





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

import numpy as np
import torch

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







@dataclass
class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   42
							UpperCAmelCase_      						=   42
							UpperCAmelCase_      						=   None







class 						lowerCamelCase       (__lowerCamelCase							,     __lowerCamelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   2






							@register_to_config
							def __init__( self     :					Optional[Any],			_UpperCAmelCase     :					float = 0.02,			_UpperCAmelCase     :					float = 1_0_0,			_UpperCAmelCase     :					float = 1.007,			_UpperCAmelCase     :					float = 8_0,			_UpperCAmelCase     :					float = 0.05,			_UpperCAmelCase     :					float = 5_0,			)				->			List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						sigma_max

														# setable values
														SCREAMING_SNAKE_CASE__       :      int     =						None
														SCREAMING_SNAKE_CASE__       :      np.IntTensor     =						None
														SCREAMING_SNAKE_CASE__       :      torch.FloatTensor     =						None  # sigma(t_i)






							def  A_     ( self     :					Optional[Any],			_UpperCAmelCase     :					torch.FloatTensor,			_UpperCAmelCase     :					Optional[int] = None						)				->			torch.FloatTensor:





														"""simple docstring"""
														return sample






							def  A_     ( self     :					Tuple,			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					Union[str, torch.device] = None						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						num_inference_steps
														SCREAMING_SNAKE_CASE__       :      int     =						np.arange(0,			self.num_inference_steps						)[::-1].copy()
														SCREAMING_SNAKE_CASE__       :      str     =						torch.from_numpy(_UpperCAmelCase						).to(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						[
														    (
														        self.config.sigma_max**2
														        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
														    )
														    for i in self.timesteps
														]
														SCREAMING_SNAKE_CASE__       :      Tuple     =						torch.tensor(_UpperCAmelCase,			dtype=torch.floataa,			device=_UpperCAmelCase						)






							def  A_     ( self     :					Any,			_UpperCAmelCase     :					torch.FloatTensor,			_UpperCAmelCase     :					float,			_UpperCAmelCase     :					Optional[torch.Generator] = None						)				->			Tuple[torch.FloatTensor, float]:





														"""simple docstring"""
														if self.config.s_min <= sigma <= self.config.s_max:
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						min(self.config.s_churn / self.num_inference_steps,			2**0.5 - 1						)
														else:
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						0

														# sample eps ~ N(0, S_noise^2 * I)
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.config.s_noise * randn_tensor(sample.shape,			generator=_UpperCAmelCase						).to(sample.device						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						sigma + gamma * sigma
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

														return sample_hat, sigma_hat






							def  A_     ( self     :					Optional[int],			_UpperCAmelCase     :					torch.FloatTensor,			_UpperCAmelCase     :					float,			_UpperCAmelCase     :					float,			_UpperCAmelCase     :					torch.FloatTensor,			_UpperCAmelCase     :					bool = True,			)				->			Union[KarrasVeOutput, Tuple]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						sample_hat + sigma_hat * model_output
														SCREAMING_SNAKE_CASE__       :      Dict     =						(sample_hat - pred_original_sample) / sigma_hat
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						sample_hat + (sigma_prev - sigma_hat) * derivative

														if not return_dict:
																					return (sample_prev, derivative)

														return KarrasVeOutput(
														    prev_sample=_UpperCAmelCase,			derivative=_UpperCAmelCase,			pred_original_sample=_UpperCAmelCase						)






							def  A_     ( self     :					Tuple,			_UpperCAmelCase     :					torch.FloatTensor,			_UpperCAmelCase     :					float,			_UpperCAmelCase     :					float,			_UpperCAmelCase     :					torch.FloatTensor,			_UpperCAmelCase     :					torch.FloatTensor,			_UpperCAmelCase     :					torch.FloatTensor,			_UpperCAmelCase     :					bool = True,			)				->			Union[KarrasVeOutput, Tuple]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      int     =						sample_prev + sigma_prev * model_output
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						(sample_prev - pred_original_sample) / sigma_prev
														SCREAMING_SNAKE_CASE__       :      str     =						sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

														if not return_dict:
																					return (sample_prev, derivative)

														return KarrasVeOutput(
														    prev_sample=_UpperCAmelCase,			derivative=_UpperCAmelCase,			pred_original_sample=_UpperCAmelCase						)






							def  A_     ( self     :					Optional[Any],			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					Optional[int]						)				->			str:





														"""simple docstring"""
														raise NotImplementedError()













from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase




def _a (  SCREAMING_SNAKE_CASE__      :  str       )	->    None:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Dict     =						analyze_text(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      Tuple     =						list(" " + ascii_lowercase       )
							# what is our total sum of probabilities.
							SCREAMING_SNAKE_CASE__       :      str     =						sum(single_char_strings.values()       )

							# one length string
							SCREAMING_SNAKE_CASE__       :      str     =						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:
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						single_char_strings[ch]
																					SCREAMING_SNAKE_CASE__       :      Any     =						my_str / all_sum
																					my_fir_sum += prob * math.loga(SCREAMING_SNAKE_CASE__       )  # entropy formula.

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

							# two len string
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						sum(two_char_strings.values()       )
							SCREAMING_SNAKE_CASE__       :      int     =						0
							# for each alpha (two in size) calculate entropy.
							for cha in my_alphas:
														for cha in my_alphas:
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						cha + cha
																					if sequence in two_char_strings:
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						two_char_strings[sequence]
																												SCREAMING_SNAKE_CASE__       :      Tuple     =						int(SCREAMING_SNAKE_CASE__       ) / all_sum
																												my_sec_sum += prob * math.loga(SCREAMING_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 _a (  SCREAMING_SNAKE_CASE__      :  str       )	->    tuple[dict, dict]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      str     =						Counter()  # type: ignore
							SCREAMING_SNAKE_CASE__       :      Dict     =						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(SCREAMING_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 _a (  )	->    str:

							'''simple docstring'''






							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()






