




import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD






class 					__magic_name__							( nn.Module       ):






				'''simple docstring'''
				__UpperCamelCase    						=		42
				__UpperCamelCase    						=		42
				__UpperCamelCase    						=		0.0
				__UpperCamelCase    						=		1
				__UpperCamelCase    						=		1
				__UpperCamelCase    						=		True
				__UpperCamelCase    						=		False
				__UpperCamelCase    						=		False
				__UpperCamelCase    						=		False
				__UpperCamelCase    						=		jnp.floataa





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      []
							lowerCamelCase       =      []

							for i in range(self.num_layers				):
										lowerCamelCase       =      self.in_channels if i == 0 else self.out_channels

										lowerCamelCase       =      FlaxResnetBlockaD(
										    in_channels=_a	,				out_channels=self.out_channels	,				dropout_prob=self.dropout	,				dtype=self.dtype	,				)
										resnets.append(_a				)

										lowerCamelCase       =      FlaxTransformeraDModel(
										    in_channels=self.out_channels	,				n_heads=self.num_attention_heads	,				d_head=self.out_channels // self.num_attention_heads	,				depth=1	,				use_linear_projection=self.use_linear_projection	,				only_cross_attention=self.only_cross_attention	,				use_memory_efficient_attention=self.use_memory_efficient_attention	,				dtype=self.dtype	,				)
										attentions.append(_a				)

							lowerCamelCase       =      resnets
							lowerCamelCase       =      attentions

							if self.add_downsample:
										lowerCamelCase       =      FlaxDownsampleaD(self.out_channels	,				dtype=self.dtype				)





				def __call__( self	,				_a	,				_a	,				_a	,				_a=True				):





							"""simple docstring"""
							lowerCamelCase       =      ()

							for resnet, attn in zip(self.resnets	,				self.attentions				):
										lowerCamelCase       =      resnet(_a	,				_a	,				deterministic=_a				)
										lowerCamelCase       =      attn(_a	,				_a	,				deterministic=_a				)
										output_states += (hidden_states,)

							if self.add_downsample:
										lowerCamelCase       =      self.downsamplers_a(_a				)
										output_states += (hidden_states,)

							return hidden_states, output_states






class 					__magic_name__							( nn.Module       ):






				'''simple docstring'''
				__UpperCamelCase    						=		42
				__UpperCamelCase    						=		42
				__UpperCamelCase    						=		0.0
				__UpperCamelCase    						=		1
				__UpperCamelCase    						=		True
				__UpperCamelCase    						=		jnp.floataa





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      []

							for i in range(self.num_layers				):
										lowerCamelCase       =      self.in_channels if i == 0 else self.out_channels

										lowerCamelCase       =      FlaxResnetBlockaD(
										    in_channels=_a	,				out_channels=self.out_channels	,				dropout_prob=self.dropout	,				dtype=self.dtype	,				)
										resnets.append(_a				)
							lowerCamelCase       =      resnets

							if self.add_downsample:
										lowerCamelCase       =      FlaxDownsampleaD(self.out_channels	,				dtype=self.dtype				)





				def __call__( self	,				_a	,				_a	,				_a=True				):





							"""simple docstring"""
							lowerCamelCase       =      ()

							for resnet in self.resnets:
										lowerCamelCase       =      resnet(_a	,				_a	,				deterministic=_a				)
										output_states += (hidden_states,)

							if self.add_downsample:
										lowerCamelCase       =      self.downsamplers_a(_a				)
										output_states += (hidden_states,)

							return hidden_states, output_states






class 					__magic_name__							( nn.Module       ):






				'''simple docstring'''
				__UpperCamelCase    						=		42
				__UpperCamelCase    						=		42
				__UpperCamelCase    						=		42
				__UpperCamelCase    						=		0.0
				__UpperCamelCase    						=		1
				__UpperCamelCase    						=		1
				__UpperCamelCase    						=		True
				__UpperCamelCase    						=		False
				__UpperCamelCase    						=		False
				__UpperCamelCase    						=		False
				__UpperCamelCase    						=		jnp.floataa





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      []
							lowerCamelCase       =      []

							for i in range(self.num_layers				):
										lowerCamelCase       =      self.in_channels if (i == self.num_layers - 1) else self.out_channels
										lowerCamelCase       =      self.prev_output_channel if i == 0 else self.out_channels

										lowerCamelCase       =      FlaxResnetBlockaD(
										    in_channels=resnet_in_channels + res_skip_channels	,				out_channels=self.out_channels	,				dropout_prob=self.dropout	,				dtype=self.dtype	,				)
										resnets.append(_a				)

										lowerCamelCase       =      FlaxTransformeraDModel(
										    in_channels=self.out_channels	,				n_heads=self.num_attention_heads	,				d_head=self.out_channels // self.num_attention_heads	,				depth=1	,				use_linear_projection=self.use_linear_projection	,				only_cross_attention=self.only_cross_attention	,				use_memory_efficient_attention=self.use_memory_efficient_attention	,				dtype=self.dtype	,				)
										attentions.append(_a				)

							lowerCamelCase       =      resnets
							lowerCamelCase       =      attentions

							if self.add_upsample:
										lowerCamelCase       =      FlaxUpsampleaD(self.out_channels	,				dtype=self.dtype				)





				def __call__( self	,				_a	,				_a	,				_a	,				_a	,				_a=True				):





