






'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



					@property
					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       Optional[Any]:



						'''simple docstring'''





						torch.manual_seed(0					)
						_lowercase					:       Tuple       =		UNetaDModel(
						    block_out_channels=(32, 64)     ,  layers_per_block=2     ,  sample_size=32     ,  in_channels=3     ,  out_channels=3     ,  down_block_types=('DownBlock2D', 'AttnDownBlock2D')     ,  up_block_types=('AttnUpBlock2D', 'UpBlock2D')     ,  )
						return model




					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       Tuple:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		self.dummy_uncond_unet
						_lowercase					:       List[str]       =		ScoreSdeVeScheduler()

						_lowercase					:       Optional[int]       =		ScoreSdeVePipeline(unet=UpperCamelCase_     ,  scheduler=UpperCamelCase_					)
						sde_ve.to(UpperCamelCase_					)
						sde_ve.set_progress_bar_config(disable=UpperCamelCase_					)

						_lowercase					:       Any       =		torch.manual_seed(0					)
						_lowercase					:       List[str]       =		sde_ve(num_inference_steps=2     ,  output_type='numpy'     ,  generator=UpperCamelCase_					).images

						_lowercase					:       str       =		torch.manual_seed(0					)
						_lowercase					:       Union[str, Any]       =		sde_ve(num_inference_steps=2     ,  output_type='numpy'     ,  generator=UpperCamelCase_     ,  return_dict=UpperCamelCase_					)[
						    0
						]

						_lowercase					:       Optional[Any]       =		image[0, -3:, -3:, -1]
						_lowercase					:       str       =		image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 32, 32, 3)
						_lowercase					:       Union[str, Any]       =		np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

						assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1E-2






@slow
@require_torch
class 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



					def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		'google/ncsnpp-church-256'
						_lowercase					:       int       =		UNetaDModel.from_pretrained(UpperCamelCase_					)

						_lowercase					:       List[Any]       =		ScoreSdeVeScheduler.from_pretrained(UpperCamelCase_					)

						_lowercase					:       Optional[Any]       =		ScoreSdeVePipeline(unet=UpperCamelCase_     ,  scheduler=UpperCamelCase_					)
						sde_ve.to(UpperCamelCase_					)
						sde_ve.set_progress_bar_config(disable=UpperCamelCase_					)

						_lowercase					:       int       =		torch.manual_seed(0					)
						_lowercase					:       Any       =		sde_ve(num_inference_steps=10     ,  output_type='numpy'     ,  generator=UpperCamelCase_					).images

						_lowercase					:       List[str]       =		image[0, -3:, -3:, -1]

						assert image.shape == (1, 256, 256, 3)

						_lowercase					:       List[str]       =		np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]					)

						assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2





'''simple docstring'''

from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def 						__UpperCamelCase				(							_lowercase					) ->			None:
	_lowercase  ,    _lowercase					:       List[Any]       =		analyze_text(_lowercase					)
	_lowercase					:       Any       =		list(' ' + ascii_lowercase					)
	# what is our total sum of probabilities.
	_lowercase					:       Union[str, Any]       =		sum(single_char_strings.values()					)

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

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

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

    # 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				(							_lowercase					) ->			tuple[dict, dict]:
	_lowercase					:       Optional[Any]       =		Counter()  # type: ignore
	_lowercase					:       List[Any]       =		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(_lowercase					) - 1					):
		single_char_strings[text[i]] += 1
		two_char_strings[text[i : i + 2]] += 1
	return single_char_strings, two_char_strings







def 						__UpperCamelCase				(							) ->			List[Any]:
	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()
