



'''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(      _lowerCAmelCase					):


						'''simple docstring'''



						UpperCAmelCase							:    torch.FloatTensor







class __snake_case(      _lowerCAmelCase		,							_lowerCAmelCase					):


						'''simple docstring'''



						@register_to_config
						def __init__(					self	,       A_ = 16	,       A_ = 88	,       A_ = None	,       A_ = None	,       A_ = 1	,       A_ = 0.0	,       A_ = 32	,       A_ = None	,       A_ = False	,       A_ = None	,       A_ = None	,       A_ = None	,       A_ = "geglu"	,       A_ = None	,       A_ = False	,       A_ = False	,       A_ = False	,       A_ = "layer_norm"	,       A_ = True	,       )	->	Tuple:
							super().__init__()
							lowerCAmelCase    =	use_linear_projection
							lowerCAmelCase    =	num_attention_heads
							lowerCAmelCase    =	attention_head_dim
							lowerCAmelCase    =	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
							lowerCAmelCase    =	(in_channels is not None) and (patch_size is None)
							lowerCAmelCase    =	num_vector_embeds is not None
							lowerCAmelCase    =	in_channels is not None and patch_size is not None

							if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
								lowerCAmelCase    =	(
								    f'The configuration file of this model: {self.__class__} is outdated.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"""	,       A_	,       standard_warn=A_      )
								lowerCAmelCase    =	"""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:
								lowerCAmelCase    =	in_channels

								lowerCAmelCase    =	torch.nn.GroupNorm(num_groups=A_	,       num_channels=A_	,       eps=1e-6	,       affine=A_      )
								if use_linear_projection:
									lowerCAmelCase    =	nn.Linear(A_	,       A_      )
								else:
									lowerCAmelCase    =	nn.Convad(A_	,       A_	,       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"

								lowerCAmelCase    =	sample_size
								lowerCAmelCase    =	sample_size
								lowerCAmelCase    =	num_vector_embeds
								lowerCAmelCase    =	self.height * self.width

								lowerCAmelCase    =	ImagePositionalEmbeddings(
								    num_embed=A_	,       embed_dim=A_	,       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"

								lowerCAmelCase    =	sample_size
								lowerCAmelCase    =	sample_size

								lowerCAmelCase    =	patch_size
								lowerCAmelCase    =	PatchEmbed(
								    height=A_	,       width=A_	,       patch_size=A_	,       in_channels=A_	,       embed_dim=A_	,       )

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

							# 4. Define output layers
							lowerCAmelCase    =	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:
									lowerCAmelCase    =	nn.Linear(A_	,       A_      )
								else:
									lowerCAmelCase    =	nn.Convad(A_	,       A_	,       kernel_size=1	,       stride=1	,       padding=0      )
							elif self.is_input_vectorized:
								lowerCAmelCase    =	nn.LayerNorm(A_      )
								lowerCAmelCase    =	nn.Linear(A_	,       self.num_vector_embeds - 1      )
							elif self.is_input_patches:
								lowerCAmelCase    =	nn.LayerNorm(A_	,       elementwise_affine=A_	,       eps=1e-6      )
								lowerCAmelCase    =	nn.Linear(A_	,       2 * inner_dim      )
								lowerCAmelCase    =	nn.Linear(A_	,       patch_size * patch_size * self.out_channels      )




						def 				__snake_case    (					self	,       A_	,       A_ = None	,       A_ = None	,       A_ = None	,       A_ = None	,       A_ = None	,       A_ = None	,       A_ = True	,       )	->	int:
							# ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
							#   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
							#   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
							# expects mask of shape:
							#   [batch, key_tokens]
							# adds singleton query_tokens dimension:
							#   [batch,                    1, key_tokens]
							# this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
							#   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
							#   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
							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)
								lowerCAmelCase    =	(1 - attention_mask.to(hidden_states.dtype      )) * -1_0_0_0_0.0
								lowerCAmelCase    =	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:
								lowerCAmelCase    =	(1 - encoder_attention_mask.to(hidden_states.dtype      )) * -1_0_0_0_0.0
								lowerCAmelCase    =	encoder_attention_mask.unsqueeze(1      )

							# 1. Input
							if self.is_input_continuous:
								lowerCAmelCase,   lowerCAmelCase,   lowerCAmelCase,   lowerCAmelCase    =	hidden_states.shape
								lowerCAmelCase    =	hidden_states

								lowerCAmelCase    =	self.norm(A_      )
								if not self.use_linear_projection:
									lowerCAmelCase    =	self.proj_in(A_      )
									lowerCAmelCase    =	hidden_states.shape[1]
									lowerCAmelCase    =	hidden_states.permute(0	,       2	,       3	,       1      ).reshape(A_	,       height * width	,       A_      )
								else:
									lowerCAmelCase    =	hidden_states.shape[1]
									lowerCAmelCase    =	hidden_states.permute(0	,       2	,       3	,       1      ).reshape(A_	,       height * width	,       A_      )
									lowerCAmelCase    =	self.proj_in(A_      )
							elif self.is_input_vectorized:
								lowerCAmelCase    =	self.latent_image_embedding(A_      )
							elif self.is_input_patches:
								lowerCAmelCase    =	self.pos_embed(A_      )

							# 2. Blocks
							for block in self.transformer_blocks:
								lowerCAmelCase    =	block(
								    A_	,       attention_mask=A_	,       encoder_hidden_states=A_	,       encoder_attention_mask=A_	,       timestep=A_	,       cross_attention_kwargs=A_	,       class_labels=A_	,       )

							# 3. Output
							if self.is_input_continuous:
								if not self.use_linear_projection:
									lowerCAmelCase    =	hidden_states.reshape(A_	,       A_	,       A_	,       A_      ).permute(0	,       3	,       1	,       2      ).contiguous()
									lowerCAmelCase    =	self.proj_out(A_      )
								else:
									lowerCAmelCase    =	self.proj_out(A_      )
									lowerCAmelCase    =	hidden_states.reshape(A_	,       A_	,       A_	,       A_      ).permute(0	,       3	,       1	,       2      ).contiguous()

								lowerCAmelCase    =	hidden_states + residual
							elif self.is_input_vectorized:
								lowerCAmelCase    =	self.norm_out(A_      )
								lowerCAmelCase    =	self.out(A_      )
								# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
								lowerCAmelCase    =	logits.permute(0	,       2	,       1      )

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

								# unpatchify
								lowerCAmelCase    =	lowerCAmelCase    =	int(hidden_states.shape[1] ** 0.5      )
								lowerCAmelCase    =	hidden_states.reshape(
								    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)      )
								lowerCAmelCase    =	torch.einsum("""nhwpqc->nchpwq"""	,       A_      )
								lowerCAmelCase    =	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=A_      )




'''simple docstring'''

def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						int = 4_000_000							)			->						int:







	"""simple docstring"""
	lowerCAmelCase    =	1
	lowerCAmelCase    =	2
	lowerCAmelCase    =	0
	while j <= n:
		if j % 2 == 0:
			total += j
		lowerCAmelCase,   lowerCAmelCase    =	j, i + j

	return total


if __name__ == "__main__":
				print(F'''{solution() = }''')