




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 UNetMidBlockaD, get_down_block, get_up_block

@dataclass
class      UpperCAmelCase						(			_a   ):
   __SCREAMING_SNAKE_CASE		:							torch.FloatTensor

class      UpperCAmelCase						(			_a   ,					_a   ):






   @register_to_config
   def __init__(	self   :       int						,					UpperCamelCase_   :       Optional[Union[int, Tuple[int, int]]] = None						,					UpperCamelCase_   :       int = 3						,					UpperCamelCase_   :       int = 3						,					UpperCamelCase_   :       bool = False						,					UpperCamelCase_   :       str = "positional"						,					UpperCamelCase_   :       int = 0						,					UpperCamelCase_   :       bool = True						,					UpperCamelCase_   :       Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")						,					UpperCamelCase_   :       Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")						,					UpperCamelCase_   :       Tuple[int] = (2_24, 4_48, 6_72, 8_96)						,					UpperCamelCase_   :       int = 2						,					UpperCamelCase_   :       float = 1						,					UpperCamelCase_   :       int = 1						,					UpperCamelCase_   :       str = "conv"						,					UpperCamelCase_   :       str = "conv"						,					UpperCamelCase_   :       str = "silu"						,					UpperCamelCase_   :       Optional[int] = 8						,					UpperCamelCase_   :       int = 32						,					UpperCamelCase_   :       float = 1E-5						,					UpperCamelCase_   :       str = "default"						,					UpperCamelCase_   :       bool = True						,					UpperCamelCase_   :       Optional[str] = None						,					UpperCamelCase_   :       Optional[int] = None						,					)->			List[Any]:
     super().__init__()

     _lowercase: str						     =    sample_size
     _lowercase: Any						     =    block_out_channels[0] * 4

     # Check inputs
     if len(UpperCamelCase_     ) != len(UpperCamelCase_     ):
       raise ValueError(
           f'Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.'     )

     if len(UpperCamelCase_     ) != len(UpperCamelCase_     ):
       raise ValueError(
           f'Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.'     )

     # input
     _lowercase: Tuple						     =    nn.Convad(UpperCamelCase_						,					block_out_channels[0]						,					kernel_size=3						,					padding=(1, 1)     )

     # time
     if time_embedding_type == "fourier":
       _lowercase: Optional[Any]						     =    GaussianFourierProjection(embedding_size=block_out_channels[0]						,					scale=16     )
       _lowercase: Tuple						     =    2 * block_out_channels[0]
     elif time_embedding_type == "positional":
       _lowercase: Optional[Any]						     =    Timesteps(block_out_channels[0]						,					UpperCamelCase_						,					UpperCamelCase_     )
       _lowercase: Optional[Any]						     =    block_out_channels[0]

     _lowercase: Tuple						     =    TimestepEmbedding(UpperCamelCase_						,					UpperCamelCase_     )

     # class embedding
     if class_embed_type is None and num_class_embeds is not None:
       _lowercase: List[Any]						     =    nn.Embedding(UpperCamelCase_						,					UpperCamelCase_     )
     elif class_embed_type == "timestep":
       _lowercase: str						     =    TimestepEmbedding(UpperCamelCase_						,					UpperCamelCase_     )
     elif class_embed_type == "identity":
       _lowercase: str						     =    nn.Identity(UpperCamelCase_						,					UpperCamelCase_     )
     else:
       _lowercase: Optional[int]						     =    None

     _lowercase: Union[str, Any]						     =    nn.ModuleList([]     )
     _lowercase: List[Any]						     =    None
     _lowercase: Optional[Any]						     =    nn.ModuleList([]     )

     # down
     _lowercase: Any						     =    block_out_channels[0]
     for i, down_block_type in enumerate(UpperCamelCase_     ):
       _lowercase: Optional[int]						     =    output_channel
       _lowercase: List[Any]						     =    block_out_channels[i]
       _lowercase: Dict						     =    i == len(UpperCamelCase_     ) - 1

