

import math
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
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class  __UpperCAmelCase					(	a__						):
    UpperCamelCase       					=					4_2
    UpperCamelCase       					=					None






def       a__					(		UpperCAmelCase							:      List[Any]    ,			UpperCAmelCase							:      Optional[Any]=0.999    ,			UpperCAmelCase							:      List[Any]="cosine"    ,			)  ->						Any:

  if alpha_transform_type == "cosine":

    def alpha_bar_fn(UpperCAmelCase							:      str      ):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2      ) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(UpperCAmelCase							:      List[str]      ):
      return math.exp(t * -12.0      )

  else:
    raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''      )

  UpperCAmelCase	:							Any						=			[]
  for i in range(_lowerCamelCase      ):
    UpperCAmelCase	:							List[Any]						=			i / num_diffusion_timesteps
    UpperCAmelCase	:							int						=			(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(_lowerCamelCase      ) / alpha_bar_fn(_lowerCamelCase      )    ,			_lowerCamelCase      )      )
  return torch.tensor(_lowerCamelCase    ,			dtype=torch.floataa      )






class  __UpperCAmelCase					(	a__      ,  a__						):



    @register_to_config
    def __init__(							self							:					List[str],		__A							:					int = 1_0_0_0,		__A							:					str = "fixed_small_log",		__A							:					bool = True,		__A							:					Optional[float] = 1.0,		__A							:					str = "epsilon",		__A							:					str = "squaredcos_cap_v2",		):
      if beta_schedule != "squaredcos_cap_v2":
        raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''						)

      UpperCAmelCase	:							Tuple						=			betas_for_alpha_bar(__A						)

      UpperCAmelCase	:							Dict						=			1.0 - self.betas
      UpperCAmelCase	:							int						=			torch.cumprod(self.alphas,		dim=0						)
      UpperCAmelCase	:							List[str]						=			torch.tensor(1.0						)

      # standard deviation of the initial noise distribution
      UpperCAmelCase	:							Any						=			1.0

      # setable values
      UpperCAmelCase	:							int						=			None
      UpperCAmelCase	:							Optional[Any]						=			torch.from_numpy(np.arange(0,		__A						)[::-1].copy()						)

      UpperCAmelCase	:							int						=			variance_type



    def 	__magic_name__   (							self							:					Any,		__A							:					torch.FloatTensor,		__A							:					Optional[int] = None						):
      return sample



    def 	__magic_name__   (							self							:					Optional[Any],		__A							:					int,		__A							:					Union[str, torch.device] = None						):
      UpperCAmelCase	:							Union[str, Any]						=			num_inference_steps
      UpperCAmelCase	:							List[str]						=			(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
      UpperCAmelCase	:							List[Any]						=			(np.arange(0,		__A						) * step_ratio).round()[::-1].copy().astype(np.intaa						)
      UpperCAmelCase	:							Tuple						=			torch.from_numpy(__A						).to(__A						)



    def 	__magic_name__   (							self							:					str,		__A							:					Dict,		__A							:					str=None,		__A							:					Any=None,		__A							:					Dict=None						):
      if prev_timestep is None:
        UpperCAmelCase	:							Tuple						=			t - 1

      UpperCAmelCase	:							List[str]						=			self.alphas_cumprod[t]
      UpperCAmelCase	:							Optional[Any]						=			self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
      UpperCAmelCase	:							Any						=			1 - alpha_prod_t
      UpperCAmelCase	:							Optional[int]						=			1 - alpha_prod_t_prev

      if prev_timestep == t - 1:
        UpperCAmelCase	:							List[Any]						=			self.betas[t]
      else:
        UpperCAmelCase	:							int						=			1 - alpha_prod_t / alpha_prod_t_prev

      # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
      # and sample from it to get previous sample
      # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
      UpperCAmelCase	:							Optional[Any]						=			beta_prod_t_prev / beta_prod_t * beta

      if variance_type is None:
        UpperCAmelCase	:							Union[str, Any]						=			self.config.variance_type

