

"""simple docstring"""




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      __lowercase			(    __lowerCamelCase					):
  snake_case_				       =       4_2
  snake_case_				       =       None







def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase=0.999							,    __UpperCamelCase="cosine"							,    ):
 '''simple docstring'''





 if alpha_transform_type == "cosine":

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

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(__UpperCamelCase      ):
   return math.exp(t * -12.0      )

 else:
  raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"      )

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







class      __lowercase			(    __lowerCamelCase	,			__lowerCamelCase					):

  @register_to_config
  def __init__(  self		:			List[Any]					,A		:			int = 1_000					,A		:			str = "fixed_small_log"					,A		:			bool = True					,A		:			Optional[float] = 1.0					,A		:			str = "epsilon"					,A		:			str = "squaredcos_cap_v2"					,):






   '''simple docstring'''
   if beta_schedule != "squaredcos_cap_v2":
    raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"""    )

   UpperCAmelCase__					:  Dict					   =					betas_for_alpha_bar(A    )

   UpperCAmelCase__					:  Dict					   =					1.0 - self.betas
   UpperCAmelCase__					:  Optional[Any]					   =					torch.cumprod(self.alphas					,dim=0    )
   UpperCAmelCase__					:  Any					   =					torch.tensor(1.0    )

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

   # setable values
   UpperCAmelCase__					:  int					   =					None
   UpperCAmelCase__					:  str					   =					torch.from_numpy(np.arange(0					,A    )[::-1].copy()    )

   UpperCAmelCase__					:  Union[str, Any]					   =					variance_type

  def    __lowercase       (  self		:			Tuple					,A		:			torch.FloatTensor					,A		:			Optional[int] = None    ):






   '''simple docstring'''
   return sample

  def    __lowercase       (  self		:			Optional[int]					,A		:			int					,A		:			Union[str, torch.device] = None    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					num_inference_steps
   UpperCAmelCase__					:  str					   =					(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
   UpperCAmelCase__					:  Any					   =					(np.arange(0					,A    ) * step_ratio).round()[::-1].copy().astype(np.intaa    )
   UpperCAmelCase__					:  List[Any]					   =					torch.from_numpy(A    ).to(A    )

  def    __lowercase       (  self		:			Dict					,A		:			int					,A		:			Any=None					,A		:			Optional[int]=None					,A		:			Union[str, Any]=None    ):






   '''simple docstring'''
   if prev_timestep is None:
    UpperCAmelCase__					:  str					   =					t - 1

   UpperCAmelCase__					:  Optional[Any]					   =					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__					:  List[str]					   =					1 - alpha_prod_t_prev

   if prev_timestep == t - 1:
    UpperCAmelCase__					:  Optional[int]					   =					self.betas[t]
   else:
    UpperCAmelCase__					:  Optional[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__					:  List[Any]					   =					beta_prod_t_prev / beta_prod_t * beta

   if variance_type is None:
    UpperCAmelCase__					:  int					   =					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__					:  List[str]					   =					torch.exp(0.5 * variance    )
   elif variance_type == "learned_range":
    # NOTE difference with DDPM scheduler
    UpperCAmelCase__					:  List[Any]					   =					variance.log()
    UpperCAmelCase__					:  List[Any]					   =					beta.log()

    UpperCAmelCase__					:  str					   =					(predicted_variance + 1) / 2
    UpperCAmelCase__					:  str					   =					frac * max_log + (1 - frac) * min_log

   return variance

  def    __lowercase       (  self		:			Dict					,A		:			torch.FloatTensor					,A		:			int					,A		:			torch.FloatTensor					,A		:			Optional[int] = None					,A		:			Any=None					,A		:			bool = True					,):






   '''simple docstring'''
   UpperCAmelCase__					:  str					   =					timestep

   if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
    UpperCAmelCase__					:  Tuple					   =					torch.split(A					,sample.shape[1]					,dim=1    )
   else:
    UpperCAmelCase__					:  int					   =					None

