

# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput


@dataclass
class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			__A						  =  4_2
			__A						  =  4_2







class SCREAMING_SNAKE_CASE  (		__snake_case							,						__snake_case							):






			"""simple docstring"""




			__A						  =  1


			@register_to_config
			def __init__(    self ,				__UpperCamelCase = 20_00 ,				__UpperCamelCase = 0.15 ,				__UpperCamelCase = 0.01 ,				__UpperCamelCase = 1348.0 ,				__UpperCamelCase = 1E-5 ,				__UpperCamelCase = 1 ,				):

				"""simple docstring"""


				snake_case_      		= sigma_max

				# setable values
				snake_case_      		= None

				self.set_sigmas(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase = None							):

				"""simple docstring"""


				return sample


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase = None ,				__UpperCamelCase = None							):

				"""simple docstring"""


				snake_case_      		= sampling_eps if sampling_eps is not None else self.config.sampling_eps

				snake_case_      		= torch.linspace(1 ,				__UpperCamelCase ,				__UpperCamelCase ,				device=__UpperCamelCase							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = None							):

				"""simple docstring"""


				snake_case_      		= sigma_min if sigma_min is not None else self.config.sigma_min
				snake_case_      		= sigma_max if sigma_max is not None else self.config.sigma_max
				snake_case_      		= sampling_eps if sampling_eps is not None else self.config.sampling_eps
				if self.timesteps is None:
					self.set_timesteps(__UpperCamelCase ,				__UpperCamelCase							)

				snake_case_      		= sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
				snake_case_      		= torch.exp(torch.linspace(math.log(__UpperCamelCase							) ,				math.log(__UpperCamelCase							) ,				__UpperCamelCase							)							)
				snake_case_      		= torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				return torch.where(
				    timesteps == 0 ,				torch.zeros_like(t.to(timesteps.device							)							) ,				self.discrete_sigmas[timesteps - 1].to(timesteps.device							) ,				)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase = None ,				__UpperCamelCase = True ,				):

				"""simple docstring"""


				if self.timesteps is None:
					raise ValueError(
					    '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'							)

				snake_case_      		= timestep * torch.ones(
				    sample.shape[0] ,				device=sample.device							)  # torch.repeat_interleave(timestep, sample.shape[0])
				snake_case_      		= (timestep * (len(self.timesteps							) - 1)).long()

				# mps requires indices to be in the same device, so we use cpu as is the default with cuda
				snake_case_      		= timesteps.to(self.discrete_sigmas.device							)

				snake_case_      		= self.discrete_sigmas[timesteps].to(sample.device							)
				snake_case_      		= self.get_adjacent_sigma(__UpperCamelCase ,				__UpperCamelCase							).to(sample.device							)
				snake_case_      		= torch.zeros_like(__UpperCamelCase							)
				snake_case_      		= (sigma**2 - adjacent_sigma**2) ** 0.5

				# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
				# also equation 47 shows the analog from SDE models to ancestral sampling methods
				snake_case_      		= diffusion.flatten()
				while len(diffusion.shape							) < len(sample.shape							):
					snake_case_      		= diffusion.unsqueeze(-1							)
				snake_case_      		= drift - diffusion**2 * model_output

				#  equation 6: sample noise for the diffusion term of
				snake_case_      		= randn_tensor(
				    sample.shape ,				layout=sample.layout ,				generator=__UpperCamelCase ,				device=sample.device ,				dtype=sample.dtype							)
				snake_case_      		= sample - drift  # subtract because `dt` is a small negative timestep
				# TODO is the variable diffusion the correct scaling term for the noise?
				snake_case_      		= prev_sample_mean + diffusion * noise  # add impact of diffusion field g

				if not return_dict:
					return (prev_sample, prev_sample_mean)

				return SdeVeOutput(prev_sample=__UpperCamelCase ,				prev_sample_mean=__UpperCamelCase							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase = None ,				__UpperCamelCase = True ,				):

				"""simple docstring"""


				if self.timesteps is None:
					raise ValueError(
					    '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'							)

				# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
				# sample noise for correction
				snake_case_      		= randn_tensor(sample.shape ,				layout=sample.layout ,				generator=__UpperCamelCase							).to(sample.device							)

				# compute step size from the model_output, the noise, and the snr
				snake_case_      		= torch.norm(model_output.reshape(model_output.shape[0] ,				-1							) ,				dim=-1							).mean()
				snake_case_      		= torch.norm(noise.reshape(noise.shape[0] ,				-1							) ,				dim=-1							).mean()
				snake_case_      		= (self.config.snr * noise_norm / grad_norm) ** 2 * 2
				snake_case_      		= step_size * torch.ones(sample.shape[0]							).to(sample.device							)
				# self.repeat_scalar(step_size, sample.shape[0])

				# compute corrected sample: model_output term and noise term
				snake_case_      		= step_size.flatten()
				while len(step_size.shape							) < len(sample.shape							):
					snake_case_      		= step_size.unsqueeze(-1							)
				snake_case_      		= sample + step_size * model_output
				snake_case_      		= prev_sample_mean + ((step_size * 2) ** 0.5) * noise

				if not return_dict:
					return (prev_sample,)

				return SchedulerOutput(prev_sample=__UpperCamelCase							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				):

				"""simple docstring"""


				snake_case_      		= timesteps.to(original_samples.device							)
				snake_case_      		= self.discrete_sigmas.to(original_samples.device							)[timesteps]
				snake_case_      		= (
				    noise * sigmas[:, None, None, None]
				    if noise is not None
				    else torch.randn_like(__UpperCamelCase							) * sigmas[:, None, None, None]
				)
				snake_case_      		= noise + original_samples
				return noisy_samples







			def __len__(    self							):

				"""simple docstring"""


				return self.config.num_train_timesteps



from collections import defaultdict





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= first_str.lower().strip()
	snake_case_      		= second_str.lower().strip()

	# Remove whitespace
	snake_case_      		= first_str.replace(' '			, ''	)
	snake_case_      		= second_str.replace(' '			, ''	)

	# Strings of different lengths are not anagrams
	if len(lowercase__	) != len(lowercase__	):
		return False

	# Default values for count should be 0
	snake_case_      		= defaultdict(lowercase__	)

	# For each character in input strings,
	# increment count in the corresponding
	for i in range(len(lowercase__	)	):
		count[first_str[i]] += 1
		count[second_str[i]] -= 1

	return all(_count == 0 for _count in count.values()	)


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		A											=     input('Enter the first string ').strip()
		A											=     input('Enter the second string ').strip()

		A											=     check_anagrams(input_a, input_b)
		print(f"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")
