


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 							lowercase__(						snake_case__			):






      '''simple docstring'''







      snake_case__       =					42




class 							lowercase__(						snake_case__			,							snake_case__			):






      '''simple docstring'''







      @register_to_config
      def __init__(		self   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = 3   ,			__SCREAMING_SNAKE_CASE = 3   ,			__SCREAMING_SNAKE_CASE = False   ,			__SCREAMING_SNAKE_CASE = "positional"   ,			__SCREAMING_SNAKE_CASE = 0   ,			__SCREAMING_SNAKE_CASE = True   ,			__SCREAMING_SNAKE_CASE = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")   ,			__SCREAMING_SNAKE_CASE = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")   ,			__SCREAMING_SNAKE_CASE = (2_24, 4_48, 6_72, 8_96)   ,			__SCREAMING_SNAKE_CASE = 2   ,			__SCREAMING_SNAKE_CASE = 1   ,			__SCREAMING_SNAKE_CASE = 1   ,			__SCREAMING_SNAKE_CASE = "conv"   ,			__SCREAMING_SNAKE_CASE = "conv"   ,			__SCREAMING_SNAKE_CASE = "silu"   ,			__SCREAMING_SNAKE_CASE = 8   ,			__SCREAMING_SNAKE_CASE = 32   ,			__SCREAMING_SNAKE_CASE = 1E-5   ,			__SCREAMING_SNAKE_CASE = "default"   ,			__SCREAMING_SNAKE_CASE = True   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = None   ,			)					->						Optional[int]:







         """simple docstring"""







         super().__init__()

         UpperCamelCase__ :			Any 						=sample_size
         UpperCamelCase__ :			Any 						=block_out_channels[0] * 4

         # Check inputs
         if len(__SCREAMING_SNAKE_CASE) != len(__SCREAMING_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(__SCREAMING_SNAKE_CASE) != len(__SCREAMING_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
         UpperCamelCase__ :			List[str] 						=nn.Convad(__SCREAMING_SNAKE_CASE   ,			block_out_channels[0]   ,			kernel_size=3   ,			padding=(1, 1))

         # time
         if time_embedding_type == "fourier":
            UpperCamelCase__ :			Tuple 						=GaussianFourierProjection(embedding_size=block_out_channels[0]   ,			scale=16)
            UpperCamelCase__ :			Union[str, Any] 						=2 * block_out_channels[0]
         elif time_embedding_type == "positional":
            UpperCamelCase__ :			Optional[int] 						=Timesteps(block_out_channels[0]   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)
            UpperCamelCase__ :			Dict 						=block_out_channels[0]

         UpperCamelCase__ :			List[Any] 						=TimestepEmbedding(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)

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

         UpperCamelCase__ :			int 						=nn.ModuleList([])
         UpperCamelCase__ :			List[Any] 						=None
         UpperCamelCase__ :			int 						=nn.ModuleList([])

         # down
         UpperCamelCase__ :			Optional[int] 						=block_out_channels[0]
         for i, down_block_type in enumerate(__SCREAMING_SNAKE_CASE):
            UpperCamelCase__ :			int 						=output_channel
            UpperCamelCase__ :			int 						=block_out_channels[i]
            UpperCamelCase__ :			Dict 						=i == len(__SCREAMING_SNAKE_CASE) - 1

            UpperCamelCase__ :			Optional[int] 						=get_down_block(
                __SCREAMING_SNAKE_CASE   ,			num_layers=__SCREAMING_SNAKE_CASE   ,			in_channels=__SCREAMING_SNAKE_CASE   ,			out_channels=__SCREAMING_SNAKE_CASE   ,			temb_channels=__SCREAMING_SNAKE_CASE   ,			add_downsample=not is_final_block   ,			resnet_eps=__SCREAMING_SNAKE_CASE   ,			resnet_act_fn=__SCREAMING_SNAKE_CASE   ,			resnet_groups=__SCREAMING_SNAKE_CASE   ,			attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel   ,			downsample_padding=__SCREAMING_SNAKE_CASE   ,			resnet_time_scale_shift=__SCREAMING_SNAKE_CASE   ,			downsample_type=__SCREAMING_SNAKE_CASE   ,			)
            self.down_blocks.append(__SCREAMING_SNAKE_CASE)

