



from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)



@flax.struct.dataclass
class 		lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     =42



@flax_register_to_config
class 		lowerCamelCase__      (							nn.Module				,    lowerCamelCase__				,    lowerCamelCase__):




		'''simple docstring'''



		snake_case_     =32
		snake_case_     =4
		snake_case_     =4
		snake_case_     =(
		    """CrossAttnDownBlock2D""",
		    """CrossAttnDownBlock2D""",
		    """CrossAttnDownBlock2D""",
		    """DownBlock2D""",
		)
		snake_case_     =("""UpBlock2D""", """CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""")
		snake_case_     =False
		snake_case_     =(320, 640, 1280, 1280)
		snake_case_     =2
		snake_case_     =8
		snake_case_     =None
		snake_case_     =1280
		snake_case_     =0.0
		snake_case_     =False
		snake_case_     =jnp.floataa
		snake_case_     =True
		snake_case_     =0
		snake_case_     =False



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      FrozenDict:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					(1, self.in_channels, self.sample_size, self.sample_size)
						lowerCAmelCase__					:    Dict												=					jnp.zeros(__lowerCamelCase		,dtype=jnp.floataa			)
						lowerCAmelCase__					:    str												=					jnp.ones((1,)		,dtype=jnp.intaa			)
						lowerCAmelCase__					:    Optional[int]												=					jnp.zeros((1, 1, self.cross_attention_dim)		,dtype=jnp.floataa			)

						lowerCAmelCase__					:    int												=					jax.random.split(__lowerCamelCase			)
						lowerCAmelCase__					:    int												=					{'''params''': params_rng, '''dropout''': dropout_rng}

						return self.init(__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)["params"]



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					self.block_out_channels
						lowerCAmelCase__					:    str												=					block_out_channels[0] * 4

						if self.num_attention_heads is not None:
										raise ValueError(
										    '''At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.'''			)

						# If `num_attention_heads` is not defined (which is the case for most models)
						# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
						# The reason for this behavior is to correct for incorrectly named variables that were introduced
						# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
						# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
						# which is why we correct for the naming here.
						lowerCAmelCase__					:    Optional[Any]												=					self.num_attention_heads or self.attention_head_dim

						# input
						lowerCAmelCase__					:    Union[str, Any]												=					nn.Conv(
						    block_out_channels[0]		,kernel_size=(3, 3)		,strides=(1, 1)		,padding=((1, 1), (1, 1))		,dtype=self.dtype		,)

						# time
						lowerCAmelCase__					:    Optional[int]												=					FlaxTimesteps(
						    block_out_channels[0]		,flip_sin_to_cos=self.flip_sin_to_cos		,freq_shift=self.config.freq_shift			)
						lowerCAmelCase__					:    Optional[int]												=					FlaxTimestepEmbedding(__lowerCamelCase		,dtype=self.dtype			)

						lowerCAmelCase__					:    Union[str, Any]												=					self.only_cross_attention
						if isinstance(__lowerCamelCase		,__lowerCamelCase			):
										lowerCAmelCase__					:    Tuple												=					(only_cross_attention,) * len(self.down_block_types			)

						if isinstance(__lowerCamelCase		,__lowerCamelCase			):
										lowerCAmelCase__					:    int												=					(num_attention_heads,) * len(self.down_block_types			)

						# down
						lowerCAmelCase__					:    Any												=					[]
						lowerCAmelCase__					:    Tuple												=					block_out_channels[0]
						for i, down_block_type in enumerate(self.down_block_types			):
										lowerCAmelCase__					:    str												=					output_channel
										lowerCAmelCase__					:    int												=					block_out_channels[i]
										lowerCAmelCase__					:    Dict												=					i == len(__lowerCamelCase			) - 1

