


'''simple docstring'''






from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload




class 		a__    :





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_ = None       )	->							None:
														if components is None:
																					lowerCAmelCase__	       =     []
														lowerCAmelCase__	       =     list(lowerCamelCase_       )







							def __len__(		self       )	->							int:
														return len(self.__components       )







							def __str__(		self       )	->							str:
														return "(" + ",".join(map(lowerCamelCase_ ,   self.__components       )       ) + ")"







							def __add__(		self ,   lowerCamelCase_       )	->							Vector:
														lowerCAmelCase__	       =     len(self       )
														if size == len(lowerCamelCase_       ):
																					lowerCAmelCase__	       =     [self.__components[i] + other.component(lowerCamelCase_       ) for i in range(lowerCamelCase_       )]
																					return Vector(lowerCamelCase_       )
														else:
																					raise Exception('''must have the same size'''       )







							def __sub__(		self ,   lowerCamelCase_       )	->							Vector:
														lowerCAmelCase__	       =     len(self       )
														if size == len(lowerCamelCase_       ):
																					lowerCAmelCase__	       =     [self.__components[i] - other.component(lowerCamelCase_       ) for i in range(lowerCamelCase_       )]
																					return Vector(lowerCamelCase_       )
														else:  # error case
																					raise Exception('''must have the same size'''       )







							@overload
							def __mul__(		self ,   lowerCamelCase_       )	->							Vector:
														...







							@overload
							def __mul__(		self ,   lowerCamelCase_       )	->							float:
														...







							def __mul__(		self ,   lowerCamelCase_       )	->							float | Vector:
														if isinstance(lowerCamelCase_ ,   (float, int)       ):
																					lowerCAmelCase__	       =     [c * other for c in self.__components]
																					return Vector(lowerCamelCase_       )
														elif isinstance(lowerCamelCase_ ,   lowerCamelCase_       ) and len(self       ) == len(lowerCamelCase_       ):
																					lowerCAmelCase__	       =     len(self       )
																					lowerCAmelCase__	       =     [self.__components[i] * other.component(lowerCamelCase_       ) for i in range(lowerCamelCase_       )]
																					return sum(lowerCamelCase_       )
														else:  # error case
																					raise Exception('''invalid operand!'''       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Vector:
														return Vector(self.__components       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							float:
														if isinstance(lowerCamelCase_ ,   lowerCamelCase_       ) and -len(self.__components       ) <= i < len(self.__components       ):
																					return self.__components[i]
														else:
																					raise Exception('''index out of range'''       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							None:
														assert -len(self.__components       ) <= pos < len(self.__components       )
														lowerCAmelCase__	       =     value







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							float:
														if len(self.__components       ) == 0:
																					raise Exception('''Vector is empty'''       )
														lowerCAmelCase__	       =     [c**2 for c in self.__components]
														return math.sqrt(sum(lowerCamelCase_       )       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = False       )	->							float:
														lowerCAmelCase__	       =     self * other
														lowerCAmelCase__	       =     self.euclidean_length() * other.euclidean_length()
														if deg:
																					return math.degrees(math.acos(num / den       )       )
														else:
																					return math.acos(num / den       )


def  _snake_case       (					A	)						->   Vector:
							assert isinstance(A			,   A	)
							return Vector([0] * dimension	)


def  _snake_case       (					A			,   A	)						->   Vector:
							assert isinstance(A			,   A	) and (isinstance(A			,   A	))
							lowerCAmelCase__	       =     [0] * dimension
							lowerCAmelCase__	       =     1
							return Vector(A	)


def  _snake_case       (					A			,   A			,   A	)						->   Vector:
							assert (
							    isinstance(A			,   A	)
							    and isinstance(A			,   A	)
							    and (isinstance(A			,   (int, float)	))
							)
							return x * scalar + y


def  _snake_case       (					A			,   A			,   A	)						->   Vector:
							random.seed(A	)
							lowerCAmelCase__	       =     [random.randint(A			,   A	) for _ in range(A	)]
							return Vector(A	)




class 		a__    :





