


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 		UpperCamelCase_		(					A   ):





						"""simple docstring"""




						UpperCAmelCase__	:       torch.FloatTensor






class 		UpperCamelCase_		(					A   ,		A   ):





						"""simple docstring"""




						@register_to_config
						def __init__( self				:					Tuple	,				_lowerCamelCase				:					int = 16	,				_lowerCamelCase				:					int = 88	,				_lowerCamelCase				:					Optional[int] = None	,				_lowerCamelCase				:					Optional[int] = None	,				_lowerCamelCase				:					int = 1	,				_lowerCamelCase				:					float = 0.0	,				_lowerCamelCase				:					int = 32	,				_lowerCamelCase				:					Optional[int] = None	,				_lowerCamelCase				:					bool = False	,				_lowerCamelCase				:					Optional[int] = None	,				_lowerCamelCase				:					Optional[int] = None	,				_lowerCamelCase				:					Optional[int] = None	,				_lowerCamelCase				:					str = "geglu"	,				_lowerCamelCase				:					Optional[int] = None	,				_lowerCamelCase				:					bool = False	,				_lowerCamelCase				:					bool = False	,				_lowerCamelCase				:					bool = False	,				_lowerCamelCase				:					str = "layer_norm"	,				_lowerCamelCase				:					bool = True	,				)							->					str:
										super().__init__()
										__magic_name__	  = use_linear_projection
										__magic_name__	  = num_attention_heads
										__magic_name__	  = attention_head_dim
										__magic_name__	  = 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
										__magic_name__	  = (in_channels is not None) and (patch_size is None)
										__magic_name__	  = num_vector_embeds is not None
										__magic_name__	  = in_channels is not None and patch_size is not None

										if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
														__magic_name__	  = (
														    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"	,				_lowerCamelCase	,				standard_warn=_lowerCamelCase  )
														__magic_name__	  = "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:
														__magic_name__	  = in_channels

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

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

														__magic_name__	  = ImagePositionalEmbeddings(
														    num_embed=_lowerCamelCase	,				embed_dim=_lowerCamelCase	,				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"

														__magic_name__	  = sample_size
														__magic_name__	  = sample_size

														__magic_name__	  = patch_size
														__magic_name__	  = PatchEmbed(
														    height=_lowerCamelCase	,				width=_lowerCamelCase	,				patch_size=_lowerCamelCase	,				in_channels=_lowerCamelCase	,				embed_dim=_lowerCamelCase	,				)

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

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

						def 					__A					( self				:					str	,				_lowerCamelCase				:					torch.Tensor	,				_lowerCamelCase				:					Optional[torch.Tensor] = None	,				_lowerCamelCase				:					Optional[torch.LongTensor] = None	,				_lowerCamelCase				:					Optional[torch.LongTensor] = None	,				_lowerCamelCase				:					Dict[str, Any] = None	,				_lowerCamelCase				:					Optional[torch.Tensor] = None	,				_lowerCamelCase				:					Optional[torch.Tensor] = None	,				_lowerCamelCase				:					bool = True	,				)							->					Dict:
										# 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)
														__magic_name__	  = (1 - attention_mask.to(hidden_states.dtype  )) * -1_00_00.0
														__magic_name__	  = 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:
														__magic_name__	  = (1 - encoder_attention_mask.to(hidden_states.dtype  )) * -1_00_00.0
														__magic_name__	  = encoder_attention_mask.unsqueeze(1  )

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

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

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

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

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

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

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







'''simple docstring'''
def      __snake_case       (       lowerCamelCase_ :				int = 100						):
				'''simple docstring'''




				__magic_name__	  = [[0 for _ in range(lowerCamelCase_						)] for _ in range(m + 1						)]
				for i in range(m + 1						):
								__magic_name__	  = 1

				for n in range(m + 1						):
								for k in range(1	,  lowerCamelCase_						):
												memo[n][k] += memo[n][k - 1]
												if n > k:
																memo[n][k] += memo[n - k - 1][k]

				return memo[m][m - 1] - 1


if __name__ == "__main__":
						print(solution(int(input('Enter a number: ').strip())))



