




"""simple docstring"""






from __future__ import annotations

from typing import Any






class lowerCAmelCase__	:






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ,      lowercase  ,      lowercase = 0  ):
							_lowerCamelCase, _lowerCamelCase     :			Tuple    =    row, column
							_lowerCamelCase     :			int    =    [[default_value for c in range(lowercase  )] for r in range(lowercase  )]
			def __str__(  self  ):
							_lowerCamelCase     :			Tuple    =    F'''Matrix consist of {self.row} rows and {self.column} columns\n'''

							# Make string identifier
							_lowerCamelCase     :			Optional[int]    =    0
							for row_vector in self.array:
											for obj in row_vector:
															_lowerCamelCase     :			int    =    max(lowercase  ,      len(str(lowercase  )  )  )
							_lowerCamelCase     :			Optional[int]    =    F'''%{max_element_length}s'''

							# Make string and return
							def single_line(lowercase  ) -> str:
											nonlocal string_format_identifier
											_lowerCamelCase     :			int    =    '['
											line += ", ".join(string_format_identifier % (obj,) for obj in row_vector  )
											line += "]"
											return line

							s += "\n".join(single_line(lowercase  ) for row_vector in self.array  )
							return s
			def __repr__(  self  ):
							return str(self  )
			def 					A_    (  self  ,      lowercase  ):
							if not (isinstance(lowercase  ,      (list, tuple)  ) and len(lowercase  ) == 2):
											return False
							elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
											return False
							else:
											return True
			def __getitem__(  self  ,      lowercase  ):
							assert self.validate_indicies(lowercase  )
							return self.array[loc[0]][loc[1]]
			def __setitem__(  self  ,      lowercase  ,      lowercase  ):
							assert self.validate_indicies(lowercase  )
							_lowerCamelCase     :			Dict    =    value
			def __add__(  self  ,      lowercase  ):
							assert isinstance(lowercase  ,      lowercase  )
							assert self.row == another.row and self.column == another.column

							# Add
							_lowerCamelCase     :			Union[str, Any]    =    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  ):
							_lowerCamelCase     :			int    =    Matrix(self.row  ,      self.column  )
							for r in range(self.row  ):
											for c in range(self.column  ):
															_lowerCamelCase     :			List[str]    =    -self[r, c]
							return result
			def __sub__(  self  ,      lowercase  ):
							return self + (-another)
			def __mul__(  self  ,      lowercase  ):

							if isinstance(lowercase  ,      (int, float)  ):  # Scalar multiplication
											_lowerCamelCase     :			int    =    Matrix(self.row  ,      self.column  )
											for r in range(self.row  ):
															for c in range(self.column  ):
																			_lowerCamelCase     :			Optional[Any]    =    self[r, c] * another
											return result
							elif isinstance(lowercase  ,      lowercase  ):  # 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     :			Optional[int]    =    F'''Unsupported type given for another ({type(lowercase  )})'''
											raise TypeError(lowercase  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			Any    =    Matrix(self.column  ,      self.row  )
							for r in range(self.row  ):
											for c in range(self.column  ):
															_lowerCamelCase     :			Optional[Any]    =    self[r, c]
							return result






			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							assert isinstance(lowercase  ,      lowercase  ) and isinstance(lowercase  ,      lowercase  )
							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     :			Optional[int]    =    v.transpose()
							_lowerCamelCase     :			Tuple    =    (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      _snake_case			(				):
							# a^(-1)
							_lowerCamelCase     :			Optional[Any]    =    Matrix(3							,    3							,    0		)
							for i in range(3		):
											_lowerCamelCase     :			Optional[Any]    =    1
							print(f'''a^(-1) is {ainv}'''		)
							# u, v
							_lowerCamelCase     :			Tuple    =    Matrix(3							,    1							,    0		)
							_lowerCamelCase, _lowerCamelCase, _lowerCamelCase     :			Optional[int]    =    1, 2, -3
							_lowerCamelCase     :			Union[str, 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(lowercase__							,    lowercase__		)}'''		)







			def      _snake_case			(				):
							import doctest

							doctest.testmod()
			testa()






