
'''simple docstring'''
from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin






@dataclass
class   snake_case__					(			a__				):







		"""simple docstring"""

		lowerCamelCase           =	42







class   snake_case__					(			a__						,				a__				):







		"""simple docstring"""

		@register_to_config
		def __init__(			self							: Any     ,  UpperCamelCase__							: str = 16     ,  UpperCamelCase__							: Union[str, Any] = 88     ,  UpperCamelCase__							: Optional[Any] = None     ,  UpperCamelCase__							: Optional[int] = None     ,  UpperCamelCase__							: Any = 1     ,  UpperCamelCase__							: Tuple = 0.0     ,  UpperCamelCase__							: Optional[Any] = 32     ,  UpperCamelCase__							: Dict = None     ,  UpperCamelCase__							: Dict = False     ,  UpperCamelCase__							: Optional[Any] = None     ,  UpperCamelCase__							: List[Any] = None     ,  UpperCamelCase__							: Any = None     ,  UpperCamelCase__							: List[str] = "geglu"     ,  UpperCamelCase__							: int = None     ,  UpperCamelCase__							: Tuple = False     ,  UpperCamelCase__							: Dict = False     ,  UpperCamelCase__							: Optional[int] = False     ,  UpperCamelCase__							: Dict = "layer_norm"     ,  UpperCamelCase__							: int = True     ,  )	-> Union[str, Any]:






				"""simple docstring"""







				super().__init__()
				snake_case     :					Tuple     =	use_linear_projection
				snake_case     :					int     =	num_attention_heads
				snake_case     :					Dict     =	attention_head_dim
				snake_case     :					Optional[int]     =	num_attention_heads * attention_head_dim

				# 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
				# Define whether input is continuous or discrete depending on configuration
				snake_case     :					Optional[Any]     =	(in_channels is not None) and (patch_size is None)
				snake_case     :					Tuple     =	num_vector_embeds is not None
				snake_case     :					Optional[int]     =	in_channels is not None and patch_size is not None

				if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
						snake_case     :					Any     =	(
						    f'The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or'
						    ' incorrectly set to `\'layer_norm\'`.Make sure to set `norm_type` to `\'ada_norm\'` in the config.'
						    ' Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect'
						    ' results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it'
						    ' would be very nice if you could open a Pull request for the `transformer/config.json` file'
						)
						deprecate('''norm_type!=num_embeds_ada_norm'''     ,  '''1.0.0'''     ,  SCREAMING_SNAKE_CASE_     ,  standard_warn=SCREAMING_SNAKE_CASE_			)
						snake_case     :					Any     =	'ada_norm'

				if self.is_input_continuous and self.is_input_vectorized:
						raise ValueError(
						    f'Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make'
						    ''' sure that either `in_channels` or `num_vector_embeds` is None.'''			)
				elif self.is_input_vectorized and self.is_input_patches:
						raise ValueError(
						    f'Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make'
						    ''' sure that either `num_vector_embeds` or `num_patches` is None.'''			)
				elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
						raise ValueError(
						    f'Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:'
						    f' {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None.'			)

				# 2. Define input layers
				if self.is_input_continuous:
						snake_case     :					Optional[int]     =	in_channels

						snake_case     :					str     =	torch.nn.GroupNorm(num_groups=SCREAMING_SNAKE_CASE_     ,  num_channels=SCREAMING_SNAKE_CASE_     ,  eps=1e-6     ,  affine=SCREAMING_SNAKE_CASE_			)
						if use_linear_projection:
								snake_case     :					Dict     =	nn.Linear(SCREAMING_SNAKE_CASE_     ,  SCREAMING_SNAKE_CASE_			)
						else:
								snake_case     :					Any     =	nn.Convad(SCREAMING_SNAKE_CASE_     ,  SCREAMING_SNAKE_CASE_     ,  kernel_size=1     ,  stride=1     ,  padding=0			)
				elif self.is_input_vectorized:
						assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
						assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

						snake_case     :					Union[str, Any]     =	sample_size
						snake_case     :					int     =	sample_size
						snake_case     :					Optional[Any]     =	num_vector_embeds
						snake_case     :					str     =	self.height * self.width

						snake_case     :					Union[str, Any]     =	ImagePositionalEmbeddings(
						    num_embed=SCREAMING_SNAKE_CASE_     ,  embed_dim=SCREAMING_SNAKE_CASE_     ,  height=self.height     ,  width=self.width			)
				elif self.is_input_patches:
						assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

						snake_case     :					int     =	sample_size
						snake_case     :					Optional[Any]     =	sample_size

						snake_case     :					List[str]     =	patch_size
						snake_case     :					Tuple     =	PatchEmbed(
						    height=SCREAMING_SNAKE_CASE_     ,  width=SCREAMING_SNAKE_CASE_     ,  patch_size=SCREAMING_SNAKE_CASE_     ,  in_channels=SCREAMING_SNAKE_CASE_     ,  embed_dim=SCREAMING_SNAKE_CASE_     ,  )

