


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  snake_case__						(	lowerCAmelCase__					):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     42




class  snake_case__						(	lowerCAmelCase__    ,					lowerCAmelCase__					):






     """simple docstring"""


     @register_to_config
     def __init__(	self  :  Optional[Any],			_snake_case  :  Optional[int] = None,			_snake_case  :  int = 3,			_snake_case  :  Dict = 3,			_snake_case  :  Tuple = False,			_snake_case  :  Optional[Any] = "positional",			_snake_case  :  List[str] = 0,			_snake_case  :  int = True,			_snake_case  :  Tuple = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D"),			_snake_case  :  Union[str, Any] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D"),			_snake_case  :  Dict = (2_2_4, 4_4_8, 6_7_2, 8_9_6),			_snake_case  :  Union[str, Any] = 2,			_snake_case  :  List[str] = 1,			_snake_case  :  Tuple = 1,			_snake_case  :  Any = "conv",			_snake_case  :  List[str] = "conv",			_snake_case  :  Any = "silu",			_snake_case  :  Dict = 8,			_snake_case  :  Optional[int] = 3_2,			_snake_case  :  Union[str, Any] = 1e-5,			_snake_case  :  Union[str, Any] = "default",			_snake_case  :  str = True,			_snake_case  :  Union[str, Any] = None,			_snake_case  :  int = None,			)       ->List[Any]:
            super().__init__()

            snake_case__							:     Optional[Any]            =     sample_size
            snake_case__							:     List[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
            snake_case__							:     int            =     nn.Convad(UpperCamelCase__,			block_out_channels[0],			kernel_size=3,			padding=(1, 1) )

            # time
            if time_embedding_type == "fourier":
                   snake_case__							:     Dict            =     GaussianFourierProjection(embedding_size=block_out_channels[0],			scale=1_6 )
                   snake_case__							:     int            =     2 * block_out_channels[0]
            elif time_embedding_type == "positional":
                   snake_case__							:     List[str]            =     Timesteps(block_out_channels[0],			UpperCamelCase__,			UpperCamelCase__ )
                   snake_case__							:     Optional[Any]            =     block_out_channels[0]

            snake_case__							:     Optional[int]            =     TimestepEmbedding(UpperCamelCase__,			UpperCamelCase__ )

            # class embedding
            if class_embed_type is None and num_class_embeds is not None:
                   snake_case__							:     List[str]            =     nn.Embedding(UpperCamelCase__,			UpperCamelCase__ )
            elif class_embed_type == "timestep":
                   snake_case__							:     Dict            =     TimestepEmbedding(UpperCamelCase__,			UpperCamelCase__ )
            elif class_embed_type == "identity":
                   snake_case__							:     str            =     nn.Identity(UpperCamelCase__,			UpperCamelCase__ )
            else:
                   snake_case__							:     Dict            =     None

            snake_case__							:     Optional[Any]            =     nn.ModuleList([] )
            snake_case__							:     List[str]            =     None
            snake_case__							:     int            =     nn.ModuleList([] )

            # down
            snake_case__							:     List[Any]            =     block_out_channels[0]
            for i, down_block_type in enumerate(UpperCamelCase__ ):
                   snake_case__							:     Union[str, Any]            =     output_channel
                   snake_case__							:     int            =     block_out_channels[i]
                   snake_case__							:     str            =     i == len(UpperCamelCase__ ) - 1

                   snake_case__							:     List[Any]            =     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
            snake_case__							:     Optional[Any]            =     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
            snake_case__							:     Dict            =     list(reversed(UpperCamelCase__ ) )
            snake_case__							:     List[str]            =     reversed_block_out_channels[0]
            for i, up_block_type in enumerate(UpperCamelCase__ ):
                   snake_case__							:     Any            =     output_channel
                   snake_case__							:     Optional[Any]            =     reversed_block_out_channels[i]
                   snake_case__							:     Optional[int]            =     reversed_block_out_channels[min(i + 1,			len(UpperCamelCase__ ) - 1 )]

                   snake_case__							:     Optional[Any]            =     i == len(UpperCamelCase__ ) - 1

                   snake_case__							:     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__ )
                   snake_case__							:     Optional[Any]            =     output_channel