         # mid
         UpperCamelCase__ :			Optional[Any] 						=UNetMidBlockaD(
             in_channels=block_out_channels[-1]   ,			temb_channels=__SCREAMING_SNAKE_CASE   ,			resnet_eps=__SCREAMING_SNAKE_CASE   ,			resnet_act_fn=__SCREAMING_SNAKE_CASE   ,			output_scale_factor=__SCREAMING_SNAKE_CASE   ,			resnet_time_scale_shift=__SCREAMING_SNAKE_CASE   ,			attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]   ,			resnet_groups=__SCREAMING_SNAKE_CASE   ,			add_attention=__SCREAMING_SNAKE_CASE   ,			)

         # up
         UpperCamelCase__ :			Any 						=list(reversed(__SCREAMING_SNAKE_CASE))
         UpperCamelCase__ :			Union[str, Any] 						=reversed_block_out_channels[0]
         for i, up_block_type in enumerate(__SCREAMING_SNAKE_CASE):
            UpperCamelCase__ :			str 						=output_channel
            UpperCamelCase__ :			Dict 						=reversed_block_out_channels[i]
            UpperCamelCase__ :			Tuple 						=reversed_block_out_channels[min(i + 1   ,			len(__SCREAMING_SNAKE_CASE) - 1)]

            UpperCamelCase__ :			Optional[int] 						=i == len(__SCREAMING_SNAKE_CASE) - 1

            UpperCamelCase__ :			Optional[Any] 						=get_up_block(
                __SCREAMING_SNAKE_CASE   ,			num_layers=layers_per_block + 1   ,			in_channels=__SCREAMING_SNAKE_CASE   ,			out_channels=__SCREAMING_SNAKE_CASE   ,			prev_output_channel=__SCREAMING_SNAKE_CASE   ,			temb_channels=__SCREAMING_SNAKE_CASE   ,			add_upsample=not is_final_block   ,			resnet_eps=__SCREAMING_SNAKE_CASE   ,			resnet_act_fn=__SCREAMING_SNAKE_CASE   ,			resnet_groups=__SCREAMING_SNAKE_CASE   ,			attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel   ,			resnet_time_scale_shift=__SCREAMING_SNAKE_CASE   ,			upsample_type=__SCREAMING_SNAKE_CASE   ,			)
            self.up_blocks.append(__SCREAMING_SNAKE_CASE)
            UpperCamelCase__ :			List[Any] 						=output_channel

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




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = True   ,			)					->						Union[UNetaDOutput, Tuple]:







         """simple docstring"""







         if self.config.center_input_sample:
            UpperCamelCase__ :			Optional[Any] 						=2 * sample - 1.0

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

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

         UpperCamelCase__ :			Tuple 						=self.time_proj(__SCREAMING_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.
         UpperCamelCase__ :			Any 						=t_emb.to(dtype=self.dtype)
         UpperCamelCase__ :			int 						=self.time_embedding(__SCREAMING_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":
               UpperCamelCase__ :			List[Any] 						=self.time_proj(__SCREAMING_SNAKE_CASE)

            UpperCamelCase__ :			Optional[Any] 						=self.class_embedding(__SCREAMING_SNAKE_CASE).to(dtype=self.dtype)
            UpperCamelCase__ :			Union[str, Any] 						=emb + class_emb

         # 2. pre-process
         UpperCamelCase__ :			Any 						=sample
         UpperCamelCase__ :			Tuple 						=self.conv_in(__SCREAMING_SNAKE_CASE)

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

            down_block_res_samples += res_samples

         # 4. mid
         UpperCamelCase__ :			Any 						=self.mid_block(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)

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

            if hasattr(__SCREAMING_SNAKE_CASE   ,			"skip_conv"):
               UpperCamelCase__						,	UpperCamelCase__ :			List[str] 						=upsample_block(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)
            else:
               UpperCamelCase__ :			Optional[int] 						=upsample_block(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)

        # 6. post-process
         UpperCamelCase__ :			List[Any] 						=self.conv_norm_out(__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			Union[str, Any] 						=self.conv_act(__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			Any 						=self.conv_out(__SCREAMING_SNAKE_CASE)

         if skip_sample is not None:
            sample += skip_sample

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

         if not return_dict:
            return (sample,)

         return UNetaDOutput(sample=__SCREAMING_SNAKE_CASE)





def 	_lowerCamelCase  (		A_  :	float							,  A_  :	float							,  A_  :	int      )			->			float:

   '''simple docstring'''






   UpperCamelCase__ :			Union[str, Any] 						=[1 / first_term]
   UpperCamelCase__ :			Tuple 						=1 / first_term
   for _ in range(number_of_terms - 1      ):
      first_term += common_difference
      arithmetic_progression.append(A_      )
   UpperCamelCase__ :			List[str] 						=[1 / step for step in arithmetic_progression]
   return sum(A_      )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(sum_of_harmonic_progression(1 / 2, 2, 2))

