.config.attention_head_dim // 2
		self.unet.set_attention_slice(__lowerCamelCase	)





	def 						_A (		self				:					Union[str, Any]	):
		self.enable_attention_slicing(__lowerCamelCase	)





	def 						_A (		self				:					Union[str, Any]	):
		if is_accelerate_available():
			from accelerate import cpu_offload
		else:
			raise ImportError("""Please install accelerate via `pip install accelerate`"""	)

		UpperCamelCase     :Any          =	torch.device("""cuda"""	)

		for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
			if cpu_offloaded_model is not None:
				cpu_offload(__lowerCamelCase ,  __lowerCamelCase	)





	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def 						_A (		self				:					Tuple	):
		if self.device != torch.device("""meta"""	) or not hasattr(self.unet ,  """_hf_hook"""	):
			return self.device
		for module in self.unet.modules():
			if (
			    hasattr(__lowerCamelCase ,  """_hf_hook"""	)
			    and hasattr(module._hf_hook ,  """execution_device"""	)
			    and module._hf_hook.execution_device is not None
			):
				return torch.device(module._hf_hook.execution_device	)
		return self.device





	@torch.no_grad()
	def __call__(		self				:					Optional[Any] ,  __lowerCamelCase				:					Union[str, List[str]] ,  __lowerCamelCase				:					Union[torch.FloatTensor, PIL.Image.Image] ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					int = 512 ,  __lowerCamelCase				:					int = 512 ,  __lowerCamelCase				:					int = 50 ,  __lowerCamelCase				:					float = 7.5 ,  __lowerCamelCase				:					Optional[Union[str, List[str]]] = None ,  __lowerCamelCase				:					Optional[int] = 1 ,  __lowerCamelCase				:					float = 0.0 ,  __lowerCamelCase				:					Optional[torch.Generator] = None ,  __lowerCamelCase				:					Optional[torch.FloatTensor] = None ,  __lowerCamelCase				:					Optional[str] = "pil" ,  __lowerCamelCase				:					bool = True ,  __lowerCamelCase				:					Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,  __lowerCamelCase				:					int = 1 ,  **__lowerCamelCase				:					List[Any] ,  ):
		UpperCamelCase     :Any          =	self.segmentation_processor(
		    text=[text] ,  images=[image] ,  padding="""max_length""" ,  return_tensors="""pt"""	).to(self.device	)
		UpperCamelCase     :Any          =	self.segmentation_model(**__lowerCamelCase	)
		UpperCamelCase     :Dict          =	torch.sigmoid(outputs.logits	).cpu().detach().unsqueeze(-1	).numpy()
		UpperCamelCase     :Optional[int]          =	self.numpy_to_pil(__lowerCamelCase	)[0].resize(image.size	)

		# Run inpainting pipeline with the generated mask
		UpperCamelCase     :Tuple          =	StableDiffusionInpaintPipeline(
		    vae=self.vae ,  text_encoder=self.text_encoder ,  tokenizer=self.tokenizer ,  unet=self.unet ,  scheduler=self.scheduler ,  safety_checker=self.safety_checker ,  feature_extractor=self.feature_extractor ,  )
		return inpainting_pipeline(
		    prompt=__lowerCamelCase ,  image=__lowerCamelCase ,  mask_image=__lowerCamelCase ,  height=__lowerCamelCase ,  width=__lowerCamelCase ,  num_inference_steps=__lowerCamelCase ,  guidance_scale=__lowerCamelCase ,  negative_prompt=__lowerCamelCase ,  num_images_per_prompt=__lowerCamelCase ,  eta=__lowerCamelCase ,  generator=__lowerCamelCase ,  latents=__lowerCamelCase ,  output_type=__lowerCamelCase ,  return_dict=__lowerCamelCase ,  callback=__lowerCamelCase ,  callback_steps=__lowerCamelCase ,  )







from math import sqrt



def 		A_				(     _lowerCAmelCase     )						-> bool:
 assert isinstance(_lowerCAmelCase							,   _lowerCAmelCase     ) and (
     number >= 0
 ), "'number' must been an int and positive"

