





from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class     UpperCamelCase_   (			__SCREAMING_SNAKE_CASE					):

    '''simple docstring'''






    def __init__(      self				:     List[Any] ,			UpperCAmelCase__				:     Any ,			UpperCAmelCase__				:     Tuple)						->str:





           '''simple docstring'''

           super().__init__()

           # make sure scheduler can always be converted to DDIM
           A__							=   DDIMScheduler.from_config(scheduler.config)

           self.register_modules(unet=_snake_case ,			scheduler=_snake_case)





    @torch.no_grad()
    def __call__(      self				:     int ,			UpperCAmelCase__				:     List[str] = 1 ,			UpperCAmelCase__				:     Union[str, Any] = None ,			UpperCAmelCase__				:     Any = 0.0 ,			UpperCAmelCase__				:     Union[str, Any] = 50 ,			UpperCAmelCase__				:     Tuple = None ,			UpperCAmelCase__				:     Optional[int] = "pil" ,			UpperCAmelCase__				:     Union[str, Any] = True ,			)						->int:





           '''simple docstring'''

           if isinstance(self.unet.config.sample_size ,			_snake_case):
                  A__							=   (
                      batch_size,
                      self.unet.config.in_channels,
                      self.unet.config.sample_size,
                      self.unet.config.sample_size,
                  )
           else:
                  A__							=   (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

           if isinstance(_snake_case ,			_snake_case) and len(_snake_case) != batch_size:
                  raise ValueError(
                      f"""You have passed a list of generators of length {len(_snake_case)}, but requested an effective batch"""
                      f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""")

           A__							=   randn_tensor(_snake_case ,			generator=_snake_case ,			device=self.device ,			dtype=self.unet.dtype)

           # set step values
           self.scheduler.set_timesteps(_snake_case)

           for t in self.progress_bar(self.scheduler.timesteps):
                  # 1. predict noise model_output
                  A__							=   self.unet(_snake_case ,			_snake_case).sample

                  # 2. predict previous mean of image x_t-1 and add variance depending on eta
                  # eta corresponds to η in paper and should be between [0, 1]
                  # do x_t -> x_t-1
                  A__							=   self.scheduler.step(
                      _snake_case ,			_snake_case ,			_snake_case ,			eta=_snake_case ,			use_clipped_model_output=_snake_case ,			generator=_snake_case).prev_sample

           A__							=   (image / 2 + 0.5).clamp(0 ,			1)
           A__							=   image.cpu().permute(0 ,			2 ,			3 ,			1).numpy()
           if output_type == "pil":
                  A__							=   self.numpy_to_pil(_snake_case)

           if not return_dict:
                  return (image,)

           return ImagePipelineOutput(images=_snake_case)








from __future__ import annotations

from typing import Any


class     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     str ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     float = 0)						->None:





           '''simple docstring'''

           A__			,							A__							=   row, column
           A__							=   [[default_value for c in range(UpperCAmelCase__)] for r in range(UpperCAmelCase__)]


    def __str__(      self				:     List[str])						->str:





           '''simple docstring'''

           A__							=   f"""Matrix consist of {self.row} rows and {self.column} columns\n"""

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

           # Make string and return
           def single_line(UpperCAmelCase__				:     list[float]) -> str:
                  nonlocal string_format_identifier
                  A__							=   '''['''
                  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				:     Tuple)						->str:





           '''simple docstring'''

           return str(self)


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			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				:     List[Any] ,			UpperCAmelCase__				:     tuple[int, int])						->Any:





           '''simple docstring'''

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


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





           '''simple docstring'''

           assert self.validate_indicies(UpperCAmelCase__)
           A__							=   value


    def __add__(      self				:     Optional[int] ,			UpperCAmelCase__				:     Matrix)						->Matrix:





           '''simple docstring'''

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

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


    def __neg__(      self				:     str)						->Matrix:





           '''simple docstring'''

           A__							=   Matrix(self.row ,			self.column)
           for r in range(self.row):
                  for c in range(self.column):
                         A__							=   -self[r, c]
           return result


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





           '''simple docstring'''

           return self + (-another)


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





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->Matrix:





           '''simple docstring'''

           A__							=   Matrix(self.column ,			self.row)
           for r in range(self.row):
                  for c in range(self.column):
                         A__							=   self[r, c]
           return result





    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple ,			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
           A__							=   v.transpose()
           A__							=   (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 SCREAMING_SNAKE_CASE    (	)       ->	None:



         """simple docstring"""

         A__							=   Matrix(3			,    3			,    0						)
         for i in range(3						):
                A__							=   1
         print(f"""a^(-1) is {ainv}"""						)
         # u, v
         A__							=   Matrix(3			,    1			,    0						)
         A__			,							A__			,							A__							=   1, 2, -3
         A__							=   Matrix(3			,    1			,    0						)
         A__			,							A__			,							A__							=   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(lowercase_			,    lowercase_						)}"""						)







  def SCREAMING_SNAKE_CASE    (	)       ->	None:



         """simple docstring"""

         import doctest

         doctest.testmod()
  testa()


