"""simple docstring"""







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

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



class  _lowerCamelCase			(nn.Module     ):
				_snake_case     =  42
				_snake_case     =  42
				_snake_case     =  0.0
				_snake_case     =  1
				_snake_case     =  1
				_snake_case     =  True
				_snake_case     =  False
				_snake_case     =  False
				_snake_case     =  False
				_snake_case     =  jnp.floataa







				def 							__UpperCAmelCase					( self    :       str						):

					"""simple docstring"""






					_lowercase :    Optional[int]							=							[]
					_lowercase :    Optional[int]							=							[]

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

						_lowercase :    Union[str, Any]							=							FlaxResnetBlockaD(
						    in_channels=lowerCamelCase_			,					out_channels=self.out_channels			,					dropout_prob=self.dropout			,					dtype=self.dtype			,					)
						resnets.append(lowerCamelCase_						)

						_lowercase :    Tuple							=							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(lowerCamelCase_						)

					_lowercase :    str							=							resnets
					_lowercase :    Tuple							=							attentions

					if self.add_downsample:
						_lowercase :    List[Any]							=							FlaxDownsampleaD(self.out_channels			,					dtype=self.dtype						)







				def __call__( self    :       int			,					lowerCamelCase_    :       List[Any]			,					lowerCamelCase_    :       Tuple			,					lowerCamelCase_    :       Union[str, Any]			,					lowerCamelCase_    :       int=True						):

					"""simple docstring"""






					_lowercase :    str							=							()

					for resnet, attn in zip(self.resnets			,					self.attentions						):
						_lowercase :    List[str]							=							resnet(lowerCamelCase_			,					lowerCamelCase_			,					deterministic=lowerCamelCase_						)
						_lowercase :    Any							=							attn(lowerCamelCase_			,					lowerCamelCase_			,					deterministic=lowerCamelCase_						)
						output_states += (hidden_states,)

					if self.add_downsample:
						_lowercase :    List[str]							=							self.downsamplers_a(lowerCamelCase_						)
						output_states += (hidden_states,)

					return hidden_states, output_states



class  _lowerCamelCase			(nn.Module     ):
				_snake_case     =  42
				_snake_case     =  42
				_snake_case     =  0.0
				_snake_case     =  1
				_snake_case     =  True
				_snake_case     =  jnp.floataa







				def 							__UpperCAmelCase					( self    :       str						):

					"""simple docstring"""






					_lowercase :    Dict							=							[]

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

						_lowercase :    Union[str, Any]							=							FlaxResnetBlockaD(
						    in_channels=lowerCamelCase_			,					out_channels=self.out_channels			,					dropout_prob=self.dropout			,					dtype=self.dtype			,					)
						resnets.append(lowerCamelCase_						)
					_lowercase :    Optional[Any]							=							resnets

					if self.add_downsample:
						_lowercase :    Union[str, Any]							=							FlaxDownsampleaD(self.out_channels			,					dtype=self.dtype						)







				def __call__( self    :       List[Any]			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       List[str]			,					lowerCamelCase_    :       Any=True						):

					"""simple docstring"""






					_lowercase :    int							=							()

					for resnet in self.resnets:
						_lowercase :    List[Any]							=							resnet(lowerCamelCase_			,					lowerCamelCase_			,					deterministic=lowerCamelCase_						)
						output_states += (hidden_states,)

					if self.add_downsample:
						_lowercase :    Dict							=							self.downsamplers_a(lowerCamelCase_						)
						output_states += (hidden_states,)

					return hidden_states, output_states



class  _lowerCamelCase			(nn.Module     ):
				_snake_case     =  42
				_snake_case     =  42
				_snake_case     =  42
				_snake_case     =  0.0
				_snake_case     =  1
				_snake_case     =  1
				_snake_case     =  True
				_snake_case     =  False
				_snake_case     =  False
				_snake_case     =  False
				_snake_case     =  jnp.floataa







				def 							__UpperCAmelCase					( self    :       Any						):

					"""simple docstring"""






					_lowercase :    Any							=							[]
					_lowercase :    int							=							[]

					for i in range(self.num_layers						):
						_lowercase :    Dict							=							self.in_channels if (i == self.num_layers - 1) else self.out_channels
						_lowercase :    Optional[Any]							=							self.prev_output_channel if i == 0 else self.out_channels

						_lowercase :    int							=							FlaxResnetBlockaD(
						    in_channels=resnet_in_channels + res_skip_channels			,					out_channels=self.out_channels			,					dropout_prob=self.dropout			,					dtype=self.dtype			,					)
						resnets.append(lowerCamelCase_						)

						_lowercase :    Dict							=							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(lowerCamelCase_						)

					_lowercase :    Union[str, Any]							=							resnets
					_lowercase :    Dict							=							attentions

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







				def __call__( self    :       int			,					lowerCamelCase_    :       Dict			,					lowerCamelCase_    :       str			,					lowerCamelCase_    :       Optional[int]			,					lowerCamelCase_    :       Tuple			,					lowerCamelCase_    :       str=True						):