            # out
            snake_case__							:     Optional[int]            =     norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4,			3_2 )
            snake_case__							:     Optional[Any]            =     nn.GroupNorm(num_channels=block_out_channels[0],			num_groups=UpperCamelCase__,			eps=UpperCamelCase__ )
            snake_case__							:     Optional[Any]            =     nn.SiLU()
            snake_case__							:     Dict            =     nn.Convad(block_out_channels[0],			UpperCamelCase__,			kernel_size=3,			padding=1 )






     def        lowercase_       (	self  :  Optional[Any],			_snake_case  :  Union[str, Any],			_snake_case  :  Tuple,			_snake_case  :  Union[str, Any] = None,			_snake_case  :  Tuple = True,			)       ->Union[UNetaDOutput, Tuple]:
            # 0. center input if necessary
            if self.config.center_input_sample:
                   snake_case__							:     Dict            =     2 * sample - 1.0

            # 1. time
            snake_case__							:     int            =     timestep
            if not torch.is_tensor(UpperCamelCase__ ):
                   snake_case__							:     Optional[int]            =     torch.tensor([timesteps],			dtype=torch.long,			device=sample.device )
            elif torch.is_tensor(UpperCamelCase__ ) and len(timesteps.shape ) == 0:
                   snake_case__							:     Dict            =     timesteps[None].to(sample.device )

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

            snake_case__							:     Dict            =     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.
            snake_case__							:     Optional[int]            =     t_emb.to(dtype=self.dtype )
            snake_case__							:     Dict            =     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":
                          snake_case__							:     List[Any]            =     self.time_proj(UpperCamelCase__ )

                   snake_case__							:     Tuple            =     self.class_embedding(UpperCamelCase__ ).to(dtype=self.dtype )
                   snake_case__							:     Tuple            =     emb + class_emb

            # 2. pre-process
            snake_case__							:     str            =     sample
            snake_case__							:     Union[str, Any]            =     self.conv_in(UpperCamelCase__ )

            # 3. down
            snake_case__							:     int            =     (sample,)
            for downsample_block in self.down_blocks:
                   if hasattr(UpperCamelCase__,			'skip_conv' ):
                          snake_case__							:     Optional[int]            =     downsample_block(
                              hidden_states=UpperCamelCase__,			temb=UpperCamelCase__,			skip_sample=UpperCamelCase__ )
                   else:
                          snake_case__							:     Union[str, Any]            =     downsample_block(hidden_states=UpperCamelCase__,			temb=UpperCamelCase__ )

                   down_block_res_samples += res_samples

            # 4. mid
            snake_case__							:     Dict            =     self.mid_block(UpperCamelCase__,			UpperCamelCase__ )

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

                   if hasattr(UpperCamelCase__,			'skip_conv' ):
                          snake_case__							:     List[str]            =     upsample_block(UpperCamelCase__,			UpperCamelCase__,			UpperCamelCase__,			UpperCamelCase__ )
                   else:
                          snake_case__							:     Union[str, Any]            =     upsample_block(UpperCamelCase__,			UpperCamelCase__,			UpperCamelCase__ )

        # 6. post-process
            snake_case__							:     Optional[int]            =     self.conv_norm_out(UpperCamelCase__ )
            snake_case__							:     Tuple            =     self.conv_act(UpperCamelCase__ )
            snake_case__							:     Optional[Any]            =     self.conv_out(UpperCamelCase__ )

            if skip_sample is not None:
                   sample += skip_sample

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

            if not return_dict:
                   return (sample,)

            return UNetaDOutput(sample=UpperCamelCase__ )











import math
def 	lowercase_							(A			:			int				):

       if 1 < number < 4:
              # 2 and 3 are primes
              return True
       elif number < 2 or number % 2 == 0 or number % 3 == 0:
              # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
              return False

       # All primes number are in format of 6k +/- 1
       for i in range(5		, int(math.sqrt(A				) + 1				)		, 6				):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True
def 	lowercase_							(A			:			int = 1_0_0_0		, A			:			int = 1_0_0_0				):
       snake_case__							:     str            =     [0, 0, 0]  # length, a, b
       for a in range((a_limit * -1) + 1		, A				):
              for b in range(2		, A				):
                     if is_prime(A				):
                            snake_case__							:     Optional[Any]            =     0
                            snake_case__							:     Optional[int]            =     0
                            while is_prime((n**2) + (a * n) + b				):
                                   count += 1
                                   n += 1
                            if count > longest[0]:
                                   snake_case__							:     Union[str, Any]            =     [count, a, b]
       snake_case__							:     List[str]            =     longest[1] * longest[2]
       return ans


if __name__ == "__main__":
 print(solution(1_000, 1_000))