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							None:
														lowerCAmelCase__	       =     matrix
														lowerCAmelCase__	       =     w
														lowerCAmelCase__	       =     h







							def __str__(		self       )	->							str:
														lowerCAmelCase__	       =     ''''''
														for i in range(self.__height       ):
																					ans += "|"
																					for j in range(self.__width       ):
																												if j < self.__width - 1:
																																			ans += str(self.__matrix[i][j]       ) + ","
																												else:
																																			ans += str(self.__matrix[i][j]       ) + "|\n"
														return ans







							def __add__(		self ,   lowerCamelCase_       )	->							Matrix:
														if self.__width == other.width() and self.__height == other.height():
																					lowerCAmelCase__	       =     []
																					for i in range(self.__height       ):
																												lowerCAmelCase__	       =     [
																												    self.__matrix[i][j] + other.component(lowerCamelCase_ ,   lowerCamelCase_       )
																												    for j in range(self.__width       )
																												]
																												matrix.append(lowerCamelCase_       )
																					return Matrix(lowerCamelCase_ ,   self.__width ,   self.__height       )
														else:
																					raise Exception('''matrix must have the same dimension!'''       )







							def __sub__(		self ,   lowerCamelCase_       )	->							Matrix:
														if self.__width == other.width() and self.__height == other.height():
																					lowerCAmelCase__	       =     []
																					for i in range(self.__height       ):
																												lowerCAmelCase__	       =     [
																												    self.__matrix[i][j] - other.component(lowerCamelCase_ ,   lowerCamelCase_       )
																												    for j in range(self.__width       )
																												]
																												matrix.append(lowerCamelCase_       )
																					return Matrix(lowerCamelCase_ ,   self.__width ,   self.__height       )
														else:
																					raise Exception('''matrices must have the same dimension!'''       )







							@overload
							def __mul__(		self ,   lowerCamelCase_       )	->							Matrix:
														...







							@overload
							def __mul__(		self ,   lowerCamelCase_       )	->							Vector:
														...







							def __mul__(		self ,   lowerCamelCase_       )	->							Vector | Matrix:
														if isinstance(lowerCamelCase_ ,   lowerCamelCase_       ):  # matrix-vector
																					if len(lowerCamelCase_       ) == self.__width:
																												lowerCAmelCase__	       =     zero_vector(self.__height       )
																												for i in range(self.__height       ):
																																			lowerCAmelCase__	       =     [
																																			    self.__matrix[i][j] * other.component(lowerCamelCase_       )
																																			    for j in range(self.__width       )
																																			]
																																			ans.change_component(lowerCamelCase_ ,   sum(lowerCamelCase_       )       )
																												return ans
																					else:
																												raise Exception(
																												    '''vector must have the same size as the '''
																												    '''number of columns of the matrix!'''       )
														elif isinstance(lowerCamelCase_ ,   (int, float)       ):  # matrix-scalar
																					lowerCAmelCase__	       =     [
																					    [self.__matrix[i][j] * other for j in range(self.__width       )]
																					    for i in range(self.__height       )
																					]
																					return Matrix(lowerCamelCase_ ,   self.__width ,   self.__height       )
														return None







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														return self.__height







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														return self.__width







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							float:
														if 0 <= x < self.__height and 0 <= y < self.__width:
																					return self.__matrix[x][y]
														else:
																					raise Exception('''change_component: indices out of bounds'''       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							None:
														if 0 <= x < self.__height and 0 <= y < self.__width:
																					lowerCAmelCase__	       =     value
														else:
																					raise Exception('''change_component: indices out of bounds'''       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							float:
														if self.__height != self.__width:
																					raise Exception('''Matrix is not square'''       )
														lowerCAmelCase__	       =     self.__matrix[:x] + self.__matrix[x + 1 :]
														for i in range(len(lowerCamelCase_       )       ):
																					lowerCAmelCase__	       =     minor[i][:y] + minor[i][y + 1 :]
														return Matrix(lowerCamelCase_ ,   self.__width - 1 ,   self.__height - 1       ).determinant()