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

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

   if prev_timestep == t - 1:
    UpperCAmelCase__					:  List[Any]					   =					self.betas[t]
    UpperCAmelCase__					:  List[str]					   =					self.alphas[t]
   else:
    UpperCAmelCase__					:  List[Any]					   =					1 - alpha_prod_t / alpha_prod_t_prev
    UpperCAmelCase__					:  Dict					   =					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__					:  Optional[Any]					   =					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
   elif self.config.prediction_type == "sample":
    UpperCAmelCase__					:  Union[str, Any]					   =					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__					:  int					   =					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__					:  str					   =					(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
   UpperCAmelCase__					:  Any					   =					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__					:  Optional[Any]					   =					pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

   # 6. Add noise
   UpperCAmelCase__					:  Optional[Any]					   =					0
   if t > 0:
    UpperCAmelCase__					:  Any					   =					randn_tensor(
        model_output.shape					,dtype=model_output.dtype					,generator=A					,device=model_output.device    )

    UpperCAmelCase__					:  List[str]					   =					self._get_variance(
        A					,predicted_variance=A					,prev_timestep=A					,)

    if self.variance_type == "fixed_small_log":
     UpperCAmelCase__					:  Union[str, Any]					   =					variance
    elif self.variance_type == "learned_range":
     UpperCAmelCase__					:  Dict					   =					(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__					:  Dict					   =					variance * variance_noise

   UpperCAmelCase__					:  Any					   =					pred_prev_sample + variance

   if not return_dict:
    return (pred_prev_sample,)

   return UnCLIPSchedulerOutput(prev_sample=A					,pred_original_sample=A    )




  def    __lowercase       (  self		:			str					,A		:			torch.FloatTensor					,A		:			torch.FloatTensor					,A		:			torch.IntTensor					,):






   '''simple docstring'''
   UpperCAmelCase__					:  str					   =					self.alphas_cumprod.to(device=original_samples.device					,dtype=original_samples.dtype    )
   UpperCAmelCase__					:  List[str]					   =					timesteps.to(original_samples.device    )

   UpperCAmelCase__					:  int					   =					alphas_cumprod[timesteps] ** 0.5
   UpperCAmelCase__					:  Optional[int]					   =					sqrt_alpha_prod.flatten()
   while len(sqrt_alpha_prod.shape    ) < len(original_samples.shape    ):
    UpperCAmelCase__					:  Any					   =					sqrt_alpha_prod.unsqueeze(-1    )

   UpperCAmelCase__					:  List[str]					   =					(1 - alphas_cumprod[timesteps]) ** 0.5
   UpperCAmelCase__					:  int					   =					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__					:  Dict					   =					sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
   return noisy_samples





"""simple docstring"""




from __future__ import annotations

from typing import Any







class      __lowercase			:

  def __init__(  self		:			Optional[int]					,A		:			int					,A		:			int					,A		:			float = 0    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__					:  str					   =					row, column
   UpperCAmelCase__					:  int					   =					[[default_value for c in range(A    )] for r in range(A    )]

  def __str__(  self		:			Union[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					f"Matrix consist of {self.row} rows and {self.column} columns\n"

   # Make string identifier
   UpperCAmelCase__					:  Optional[Any]					   =					0
   for row_vector in self.array:
    for obj in row_vector:
     UpperCAmelCase__					:  Optional[int]					   =					max(A					,len(str(A    )    )    )
   UpperCAmelCase__					:  int					   =					f"%{max_element_length}s"

   # Make string and return
   def single_line(A		:			list[float]    ) -> str:
    nonlocal string_format_identifier
    UpperCAmelCase__					:  List[str]					   =					"""["""
    line += ", ".join(string_format_identifier % (obj,) for obj in row_vector    )
    line += "]"
    return line

   s += "\n".join(single_line(A    ) for row_vector in self.array    )
   return s

  def __repr__(  self		:			Dict    ):






   '''simple docstring'''
   return str(self    )

  def    __lowercase       (  self		:			int					,A		:			tuple[int, int]    ):






   '''simple docstring'''
   if not (isinstance(A					,(list, tuple)    ) and len(A    ) == 2):
    return False
   elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
    return False
   else:
    return True

  def __getitem__(  self		:			Optional[int]					,A		:			tuple[int, int]    ):






