





'''simple docstring'''




import math
from typing import Union

import torch

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




class      UpperCamelCase__					(					lowercase_						,					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	1


					@register_to_config
					def __init__(						self				:			Optional[int]      ,						lowerCamelCase_				:			Optional[Any]=20_00      ,						lowerCamelCase_				:			List[str]=0.1      ,						lowerCamelCase_				:			Dict=20      ,						lowerCamelCase_				:			Optional[int]=1e-3						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			None
							SCREAMING_SNAKE_CASE :				Dict		=			None
							SCREAMING_SNAKE_CASE :				Any		=			None


					def lowerCamelCase_			(						self				:			Any      ,						lowerCamelCase_				:			str      ,						lowerCamelCase_				:			Union[str, torch.device] = None						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[int]		=			torch.linspace(1      ,						self.config.sampling_eps      ,						snake_case__      ,						device=snake_case__						)


					def lowerCamelCase_			(						self				:			Tuple      ,						lowerCamelCase_				:			str      ,						lowerCamelCase_				:			int      ,						lowerCamelCase_				:			Dict      ,						lowerCamelCase_				:			Any=None						):

							'''simple docstring'''



							if self.timesteps is None:
									raise ValueError(
									    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""						)

							# TODO(Patrick) better comments + non-PyTorch
							# postprocess model score
							SCREAMING_SNAKE_CASE :				Tuple		=			(
							    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
							)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff						)						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			std.flatten()
							while len(std.shape						) < len(score.shape						):
									SCREAMING_SNAKE_CASE :				Optional[int]		=			std.unsqueeze(-1						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			-score / std

							# compute
							SCREAMING_SNAKE_CASE :				Tuple		=			-1.0 / len(self.timesteps						)

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
							SCREAMING_SNAKE_CASE :				Tuple		=			beta_t.flatten()
							while len(beta_t.shape						) < len(x.shape						):
									SCREAMING_SNAKE_CASE :				str		=			beta_t.unsqueeze(-1						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			-0.5 * beta_t * x

							SCREAMING_SNAKE_CASE :				List[Any]		=			torch.sqrt(snake_case__						)
							SCREAMING_SNAKE_CASE :				Dict		=			drift - diffusion**2 * score
							SCREAMING_SNAKE_CASE :				List[str]		=			x + drift * dt

							# add noise
							SCREAMING_SNAKE_CASE :				str		=			randn_tensor(x.shape      ,						layout=x.layout      ,						generator=snake_case__      ,						device=x.device      ,						dtype=x.dtype						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			x_mean + diffusion * math.sqrt(-dt						) * noise

							return x, x_mean



					def __len__(						self				:			Dict						):

							'''simple docstring'''



							return self.config.num_train_timesteps





'''simple docstring'''





def snake_case_     (					lowercase__ = "The quick brown fox jumps over the lazy dog"   ,       ):
  UpperCAmelCase__      :					Dict				       =							set()

  # Replace all the whitespace in our sentence
  UpperCAmelCase__      :					str				       =							input_str.replace(" "   ,       ""		)
  for alpha in input_str:
    if "a" <= alpha.lower() <= "z":
      frequency.add(alpha.lower()		)
  return len(lowercase__		) == 2_6







def snake_case_     (					lowercase__ = "The quick brown fox jumps over the lazy dog"   ,       ):
  UpperCAmelCase__      :					str				       =							[False] * 2_6
  for char in input_str:
    if char.islower():
      UpperCAmelCase__      :					List[Any]				       =							True
    elif char.isupper():
      UpperCAmelCase__      :					List[Any]				       =							True
  return all(lowercase__		)







def snake_case_     (					lowercase__ = "The quick brown fox jumps over the lazy dog"   ,       ):
  return len({char for char in input_str.lower() if char.isalpha()}		) == 2_6







def snake_case_     (					):
  from timeit import timeit

  UpperCAmelCase__      :					Union[str, Any]				       =							"from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
  print(timeit("is_pangram()"   ,       setup=lowercase__		)		)
  print(timeit("is_pangram_faster()"   ,       setup=lowercase__		)		)
  print(timeit("is_pangram_fastest()"   ,       setup=lowercase__		)		)
  # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
  # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()



