

from __future__ import annotations

from typing import Any







class 	lowercase    :


		"""simple docstring"""




		def __init__(				self	,       __snake_case	,       __snake_case	,       __snake_case = 0):
			_UpperCamelCase	,		_UpperCamelCase   :		Optional[Any]			=						row, column
			_UpperCamelCase   :		Any			=						[[default_value for c in range(UpperCAmelCase_)] for r in range(UpperCAmelCase_)]


		def __str__(				self):
			_UpperCamelCase   :		str			=						f'''Matrix consist of {self.row} rows and {self.column} columns\n'''

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

			# Make string and return
			def single_line(__snake_case) -> str:
				nonlocal string_format_identifier
				_UpperCamelCase   :		List[Any]			=						'['
				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):
			return str(self)


		def 		A__					(				self	,       __snake_case):



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


		def __setitem__(				self	,       __snake_case	,       __snake_case):
			assert self.validate_indicies(UpperCAmelCase_)
			_UpperCamelCase   :		Optional[Any]			=						value


		def __add__(				self	,       __snake_case):
			assert isinstance(UpperCAmelCase_	,       UpperCAmelCase_)
			assert self.row == another.row and self.column == another.column

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


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


		def __sub__(				self	,       __snake_case):
			return self + (-another)


		def __mul__(				self	,       __snake_case):



			if isinstance(UpperCAmelCase_	,       (int, float)):  # Scalar multiplication
				_UpperCamelCase   :		Any			=						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
				return result
			elif isinstance(UpperCAmelCase_	,       UpperCAmelCase_):  # Matrix multiplication
				assert self.column == another.row
				_UpperCamelCase   :		List[Any]			=						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   :		Optional[int]			=						f'''Unsupported type given for another ({type(UpperCAmelCase_)})'''
				raise TypeError(UpperCAmelCase_)


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






		def 		A__					(				self	,       __snake_case	,       __snake_case):
			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
			_UpperCamelCase   :		List[Any]			=						v.transpose()
			_UpperCamelCase   :		str			=						(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'''




								_UpperCamelCase   :		List[Any]			=						Matrix(3							,       3							,       0	)
								for i in range(3	):
									_UpperCamelCase   :		str			=						1
								print(F'''a^(-1) is {ainv}'''	)
								# u, v
								_UpperCamelCase   :		Dict			=						Matrix(3							,       1							,       0	)
								_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase   :		int			=						1, 2, -3
								_UpperCamelCase   :		List[Any]			=						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(_SCREAMING_SNAKE_CASE							,       _SCREAMING_SNAKE_CASE	)}'''	)
							def 					lowerCamelCase_     (							)						->				None:




								'''simple docstring'''




								import doctest

								doctest.testmod()







							testa()







import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


lowerCAmelCase__								=		False







class 	lowercase    (				unittest.TestCase							):


		"""simple docstring"""




		def 		A__					(				self	,       __snake_case=32):
			set_seed(0)
			_UpperCamelCase   :		int			=						UNetaDModel(sample_size=__snake_case	,       in_channels=3	,       out_channels=3)
			_UpperCamelCase   :		str			=						torch.optim.SGD(model.parameters()	,       lr=0.0_0_0_1)
			return model, optimizer






		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
			_UpperCamelCase   :		List[Any]			=						DDPMScheduler(
			    num_train_timesteps=10_00	,       beta_start=0.0_0_0_1	,       beta_end=0.0_2	,       beta_schedule='linear'	,       clip_sample=__snake_case	,       )
			_UpperCamelCase   :		List[Any]			=						DDIMScheduler(
			    num_train_timesteps=10_00	,       beta_start=0.0_0_0_1	,       beta_end=0.0_2	,       beta_schedule='linear'	,       clip_sample=__snake_case	,       )

			assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

			# shared batches for DDPM and DDIM
			set_seed(0)
			_UpperCamelCase   :		Optional[Any]			=						[torch.randn((4, 3, 32, 32)).clip(-1	,       1).to(__snake_case) for _ in range(4)]
			_UpperCamelCase   :		str			=						[torch.randn((4, 3, 32, 32)).to(__snake_case) for _ in range(4)]
			_UpperCamelCase   :		int			=						[torch.randint(0	,       10_00	,       (4,)).long().to(__snake_case) for _ in range(4)]

			# train with a DDPM scheduler
			_UpperCamelCase	,		_UpperCamelCase   :		List[Any]			=						self.get_model_optimizer(resolution=32)
			model.train().to(__snake_case)
			for i in range(4):
				optimizer.zero_grad()
				_UpperCamelCase   :		int			=						ddpm_scheduler.add_noise(clean_images[i]	,       noise[i]	,       timesteps[i])
				_UpperCamelCase   :		Any			=						model(__snake_case	,       timesteps[i]).sample
				_UpperCamelCase   :		str			=						torch.nn.functional.mse_loss(__snake_case	,       noise[i])
				loss.backward()
				optimizer.step()
			del model, optimizer

			# recreate the model and optimizer, and retry with DDIM
			_UpperCamelCase	,		_UpperCamelCase   :		Union[str, Any]			=						self.get_model_optimizer(resolution=32)
			model.train().to(__snake_case)
			for i in range(4):
				optimizer.zero_grad()
				_UpperCamelCase   :		Dict			=						ddim_scheduler.add_noise(clean_images[i]	,       noise[i]	,       timesteps[i])
				_UpperCamelCase   :		Dict			=						model(__snake_case	,       timesteps[i]).sample
				_UpperCamelCase   :		Tuple			=						torch.nn.functional.mse_loss(__snake_case	,       noise[i])
				loss.backward()
				optimizer.step()
			del model, optimizer

			self.assertTrue(torch.allclose(__snake_case	,       __snake_case	,       atol=1e-5))
			self.assertTrue(torch.allclose(__snake_case	,       __snake_case	,       atol=1e-5))