   '''simple docstring'''
   assert self.validate_indicies(A    )
   return self.array[loc[0]][loc[1]]

  def __setitem__(  self		:			List[Any]					,A		:			tuple[int, int]					,A		:			float    ):






   '''simple docstring'''
   assert self.validate_indicies(A    )
   UpperCAmelCase__					:  str					   =					value

  def __add__(  self		:			Any					,A		:			Matrix    ):






   '''simple docstring'''
   assert isinstance(A					,A    )
   assert self.row == another.row and self.column == another.column

   # Add
   UpperCAmelCase__					:  Optional[int]					   =					Matrix(self.row					,self.column    )
   for r in range(self.row    ):
    for c in range(self.column    ):
     UpperCAmelCase__					:  Union[str, Any]					   =					self[r, c] + another[r, c]
   return result

  def __neg__(  self		:			Union[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					Matrix(self.row					,self.column    )
   for r in range(self.row    ):
    for c in range(self.column    ):
     UpperCAmelCase__					:  int					   =					-self[r, c]
   return result

  def __sub__(  self		:			str					,A		:			Matrix    ):






   '''simple docstring'''
   return self + (-another)

  def __mul__(  self		:			Union[str, Any]					,A		:			int | float | Matrix    ):






   '''simple docstring'''

   if isinstance(A					,(int, float)    ):  # Scalar multiplication
    UpperCAmelCase__					:  Dict					   =					Matrix(self.row					,self.column    )
    for r in range(self.row    ):
     for c in range(self.column    ):
      UpperCAmelCase__					:  Any					   =					self[r, c] * another
    return result
   elif isinstance(A					,A    ):  # Matrix multiplication
    assert self.column == another.row
    UpperCAmelCase__					:  Optional[int]					   =					Matrix(self.row					,another.column    )
    for r in range(self.row    ):
     for c in range(another.column    ):
      for i in range(self.column    ):
       result[r, c] += self[r, i] * another[i, c]
    return result
   else:
    UpperCAmelCase__					:  Union[str, Any]					   =					f"Unsupported type given for another ({type(A    )})"
    raise TypeError(A    )

  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					Matrix(self.column					,self.row    )
   for r in range(self.row    ):
    for c in range(self.column    ):
     UpperCAmelCase__					:  Optional[int]					   =					self[r, c]
   return result




  def    __lowercase       (  self		:			Any					,A		:			Matrix					,A		:			Matrix    ):






   '''simple docstring'''
   assert isinstance(A					,A    ) and isinstance(A					,A    )
   assert self.row == self.column == u.row == v.row  # u, v should be column vector
   assert u.column == v.column == 1  # u, v should be column vector

   # Calculate
   UpperCAmelCase__					:  Optional[Any]					   =					v.transpose()
   UpperCAmelCase__					:  Union[str, Any]					   =					(v_t * self * u)[0, 0] + 1
   if numerator_factor == 0:
    return None  # It's not invertable
   return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":
      def   lowerCAmelCase						(					):
       '''simple docstring'''





       UpperCAmelCase__					:  List[str]					   =					Matrix(3							,    3							,    0      )
       for i in range(3      ):
        UpperCAmelCase__					:  List[str]					   =					1
       print(F"a^(-1) is {ainv}"      )
       # u, v
       UpperCAmelCase__					:  List[Any]					   =					Matrix(3							,    1							,    0      )
       UpperCAmelCase__   ,      UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[int]					   =					1, 2, -3
       UpperCAmelCase__					:  List[str]					   =					Matrix(3							,    1							,    0      )
       UpperCAmelCase__   ,      UpperCAmelCase__   ,      UpperCAmelCase__					:  Tuple					   =					4, -2, 5
       print(F"u is {u}"      )
       print(F"v is {v}"      )
       print(F"uv^T is {u * v.transpose()}"      )
       # Sherman Morrison
       print(F"(a + uv^T)^(-1) is {ainv.sherman_morrison(__UpperCamelCase							,    __UpperCamelCase      )}"      )
      def   lowerCAmelCase						(					):
       '''simple docstring'''





       import doctest

       doctest.testmod()




      testa()