 UpperCamelCase	:					List[Any]       	=   True

 # 0 and 1 are none primes.
 if number <= 1:
  UpperCamelCase	:					List[Any]       	=   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:
   UpperCamelCase	:					Union[str, Any]       	=   False
   break

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

 return status



def 		A_				(     _lowerCAmelCase     )						-> Any:
 assert isinstance(_lowerCAmelCase							,   _lowerCAmelCase     ) and (n > 2), "'N' must been an int and > 2"

 # beginList: contains all natural numbers from 2 up to N
 UpperCamelCase	:					int       	=   list(range(2							,   n + 1     )     )

 UpperCamelCase	:					Optional[int]       	=   []  # 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):
    UpperCamelCase	:					Tuple       	=   0

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

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

 return ans



def 		A_				(     _lowerCAmelCase     )						-> Optional[Any]:
 assert isinstance(_lowerCAmelCase							,   _lowerCAmelCase     ) and (n > 2), "'N' must been an int and > 2"

 UpperCamelCase	:					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 		A_				(     _lowerCAmelCase     )						-> Any:
 assert isinstance(_lowerCAmelCase							,   _lowerCAmelCase     ) and number >= 0, "'number' must been an int and >= 0"

 UpperCamelCase	:					Optional[Any]       	=   []  # this list will be returns of the function.

 # potential prime number factors.

 UpperCamelCase	:					Tuple       	=   2

 UpperCamelCase	:					str       	=   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 		A_				(     _lowerCAmelCase     )						-> Any:
 assert isinstance(_lowerCAmelCase							,   _lowerCAmelCase     ) and (
     number >= 0
 ), "'number' bust been an int and >= 0"

 UpperCamelCase	:					List[Any]       	=   0

 # prime factorization of 'number'
 UpperCamelCase	:					Any       	=   prime_factorization(_lowerCAmelCase     )

 UpperCamelCase	:					List[Any]       	=   max(_lowerCAmelCase     )

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

 return ans



def 		A_				(     _lowerCAmelCase     )						-> Union[str, Any]:
 assert isinstance(_lowerCAmelCase							,   _lowerCAmelCase     ) and (
     number >= 0
 ), "'number' bust been an int and >= 0"

 UpperCamelCase	:					List[Any]       	=   0

 # prime factorization of 'number'
 UpperCamelCase	:					Dict       	=   prime_factorization(_lowerCAmelCase     )

 UpperCamelCase	:					List[Any]       	=   min(_lowerCAmelCase     )

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

 return ans



def 		A_				(     _lowerCAmelCase     )						-> Optional[Any]:
 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 		A_				(     _lowerCAmelCase     )						-> List[Any]:
 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 		A_				(     _lowerCAmelCase     )						-> Any:
 assert (
     isinstance(_lowerCAmelCase							,   _lowerCAmelCase     ) and (number > 2) and is_even(_lowerCAmelCase     )
 ), "'number' must been an int, even and > 2"

 UpperCamelCase	:					List[str]       	=   []  # this list will returned

 # creates a list of prime numbers between 2 up to 'number'
 UpperCamelCase	:					Dict       	=   get_prime_numbers(_lowerCAmelCase     )
 UpperCamelCase	:					Tuple       	=   len(_lowerCAmelCase     )

 # run variable for while-loops.
 UpperCamelCase	:					Optional[int]       	=   0
 UpperCamelCase	:					int       	=   None

 # exit variable. for break up the loops
 UpperCamelCase	:					Union[str, Any]       	=   True

 while i < len_pn and loop:
  UpperCamelCase	:					Tuple       	=   i + 1

  while j < len_pn and loop:
   if prime_numbers[i] + prime_numbers[j] == number:
    UpperCamelCase	:					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 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> str:
 assert (
     isinstance(_lowerCAmelCase							,   _lowerCAmelCase     )
     and isinstance(_lowerCAmelCase							,   _lowerCAmelCase     )
     and (numbera >= 0)
     and (numbera >= 0)
 ), "'number1' and 'number2' must been positive integer."

