

"""simple docstring"""





import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			(EulerDiscreteScheduler,)
					lowercase__												=			10







					def 			__lowerCAmelCase					(			self     :							str  ,**lowercase_     :							Union[str, Any]						):
						lowerCAmelCase__      :		Optional[Any]    							=      {
						    '''num_train_timesteps''': 1_1_0_0,
						    '''beta_start''': 0.0001,
						    '''beta_end''': 0.02,
						    '''beta_schedule''': '''linear''',
						}

						config.update(**lowercase_						)
						return config







					def 			__lowerCAmelCase					(			self     :							Tuple						):
						for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowercase_						)







					def 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]  ,[0.0002, 0.002, 0.02]						):
							self.check_over_configs(beta_start=lowercase_  ,beta_end=lowercase_						)







					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						for schedule in ["linear", "scaled_linear"]:
							self.check_over_configs(beta_schedule=lowercase_						)







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						for prediction_type in ["epsilon", "v_prediction"]:
							self.check_over_configs(prediction_type=lowercase_						)







					def 			__lowerCAmelCase					(			self     :							str						):
						lowerCAmelCase__      :		Optional[int]    							=      self.scheduler_classes[0]
						lowerCAmelCase__      :		Any    							=      self.get_scheduler_config()
						lowerCAmelCase__      :		Any    							=      scheduler_class(**lowercase_						)

						scheduler.set_timesteps(self.num_inference_steps						)

						lowerCAmelCase__      :		Union[str, Any]    							=      torch.manual_seed(0						)

						lowerCAmelCase__      :		Tuple    							=      self.dummy_model()
						lowerCAmelCase__      :		Union[str, Any]    							=      self.dummy_sample_deter * scheduler.init_noise_sigma
						lowerCAmelCase__      :		int    							=      sample.to(lowercase_						)

						for i, t in enumerate(scheduler.timesteps						):
							lowerCAmelCase__      :		Optional[int]    							=      scheduler.scale_model_input(lowercase_  ,lowercase_						)

							lowerCAmelCase__      :		Optional[Any]    							=      model(lowercase_  ,lowercase_						)

							lowerCAmelCase__      :		Dict    							=      scheduler.step(lowercase_  ,lowercase_  ,lowercase_  ,generator=lowercase_						)
							lowerCAmelCase__      :		str    							=      output.prev_sample

						lowerCAmelCase__      :		str    							=      torch.sum(torch.abs(lowercase_						)						)
						lowerCAmelCase__      :		List[str]    							=      torch.mean(torch.abs(lowercase_						)						)

						assert abs(result_sum.item() - 10.0807						) < 1E-2
						assert abs(result_mean.item() - 0.0131						) < 1E-3







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		Optional[int]    							=      self.scheduler_classes[0]
						lowerCAmelCase__      :		Optional[int]    							=      self.get_scheduler_config(prediction_type='''v_prediction'''						)
						lowerCAmelCase__      :		Optional[int]    							=      scheduler_class(**lowercase_						)

						scheduler.set_timesteps(self.num_inference_steps						)

						lowerCAmelCase__      :		Optional[Any]    							=      torch.manual_seed(0						)

						lowerCAmelCase__      :		Optional[int]    							=      self.dummy_model()
						lowerCAmelCase__      :		List[Any]    							=      self.dummy_sample_deter * scheduler.init_noise_sigma
						lowerCAmelCase__      :		str    							=      sample.to(lowercase_						)

						for i, t in enumerate(scheduler.timesteps						):
							lowerCAmelCase__      :		Tuple    							=      scheduler.scale_model_input(lowercase_  ,lowercase_						)

							lowerCAmelCase__      :		List[Any]    							=      model(lowercase_  ,lowercase_						)

							lowerCAmelCase__      :		List[str]    							=      scheduler.step(lowercase_  ,lowercase_  ,lowercase_  ,generator=lowercase_						)
							lowerCAmelCase__      :		Any    							=      output.prev_sample

						lowerCAmelCase__      :		Dict    							=      torch.sum(torch.abs(lowercase_						)						)
						lowerCAmelCase__      :		int    							=      torch.mean(torch.abs(lowercase_						)						)

						assert abs(result_sum.item() - 0.0002						) < 1E-2
						assert abs(result_mean.item() - 2.2_676E-06						) < 1E-3







