

"""simple docstring"""



from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase





def        _snake_case   (			UpperCAmelCase_     :    str	):
     A__								=     analyze_text(SCREAMING_SNAKE_CASE_	)
     A__								=     list(""" """ + ascii_lowercase	)
     # what is our total sum of probabilities.
     A__								=     sum(single_char_strings.values()	)

     # one length string
     A__								=     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:
               A__								=     single_char_strings[ch]
               A__								=     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
     A__								=     sum(two_char_strings.values()	)
     A__								=     0
     # for each alpha (two in size) calculate entropy.
     for cha in my_alphas:
          for cha in my_alphas:
               A__								=     cha + cha
               if sequence in two_char_strings:
                    A__								=     two_char_strings[sequence]
                    A__								=     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        _snake_case   (			UpperCAmelCase_     :    Any	):
     A__								=     Counter()  # type: ignore
     A__								=     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        _snake_case   (			):
     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()








from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class      A__							(     __magic_name__	):
				lowercase			    =  42
				lowercase			    =  42






				def __init__(						self					:      Any    ,     a					:      UNetaDModel    ,     a					:      ScoreSdeVeScheduler   ):






							'''simple docstring'''







							super().__init__()
							self.register_modules(unet=a    ,     scheduler=a   )






				@torch.no_grad()
				def __call__(						self					:      List[str]    ,     a					:      int = 1    ,     a					:      int = 2_000    ,     a					:      Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,     a					:      Optional[str] = "pil"    ,     a					:      bool = True    ,     **a					:      int    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		self.unet.config.sample_size
							lowerCAmelCase__      :      Union[str, Any]         =		(batch_size, 3, img_size, img_size)

							lowerCAmelCase__      :      Tuple         =		self.unet

							lowerCAmelCase__      :      Optional[Any]         =		randn_tensor(a    ,     generator=a   ) * self.scheduler.init_noise_sigma
							lowerCAmelCase__      :      int         =		sample.to(self.device   )

							self.scheduler.set_timesteps(a   )
							self.scheduler.set_sigmas(a   )

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps   )   ):
										lowerCAmelCase__      :      Dict         =		self.scheduler.sigmas[i] * torch.ones(shape[0]    ,     device=self.device   )

										# correction step
										for _ in range(self.scheduler.config.correct_steps   ):
													lowerCAmelCase__      :      Optional[Any]         =		self.unet(a    ,     a   ).sample
													lowerCAmelCase__      :      Dict         =		self.scheduler.step_correct(a    ,     a    ,     generator=a   ).prev_sample

										# prediction step
										lowerCAmelCase__      :      Optional[int]         =		model(a    ,     a   ).sample
										lowerCAmelCase__      :      Optional[Any]         =		self.scheduler.step_pred(a    ,     a    ,     a    ,     generator=a   )

										lowerCAmelCase__      ,				lowerCAmelCase__      :      str         =		output.prev_sample, output.prev_sample_mean

							lowerCAmelCase__      :      Any         =		sample_mean.clamp(0    ,     1   )
							lowerCAmelCase__      :      List[str]         =		sample.cpu().permute(0    ,     2    ,     3    ,     1   ).numpy()
							if output_type == "pil":
										lowerCAmelCase__      :      int         =		self.numpy_to_pil(a   )

							if not return_dict:
										return (sample,)

							return ImagePipelineOutput(images=a   )