       _lowercase: str						     =    get_down_block(
           UpperCamelCase_						,					num_layers=UpperCamelCase_						,					in_channels=UpperCamelCase_						,					out_channels=UpperCamelCase_						,					temb_channels=UpperCamelCase_						,					add_downsample=not is_final_block						,					resnet_eps=UpperCamelCase_						,					resnet_act_fn=UpperCamelCase_						,					resnet_groups=UpperCamelCase_						,					attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel						,					downsample_padding=UpperCamelCase_						,					resnet_time_scale_shift=UpperCamelCase_						,					downsample_type=UpperCamelCase_						,					)
       self.down_blocks.append(UpperCamelCase_     )

     # mid
     _lowercase: List[str]						     =    UNetMidBlockaD(
         in_channels=block_out_channels[-1]						,					temb_channels=UpperCamelCase_						,					resnet_eps=UpperCamelCase_						,					resnet_act_fn=UpperCamelCase_						,					output_scale_factor=UpperCamelCase_						,					resnet_time_scale_shift=UpperCamelCase_						,					attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]						,					resnet_groups=UpperCamelCase_						,					add_attention=UpperCamelCase_						,					)

     # up
     _lowercase: int						     =    list(reversed(UpperCamelCase_     )     )
     _lowercase: Dict						     =    reversed_block_out_channels[0]
     for i, up_block_type in enumerate(UpperCamelCase_     ):
       _lowercase: Any						     =    output_channel
       _lowercase: Any						     =    reversed_block_out_channels[i]
       _lowercase: Tuple						     =    reversed_block_out_channels[min(i + 1						,					len(UpperCamelCase_     ) - 1     )]

       _lowercase: Optional[Any]						     =    i == len(UpperCamelCase_     ) - 1

       _lowercase: Tuple						     =    get_up_block(
           UpperCamelCase_						,					num_layers=layers_per_block + 1						,					in_channels=UpperCamelCase_						,					out_channels=UpperCamelCase_						,					prev_output_channel=UpperCamelCase_						,					temb_channels=UpperCamelCase_						,					add_upsample=not is_final_block						,					resnet_eps=UpperCamelCase_						,					resnet_act_fn=UpperCamelCase_						,					resnet_groups=UpperCamelCase_						,					attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel						,					resnet_time_scale_shift=UpperCamelCase_						,					upsample_type=UpperCamelCase_						,					)
       self.up_blocks.append(UpperCamelCase_     )
       _lowercase: Tuple						     =    output_channel

     # out
     _lowercase: Optional[int]						     =    norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4						,					32     )
     _lowercase: Optional[Any]						     =    nn.GroupNorm(num_channels=block_out_channels[0]						,					num_groups=UpperCamelCase_						,					eps=UpperCamelCase_     )
     _lowercase: Tuple						     =    nn.SiLU()
     _lowercase: Optional[int]						     =    nn.Convad(block_out_channels[0]						,					UpperCamelCase_						,					kernel_size=3						,					padding=1     )






   def  _a						(	self   :       List[Any]						,					UpperCamelCase_   :       torch.FloatTensor						,					UpperCamelCase_   :       Union[torch.Tensor, float, int]						,					UpperCamelCase_   :       Optional[torch.Tensor] = None						,					UpperCamelCase_   :       bool = True						,					)->			Union[UNetaDOutput, Tuple]:
     # 0. center input if necessary
     if self.config.center_input_sample:
       _lowercase: str						     =    2 * sample - 1.0

     # 1. time
     _lowercase: List[str]						     =    timestep
     if not torch.is_tensor(UpperCamelCase_     ):
       _lowercase: str						     =    torch.tensor([timesteps]						,					dtype=torch.long						,					device=sample.device     )
     elif torch.is_tensor(UpperCamelCase_     ) and len(timesteps.shape     ) == 0:
       _lowercase: Optional[Any]						     =    timesteps[None].to(sample.device     )

     # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
     _lowercase: Union[str, Any]						     =    timesteps * torch.ones(sample.shape[0]						,					dtype=timesteps.dtype						,					device=timesteps.device     )

     _lowercase: List[Any]						     =    self.time_proj(UpperCamelCase_     )