										if down_block_type == "CrossAttnDownBlock2D":
														lowerCAmelCase__					:    str												=					FlaxCrossAttnDownBlockaD(
														    in_channels=__lowerCamelCase		,out_channels=__lowerCamelCase		,dropout=self.dropout		,num_layers=self.layers_per_block		,num_attention_heads=num_attention_heads[i]		,add_downsample=not is_final_block		,use_linear_projection=self.use_linear_projection		,only_cross_attention=only_cross_attention[i]		,use_memory_efficient_attention=self.use_memory_efficient_attention		,dtype=self.dtype		,)
										else:
														lowerCAmelCase__					:    Any												=					FlaxDownBlockaD(
														    in_channels=__lowerCamelCase		,out_channels=__lowerCamelCase		,dropout=self.dropout		,num_layers=self.layers_per_block		,add_downsample=not is_final_block		,dtype=self.dtype		,)

										down_blocks.append(__lowerCamelCase			)
						lowerCAmelCase__					:    str												=					down_blocks

						# mid
						lowerCAmelCase__					:    Optional[int]												=					FlaxUNetMidBlockaDCrossAttn(
						    in_channels=block_out_channels[-1]		,dropout=self.dropout		,num_attention_heads=num_attention_heads[-1]		,use_linear_projection=self.use_linear_projection		,use_memory_efficient_attention=self.use_memory_efficient_attention		,dtype=self.dtype		,)

						# up
						lowerCAmelCase__					:    Dict												=					[]
						lowerCAmelCase__					:    Union[str, Any]												=					list(reversed(__lowerCamelCase			)			)
						lowerCAmelCase__					:    int												=					list(reversed(__lowerCamelCase			)			)
						lowerCAmelCase__					:    List[Any]												=					list(reversed(__lowerCamelCase			)			)
						lowerCAmelCase__					:    str												=					reversed_block_out_channels[0]
						for i, up_block_type in enumerate(self.up_block_types			):
										lowerCAmelCase__					:    str												=					output_channel
										lowerCAmelCase__					:    Any												=					reversed_block_out_channels[i]
										lowerCAmelCase__					:    Tuple												=					reversed_block_out_channels[min(i + 1		,len(__lowerCamelCase			) - 1			)]

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

										if up_block_type == "CrossAttnUpBlock2D":
														lowerCAmelCase__					:    str												=					FlaxCrossAttnUpBlockaD(
														    in_channels=__lowerCamelCase		,out_channels=__lowerCamelCase		,prev_output_channel=__lowerCamelCase		,num_layers=self.layers_per_block + 1		,num_attention_heads=reversed_num_attention_heads[i]		,add_upsample=not is_final_block		,dropout=self.dropout		,use_linear_projection=self.use_linear_projection		,only_cross_attention=only_cross_attention[i]		,use_memory_efficient_attention=self.use_memory_efficient_attention		,dtype=self.dtype		,)
										else:
														lowerCAmelCase__					:    List[Any]												=					FlaxUpBlockaD(
														    in_channels=__lowerCamelCase		,out_channels=__lowerCamelCase		,prev_output_channel=__lowerCamelCase		,num_layers=self.layers_per_block + 1		,add_upsample=not is_final_block		,dropout=self.dropout		,dtype=self.dtype		,)

										up_blocks.append(__lowerCamelCase			)
										lowerCAmelCase__					:    str												=					output_channel
						lowerCAmelCase__					:    Optional[Any]												=					up_blocks

						# out
						lowerCAmelCase__					:    List[Any]												=					nn.GroupNorm(num_groups=32		,epsilon=1e-5			)
						lowerCAmelCase__					:    Optional[int]												=					nn.Conv(
						    self.out_channels		,kernel_size=(3, 3)		,strides=(1, 1)		,padding=((1, 1), (1, 1))		,dtype=self.dtype		,)



		def __call__(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase=None		,__lowerCamelCase=None		,__lowerCamelCase = True		,__lowerCamelCase = False		,)					->      Union[FlaxUNetaDConditionOutput, Tuple]:

