




"""simple docstring"""




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 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''
				__UpperCamelCase    						=		42






@flax_register_to_config
class 					__magic_name__							( nn.Module		,      UpperCAmelCase__		,      UpperCAmelCase__       ):






				'''simple docstring'''
				__UpperCamelCase    						=		32
				__UpperCamelCase    						=		4
				__UpperCamelCase    						=		4
				__UpperCamelCase    						=		(
				    "CrossAttnDownBlock2D",
				    "CrossAttnDownBlock2D",
				    "CrossAttnDownBlock2D",
				    "DownBlock2D",
				)
				__UpperCamelCase    						=		("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
				__UpperCamelCase    						=		False
				__UpperCamelCase    						=		(3_20, 6_40, 12_80, 12_80)
				__UpperCamelCase    						=		2
				__UpperCamelCase    						=		8
				__UpperCamelCase    						=		None
				__UpperCamelCase    						=		12_80
				__UpperCamelCase    						=		0.0
				__UpperCamelCase    						=		False
				__UpperCamelCase    						=		jnp.floataa
				__UpperCamelCase    						=		True
				__UpperCamelCase    						=		0
				__UpperCamelCase    						=		False





				def      _lowerCAmelCase       ( self	,				_a				):





							"""simple docstring"""
							# init input tensors
							lowerCamelCase       =      (1, self.in_channels, self.sample_size, self.sample_size)
							lowerCamelCase       =      jnp.zeros(_a	,				dtype=jnp.floataa				)
							lowerCamelCase       =      jnp.ones((1,)	,				dtype=jnp.intaa				)
							lowerCamelCase       =      jnp.zeros((1, 1, self.cross_attention_dim)	,				dtype=jnp.floataa				)

							lowerCamelCase   ,     lowerCamelCase       =      jax.random.split(_a				)
							lowerCamelCase       =      {"""params""": params_rng, """dropout""": dropout_rng}

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





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.block_out_channels
							lowerCamelCase       =      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       =      self.num_attention_heads or self.attention_head_dim

							# input
							lowerCamelCase       =      nn.Conv(
							    block_out_channels[0]	,				kernel_size=(3, 3)	,				strides=(1, 1)	,				padding=((1, 1), (1, 1))	,				dtype=self.dtype	,				)

							# time
							lowerCamelCase       =      FlaxTimesteps(
							    block_out_channels[0]	,				flip_sin_to_cos=self.flip_sin_to_cos	,				freq_shift=self.config.freq_shift				)
							lowerCamelCase       =      FlaxTimestepEmbedding(_a	,				dtype=self.dtype				)

							lowerCamelCase       =      self.only_cross_attention
							if isinstance(_a	,				_a				):
										lowerCamelCase       =      (only_cross_attention,) * len(self.down_block_types				)

							if isinstance(_a	,				_a				):
										lowerCamelCase       =      (num_attention_heads,) * len(self.down_block_types				)

							# down
							lowerCamelCase       =      []
							lowerCamelCase       =      block_out_channels[0]
							for i, down_block_type in enumerate(self.down_block_types				):
										lowerCamelCase       =      output_channel
										lowerCamelCase       =      block_out_channels[i]
										lowerCamelCase       =      i == len(_a				) - 1

										if down_block_type == "CrossAttnDownBlock2D":
													lowerCamelCase       =      FlaxCrossAttnDownBlockaD(
													    in_channels=_a	,				out_channels=_a	,				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       =      FlaxDownBlockaD(
													    in_channels=_a	,				out_channels=_a	,				dropout=self.dropout	,				num_layers=self.layers_per_block	,				add_downsample=not is_final_block	,				dtype=self.dtype	,				)

										down_blocks.append(_a				)
							lowerCamelCase       =      down_blocks

							# mid
							lowerCamelCase       =      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       =      []
							lowerCamelCase       =      list(reversed(_a				)				)
							lowerCamelCase       =      list(reversed(_a				)				)
							lowerCamelCase       =      list(reversed(_a				)				)
							lowerCamelCase       =      reversed_block_out_channels[0]
							for i, up_block_type in enumerate(self.up_block_types				):
										lowerCamelCase       =      output_channel
										lowerCamelCase       =      reversed_block_out_channels[i]
										lowerCamelCase       =      reversed_block_out_channels[min(i + 1	,				len(_a				) - 1				)]