				# 3. Define transformers blocks
				snake_case     :					Any     =	nn.ModuleList(
				    [
				        BasicTransformerBlock(
				            SCREAMING_SNAKE_CASE_     ,  SCREAMING_SNAKE_CASE_     ,  SCREAMING_SNAKE_CASE_     ,  dropout=SCREAMING_SNAKE_CASE_     ,  cross_attention_dim=SCREAMING_SNAKE_CASE_     ,  activation_fn=SCREAMING_SNAKE_CASE_     ,  num_embeds_ada_norm=SCREAMING_SNAKE_CASE_     ,  attention_bias=SCREAMING_SNAKE_CASE_     ,  only_cross_attention=SCREAMING_SNAKE_CASE_     ,  upcast_attention=SCREAMING_SNAKE_CASE_     ,  norm_type=SCREAMING_SNAKE_CASE_     ,  norm_elementwise_affine=SCREAMING_SNAKE_CASE_     ,  )
				        for d in range(SCREAMING_SNAKE_CASE_			)
				    ]			)

				# 4. Define output layers
				snake_case     :					Optional[int]     =	in_channels if out_channels is None else out_channels
				if self.is_input_continuous:
						# TODO: should use out_channels for continuous projections
						if use_linear_projection:
								snake_case     :					Optional[Any]     =	nn.Linear(SCREAMING_SNAKE_CASE_     ,  SCREAMING_SNAKE_CASE_			)
						else:
								snake_case     :					List[str]     =	nn.Convad(SCREAMING_SNAKE_CASE_     ,  SCREAMING_SNAKE_CASE_     ,  kernel_size=1     ,  stride=1     ,  padding=0			)
				elif self.is_input_vectorized:
						snake_case     :					Optional[Any]     =	nn.LayerNorm(SCREAMING_SNAKE_CASE_			)
						snake_case     :					Any     =	nn.Linear(SCREAMING_SNAKE_CASE_     ,  self.num_vector_embeds - 1			)
				elif self.is_input_patches:
						snake_case     :					List[str]     =	nn.LayerNorm(SCREAMING_SNAKE_CASE_     ,  elementwise_affine=SCREAMING_SNAKE_CASE_     ,  eps=1e-6			)
						snake_case     :					Tuple     =	nn.Linear(SCREAMING_SNAKE_CASE_     ,  2 * inner_dim			)
						snake_case     :					Any     =	nn.Linear(SCREAMING_SNAKE_CASE_     ,  patch_size * patch_size * self.out_channels			)



		def 			lowerCAmelCase							(			self							: Optional[Any]     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: Optional[int] = None     ,  UpperCamelCase__							: List[str] = None     ,  UpperCamelCase__							: List[Any] = None     ,  UpperCamelCase__							: Tuple = None     ,  UpperCamelCase__							: Any = None     ,  UpperCamelCase__							: List[str] = None     ,  UpperCamelCase__							: List[Any] = True     ,  )	-> Optional[Any]:






				"""simple docstring"""







				if attention_mask is not None and attention_mask.ndim == 2:
						# assume that mask is expressed as:
						#   (1 = keep,      0 = discard)
						# convert mask into a bias that can be added to attention scores:
						#       (keep = +0,     discard = -10000.0)
						snake_case     :					List[str]     =	(1 - attention_mask.to(hidden_states.dtype			)) * -1_0000.0
						snake_case     :					Tuple     =	attention_mask.unsqueeze(1			)

				# convert encoder_attention_mask to a bias the same way we do for attention_mask
				if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
						snake_case     :					int     =	(1 - encoder_attention_mask.to(hidden_states.dtype			)) * -1_0000.0
						snake_case     :					Dict     =	encoder_attention_mask.unsqueeze(1			)

				# 1. Input
				if self.is_input_continuous:
						snake_case     :					Union[str, Any]     =	hidden_states.shape
						snake_case     :					str     =	hidden_states

						snake_case     :					Union[str, Any]     =	self.norm(SCREAMING_SNAKE_CASE_			)
						if not self.use_linear_projection:
								snake_case     :					Tuple     =	self.proj_in(SCREAMING_SNAKE_CASE_			)
								snake_case     :					Optional[int]     =	hidden_states.shape[1]
								snake_case     :					Tuple     =	hidden_states.permute(0     ,  2     ,  3     ,  1			).reshape(SCREAMING_SNAKE_CASE_     ,  height * width     ,  SCREAMING_SNAKE_CASE_			)
						else:
								snake_case     :					Optional[int]     =	hidden_states.shape[1]
								snake_case     :					Dict     =	hidden_states.permute(0     ,  2     ,  3     ,  1			).reshape(SCREAMING_SNAKE_CASE_     ,  height * width     ,  SCREAMING_SNAKE_CASE_			)
								snake_case     :					Union[str, Any]     =	self.proj_in(SCREAMING_SNAKE_CASE_			)
				elif self.is_input_vectorized:
						snake_case     :					Union[str, Any]     =	self.latent_image_embedding(SCREAMING_SNAKE_CASE_			)
				elif self.is_input_patches:
						snake_case     :					Any     =	self.pos_embed(SCREAMING_SNAKE_CASE_			)