      # hacks - were probably added for training stability
      if variance_type == "fixed_small_log":
        UpperCAmelCase	:							List[str]						=			torch.log(torch.clamp(__A,		min=1E-20						)						)
        UpperCAmelCase	:							str						=			torch.exp(0.5 * variance						)
      elif variance_type == "learned_range":
        # NOTE difference with DDPM scheduler
        UpperCAmelCase	:							Optional[int]						=			variance.log()
        UpperCAmelCase	:							Optional[int]						=			beta.log()

        UpperCAmelCase	:							Tuple						=			(predicted_variance + 1) / 2
        UpperCAmelCase	:							Dict						=			frac * max_log + (1 - frac) * min_log

      return variance



    def 	__magic_name__   (							self							:					str,		__A							:					torch.FloatTensor,		__A							:					int,		__A							:					torch.FloatTensor,		__A							:					Optional[int] = None,		__A							:					Union[str, Any]=None,		__A							:					bool = True,		):
      UpperCAmelCase	:							Tuple						=			timestep

      if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
        UpperCAmelCase	:							Optional[int]						=			torch.split(__A,		sample.shape[1],		dim=1						)
      else:
        UpperCAmelCase	:							Optional[Any]						=			None

      # 1. compute alphas, betas
      if prev_timestep is None:
        UpperCAmelCase	:							Optional[Any]						=			t - 1

      UpperCAmelCase	:							Any						=			self.alphas_cumprod[t]
      UpperCAmelCase	:							Union[str, Any]						=			self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
      UpperCAmelCase	:							Dict						=			1 - alpha_prod_t
      UpperCAmelCase	:							Optional[int]						=			1 - alpha_prod_t_prev

      if prev_timestep == t - 1:
        UpperCAmelCase	:							str						=			self.betas[t]
        UpperCAmelCase	:							int						=			self.alphas[t]
      else:
        UpperCAmelCase	:							Tuple						=			1 - alpha_prod_t / alpha_prod_t_prev
        UpperCAmelCase	:							Optional[int]						=			1 - beta

      # 2. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
      if self.config.prediction_type == "epsilon":
        UpperCAmelCase	:							str						=			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif self.config.prediction_type == "sample":
        UpperCAmelCase	:							Dict						=			model_output
      else:
        raise ValueError(
            F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
            ''' for the UnCLIPScheduler.'''						)

      # 3. Clip "predicted x_0"
      if self.config.clip_sample:
        UpperCAmelCase	:							Tuple						=			torch.clamp(
            __A,		-self.config.clip_sample_range,		self.config.clip_sample_range						)

      # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      UpperCAmelCase	:							Optional[Any]						=			(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
      UpperCAmelCase	:							List[str]						=			alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

      # 5. Compute predicted previous sample µ_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      UpperCAmelCase	:							Union[str, Any]						=			pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

      # 6. Add noise
      UpperCAmelCase	:							Union[str, Any]						=			0
      if t > 0:
        UpperCAmelCase	:							Union[str, Any]						=			randn_tensor(
            model_output.shape,		dtype=model_output.dtype,		generator=__A,		device=model_output.device						)

        UpperCAmelCase	:							Dict						=			self._get_variance(
            __A,		predicted_variance=__A,		prev_timestep=__A,		)

        if self.variance_type == "fixed_small_log":
          UpperCAmelCase	:							Tuple						=			variance
        elif self.variance_type == "learned_range":
          UpperCAmelCase	:							Tuple						=			(0.5 * variance).exp()
        else:
          raise ValueError(
              F'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
              ''' for the UnCLIPScheduler.'''						)

        UpperCAmelCase	:							List[Any]						=			variance * variance_noise

      UpperCAmelCase	:							Optional[Any]						=			pred_prev_sample + variance

      if not return_dict:
        return (pred_prev_sample,)

      return UnCLIPSchedulerOutput(prev_sample=__A,		pred_original_sample=__A						)





    def 	__magic_name__   (							self							:					Tuple,		__A							:					torch.FloatTensor,		__A							:					torch.FloatTensor,		__A							:					torch.IntTensor,		):
      # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
      UpperCAmelCase	:							Union[str, Any]						=			self.alphas_cumprod.to(device=original_samples.device,		dtype=original_samples.dtype						)
      UpperCAmelCase	:							Optional[int]						=			timesteps.to(original_samples.device						)

