


"""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    __A   (_lowerCamelCase):





							'''simple docstring'''

							__lowercase:						str												=			42





class    __A   (_lowerCamelCase  ,						_lowerCamelCase):





							'''simple docstring'''

							@register_to_config
							def __init__( self							:					List[str]						,				UpperCAmelCase_							:					List[str] = 65_536						,				UpperCAmelCase_							:					List[str] = None						,				UpperCAmelCase_							:					List[str] = 2						,				UpperCAmelCase_							:					Any = 2						,				UpperCAmelCase_							:					Dict = 0						,				UpperCAmelCase_							:					int = "fourier"						,				UpperCAmelCase_							:					Dict = True						,				UpperCAmelCase_							:					int = False						,				UpperCAmelCase_							:					List[Any] = 0.0						,				UpperCAmelCase_							:					List[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")						,				UpperCAmelCase_							:					Union[str, Any] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")						,				UpperCAmelCase_							:					Dict = "UNetMidBlock1D"						,				UpperCAmelCase_							:					int = None						,				UpperCAmelCase_							:					Any = (32, 32, 64)						,				UpperCAmelCase_							:					str = None						,				UpperCAmelCase_							:					Dict = 8						,				UpperCAmelCase_							:					Union[str, Any] = 1						,				UpperCAmelCase_							:					Optional[int] = False						,				)    ->Any:



														"""simple docstring"""



														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 						lowerCAmelCase				( self							:					Tuple						,				UpperCAmelCase_							:					Dict						,				UpperCAmelCase_							:					Any						,				UpperCAmelCase_							:					Any = True						,				)    ->Any:



														"""simple docstring"""



														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_				)





'''simple docstring'''







def 				SCREAMING_SNAKE_CASE__	(   __A = 1_000_000	)       ->	int:
	_snake_case   						=		limit + 1
	_snake_case   						=		[0] * limit
	for first_term in range(1			,				__A	):
		for n in range(__A			,				__A			,				__A	):
			_snake_case   						=		first_term + n / first_term
			if common_difference % 4:  # d must be divisble by 4
				continue
			else:
				common_difference /= 4
				if (
				    first_term > common_difference
				    and first_term < 4 * common_difference
				):  # since x,y,z are positive integers
					frequency[n] += 1  # so z>0 and a>d ,also 4d<a

	_snake_case   						=		sum(1 for x in frequency[1:limit] if x == 10	)

	return count


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