						"""simple docstring"""






						if not isinstance(__lowerCamelCase		,jnp.ndarray			):
										lowerCAmelCase__					:    Union[str, Any]												=					jnp.array([timesteps]		,dtype=jnp.intaa			)
						elif isinstance(__lowerCamelCase		,jnp.ndarray			) and len(timesteps.shape			) == 0:
										lowerCAmelCase__					:    Tuple												=					timesteps.astype(dtype=jnp.floataa			)
										lowerCAmelCase__					:    List[str]												=					jnp.expand_dims(__lowerCamelCase		,0			)

						lowerCAmelCase__					:    List[str]												=					self.time_proj(__lowerCamelCase			)
						lowerCAmelCase__					:    Tuple												=					self.time_embedding(__lowerCamelCase			)

						# 2. pre-process
						lowerCAmelCase__					:    Dict												=					jnp.transpose(__lowerCamelCase		,(0, 2, 3, 1)			)
						lowerCAmelCase__					:    Any												=					self.conv_in(__lowerCamelCase			)

						# 3. down
						lowerCAmelCase__					:    int												=					(sample,)
						for down_block in self.down_blocks:
										if isinstance(__lowerCamelCase		,__lowerCamelCase			):
														lowerCAmelCase__					:    Dict												=					down_block(__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,deterministic=not train			)
										else:
														lowerCAmelCase__					:    Optional[int]												=					down_block(__lowerCamelCase		,__lowerCamelCase		,deterministic=not train			)
										down_block_res_samples += res_samples

						if down_block_additional_residuals is not None:
										lowerCAmelCase__					:    Any												=					()

										for down_block_res_sample, down_block_additional_residual in zip(
										    __lowerCamelCase		,__lowerCamelCase			):
														down_block_res_sample += down_block_additional_residual
														new_down_block_res_samples += (down_block_res_sample,)

										lowerCAmelCase__					:    List[str]												=					new_down_block_res_samples

						# 4. mid
						lowerCAmelCase__					:    Any												=					self.mid_block(__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,deterministic=not train			)

						if mid_block_additional_residual is not None:
										sample += mid_block_additional_residual

						# 5. up
						for up_block in self.up_blocks:
										lowerCAmelCase__					:    List[Any]												=					down_block_res_samples[-(self.layers_per_block + 1) :]
										lowerCAmelCase__					:    List[Any]												=					down_block_res_samples[: -(self.layers_per_block + 1)]
										if isinstance(__lowerCamelCase		,__lowerCamelCase			):
														lowerCAmelCase__					:    List[Any]												=					up_block(
														    __lowerCamelCase		,temb=__lowerCamelCase		,encoder_hidden_states=__lowerCamelCase		,res_hidden_states_tuple=__lowerCamelCase		,deterministic=not train		,)
										else:
														lowerCAmelCase__					:    str												=					up_block(__lowerCamelCase		,temb=__lowerCamelCase		,res_hidden_states_tuple=__lowerCamelCase		,deterministic=not train			)

        # 6. post-process
						lowerCAmelCase__					:    Any												=					self.conv_norm_out(__lowerCamelCase			)
						lowerCAmelCase__					:    List[str]												=					nn.silu(__lowerCamelCase			)
						lowerCAmelCase__					:    Any												=					self.conv_out(__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[int]												=					jnp.transpose(__lowerCamelCase		,(0, 3, 1, 2)			)

						if not return_dict:
										return (sample,)

						return FlaxUNetaDConditionOutput(sample=__lowerCamelCase			)






def      lowerCAmelCase__		(   lowerCamelCase_		:		int = 1000000):





				'''simple docstring'''

				lowerCAmelCase__					:    int												=					set(range(3	,lowerCamelCase_	,2))
				primes.add(2)
				for p in range(3	,lowerCamelCase_	,2):
								if p not in primes:
												continue
								primes.difference_update(set(range(p * p	,lowerCamelCase_	,lowerCamelCase_)))

				lowerCAmelCase__					:    int												=					[float(lowerCamelCase_) for n in range(limit + 1)]

				for p in primes:
								for n in range(lowerCamelCase_	,limit + 1	,lowerCamelCase_):
												phi[n] *= 1 - 1 / p

				return int(sum(phi[2:]))


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