										lowerCamelCase       =      i == len(_a				) - 1

										if up_block_type == "CrossAttnUpBlock2D":
													lowerCamelCase       =      FlaxCrossAttnUpBlockaD(
													    in_channels=_a	,				out_channels=_a	,				prev_output_channel=_a	,				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       =      FlaxUpBlockaD(
													    in_channels=_a	,				out_channels=_a	,				prev_output_channel=_a	,				num_layers=self.layers_per_block + 1	,				add_upsample=not is_final_block	,				dropout=self.dropout	,				dtype=self.dtype	,				)

										up_blocks.append(_a				)
										lowerCamelCase       =      output_channel
							lowerCamelCase       =      up_blocks

							# out
							lowerCamelCase       =      nn.GroupNorm(num_groups=32	,				epsilon=1e-5				)
							lowerCamelCase       =      nn.Conv(
							    self.out_channels	,				kernel_size=(3, 3)	,				strides=(1, 1)	,				padding=((1, 1), (1, 1))	,				dtype=self.dtype	,				)





				def __call__( self	,				_a	,				_a	,				_a	,				_a=None	,				_a=None	,				_a = True	,				_a = False	,				):





							"""simple docstring"""
							# 1. time
							if not isinstance(_a	,				jnp.ndarray				):
										lowerCamelCase       =      jnp.array([timesteps]	,				dtype=jnp.intaa				)
							elif isinstance(_a	,				jnp.ndarray				) and len(timesteps.shape				) == 0:
										lowerCamelCase       =      timesteps.astype(dtype=jnp.floataa				)
										lowerCamelCase       =      jnp.expand_dims(_a	,				0				)

							lowerCamelCase       =      self.time_proj(_a				)
							lowerCamelCase       =      self.time_embedding(_a				)

							# 2. pre-process
							lowerCamelCase       =      jnp.transpose(_a	,				(0, 2, 3, 1)				)
							lowerCamelCase       =      self.conv_in(_a				)

							# 3. down
							lowerCamelCase       =      (sample,)
							for down_block in self.down_blocks:
										if isinstance(_a	,				_a				):
													lowerCamelCase   ,     lowerCamelCase       =      down_block(_a	,				_a	,				_a	,				deterministic=not train				)
										else:
													lowerCamelCase   ,     lowerCamelCase       =      down_block(_a	,				_a	,				deterministic=not train				)
										down_block_res_samples += res_samples

							if down_block_additional_residuals is not None:
										lowerCamelCase       =      ()

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

										lowerCamelCase       =      new_down_block_res_samples

							# 4. mid
							lowerCamelCase       =      self.mid_block(_a	,				_a	,				_a	,				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       =      down_block_res_samples[-(self.layers_per_block + 1) :]
										lowerCamelCase       =      down_block_res_samples[: -(self.layers_per_block + 1)]
										if isinstance(_a	,				_a				):
													lowerCamelCase       =      up_block(
													    _a	,				temb=_a	,				encoder_hidden_states=_a	,				res_hidden_states_tuple=_a	,				deterministic=not train	,				)
										else:
													lowerCamelCase       =      up_block(_a	,				temb=_a	,				res_hidden_states_tuple=_a	,				deterministic=not train				)

        # 6. post-process
							lowerCamelCase       =      self.conv_norm_out(_a				)
							lowerCamelCase       =      nn.silu(_a				)
							lowerCamelCase       =      self.conv_out(_a				)
							lowerCamelCase       =      jnp.transpose(_a	,				(0, 3, 1, 2)				)

							if not return_dict:
										return (sample,)

							return FlaxUNetaDConditionOutput(sample=_a				)









"""simple docstring"""




def 					a__				(       snake_case__       )		->      list:
			if n_term == "":
						return []
			lowerCamelCase       =      []
			for temp in range(int(snake_case__       )       ):
						series.append(F'1/{temp + 1}' if series else """1"""       )
			return series


if __name__ == "__main__":
					lowerCAmelCase							:    Optional[int]  							=							input("""Enter the last number (nth term) of the Harmonic Series""")
					print("""Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n""")
					print(harmonic_series(nth_term))