     # timesteps does not contain any weights and will always return f32 tensors
     # but time_embedding might actually be running in fp16. so we need to cast here.
     # there might be better ways to encapsulate this.
     _lowercase: Optional[int]						     =    t_emb.to(dtype=self.dtype     )
     _lowercase: Any						     =    self.time_embedding(UpperCamelCase_     )

     if self.class_embedding is not None:
       if class_labels is None:
         raise ValueError('class_labels should be provided when doing class conditioning'     )

       if self.config.class_embed_type == "timestep":
         _lowercase: Union[str, Any]						     =    self.time_proj(UpperCamelCase_     )

       _lowercase: Optional[int]						     =    self.class_embedding(UpperCamelCase_     ).to(dtype=self.dtype     )
       _lowercase: Dict						     =    emb + class_emb

     # 2. pre-process
     _lowercase: Optional[int]						     =    sample
     _lowercase: Dict						     =    self.conv_in(UpperCamelCase_     )

     # 3. down
     _lowercase: List[Any]						     =    (sample,)
     for downsample_block in self.down_blocks:
       if hasattr(UpperCamelCase_						,					'skip_conv'     ):
         _lowercase     ,					_lowercase     ,					_lowercase: Optional[Any]						     =    downsample_block(
             hidden_states=UpperCamelCase_						,					temb=UpperCamelCase_						,					skip_sample=UpperCamelCase_     )
       else:
         _lowercase     ,					_lowercase: Dict						     =    downsample_block(hidden_states=UpperCamelCase_						,					temb=UpperCamelCase_     )

       down_block_res_samples += res_samples

     # 4. mid
     _lowercase: Union[str, Any]						     =    self.mid_block(UpperCamelCase_						,					UpperCamelCase_     )

     # 5. up
     _lowercase: List[Any]						     =    None
     for upsample_block in self.up_blocks:
       _lowercase: Tuple						     =    down_block_res_samples[-len(upsample_block.resnets     ) :]
       _lowercase: Dict						     =    down_block_res_samples[: -len(upsample_block.resnets     )]

       if hasattr(UpperCamelCase_						,					'skip_conv'     ):
         _lowercase     ,					_lowercase: str						     =    upsample_block(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     )
       else:
         _lowercase: Dict						     =    upsample_block(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     )

       # 6. post-process
     _lowercase: Optional[Any]						     =    self.conv_norm_out(UpperCamelCase_     )
     _lowercase: int						     =    self.conv_act(UpperCamelCase_     )
     _lowercase: List[str]						     =    self.conv_out(UpperCamelCase_     )

     if skip_sample is not None:
       sample += skip_sample

     if self.config.time_embedding_type == "fourier":
       _lowercase: List[Any]						     =    timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]     )))     )
       _lowercase: Optional[int]						     =    sample / timesteps

     if not return_dict:
       return (sample,)

     return UNetaDOutput(sample=UpperCamelCase_     )






from __future__ import annotations

from pathlib import Path


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		int:





  """simple docstring"""





  return pointa[0] * pointa[1] - pointa[1] * pointa[0]


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		bool:





  """simple docstring"""





  _lowercase: tuple[int, int]						     =    (xa, ya)
  _lowercase: tuple[int, int]						     =    (xa - xa, ya - ya)
  _lowercase: tuple[int, int]						     =    (xa - xa, ya - ya)
  _lowercase: float						     =    -vector_product(UpperCamelCase					,     UpperCamelCase							) / vector_product(
      UpperCamelCase					,     UpperCamelCase							)
  _lowercase: float						     =    +vector_product(UpperCamelCase					,     UpperCamelCase							) / vector_product(
      UpperCamelCase					,     UpperCamelCase							)

  return a > 0 and b > 0 and a + b < 1


def 		_lowerCamelCase				(					UpperCamelCase = "p102_triangles.txt"							)			->		int:





  """simple docstring"""





  _lowercase: str						     =    Path(UpperCamelCase							).parent.joinpath(UpperCamelCase							).read_text(encoding='utf-8'							)

  _lowercase: list[list[int]]						     =    []
  for line in data.strip().split('\n'							):
    triangles.append([int(UpperCamelCase							) for number in line.split(','							)]							)

  _lowercase: int						     =    0
  _lowercase: list[int]

  for triangle in triangles:
    ret += contains_origin(*UpperCamelCase							)

  return ret


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