				# 2. Blocks
				for block in self.transformer_blocks:
						snake_case     :					Optional[int]     =	block(
						    SCREAMING_SNAKE_CASE_     ,  attention_mask=SCREAMING_SNAKE_CASE_     ,  encoder_hidden_states=SCREAMING_SNAKE_CASE_     ,  encoder_attention_mask=SCREAMING_SNAKE_CASE_     ,  timestep=SCREAMING_SNAKE_CASE_     ,  cross_attention_kwargs=SCREAMING_SNAKE_CASE_     ,  class_labels=SCREAMING_SNAKE_CASE_     ,  )

				# 3. Output
				if self.is_input_continuous:
						if not self.use_linear_projection:
								snake_case     :					Tuple     =	hidden_states.reshape(SCREAMING_SNAKE_CASE_     ,  SCREAMING_SNAKE_CASE_     ,  SCREAMING_SNAKE_CASE_     ,  SCREAMING_SNAKE_CASE_			).permute(0     ,  3     ,  1     ,  2			).contiguous()
								snake_case     :					int     =	self.proj_out(SCREAMING_SNAKE_CASE_			)
						else:
								snake_case     :					Optional[int]     =	self.proj_out(SCREAMING_SNAKE_CASE_			)
								snake_case     :					Optional[int]     =	hidden_states.reshape(SCREAMING_SNAKE_CASE_     ,  SCREAMING_SNAKE_CASE_     ,  SCREAMING_SNAKE_CASE_     ,  SCREAMING_SNAKE_CASE_			).permute(0     ,  3     ,  1     ,  2			).contiguous()

						snake_case     :					Any     =	hidden_states + residual
				elif self.is_input_vectorized:
						snake_case     :					List[Any]     =	self.norm_out(SCREAMING_SNAKE_CASE_			)
						snake_case     :					Optional[int]     =	self.out(SCREAMING_SNAKE_CASE_			)
						# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
						snake_case     :					Tuple     =	logits.permute(0     ,  2     ,  1			)

						# log(p(x_0))
						snake_case     :					List[str]     =	F.log_softmax(logits.double()     ,  dim=1			).float()
				elif self.is_input_patches:
						# TODO: cleanup!
						snake_case     :					int     =	self.transformer_blocks[0].norma.emb(
						    SCREAMING_SNAKE_CASE_     ,  SCREAMING_SNAKE_CASE_     ,  hidden_dtype=hidden_states.dtype			)
						snake_case     :					int     =	self.proj_out_a(F.silu(SCREAMING_SNAKE_CASE_			)			).chunk(2     ,  dim=1			)
						snake_case     :					Dict     =	self.norm_out(SCREAMING_SNAKE_CASE_			) * (1 + scale[:, None]) + shift[:, None]
						snake_case     :					Any     =	self.proj_out_a(SCREAMING_SNAKE_CASE_			)

						# unpatchify
						snake_case     :					Union[str, Any]     =	int(hidden_states.shape[1] ** 0.5			)
						snake_case     :					Tuple     =	hidden_states.reshape(
						    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)			)
						snake_case     :					int     =	torch.einsum('''nhwpqc->nchpwq'''     ,  SCREAMING_SNAKE_CASE_			)
						snake_case     :					Any     =	hidden_states.reshape(
						    shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)			)

				if not return_dict:
						return (output,)

				return TransformeraDModelOutput(sample=SCREAMING_SNAKE_CASE_			)




'''simple docstring'''
import numpy as np




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       Optional[Any]:


		'''simple docstring'''


		snake_case ,snake_case     :					str     =	a.shape
		snake_case     :					Union[str, Any]     =	min(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
		snake_case     :					List[Any]     =	np.eye(SCREAMING_SNAKE_CASE__ )
		snake_case     :					Union[str, Any]     =	a.copy()

		for k in range(t - 1 ):
				# select a column of modified matrix A':
				snake_case     :					Union[str, Any]     =	r[k:, [k]]
				# construct first basis vector
				snake_case     :					str     =	np.zeros_like(SCREAMING_SNAKE_CASE__ )
				snake_case     :					List[str]     =	1.0
				# determine scaling factor
				snake_case     :					int     =	np.linalg.norm(SCREAMING_SNAKE_CASE__ )
				# construct vector v for Householder reflection
				snake_case     :					Optional[int]     =	x + np.sign(x[0] ) * alpha * ea
				v /= np.linalg.norm(SCREAMING_SNAKE_CASE__ )

				# construct the Householder matrix
				snake_case     :					Tuple     =	np.eye(m - k ) - 2.0 * v @ v.T
				# pad with ones and zeros as necessary
				snake_case     :					int     =	np.block([[np.eye(SCREAMING_SNAKE_CASE__ ), np.zeros((k, m - k) )], [np.zeros((m - k, k) ), q_k]] )

				snake_case     :					int     =	q @ q_k.T
				snake_case     :					Dict     =	q_k @ r

		return q, r


if __name__ == "__main__":
						import doctest

						doctest.testmod()