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						lowerCAmelCase__      :		Optional[int]    							=      self.scheduler_classes[0]
						lowerCAmelCase__      :		int    							=      self.get_scheduler_config()
						lowerCAmelCase__      :		List[str]    							=      scheduler_class(**lowercase_						)

						scheduler.set_timesteps(self.num_inference_steps  ,device=lowercase_						)

						lowerCAmelCase__      :		Any    							=      torch.manual_seed(0						)

						lowerCAmelCase__      :		int    							=      self.dummy_model()
						lowerCAmelCase__      :		Dict    							=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						lowerCAmelCase__      :		int    							=      sample.to(lowercase_						)

						for t in scheduler.timesteps:
							lowerCAmelCase__      :		Optional[int]    							=      scheduler.scale_model_input(lowercase_  ,lowercase_						)

							lowerCAmelCase__      :		Any    							=      model(lowercase_  ,lowercase_						)

							lowerCAmelCase__      :		List[str]    							=      scheduler.step(lowercase_  ,lowercase_  ,lowercase_  ,generator=lowercase_						)
							lowerCAmelCase__      :		Union[str, Any]    							=      output.prev_sample

						lowerCAmelCase__      :		int    							=      torch.sum(torch.abs(lowercase_						)						)
						lowerCAmelCase__      :		List[Any]    							=      torch.mean(torch.abs(lowercase_						)						)

						assert abs(result_sum.item() - 10.0807						) < 1E-2
						assert abs(result_mean.item() - 0.0131						) < 1E-3







					def 			__lowerCAmelCase					(			self     :							Tuple						):
						lowerCAmelCase__      :		int    							=      self.scheduler_classes[0]
						lowerCAmelCase__      :		Any    							=      self.get_scheduler_config()
						lowerCAmelCase__      :		int    							=      scheduler_class(**lowercase_  ,use_karras_sigmas=lowercase_						)

						scheduler.set_timesteps(self.num_inference_steps  ,device=lowercase_						)

						lowerCAmelCase__      :		Optional[int]    							=      torch.manual_seed(0						)

						lowerCAmelCase__      :		Optional[int]    							=      self.dummy_model()
						lowerCAmelCase__      :		Any    							=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						lowerCAmelCase__      :		Union[str, Any]    							=      sample.to(lowercase_						)

						for t in scheduler.timesteps:
							lowerCAmelCase__      :		Dict    							=      scheduler.scale_model_input(lowercase_  ,lowercase_						)

							lowerCAmelCase__      :		int    							=      model(lowercase_  ,lowercase_						)

							lowerCAmelCase__      :		Tuple    							=      scheduler.step(lowercase_  ,lowercase_  ,lowercase_  ,generator=lowercase_						)
							lowerCAmelCase__      :		List[Any]    							=      output.prev_sample

						lowerCAmelCase__      :		List[str]    							=      torch.sum(torch.abs(lowercase_						)						)
						lowerCAmelCase__      :		str    							=      torch.mean(torch.abs(lowercase_						)						)

						assert abs(result_sum.item() - 124.52_2994_9951_1719						) < 1E-2
						assert abs(result_mean.item() - 0.1_6213_9326_3339_9963						) < 1E-3







"""simple docstring"""





from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase

def     __SCREAMING_SNAKE_CASE    ( A_					):
	lowerCAmelCase__							,lowerCAmelCase__      :		Dict    							=      analyze_text(A_					)
	lowerCAmelCase__      :		Any    							=      list(''' ''' + ascii_lowercase					)
	# what is our total sum of probabilities.
	lowerCAmelCase__      :		Optional[Any]    							=      sum(single_char_strings.values()					)

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

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

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

    # 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     __SCREAMING_SNAKE_CASE    ( A_					):
	lowerCAmelCase__      :		List[str]    							=      Counter()  # type: ignore
	lowerCAmelCase__      :		Optional[int]    							=      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(A_					) - 1					):
		single_char_strings[text[i]] += 1
		two_char_strings[text[i : i + 2]] += 1
	return single_char_strings, two_char_strings

def     __SCREAMING_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()