 UpperCamelCase	:					Tuple       	=   0

 while numbera != 0:
  UpperCamelCase	:					Tuple       	=   numbera % numbera
  UpperCamelCase	:					Any       	=   numbera
  UpperCamelCase	:					Union[str, Any]       	=   rest

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

 return numbera



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> int:
 assert (
     isinstance(_lowerCAmelCase							,   _lowerCAmelCase     )
     and isinstance(_lowerCAmelCase							,   _lowerCAmelCase     )
     and (numbera >= 1)
     and (numbera >= 1)
 ), "'number1' and 'number2' must been positive integer."

 UpperCamelCase	:					Optional[int]       	=   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'
  UpperCamelCase	:					List[Any]       	=   prime_factorization(_lowerCAmelCase     )
  UpperCamelCase	:					Union[str, Any]       	=   prime_factorization(_lowerCAmelCase     )

 elif numbera == 1 or numbera == 1:
  UpperCamelCase	:					Optional[Any]       	=   []
  UpperCamelCase	:					int       	=   []
  UpperCamelCase	:					List[Any]       	=   max(_lowerCAmelCase							,   _lowerCAmelCase     )

 UpperCamelCase	:					Optional[int]       	=   0
 UpperCamelCase	:					Tuple       	=   0

 UpperCamelCase	:					List[str]       	=   []  # 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:
    UpperCamelCase	:					str       	=   prime_fac_a.count(_lowerCAmelCase     )
    UpperCamelCase	:					Tuple       	=   prime_fac_a.count(_lowerCAmelCase     )

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

   else:
    UpperCamelCase	:					str       	=   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:
   UpperCamelCase	:					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 		A_				(     _lowerCAmelCase     )						-> Tuple:
 assert isinstance(_lowerCAmelCase							,   _lowerCAmelCase     ) and (n >= 0), "'number' must been a positive int"

 UpperCamelCase	:					int       	=   0
 UpperCamelCase	:					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 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> int:
 assert (
     is_prime(_lowerCAmelCase     ) and is_prime(_lowerCAmelCase     ) and (p_number_a < p_number_a)
 ), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"

 UpperCamelCase	:					str       	=   p_number_a + 1  # jump to the next number

 UpperCamelCase	:					Dict       	=   []  # 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 		A_				(     _lowerCAmelCase     )						-> List[str]:
 assert isinstance(_lowerCAmelCase							,   _lowerCAmelCase     ) and (n >= 1), "'n' must been int and >= 1"

 UpperCamelCase	:					Dict       	=   []  # 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 		A_				(     _lowerCAmelCase     )						-> int:
 assert isinstance(_lowerCAmelCase							,   _lowerCAmelCase     ) and (
     number > 1
 ), "'number' must been an int and >= 1"

 UpperCamelCase	:					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 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> Optional[Any]:
 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.
 UpperCamelCase	:					List[str]       	=   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 		A_				(     _lowerCAmelCase     )						-> Dict:
 assert isinstance(_lowerCAmelCase							,   _lowerCAmelCase     ) and (n >= 0), "'n' must been a int and >= 0"

 UpperCamelCase	:					str       	=   1  # this will be return.

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

 return ans



def 		A_				(     _lowerCAmelCase     )						-> Tuple:
 assert isinstance(_lowerCAmelCase							,   _lowerCAmelCase     ) and (n >= 0), "'n' must been an int and >= 0"

 UpperCamelCase	:					Dict       	=   0
 UpperCamelCase	:					Dict       	=   1
 UpperCamelCase	:					Union[str, Any]       	=   1  # this will be return

 for _ in range(n - 1     ):
  UpperCamelCase	:					Any       	=   ans
  ans += fiba
  UpperCamelCase	:					str       	=   tmp

 return ans