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							float:
														if self.__height != self.__width:
																					raise Exception('''Matrix is not square'''       )
														if 0 <= x < self.__height and 0 <= y < self.__width:
																					return (-1) ** (x + y) * self.minor(lowerCamelCase_ ,   lowerCamelCase_       )
														else:
																					raise Exception('''Indices out of bounds'''       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							float:
														if self.__height != self.__width:
																					raise Exception('''Matrix is not square'''       )
														if self.__height < 1:
																					raise Exception('''Matrix has no element'''       )
														elif self.__height == 1:
																					return self.__matrix[0][0]
														elif self.__height == 2:
																					return (
																					    self.__matrix[0][0] * self.__matrix[1][1]
																					    - self.__matrix[0][1] * self.__matrix[1][0]
																					)
														else:
																					lowerCAmelCase__	       =     [
																					    self.__matrix[0][y] * self.cofactor(0 ,   lowerCamelCase_       ) for y in range(self.__width       )
																					]
																					return sum(lowerCamelCase_       )


def  _snake_case       (					A	)						->   Matrix:
							lowerCAmelCase__	       =     [[0] * n for _ in range(A	)]
							return Matrix(A			,   A			,   A	)






def  _snake_case       (					A			,   A			,   A			,   A	)						->   Matrix:
							random.seed(A	)
							lowerCAmelCase__	       =     [
							    [random.randint(A			,   A	) for _ in range(A	)] for _ in range(A	)
							]
							return Matrix(A			,   A			,   A	)



'''simple docstring'''






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 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       torch.FloatTensor




class 		a__    (							a__    ,    a__  ):





							'''simple docstring'''






							@register_to_config
							def __init__(		self ,   lowerCamelCase_ = 6_55_36 ,   lowerCamelCase_ = None ,   lowerCamelCase_ = 2 ,   lowerCamelCase_ = 2 ,   lowerCamelCase_ = 0 ,   lowerCamelCase_ = "fourier" ,   lowerCamelCase_ = True ,   lowerCamelCase_ = False ,   lowerCamelCase_ = 0.0 ,   lowerCamelCase_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") ,   lowerCamelCase_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") ,   lowerCamelCase_ = "UNetMidBlock1D" ,   lowerCamelCase_ = None ,   lowerCamelCase_ = (32, 32, 64) ,   lowerCamelCase_ = None ,   lowerCamelCase_ = 8 ,   lowerCamelCase_ = 1 ,   lowerCamelCase_ = False ,   )	->							Optional[int]:
														super().__init__()
														lowerCAmelCase__	       =     sample_size

														# time
														if time_embedding_type == "fourier":
																					lowerCAmelCase__	       =     GaussianFourierProjection(
																					    embedding_size=8 ,   set_W_to_weight=lowerCamelCase_ ,   log=lowerCamelCase_ ,   flip_sin_to_cos=lowerCamelCase_       )
																					lowerCAmelCase__	       =     2 * block_out_channels[0]
														elif time_embedding_type == "positional":
																					lowerCAmelCase__	       =     Timesteps(
																					    block_out_channels[0] ,   flip_sin_to_cos=lowerCamelCase_ ,   downscale_freq_shift=lowerCamelCase_       )
																					lowerCAmelCase__	       =     block_out_channels[0]

														if use_timestep_embedding:
																					lowerCAmelCase__	       =     block_out_channels[0] * 4
																					lowerCAmelCase__	       =     TimestepEmbedding(
																					    in_channels=lowerCamelCase_ ,   time_embed_dim=lowerCamelCase_ ,   act_fn=lowerCamelCase_ ,   out_dim=block_out_channels[0] ,   )

														lowerCAmelCase__	       =     nn.ModuleList([]       )
														lowerCAmelCase__	       =     None
														lowerCAmelCase__	       =     nn.ModuleList([]       )
														lowerCAmelCase__	       =     None

														# down
														lowerCAmelCase__	       =     in_channels
														for i, down_block_type in enumerate(lowerCamelCase_       ):
																					lowerCAmelCase__	       =     output_channel
																					lowerCAmelCase__	       =     block_out_channels[i]

																					if i == 0:
																												input_channel += extra_in_channels

																					lowerCAmelCase__	       =     i == len(lowerCamelCase_       ) - 1

																					lowerCAmelCase__	       =     get_down_block(
																					    lowerCamelCase_ ,   num_layers=lowerCamelCase_ ,   in_channels=lowerCamelCase_ ,   out_channels=lowerCamelCase_ ,   temb_channels=block_out_channels[0] ,   add_downsample=not is_final_block or downsample_each_block ,   )
																					self.down_blocks.append(lowerCamelCase_       )