      UpperCAmelCase	:							List[str]						=			alphas_cumprod[timesteps] ** 0.5
      UpperCAmelCase	:							Optional[int]						=			sqrt_alpha_prod.flatten()
      while len(sqrt_alpha_prod.shape						) < len(original_samples.shape						):
        UpperCAmelCase	:							Tuple						=			sqrt_alpha_prod.unsqueeze(-1						)

      UpperCAmelCase	:							List[str]						=			(1 - alphas_cumprod[timesteps]) ** 0.5
      UpperCAmelCase	:							Optional[Any]						=			sqrt_one_minus_alpha_prod.flatten()
      while len(sqrt_one_minus_alpha_prod.shape						) < len(original_samples.shape						):
        UpperCAmelCase	:							int						=			sqrt_one_minus_alpha_prod.unsqueeze(-1						)

      UpperCAmelCase	:							Any						=			sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
      return noisy_samples








from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean

def       a__					(		UpperCAmelCase							:      int      )  ->						Dict:

  # A local function to see if a dot lands in the circle.
  def is_in_circle(UpperCAmelCase							:      float    ,			UpperCAmelCase							:      float      ) -> bool:
    UpperCAmelCase	:							Dict						=			sqrt((x**2) + (y**2)      )
    # Our circle has a radius of 1, so a distance
    # greater than 1 would land outside the circle.
    return distance_from_centre <= 1

  # The proportion of guesses that landed in the circle
  UpperCAmelCase	:							List[str]						=			mean(
      int(is_in_circle(uniform(-1.0    ,			1.0      )    ,			uniform(-1.0    ,			1.0      )      )      )
      for _ in range(UpperCAmelCase      )      )
  # The ratio of the area for circle to square is pi/4.
  UpperCAmelCase	:							int						=			proportion * 4
  print(f'''The estimated value of pi is {pi_estimate}'''      )
  print(f'''The numpy value of pi is {pi}'''      )
  print(f'''The total error is {abs(pi - pi_estimate      )}'''      )

def       a__					(		UpperCAmelCase							:      int    ,			UpperCAmelCase							:      Callable[[float], float]    ,			UpperCAmelCase							:      float = 0.0    ,			UpperCAmelCase							:      float = 1.0    ,			)  ->						float:
  return mean(
      function_to_integrate(uniform(UpperCAmelCase    ,			UpperCAmelCase      )      ) for _ in range(UpperCAmelCase      )      ) * (max_value - min_value)

def       a__					(		UpperCAmelCase							:      int    ,			UpperCAmelCase							:      float = 0.0    ,			UpperCAmelCase							:      float = 1.0      )  ->						None:

  def identity_function(UpperCAmelCase							:      float      ) -> float:
    return x

  UpperCAmelCase	:							int						=			area_under_curve_estimator(
      UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase      )
  UpperCAmelCase	:							Tuple						=			(max_value * max_value - min_value * min_value) / 2

  print('''******************'''      )
  print(f'''Estimating area under y=x where x varies from {min_value} to {max_value}'''      )
  print(f'''Estimated value is {estimated_value}'''      )
  print(f'''Expected value is {expected_value}'''      )
  print(f'''Total error is {abs(estimated_value - expected_value      )}'''      )
  print('''******************'''      )

def       a__					(		UpperCAmelCase							:      int      )  ->						None:

  def function_to_integrate(UpperCAmelCase							:      float      ) -> float:
    return sqrt(4.0 - x * x      )

  UpperCAmelCase	:							Optional[int]						=			area_under_curve_estimator(
      UpperCAmelCase    ,			UpperCAmelCase    ,			0.0    ,			2.0      )

  print('''******************'''      )
  print('''Estimating pi using area_under_curve_estimator'''      )
  print(f'''Estimated value is {estimated_value}'''      )
  print(f'''Expected value is {pi}'''      )
  print(f'''Total error is {abs(estimated_value - pi      )}'''      )
  print('''******************'''      )


if __name__ == "__main__":
     import doctest

     doctest.testmod()





