'''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     __magic_name__						(    _UpperCAmelCase):
			UpperCamelCase__				=       42





class     __magic_name__						(    _UpperCAmelCase,     _UpperCAmelCase):







			@register_to_config
			def __init__(	self				:  Optional[int]				,			lowercase_				:  int = 65536				,			lowercase_				:  Optional[int] = None				,			lowercase_				:  int = 2				,			lowercase_				:  int = 2				,			lowercase_				:  int = 0				,			lowercase_				:  str = "fourier"				,			lowercase_				:  bool = True				,			lowercase_				:  bool = False				,			lowercase_				:  float = 0.0				,			lowercase_				:  Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")				,			lowercase_				:  Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")				,			lowercase_				:  Tuple[str] = "UNetMidBlock1D"				,			lowercase_				:  str = None				,			lowercase_				:  Tuple[int] = (32, 32, 64)				,			lowercase_				:  str = None				,			lowercase_				:  int = 8				,			lowercase_				:  int = 1				,			lowercase_				:  bool = False				,			):
										super().__init__()
										lowercase_					:			Any										=       sample_size

										# time
										if time_embedding_type == "fourier":
																	lowercase_					:			Union[str, Any]										=       GaussianFourierProjection(
																	    embedding_size=8				,			set_W_to_weight=lowercase_				,			log=lowercase_				,			flip_sin_to_cos=lowercase_    )
																	lowercase_					:			List[str]										=       2 * block_out_channels[0]
										elif time_embedding_type == "positional":
																	lowercase_					:			List[str]										=       Timesteps(
																	    block_out_channels[0]				,			flip_sin_to_cos=lowercase_				,			downscale_freq_shift=lowercase_    )
																	lowercase_					:			str										=       block_out_channels[0]

										if use_timestep_embedding:
																	lowercase_					:			Any										=       block_out_channels[0] * 4
																	lowercase_					:			List[str]										=       TimestepEmbedding(
																	    in_channels=lowercase_				,			time_embed_dim=lowercase_				,			act_fn=lowercase_				,			out_dim=block_out_channels[0]				,			)

										lowercase_					:			Optional[Any]										=       nn.ModuleList([]    )
										lowercase_					:			List[Any]										=       None
										lowercase_					:			Tuple										=       nn.ModuleList([]    )
										lowercase_					:			Tuple										=       None

										# down
										lowercase_					:			Union[str, Any]										=       in_channels
										for i, down_block_type in enumerate(lowercase_    ):
																	lowercase_					:			Tuple										=       output_channel
																	lowercase_					:			int										=       block_out_channels[i]

																	if i == 0:
																								input_channel += extra_in_channels

																	lowercase_					:			List[str]										=       i == len(lowercase_    ) - 1

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

										# mid
										lowercase_					:			Optional[Any]										=       get_mid_block(
										    lowercase_				,			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=lowercase_				,			add_downsample=lowercase_				,			)

										# up
										lowercase_					:			Any										=       list(reversed(lowercase_    )    )
										lowercase_					:			Dict										=       reversed_block_out_channels[0]
										if out_block_type is None:
																	lowercase_					:			Dict										=       out_channels
										else:
																	lowercase_					:			int										=       block_out_channels[0]

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

																	lowercase_					:			Any										=       i == len(lowercase_    ) - 1

																	lowercase_					:			Optional[Any]										=       get_up_block(
																	    lowercase_				,			num_layers=lowercase_				,			in_channels=lowercase_				,			out_channels=lowercase_				,			temb_channels=block_out_channels[0]				,			add_upsample=not is_final_block				,			)
																	self.up_blocks.append(lowercase_    )
																	lowercase_					:			List[Any]										=       output_channel

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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  torch.FloatTensor				,			lowercase_				:  Union[torch.Tensor, float, int]				,			lowercase_				:  bool = True				,			):
										lowercase_					:			List[Any]										=       timestep
										if not torch.is_tensor(lowercase_    ):
																	lowercase_					:			Dict										=       torch.tensor([timesteps]				,			dtype=torch.long				,			device=sample.device    )
										elif torch.is_tensor(lowercase_    ) and len(timesteps.shape    ) == 0:
																	lowercase_					:			List[str]										=       timesteps[None].to(sample.device    )

										lowercase_					:			Optional[int]										=       self.time_proj(lowercase_    )
										if self.config.use_timestep_embedding:
																	lowercase_					:			int										=       self.time_mlp(lowercase_    )
										else:
																	lowercase_					:			str										=       timestep_embed[..., None]
																	lowercase_					:			Optional[Any]										=       timestep_embed.repeat([1, 1, sample.shape[2]]    ).to(sample.dtype    )
																	lowercase_					:			Optional[int]										=       timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])    )

										# 2. down
										lowercase_					:			List[Any]										=       ()
										for downsample_block in self.down_blocks:
																	lowercase_						,						lowercase_					:			Dict										=       downsample_block(hidden_states=lowercase_				,			temb=lowercase_    )
																	down_block_res_samples += res_samples

										# 3. mid
										if self.mid_block:
																	lowercase_					:			int										=       self.mid_block(lowercase_				,			lowercase_    )

										# 4. up
										for i, upsample_block in enumerate(self.up_blocks    ):
																	lowercase_					:			List[str]										=       down_block_res_samples[-1:]
																	lowercase_					:			List[str]										=       down_block_res_samples[:-1]
																	lowercase_					:			Union[str, Any]										=       upsample_block(lowercase_				,			res_hidden_states_tuple=lowercase_				,			temb=lowercase_    )

										# 5. post-process
										if self.out_block:
																	lowercase_					:			Optional[Any]										=       self.out_block(lowercase_				,			lowercase_    )

										if not return_dict:
																	return (sample,)

										return UNetaDOutput(sample=lowercase_    )






'''simple docstring'''




import math
import unittest




def 							lowerCamelCase	(      UpperCAmelCase__       :					int )			->      bool:
							assert isinstance(UpperCAmelCase__      ,      UpperCAmelCase__ ) and (
							    number >= 0
							), "'number' must been an int and positive"

							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(UpperCAmelCase__ ) + 1 )      ,      6 ):
														if number % i == 0 or number % (i + 2) == 0:
																					return False
							return True





class     __magic_name__						(    unittest.TestCase):







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict    ):
										self.assertTrue(is_prime(2    )    )
										self.assertTrue(is_prime(3    )    )
										self.assertTrue(is_prime(5    )    )
										self.assertTrue(is_prime(7    )    )
										self.assertTrue(is_prime(11    )    )
										self.assertTrue(is_prime(13    )    )
										self.assertTrue(is_prime(17    )    )
										self.assertTrue(is_prime(19    )    )
										self.assertTrue(is_prime(23    )    )
										self.assertTrue(is_prime(29    )    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										with self.assertRaises(lowercase_    ):
																	is_prime(-19    )
										self.assertFalse(
										    is_prime(0    )				,			"""Zero doesn't have any positive factors, primes must have exactly two."""				,			)
										self.assertFalse(
										    is_prime(1    )				,			"""One only has 1 positive factor, primes must have exactly two."""				,			)
										self.assertFalse(is_prime(2 * 2    )    )
										self.assertFalse(is_prime(2 * 3    )    )
										self.assertFalse(is_prime(3 * 3    )    )
										self.assertFalse(is_prime(3 * 5    )    )
										self.assertFalse(is_prime(3 * 5 * 7    )    )


if __name__ == "__main__":
						unittest.main()





