





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      (UnCLIPScheduler,)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int]	,			**UpperCAmelCase_    :       Any)	->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        ={
       "num_train_timesteps": 1_000,
       "variance_type": "fixed_small_log",
       "clip_sample": True,
       "clip_sample_range": 1.0,
       "prediction_type": "epsilon",
   }

   config.update(**UpperCAmelCase_)
   return config

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->int:



   '''simple docstring'''



   for timesteps in [1, 5, 100, 1_000]:
     self.check_over_configs(num_train_timesteps=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->Optional[Any]:



   '''simple docstring'''



   for variance in ["fixed_small_log", "learned_range"]:
     self.check_over_configs(variance_type=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->Any:



   '''simple docstring'''



   for clip_sample in [True, False]:
     self.check_over_configs(clip_sample=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->List[Any]:



   '''simple docstring'''



   for clip_sample_range in [1, 5, 10, 20]:
     self.check_over_configs(clip_sample_range=UpperCAmelCase_)

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



   '''simple docstring'''



   for prediction_type in ["epsilon", "sample"]:
     self.check_over_configs(prediction_type=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->int:



   '''simple docstring'''



   for time_step in [0, 500, 999]:
     for prev_timestep in [None, 5, 100, 250, 500, 750]:
       if prev_timestep is not None and prev_timestep >= time_step:
         continue

       self.check_over_forward(time_step=UpperCAmelCase_	,			prev_timestep=UpperCAmelCase_)

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



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        =self.scheduler_classes[0]
   lowerCamelCase__:				Optional[Any]        =self.get_scheduler_config(variance_type="fixed_small_log")
   lowerCamelCase__:				Optional[int]        =scheduler_class(**UpperCAmelCase_)

   assert torch.sum(torch.abs(scheduler._get_variance(0) - 1.0_0_0_0E-1_0)) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.054_9625)) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.999_4987)) < 1E-5

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->Any:



   '''simple docstring'''



   lowerCamelCase__:				Dict        =self.scheduler_classes[0]
   lowerCamelCase__:				Dict        =self.get_scheduler_config(variance_type="learned_range")
   lowerCamelCase__:				List[str]        =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:				Dict        =0.5

   assert scheduler._get_variance(1	,			predicted_variance=UpperCAmelCase_) - -10.171_2790 < 1E-5
   assert scheduler._get_variance(487	,			predicted_variance=UpperCAmelCase_) - -5.799_8052 < 1E-5
   assert scheduler._get_variance(999	,			predicted_variance=UpperCAmelCase_) - -0.001_0011 < 1E-5

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



   '''simple docstring'''



   lowerCamelCase__:				str        =self.scheduler_classes[0]
   lowerCamelCase__:				List[str]        =self.get_scheduler_config()
   lowerCamelCase__:				Dict        =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:				Optional[int]        =scheduler.timesteps

   lowerCamelCase__:				Dict        =self.dummy_model()
   lowerCamelCase__:				List[str]        =self.dummy_sample_deter
   lowerCamelCase__:				Any        =torch.manual_seed(0)

   for i, t in enumerate(UpperCAmelCase_):
     # 1. predict noise residual
     lowerCamelCase__:				Dict        =model(UpperCAmelCase_	,			UpperCAmelCase_)

     # 2. predict previous mean of sample x_t-1
     lowerCamelCase__:				Union[str, Any]        =scheduler.step(UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_	,			generator=UpperCAmelCase_).prev_sample

     lowerCamelCase__:				int        =pred_prev_sample

   lowerCamelCase__:				Dict        =torch.sum(torch.abs(UpperCAmelCase_))
   lowerCamelCase__:				List[Any]        =torch.mean(torch.abs(UpperCAmelCase_))

   assert abs(result_sum.item() - 252.268_2495) < 1E-2
   assert abs(result_mean.item() - 0.328_4743) < 1E-3

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =self.scheduler_classes[0]
   lowerCamelCase__:				str        =self.get_scheduler_config()
   lowerCamelCase__:				Dict        =scheduler_class(**UpperCAmelCase_)

   scheduler.set_timesteps(25)

   lowerCamelCase__:				Tuple        =scheduler.timesteps

   lowerCamelCase__:				Tuple        =self.dummy_model()
   lowerCamelCase__:				Dict        =self.dummy_sample_deter
   lowerCamelCase__:				Tuple        =torch.manual_seed(0)

   for i, t in enumerate(UpperCAmelCase_):
     # 1. predict noise residual
     lowerCamelCase__:				List[str]        =model(UpperCAmelCase_	,			UpperCAmelCase_)

     if i + 1 == timesteps.shape[0]:
       lowerCamelCase__:				Optional[Any]        =None
     else:
       lowerCamelCase__:				Dict        =timesteps[i + 1]

     # 2. predict previous mean of sample x_t-1
     lowerCamelCase__:				Any        =scheduler.step(
         UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_	,			prev_timestep=UpperCAmelCase_	,			generator=UpperCAmelCase_).prev_sample

     lowerCamelCase__:				str        =pred_prev_sample

   lowerCamelCase__:				Optional[Any]        =torch.sum(torch.abs(UpperCAmelCase_))
   lowerCamelCase__:				Any        =torch.mean(torch.abs(UpperCAmelCase_))

   assert abs(result_sum.item() - 258.204_4983) < 1E-2
   assert abs(result_mean.item() - 0.336_2038) < 1E-3

