




'''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 UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class      __UpperCAmelCase							(    snake_case__							):



     '''simple docstring'''





     _UpperCamelCase  	=		42





class      __UpperCAmelCase							(    snake_case__ ,snake_case__							):



     '''simple docstring'''





     @register_to_config
     def __init__(  self    :	Optional[Any]			,					_lowercase    :	Optional[int] = None			,					_lowercase    :	Tuple = 3			,					_lowercase    :	int = 3			,					_lowercase    :	Optional[Any] = False			,					_lowercase    :	Any = "positional"			,					_lowercase    :	List[Any] = 0			,					_lowercase    :	Dict = True			,					_lowercase    :	Union[str, Any] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")			,					_lowercase    :	Optional[int] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")			,					_lowercase    :	Optional[int] = (224, 448, 672, 896)			,					_lowercase    :	List[Any] = 2			,					_lowercase    :	Optional[Any] = 1			,					_lowercase    :	str = 1			,					_lowercase    :	Optional[Any] = "conv"			,					_lowercase    :	Union[str, Any] = "conv"			,					_lowercase    :	Any = "silu"			,					_lowercase    :	Any = 8			,					_lowercase    :	Tuple = 32			,					_lowercase    :	Optional[Any] = 1E-5			,					_lowercase    :	Union[str, Any] = "default"			,					_lowercase    :	List[Any] = True			,					_lowercase    :	Optional[Any] = None			,					_lowercase    :	Tuple = None			,					)						->       Optional[int]:
        super().__init__()

        A_       		=   sample_size
        A_       		=   block_out_channels[0] * 4

        # Check inputs
        if len(__snake_case) != len(__snake_case):
           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(__snake_case) != len(__snake_case):
           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
        A_       		=   nn.Convad(__snake_case			,					block_out_channels[0]			,					kernel_size=3			,					padding=(1, 1))

        # time
        if time_embedding_type == "fourier":
           A_       		=   GaussianFourierProjection(embedding_size=block_out_channels[0]			,					scale=16)
           A_       		=   2 * block_out_channels[0]
        elif time_embedding_type == "positional":
           A_       		=   Timesteps(block_out_channels[0]			,					__snake_case			,					__snake_case)
           A_       		=   block_out_channels[0]

        A_       		=   TimestepEmbedding(__snake_case			,					__snake_case)

        # class embedding
        if class_embed_type is None and num_class_embeds is not None:
           A_       		=   nn.Embedding(__snake_case			,					__snake_case)
        elif class_embed_type == "timestep":
           A_       		=   TimestepEmbedding(__snake_case			,					__snake_case)
        elif class_embed_type == "identity":
           A_       		=   nn.Identity(__snake_case			,					__snake_case)
        else:
           A_       		=   None

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

        # down
        A_       		=   block_out_channels[0]
        for i, down_block_type in enumerate(__snake_case):
           A_       		=   output_channel
           A_       		=   block_out_channels[i]
           A_       		=   i == len(__snake_case) - 1

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

        # mid
        A_       		=   UNetMidBlockaD(
            in_channels=block_out_channels[-1]			,					temb_channels=__snake_case			,					resnet_eps=__snake_case			,					resnet_act_fn=__snake_case			,					output_scale_factor=__snake_case			,					resnet_time_scale_shift=__snake_case			,					attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]			,					resnet_groups=__snake_case			,					add_attention=__snake_case			,					)

        # up
        A_       		=   list(reversed(__snake_case))
        A_       		=   reversed_block_out_channels[0]
        for i, up_block_type in enumerate(__snake_case):
           A_       		=   output_channel
           A_       		=   reversed_block_out_channels[i]
           A_       		=   reversed_block_out_channels[min(i + 1			,					len(__snake_case) - 1)]

           A_       		=   i == len(__snake_case) - 1

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

        # out
        A_       		=   norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4			,					32)
        A_       		=   nn.GroupNorm(num_channels=block_out_channels[0]			,					num_groups=__snake_case			,					eps=__snake_case)
        A_       		=   nn.SiLU()
        A_       		=   nn.Convad(block_out_channels[0]			,					__snake_case			,					kernel_size=3			,					padding=1)



     def 						__snake_case	(  self    :	int			,					_lowercase    :	Dict			,					_lowercase    :	Dict			,					_lowercase    :	Optional[int] = None			,					_lowercase    :	List[Any] = True			,					)						->       List[Any]:
        # 0. center input if necessary
        if self.config.center_input_sample:
           A_       		=   2 * sample - 1.0

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

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

        A_       		=   self.time_proj(__snake_case)

        # 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.
        A_       		=   t_emb.to(dtype=self.dtype)
        A_       		=   self.time_embedding(__snake_case)

        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":
              A_       		=   self.time_proj(__snake_case)

           A_       		=   self.class_embedding(__snake_case).to(dtype=self.dtype)
           A_       		=   emb + class_emb

        # 2. pre-process
        A_       		=   sample
        A_       		=   self.conv_in(__snake_case)

        # 3. down
        A_       		=   (sample,)
        for downsample_block in self.down_blocks:
           if hasattr(__snake_case			,					'skip_conv'):
              A_		,   A_		,   A_       		=   downsample_block(
                  hidden_states=__snake_case			,					temb=__snake_case			,					skip_sample=__snake_case)
           else:
              A_		,   A_       		=   downsample_block(hidden_states=__snake_case			,					temb=__snake_case)

           down_block_res_samples += res_samples

        # 4. mid
        A_       		=   self.mid_block(__snake_case			,					__snake_case)

        # 5. up
        A_       		=   None
        for upsample_block in self.up_blocks:
           A_       		=   down_block_res_samples[-len(upsample_block.resnets) :]
           A_       		=   down_block_res_samples[: -len(upsample_block.resnets)]

           if hasattr(__snake_case			,					'skip_conv'):
              A_		,   A_       		=   upsample_block(__snake_case			,					__snake_case			,					__snake_case			,					__snake_case)
           else:
              A_       		=   upsample_block(__snake_case			,					__snake_case			,					__snake_case)

        # 6. post-process
        A_       		=   self.conv_norm_out(__snake_case)
        A_       		=   self.conv_act(__snake_case)
        A_       		=   self.conv_out(__snake_case)

        if skip_sample is not None:
           sample += skip_sample

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

        if not return_dict:
           return (sample,)

        return UNetaDOutput(sample=__snake_case)










'''simple docstring'''
def  lowerCamelCase(       SCREAMING_SNAKE_CASE_    )		->		list:
			A_       		=   [True for i in range(n + 1    )]
			A_       		=   [i - 1 for i in range(n + 1    )]
			A_       		=   []
			for i in range(2					,n + 1    ):
						if is_prime[i]:
									primes.append(SCREAMING_SNAKE_CASE_    )
						for j in range(0					,len(SCREAMING_SNAKE_CASE_    )    ):
									if i * primes[j] >= n:
												break
									A_       		=   False

									if i % primes[j] == 0:
												A_       		=   totients[i] * primes[j]
												break

									A_       		=   totients[i] * (primes[j] - 1)

			return totients


if __name__ == "__main__":
							import doctest

							doctest.testmod()