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block

@dataclass
class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		42


class    lowerCAmelCase							(					__UpperCamelCase,  __UpperCamelCase			):
 @register_to_config
 def __init__(      self  :      List[str]			,     UpperCAmelCase  :      int = 65536			,     UpperCAmelCase  :      Optional[int] = None			,     UpperCAmelCase  :      int = 2			,     UpperCAmelCase  :      int = 2			,     UpperCAmelCase  :      int = 0			,     UpperCAmelCase  :      str = "fourier"			,     UpperCAmelCase  :      bool = True			,     UpperCAmelCase  :      bool = False			,     UpperCAmelCase  :      float = 0.0			,     UpperCAmelCase  :      Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")			,     UpperCAmelCase  :      Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")			,     UpperCAmelCase  :      Tuple[str] = "UNetMidBlock1D"			,     UpperCAmelCase  :      str = None			,     UpperCAmelCase  :      Tuple[int] = (32, 32, 64)			,     UpperCAmelCase  :      str = None			,     UpperCAmelCase  :      int = 8			,     UpperCAmelCase  :      int = 1			,     UpperCAmelCase  :      bool = False			,     )			->	List[Any]:
     super().__init__()
     lowerCamelCase__    :      Optional[int]					 =		sample_size

     # time
     if time_embedding_type == "fourier":
         lowerCamelCase__    :      Optional[Any]					 =		GaussianFourierProjection(
             embedding_size=8			,     set_W_to_weight=UpperCAmelCase			,     log=UpperCAmelCase			,     flip_sin_to_cos=UpperCAmelCase	)
         lowerCamelCase__    :      Any					 =		2 * block_out_channels[0]
     elif time_embedding_type == "positional":
         lowerCamelCase__    :      List[Any]					 =		Timesteps(
             block_out_channels[0]			,     flip_sin_to_cos=UpperCAmelCase			,     downscale_freq_shift=UpperCAmelCase	)
         lowerCamelCase__    :      Dict					 =		block_out_channels[0]

     if use_timestep_embedding:
         lowerCamelCase__    :      str					 =		block_out_channels[0] * 4
         lowerCamelCase__    :      List[Any]					 =		TimestepEmbedding(
             in_channels=UpperCAmelCase			,     time_embed_dim=UpperCAmelCase			,     act_fn=UpperCAmelCase			,     out_dim=block_out_channels[0]			,     )

     lowerCamelCase__    :      Any					 =		nn.ModuleList([]	)
     lowerCamelCase__    :      Tuple					 =		None
     lowerCamelCase__    :      List[str]					 =		nn.ModuleList([]	)
     lowerCamelCase__    :      Optional[int]					 =		None

     # down
     lowerCamelCase__    :      Optional[int]					 =		in_channels
     for i, down_block_type in enumerate(UpperCAmelCase	):
         lowerCamelCase__    :      Union[str, Any]					 =		output_channel
         lowerCamelCase__    :      Tuple					 =		block_out_channels[i]

         if i == 0:
             input_channel += extra_in_channels

         lowerCamelCase__    :      Union[str, Any]					 =		i == len(UpperCAmelCase	) - 1

         lowerCamelCase__    :      Optional[int]					 =		get_down_block(
             UpperCAmelCase			,     num_layers=UpperCAmelCase			,     in_channels=UpperCAmelCase			,     out_channels=UpperCAmelCase			,     temb_channels=block_out_channels[0]			,     add_downsample=not is_final_block or downsample_each_block			,     )
         self.down_blocks.append(UpperCAmelCase	)

     # mid
     lowerCamelCase__    :      Optional[int]					 =		get_mid_block(
         UpperCAmelCase			,     in_channels=block_out_channels[-1]			,     mid_channels=block_out_channels[-1]			,     out_channels=block_out_channels[-1]			,     embed_dim=block_out_channels[0]			,     num_layers=UpperCAmelCase			,     add_downsample=UpperCAmelCase			,     )