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->Optional[int]:



   '''simple docstring'''



   pass







 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Tuple:



   '''simple docstring'''



   pass








from __future__ import annotations

from typing import Any



class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def __init__(self    :       Tuple	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       float = 0)	->None:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				Any        =row, column
   lowerCamelCase__:				List[str]        =[[default_value for c in range(UpperCAmelCase_)] for r in range(UpperCAmelCase_)]

 def __str__(self    :       Tuple)	->str:



   '''simple docstring'''



   lowerCamelCase__:				Union[str, Any]        =F"""Matrix consist of {self.row} rows and {self.column} columns\n"""

   # Make string identifier
   lowerCamelCase__:				List[str]        =0
   for row_vector in self.array:
     for obj in row_vector:
       lowerCamelCase__:				int        =max(UpperCAmelCase_	,			len(str(UpperCAmelCase_)))
   lowerCamelCase__:				Any        =F"""%{max_element_length}s"""

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

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

 def __repr__(self    :       Optional[int])	->str:



   '''simple docstring'''



   return str(self)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int]	,			UpperCAmelCase_    :       tuple[int, int])	->bool:



   '''simple docstring'''



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

 def __getitem__(self    :       int	,			UpperCAmelCase_    :       tuple[int, int])	->Any:



   '''simple docstring'''



   assert self.validate_indicies(UpperCAmelCase_)
   return self.array[loc[0]][loc[1]]

 def __setitem__(self    :       Optional[Any]	,			UpperCAmelCase_    :       tuple[int, int]	,			UpperCAmelCase_    :       float)	->None:



   '''simple docstring'''



   assert self.validate_indicies(UpperCAmelCase_)
   lowerCamelCase__:				str        =value

 def __add__(self    :       Dict	,			UpperCAmelCase_    :       Matrix)	->Matrix:



   '''simple docstring'''



   assert isinstance(UpperCAmelCase_	,			UpperCAmelCase_)
   assert self.row == another.row and self.column == another.column

   # Add
   lowerCamelCase__:				Dict        =Matrix(self.row	,			self.column)
   for r in range(self.row):
     for c in range(self.column):
       lowerCamelCase__:				List[str]        =self[r, c] + another[r, c]
   return result

 def __neg__(self    :       str)	->Matrix:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        =Matrix(self.row	,			self.column)
   for r in range(self.row):
     for c in range(self.column):
       lowerCamelCase__:				Union[str, Any]        =-self[r, c]
   return result

 def __sub__(self    :       str	,			UpperCAmelCase_    :       Matrix)	->Matrix:



   '''simple docstring'''



   return self + (-another)

 def __mul__(self    :       List[str]	,			UpperCAmelCase_    :       int | float | Matrix)	->Matrix:



   '''simple docstring'''



   if isinstance(UpperCAmelCase_	,			(int, float)):  # Scalar multiplication
     lowerCamelCase__:				List[Any]        =Matrix(self.row	,			self.column)
     for r in range(self.row):
       for c in range(self.column):
         lowerCamelCase__:				Union[str, Any]        =self[r, c] * another
     return result
   elif isinstance(UpperCAmelCase_	,			UpperCAmelCase_):  # Matrix multiplication
     assert self.column == another.row
     lowerCamelCase__:				Dict        =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:
     lowerCamelCase__:				int        =F"""Unsupported type given for another ({type(UpperCAmelCase_)})"""
     raise TypeError(UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any])	->Matrix:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =Matrix(self.column	,			self.row)
   for r in range(self.row):
     for c in range(self.column):
       lowerCamelCase__:				Optional[int]        =self[r, c]
   return result







 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any]	,			UpperCAmelCase_    :       Matrix	,			UpperCAmelCase_    :       Matrix)	->Any:



   '''simple docstring'''



   assert isinstance(UpperCAmelCase_	,			UpperCAmelCase_) and isinstance(UpperCAmelCase_	,			UpperCAmelCase_)
   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
   lowerCamelCase__:				Tuple        =v.transpose()
   lowerCamelCase__:				Optional[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_			(	)					->		None:







       """simple docstring"""





       lowerCamelCase__:				List[str]        =Matrix(3     ,   3     ,   0		)
       for i in range(3		):
         lowerCamelCase__:				Union[str, Any]        =1
       print(F"""a^(-1) is {ainv}"""		)
       # u, v
       lowerCamelCase__:				Optional[int]        =Matrix(3     ,   1     ,   0		)
       lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				int        =1, 2, -3
       lowerCamelCase__:				Optional[Any]        =Matrix(3     ,   1     ,   0		)
       lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				Any        =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(__a     ,   __a		)}"""		)





     def 					lowerCAmelCase_			(	)					->		None:







       """simple docstring"""





       import doctest

       doctest.testmod()






     testa()

