




# 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 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: torch.FloatTensor
				__a							: torch.FloatTensor




class 							__UpperCamelCase			( __UpperCAmelCase     ,					__UpperCAmelCase  ):





				'''simple docstring'''



				__a							: int										=1



				@register_to_config
				def __init__( self       , UpperCAmelCase_ = 20_00       , UpperCAmelCase_ = 0.15       , UpperCAmelCase_ = 0.01       , UpperCAmelCase_ = 1348.0       , UpperCAmelCase_ = 1E-5       , UpperCAmelCase_ = 1       , ):
											# standard deviation of the initial noise distribution
											lowerCAmelCase   =		sigma_max

											# setable values
											lowerCAmelCase   =		None

											self.set_sigmas(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						)



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = None						):
											return sample



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None						):
											lowerCAmelCase   =		sampling_eps if sampling_eps is not None else self.config.sampling_eps

											lowerCAmelCase   =		torch.linspace(1       , UpperCAmelCase_       , UpperCAmelCase_       , device=UpperCAmelCase_						)



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None						):
											lowerCAmelCase   =		sigma_min if sigma_min is not None else self.config.sigma_min
											lowerCAmelCase   =		sigma_max if sigma_max is not None else self.config.sigma_max
											lowerCAmelCase   =		sampling_eps if sampling_eps is not None else self.config.sampling_eps
											if self.timesteps is None:
																		self.set_timesteps(UpperCAmelCase_       , UpperCAmelCase_						)

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



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



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

											lowerCAmelCase   =		timestep * torch.ones(
											    sample.shape[0]       , device=sample.device						)  # torch.repeat_interleave(timestep, sample.shape[0])
											lowerCAmelCase   =		(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
											lowerCAmelCase   =		timesteps.to(self.discrete_sigmas.device						)

											lowerCAmelCase   =		self.discrete_sigmas[timesteps].to(sample.device						)
											lowerCAmelCase   =		self.get_adjacent_sigma(UpperCAmelCase_       , UpperCAmelCase_						).to(sample.device						)
											lowerCAmelCase   =		torch.zeros_like(UpperCAmelCase_						)
											lowerCAmelCase   =		(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
											lowerCAmelCase   =		diffusion.flatten()
											while len(diffusion.shape						) < len(sample.shape						):
																		lowerCAmelCase   =		diffusion.unsqueeze(-1						)
											lowerCAmelCase   =		drift - diffusion**2 * model_output

											#  equation 6: sample noise for the diffusion term of
											lowerCAmelCase   =		randn_tensor(
											    sample.shape       , layout=sample.layout       , generator=UpperCAmelCase_       , device=sample.device       , dtype=sample.dtype						)
											lowerCAmelCase   =		sample - drift  # subtract because `dt` is a small negative timestep
											# TODO is the variable diffusion the correct scaling term for the noise?
											lowerCAmelCase   =		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       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_ = None       , UpperCAmelCase_ = True       , ):
											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
											lowerCAmelCase   =		randn_tensor(sample.shape       , layout=sample.layout       , generator=UpperCAmelCase_						).to(sample.device						)

											# compute step size from the model_output, the noise, and the snr
											lowerCAmelCase   =		torch.norm(model_output.reshape(model_output.shape[0]       , -1						)       , dim=-1						).mean()
											lowerCAmelCase   =		torch.norm(noise.reshape(noise.shape[0]       , -1						)       , dim=-1						).mean()
											lowerCAmelCase   =		(self.config.snr * noise_norm / grad_norm) ** 2 * 2
											lowerCAmelCase   =		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
											lowerCAmelCase   =		step_size.flatten()
											while len(step_size.shape						) < len(sample.shape						):
																		lowerCAmelCase   =		step_size.unsqueeze(-1						)
											lowerCAmelCase   =		sample + step_size * model_output
											lowerCAmelCase   =		prev_sample_mean + ((step_size * 2) ** 0.5) * noise

											if not return_dict:
																		return (prev_sample,)

											return SchedulerOutput(prev_sample=UpperCAmelCase_						)



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , ):
											# Make sure sigmas and timesteps have the same device and dtype as original_samples
											lowerCAmelCase   =		timesteps.to(original_samples.device						)
											lowerCAmelCase   =		self.discrete_sigmas.to(original_samples.device						)[timesteps]
											lowerCAmelCase   =		(
											    noise * sigmas[:, None, None, None]
											    if noise is not None
											    else torch.randn_like(UpperCAmelCase_						) * sigmas[:, None, None, None]
											)
											lowerCAmelCase   =		noise + original_samples
											return noisy_samples






				def __len__( self						):
											return self.config.num_train_timesteps









from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   ,     lowerCAmelCase   =		analyze_text(_snake_case							)
							lowerCAmelCase   =		list(''' ''' + ascii_lowercase							)
							# what is our total sum of probabilities.
							lowerCAmelCase   =		sum(single_char_strings.values()							)

							# one length string
							lowerCAmelCase   =		0
							# for each alpha we go in our dict and if it is in it we calculate entropy
							for ch in my_alphas:
														if ch in single_char_strings:
																					lowerCAmelCase   =		single_char_strings[ch]
																					lowerCAmelCase   =		my_str / all_sum
																					my_fir_sum += prob * math.loga(_snake_case							)  # entropy formula.

    # print entropy
							print(F"""{round(-1 * my_fir_sum							):.1f}"""							)

							# two len string
							lowerCAmelCase   =		sum(two_char_strings.values()							)
							lowerCAmelCase   =		0
							# for each alpha (two in size) calculate entropy.
							for cha in my_alphas:
														for cha in my_alphas:
																					lowerCAmelCase   =		cha + cha
																					if sequence in two_char_strings:
																												lowerCAmelCase   =		two_char_strings[sequence]
																												lowerCAmelCase   =		int(_snake_case							) / all_sum
																												my_sec_sum += prob * math.loga(_snake_case							)

    # print second entropy
							print(F"""{round(-1 * my_sec_sum							):.1f}"""							)

							# print the difference between them
							print(F"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum)							):.1f}"""							)



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =		Counter()  # type: ignore
							lowerCAmelCase   =		Counter()  # type: ignore
							single_char_strings[text[-1]] += 1

							# first case when we have space at start.
							two_char_strings[" " + text[0]] += 1
							for i in range(0						, len(_snake_case							) - 1							):
														single_char_strings[text[i]] += 1
														two_char_strings[text[i : i + 2]] += 1
							return single_char_strings, two_char_strings



def       UpperCAmelCase       (     ):
							import doctest

							doctest.testmod()
							# text = (
							#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
							#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
							#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
							#     "beloved evident through no service elderly is. Blind there if every no so "
							#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
							#     "of message cottage windows do besides against uncivil.  Delightful "
							#     "unreserved impossible few estimating men favourable see entreaties. She "
							#     "propriety immediate was improving. He or entrance humoured likewise "
							#     "moderate. Much nor game son say feel. Fat make met can must form into "
							#     "gate. Me we offending prevailed discovery. "
							# )

							# calculate_prob(text)


if __name__ == "__main__":
		main()