							"""simple docstring"""
							for resnet, attn in zip(self.resnets	,				self.attentions				):
										# pop res hidden states
										lowerCamelCase       =      res_hidden_states_tuple[-1]
										lowerCamelCase       =      res_hidden_states_tuple[:-1]
										lowerCamelCase       =      jnp.concatenate((hidden_states, res_hidden_states)	,				axis=-1				)

										lowerCamelCase       =      resnet(_a	,				_a	,				deterministic=_a				)
										lowerCamelCase       =      attn(_a	,				_a	,				deterministic=_a				)

							if self.add_upsample:
										lowerCamelCase       =      self.upsamplers_a(_a				)

							return hidden_states






class 					__magic_name__							( nn.Module       ):






				'''simple docstring'''
				__UpperCamelCase    						=		42
				__UpperCamelCase    						=		42
				__UpperCamelCase    						=		42
				__UpperCamelCase    						=		0.0
				__UpperCamelCase    						=		1
				__UpperCamelCase    						=		True
				__UpperCamelCase    						=		jnp.floataa





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      []

							for i in range(self.num_layers				):
										lowerCamelCase       =      self.in_channels if (i == self.num_layers - 1) else self.out_channels
										lowerCamelCase       =      self.prev_output_channel if i == 0 else self.out_channels

										lowerCamelCase       =      FlaxResnetBlockaD(
										    in_channels=resnet_in_channels + res_skip_channels	,				out_channels=self.out_channels	,				dropout_prob=self.dropout	,				dtype=self.dtype	,				)
										resnets.append(_a				)

							lowerCamelCase       =      resnets

							if self.add_upsample:
										lowerCamelCase       =      FlaxUpsampleaD(self.out_channels	,				dtype=self.dtype				)





				def __call__( self	,				_a	,				_a	,				_a	,				_a=True				):





							"""simple docstring"""
							for resnet in self.resnets:
										# pop res hidden states
										lowerCamelCase       =      res_hidden_states_tuple[-1]
										lowerCamelCase       =      res_hidden_states_tuple[:-1]
										lowerCamelCase       =      jnp.concatenate((hidden_states, res_hidden_states)	,				axis=-1				)

										lowerCamelCase       =      resnet(_a	,				_a	,				deterministic=_a				)

							if self.add_upsample:
										lowerCamelCase       =      self.upsamplers_a(_a				)

							return hidden_states






class 					__magic_name__							( nn.Module       ):






				'''simple docstring'''
				__UpperCamelCase    						=		42
				__UpperCamelCase    						=		0.0
				__UpperCamelCase    						=		1
				__UpperCamelCase    						=		1
				__UpperCamelCase    						=		False
				__UpperCamelCase    						=		False
				__UpperCamelCase    						=		jnp.floataa





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      [
							    FlaxResnetBlockaD(
							        in_channels=self.in_channels	,				out_channels=self.in_channels	,				dropout_prob=self.dropout	,				dtype=self.dtype	,				)
							]

							lowerCamelCase       =      []

							for _ in range(self.num_layers				):
										lowerCamelCase       =      FlaxTransformeraDModel(
										    in_channels=self.in_channels	,				n_heads=self.num_attention_heads	,				d_head=self.in_channels // self.num_attention_heads	,				depth=1	,				use_linear_projection=self.use_linear_projection	,				use_memory_efficient_attention=self.use_memory_efficient_attention	,				dtype=self.dtype	,				)
										attentions.append(_a				)

										lowerCamelCase       =      FlaxResnetBlockaD(
										    in_channels=self.in_channels	,				out_channels=self.in_channels	,				dropout_prob=self.dropout	,				dtype=self.dtype	,				)
										resnets.append(_a				)

							lowerCamelCase       =      resnets
							lowerCamelCase       =      attentions





				def __call__( self	,				_a	,				_a	,				_a	,				_a=True				):





							"""simple docstring"""
							lowerCamelCase       =      self.resnets[0](_a	,				_a				)
							for attn, resnet in zip(self.attentions	,				self.resnets[1:]				):
										lowerCamelCase       =      attn(_a	,				_a	,				deterministic=_a				)
										lowerCamelCase       =      resnet(_a	,				_a	,				deterministic=_a				)

							return hidden_states









"""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))