														# mid
														lowerCAmelCase__	       =     get_mid_block(
														    lowerCamelCase_ ,   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=lowerCamelCase_ ,   add_downsample=lowerCamelCase_ ,   )

														# up
														lowerCAmelCase__	       =     list(reversed(lowerCamelCase_       )       )
														lowerCAmelCase__	       =     reversed_block_out_channels[0]
														if out_block_type is None:
																					lowerCAmelCase__	       =     out_channels
														else:
																					lowerCAmelCase__	       =     block_out_channels[0]

														for i, up_block_type in enumerate(lowerCamelCase_       ):
																					lowerCAmelCase__	       =     output_channel
																					lowerCAmelCase__	       =     (
																					    reversed_block_out_channels[i + 1] if i < len(lowerCamelCase_       ) - 1 else final_upsample_channels
																					)

																					lowerCAmelCase__	       =     i == len(lowerCamelCase_       ) - 1

																					lowerCAmelCase__	       =     get_up_block(
																					    lowerCamelCase_ ,   num_layers=lowerCamelCase_ ,   in_channels=lowerCamelCase_ ,   out_channels=lowerCamelCase_ ,   temb_channels=block_out_channels[0] ,   add_upsample=not is_final_block ,   )
																					self.up_blocks.append(lowerCamelCase_       )
																					lowerCAmelCase__	       =     output_channel

														# out
														lowerCAmelCase__	       =     norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 ,   32       )
														lowerCAmelCase__	       =     get_out_block(
														    out_block_type=lowerCamelCase_ ,   num_groups_out=lowerCamelCase_ ,   embed_dim=block_out_channels[0] ,   out_channels=lowerCamelCase_ ,   act_fn=lowerCamelCase_ ,   fc_dim=block_out_channels[-1] // 4 ,   )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ = True ,   )	->							Union[UNetaDOutput, Tuple]:
														lowerCAmelCase__	       =     timestep
														if not torch.is_tensor(lowerCamelCase_       ):
																					lowerCAmelCase__	       =     torch.tensor([timesteps] ,   dtype=torch.long ,   device=sample.device       )
														elif torch.is_tensor(lowerCamelCase_       ) and len(timesteps.shape       ) == 0:
																					lowerCAmelCase__	       =     timesteps[None].to(sample.device       )

														lowerCAmelCase__	       =     self.time_proj(lowerCamelCase_       )
														if self.config.use_timestep_embedding:
																					lowerCAmelCase__	       =     self.time_mlp(lowerCamelCase_       )
														else:
																					lowerCAmelCase__	       =     timestep_embed[..., None]
																					lowerCAmelCase__	       =     timestep_embed.repeat([1, 1, sample.shape[2]]       ).to(sample.dtype       )
																					lowerCAmelCase__	       =     timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])       )

														# 2. down
														lowerCAmelCase__	       =     ()
														for downsample_block in self.down_blocks:
																					lowerCAmelCase__       , lowerCAmelCase__	       =     downsample_block(hidden_states=lowerCamelCase_ ,   temb=lowerCamelCase_       )
																					down_block_res_samples += res_samples

														# 3. mid
														if self.mid_block:
																					lowerCAmelCase__	       =     self.mid_block(lowerCamelCase_ ,   lowerCamelCase_       )

														# 4. up
														for i, upsample_block in enumerate(self.up_blocks       ):
																					lowerCAmelCase__	       =     down_block_res_samples[-1:]
																					lowerCAmelCase__	       =     down_block_res_samples[:-1]
																					lowerCAmelCase__	       =     upsample_block(lowerCamelCase_ ,   res_hidden_states_tuple=lowerCamelCase_ ,   temb=lowerCamelCase_       )

														# 5. post-process
														if self.out_block:
																					lowerCAmelCase__	       =     self.out_block(lowerCamelCase_ ,   lowerCamelCase_       )

														if not return_dict:
																					return (sample,)

														return UNetaDOutput(sample=lowerCamelCase_       )