     # up
     lowerCamelCase__    :      Optional[int]					 =		list(reversed(UpperCAmelCase	)	)
     lowerCamelCase__    :      Optional[int]					 =		reversed_block_out_channels[0]
     if out_block_type is None:
         lowerCamelCase__    :      List[str]					 =		out_channels
     else:
         lowerCamelCase__    :      Any					 =		block_out_channels[0]

     for i, up_block_type in enumerate(UpperCAmelCase	):
         lowerCamelCase__    :      Tuple					 =		output_channel
         lowerCamelCase__    :      Union[str, Any]					 =		(
             reversed_block_out_channels[i + 1] if i < len(UpperCAmelCase	) - 1 else final_upsample_channels
         )

         lowerCamelCase__    :      List[str]					 =		i == len(UpperCAmelCase	) - 1

         lowerCamelCase__    :      Dict					 =		get_up_block(
             UpperCAmelCase			,     num_layers=UpperCAmelCase			,     in_channels=UpperCAmelCase			,     out_channels=UpperCAmelCase			,     temb_channels=block_out_channels[0]			,     add_upsample=not is_final_block			,     )
         self.up_blocks.append(UpperCAmelCase	)
         lowerCamelCase__    :      int					 =		output_channel

     # out
     lowerCamelCase__    :      int					 =		norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4			,     32	)
     lowerCamelCase__    :      List[Any]					 =		get_out_block(
         out_block_type=UpperCAmelCase			,     num_groups_out=UpperCAmelCase			,     embed_dim=block_out_channels[0]			,     out_channels=UpperCAmelCase			,     act_fn=UpperCAmelCase			,     fc_dim=block_out_channels[-1] // 4			,     )







 def      A_	(      self  :      List[Any]			,     UpperCAmelCase  :      torch.FloatTensor			,     UpperCAmelCase  :      Union[torch.Tensor, float, int]			,     UpperCAmelCase  :      bool = True			,     )			->	Union[UNetaDOutput, Tuple]:
     lowerCamelCase__    :      Optional[Any]					 =		timestep
     if not torch.is_tensor(UpperCAmelCase	):
         lowerCamelCase__    :      Optional[int]					 =		torch.tensor([timesteps]			,     dtype=torch.long			,     device=sample.device	)
     elif torch.is_tensor(UpperCAmelCase	) and len(timesteps.shape	) == 0:
         lowerCamelCase__    :      List[str]					 =		timesteps[None].to(sample.device	)

     lowerCamelCase__    :      Optional[int]					 =		self.time_proj(UpperCAmelCase	)
     if self.config.use_timestep_embedding:
         lowerCamelCase__    :      str					 =		self.time_mlp(UpperCAmelCase	)
     else:
         lowerCamelCase__    :      List[str]					 =		timestep_embed[..., None]
         lowerCamelCase__    :      str					 =		timestep_embed.repeat([1, 1, sample.shape[2]]	).to(sample.dtype	)
         lowerCamelCase__    :      str					 =		timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])	)

     # 2. down
     lowerCamelCase__    :      str					 =		()
     for downsample_block in self.down_blocks:
         lowerCamelCase__ ,							lowerCamelCase__    :      Union[str, Any]					 =		downsample_block(hidden_states=UpperCAmelCase			,     temb=UpperCAmelCase	)
         down_block_res_samples += res_samples

     # 3. mid
     if self.mid_block:
         lowerCamelCase__    :      Optional[Any]					 =		self.mid_block(UpperCAmelCase			,     UpperCAmelCase	)

     # 4. up
     for i, upsample_block in enumerate(self.up_blocks	):
         lowerCamelCase__    :      Dict					 =		down_block_res_samples[-1:]
         lowerCamelCase__    :      Optional[Any]					 =		down_block_res_samples[:-1]
         lowerCamelCase__    :      Any					 =		upsample_block(UpperCAmelCase			,     res_hidden_states_tuple=UpperCAmelCase			,     temb=UpperCAmelCase	)

     # 5. post-process
     if self.out_block:
         lowerCamelCase__    :      Any					 =		self.out_block(UpperCAmelCase			,     UpperCAmelCase	)

     if not return_dict:
         return (sample,)

     return UNetaDOutput(sample=UpperCAmelCase	)





