"""simple docstring"""


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 get_down_block, get_mid_block, get_out_block, get_up_block




@dataclass
class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      42




class  UpperCamelCase					(				lowerCAmelCase__    ,		lowerCAmelCase__   ):







       @register_to_config
       def __init__( self,							lowerCAmelCase__ = 6_5536,							lowerCAmelCase__ = None,							lowerCAmelCase__ = 2,							lowerCAmelCase__ = 2,							lowerCAmelCase__ = 0,							lowerCAmelCase__ = "fourier",							lowerCAmelCase__ = True,							lowerCAmelCase__ = False,							lowerCAmelCase__ = 0.0,							lowerCAmelCase__ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D"),							lowerCAmelCase__ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip"),							lowerCAmelCase__ = "UNetMidBlock1D",							lowerCAmelCase__ = None,							lowerCAmelCase__ = (32, 32, 64),							lowerCAmelCase__ = None,							lowerCAmelCase__ = 8,							lowerCAmelCase__ = 1,							lowerCAmelCase__ = False,							)      ->      Union[str, Any]:
            super().__init__()
            snake_case_					       =					sample_size

            # time
            if time_embedding_type == "fourier":
                 snake_case_					       =					GaussianFourierProjection(
                     embedding_size=8,							set_W_to_weight=lowerCAmelCase__,							log=lowerCAmelCase__,							flip_sin_to_cos=lowerCAmelCase__)
                 snake_case_					       =					2 * block_out_channels[0]
            elif time_embedding_type == "positional":
                 snake_case_					       =					Timesteps(
                     block_out_channels[0],							flip_sin_to_cos=lowerCAmelCase__,							downscale_freq_shift=lowerCAmelCase__)
                 snake_case_					       =					block_out_channels[0]

            if use_timestep_embedding:
                 snake_case_					       =					block_out_channels[0] * 4
                 snake_case_					       =					TimestepEmbedding(
                     in_channels=lowerCAmelCase__,							time_embed_dim=lowerCAmelCase__,							act_fn=lowerCAmelCase__,							out_dim=block_out_channels[0],							)

            snake_case_					       =					nn.ModuleList([])
            snake_case_					       =					None
            snake_case_					       =					nn.ModuleList([])
            snake_case_					       =					None

            # down
            snake_case_					       =					in_channels
            for i, down_block_type in enumerate(lowerCAmelCase__):
                 snake_case_					       =					output_channel
                 snake_case_					       =					block_out_channels[i]

                 if i == 0:
                      input_channel += extra_in_channels

                 snake_case_					       =					i == len(lowerCAmelCase__) - 1

                 snake_case_					       =					get_down_block(
                     lowerCAmelCase__,							num_layers=lowerCAmelCase__,							in_channels=lowerCAmelCase__,							out_channels=lowerCAmelCase__,							temb_channels=block_out_channels[0],							add_downsample=not is_final_block or downsample_each_block,							)
                 self.down_blocks.append(lowerCAmelCase__)

            # mid
            snake_case_					       =					get_mid_block(
                lowerCAmelCase__,							in_channels=block_out_channels[-1],							mid_channels=block_out_channels[-1],							out_channels=block_out_channels[-1],							embed_dim=block_out_channels[0],							num_layers=lowerCAmelCase__,							add_downsample=lowerCAmelCase__,							)

            # up
            snake_case_					       =					list(reversed(lowerCAmelCase__))
            snake_case_					       =					reversed_block_out_channels[0]
            if out_block_type is None:
                 snake_case_					       =					out_channels
            else:
                 snake_case_					       =					block_out_channels[0]

            for i, up_block_type in enumerate(lowerCAmelCase__):
                 snake_case_					       =					output_channel
                 snake_case_					       =					(
                     reversed_block_out_channels[i + 1] if i < len(lowerCAmelCase__) - 1 else final_upsample_channels
                 )

                 snake_case_					       =					i == len(lowerCAmelCase__) - 1

                 snake_case_					       =					get_up_block(
                     lowerCAmelCase__,							num_layers=lowerCAmelCase__,							in_channels=lowerCAmelCase__,							out_channels=lowerCAmelCase__,							temb_channels=block_out_channels[0],							add_upsample=not is_final_block,							)
                 self.up_blocks.append(lowerCAmelCase__)
                 snake_case_					       =					output_channel

            # out
            snake_case_					       =					norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4,							32)
            snake_case_					       =					get_out_block(
                out_block_type=lowerCAmelCase__,							num_groups_out=lowerCAmelCase__,							embed_dim=block_out_channels[0],							out_channels=lowerCAmelCase__,							act_fn=lowerCAmelCase__,							fc_dim=block_out_channels[-1] // 4,							)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__ = True,							)      ->      Union[UNetaDOutput, Tuple]:
            snake_case_					       =					timestep
            if not torch.is_tensor(lowerCAmelCase__):
                 snake_case_					       =					torch.tensor([timesteps],							dtype=torch.long,							device=sample.device)
            elif torch.is_tensor(lowerCAmelCase__) and len(timesteps.shape) == 0:
                 snake_case_					       =					timesteps[None].to(sample.device)

            snake_case_					       =					self.time_proj(lowerCAmelCase__)
            if self.config.use_timestep_embedding:
                 snake_case_					       =					self.time_mlp(lowerCAmelCase__)
            else:
                 snake_case_					       =					timestep_embed[..., None]
                 snake_case_					       =					timestep_embed.repeat([1, 1, sample.shape[2]]).to(sample.dtype)
                 snake_case_					       =					timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]))

            # 2. down
            snake_case_					       =					()
            for downsample_block in self.down_blocks:
                 snake_case_       ,	snake_case_					       =					downsample_block(hidden_states=lowerCAmelCase__,							temb=lowerCAmelCase__)
                 down_block_res_samples += res_samples

            # 3. mid
            if self.mid_block:
                 snake_case_					       =					self.mid_block(lowerCAmelCase__,							lowerCAmelCase__)

            # 4. up
            for i, upsample_block in enumerate(self.up_blocks):
                 snake_case_					       =					down_block_res_samples[-1:]
                 snake_case_					       =					down_block_res_samples[:-1]
                 snake_case_					       =					upsample_block(lowerCAmelCase__,							res_hidden_states_tuple=lowerCAmelCase__,							temb=lowerCAmelCase__)

            # 5. post-process
            if self.out_block:
                 snake_case_					       =					self.out_block(lowerCAmelCase__,							lowerCAmelCase__)

            if not return_dict:
                 return (sample,)

            return UNetaDOutput(sample=lowerCAmelCase__)





from math import isqrt







def        _lowerCamelCase(	lowercase__	)						->	bool:

		'''simple docstring'''







		return all(number % divisor != 0 for divisor in range(2   , isqrt(lowercase__	) + 1	)	)







def        _lowerCamelCase(	lowercase__ = 1_0**6	)						->	int:

		'''simple docstring'''







		__lowercase=			0
		__lowercase=			1
		__lowercase=			7
		while prime_candidate < max_prime:
				primes_count += is_prime(lowercase__	)

				cube_index += 1
				prime_candidate += 6 * cube_index

		return primes_count


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






