
"""simple docstring"""


import math



def     _lowerCamelCase(__UpperCamelCase       )				-> str:
	if number == 0:
		_lowerCAmelCase								="""Zero"""
		return words
	else:
		_lowerCAmelCase								=math.logaa(__UpperCamelCase       )
		_lowerCAmelCase								=digits + 1
		_lowerCAmelCase								={}
		_lowerCAmelCase								=""""""
		_lowerCAmelCase								="""One"""
		_lowerCAmelCase								="""Two"""
		_lowerCAmelCase								="""Three"""
		_lowerCAmelCase								="""Four"""
		_lowerCAmelCase								="""Five"""
		_lowerCAmelCase								="""Six"""
		_lowerCAmelCase								="""Seven"""
		_lowerCAmelCase								="""Eight"""
		_lowerCAmelCase								="""Nine"""

		_lowerCAmelCase								={}
		_lowerCAmelCase								=""""""
		_lowerCAmelCase								="""Twenty"""
		_lowerCAmelCase								="""Thirty"""
		_lowerCAmelCase								="""Forty"""
		_lowerCAmelCase								="""Fifty"""
		_lowerCAmelCase								="""Sixty"""
		_lowerCAmelCase								="""Seventy"""
		_lowerCAmelCase								="""Eighty"""
		_lowerCAmelCase								="""Ninety"""

		_lowerCAmelCase								={}
		_lowerCAmelCase								="""Ten"""
		_lowerCAmelCase								="""Eleven"""
		_lowerCAmelCase								="""Twelve"""
		_lowerCAmelCase								="""Thirteen"""
		_lowerCAmelCase								="""Fourteen"""
		_lowerCAmelCase								="""Fifteen"""
		_lowerCAmelCase								="""Sixteen"""
		_lowerCAmelCase								="""Seventeen"""
		_lowerCAmelCase								="""Eighteen"""
		_lowerCAmelCase								="""Nineteen"""

		_lowerCAmelCase								={}
		_lowerCAmelCase								="""Hundred,"""
		_lowerCAmelCase								="""Thousand,"""
		_lowerCAmelCase								="""Lakh,"""
		_lowerCAmelCase								="""Crore,"""

		_lowerCAmelCase								=number
		_lowerCAmelCase								=""""""
		_lowerCAmelCase								=0
		_lowerCAmelCase								=int(__UpperCamelCase       )
		while counter < digits:
			_lowerCAmelCase								=temp_num % 10
			if counter % 2 == 0:
				_lowerCAmelCase								=""""""
				if counter in placevalue and current != 0:
					_lowerCAmelCase								=placevalue[counter]
				if counter == 2:
					_lowerCAmelCase								=singles[current] + addition + words
				elif counter == 0:
					if ((temp_num % 100) // 10) == 1:
						_lowerCAmelCase								=teens[current] + addition + words
						_lowerCAmelCase								=temp_num // 10
						counter += 1
					else:
						_lowerCAmelCase								=singles[current] + addition + words

				else:
					_lowerCAmelCase								=doubles[current] + addition + words

			else:
				if counter == 1:
					if current == 1:
						_lowerCAmelCase								=teens[number % 10] + words
					else:
						_lowerCAmelCase								=""""""
						if counter in placevalue:
							_lowerCAmelCase								=placevalue[counter]
						_lowerCAmelCase								=doubles[current] + addition + words
				else:
					_lowerCAmelCase								=""""""
					if counter in placevalue:
						if current == 0 and ((temp_num % 100) // 10) == 0:
							_lowerCAmelCase								=""""""
						else:
							_lowerCAmelCase								=placevalue[counter]
					if ((temp_num % 100) // 10) == 1:
						_lowerCAmelCase								=teens[current] + addition + words
						_lowerCAmelCase								=temp_num // 10
						counter += 1
					else:
						_lowerCAmelCase								=singles[current] + addition + words
			counter += 1
			_lowerCAmelCase								=temp_num // 10
	return words


if __name__ == "__main__":
			import doctest

			doctest.testmod()









from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     def __init__(			self     :      Union[str, Any]		,     A__     :      Dict		,     A__     :      int	) ->				Union[str, Any]:
           super().__init__()
           self.register_modules(unet=A__		,     scheduler=A__	)




     @torch.no_grad()
     def __call__(			self     :      int		,     A__     :      int = 1		,     A__     :      Optional[Union[torch.Generator, List[torch.Generator]]] = None		,     A__     :      int = 1_0_0_0		,     A__     :      Optional[str] = "pil"		,     A__     :      bool = True		,     ) ->				Union[ImagePipelineOutput, Tuple]:
           # Sample gaussian noise to begin loop
           if isinstance(self.unet.config.sample_size		,     A__	):
                 lowerCamelCase__					     = (
                     batch_size,
                     self.unet.config.in_channels,
                     self.unet.config.sample_size,
                     self.unet.config.sample_size,
                 )
           else:
                 lowerCamelCase__					     = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

           if self.device.type == "mps":
                 # randn does not work reproducibly on mps
                 lowerCamelCase__					     = randn_tensor(A__		,     generator=A__	)
                 lowerCamelCase__					     = image.to(self.device	)
           else:
                 lowerCamelCase__					     = randn_tensor(A__		,     generator=A__		,     device=self.device	)

           # set step values
           self.scheduler.set_timesteps(A__	)

           for t in self.progress_bar(self.scheduler.timesteps	):
                 # 1. predict noise model_output
                 lowerCamelCase__					     = self.unet(A__		,     A__	).sample

                 # 2. compute previous image: x_t -> x_t-1
                 lowerCamelCase__					     = self.scheduler.step(A__		,     A__		,     A__		,     generator=A__	).prev_sample

           lowerCamelCase__					     = (image / 2 + 0.5).clamp(0		,     1	)
           lowerCamelCase__					     = image.cpu().permute(0		,     2		,     3		,     1	).numpy()
           if output_type == "pil":
                 lowerCamelCase__					     = self.numpy_to_pil(A__	)

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=A__	)