					"""simple docstring"""






					for resnet, attn in zip(self.resnets			,					self.attentions						):
						# pop res hidden states
						_lowercase :    Optional[int]							=							res_hidden_states_tuple[-1]
						_lowercase :    List[Any]							=							res_hidden_states_tuple[:-1]
						_lowercase :    int							=							jnp.concatenate((hidden_states, res_hidden_states)			,					axis=-1						)

						_lowercase :    Optional[Any]							=							resnet(lowerCamelCase_			,					lowerCamelCase_			,					deterministic=lowerCamelCase_						)
						_lowercase :    List[Any]							=							attn(lowerCamelCase_			,					lowerCamelCase_			,					deterministic=lowerCamelCase_						)

					if self.add_upsample:
						_lowercase :    int							=							self.upsamplers_a(lowerCamelCase_						)

					return hidden_states



class  _lowerCamelCase			(nn.Module     ):
				_snake_case     =  42
				_snake_case     =  42
				_snake_case     =  42
				_snake_case     =  0.0
				_snake_case     =  1
				_snake_case     =  True
				_snake_case     =  jnp.floataa







				def 							__UpperCAmelCase					( self    :       Tuple						):

					"""simple docstring"""






					_lowercase :    List[Any]							=							[]

					for i in range(self.num_layers						):
						_lowercase :    Union[str, Any]							=							self.in_channels if (i == self.num_layers - 1) else self.out_channels
						_lowercase :    int							=							self.prev_output_channel if i == 0 else self.out_channels

						_lowercase :    Dict							=							FlaxResnetBlockaD(
						    in_channels=resnet_in_channels + res_skip_channels			,					out_channels=self.out_channels			,					dropout_prob=self.dropout			,					dtype=self.dtype			,					)
						resnets.append(lowerCamelCase_						)

					_lowercase :    Tuple							=							resnets

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







				def __call__( self    :       Any			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       List[Any]			,					lowerCamelCase_    :       str			,					lowerCamelCase_    :       Any=True						):

					"""simple docstring"""






					for resnet in self.resnets:
						# pop res hidden states
						_lowercase :    str							=							res_hidden_states_tuple[-1]
						_lowercase :    Tuple							=							res_hidden_states_tuple[:-1]
						_lowercase :    List[Any]							=							jnp.concatenate((hidden_states, res_hidden_states)			,					axis=-1						)

						_lowercase :    Dict							=							resnet(lowerCamelCase_			,					lowerCamelCase_			,					deterministic=lowerCamelCase_						)

					if self.add_upsample:
						_lowercase :    Any							=							self.upsamplers_a(lowerCamelCase_						)

					return hidden_states



class  _lowerCamelCase			(nn.Module     ):
				_snake_case     =  42
				_snake_case     =  0.0
				_snake_case     =  1
				_snake_case     =  1
				_snake_case     =  False
				_snake_case     =  False
				_snake_case     =  jnp.floataa







				def 							__UpperCAmelCase					( self    :       List[str]						):

					"""simple docstring"""






					_lowercase :    List[Any]							=							[
					    FlaxResnetBlockaD(
					        in_channels=self.in_channels			,					out_channels=self.in_channels			,					dropout_prob=self.dropout			,					dtype=self.dtype			,					)
					]

					_lowercase :    Dict							=							[]

					for _ in range(self.num_layers						):
						_lowercase :    Any							=							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(lowerCamelCase_						)

						_lowercase :    Tuple							=							FlaxResnetBlockaD(
						    in_channels=self.in_channels			,					out_channels=self.in_channels			,					dropout_prob=self.dropout			,					dtype=self.dtype			,					)
						resnets.append(lowerCamelCase_						)

					_lowercase :    Dict							=							resnets
					_lowercase :    Tuple							=							attentions







				def __call__( self    :       Optional[int]			,					lowerCamelCase_    :       Any			,					lowerCamelCase_    :       Optional[int]			,					lowerCamelCase_    :       Optional[int]			,					lowerCamelCase_    :       Optional[Any]=True						):

					"""simple docstring"""






					_lowercase :    Dict							=							self.resnets[0](lowerCamelCase_			,					lowerCamelCase_						)
					for attn, resnet in zip(self.attentions			,					self.resnets[1:]						):
						_lowercase :    Tuple							=							attn(lowerCamelCase_			,					lowerCamelCase_			,					deterministic=lowerCamelCase_						)
						_lowercase :    Union[str, Any]							=							resnet(lowerCamelCase_			,					lowerCamelCase_			,					deterministic=lowerCamelCase_						)

					return hidden_states




"""simple docstring"""







def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	stooge(__UpperCAmelCase     ,0     ,len(__UpperCAmelCase			) - 1			)
	return arr


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	if i >= h:
		return

	# If first element is smaller than the last then swap them
	if arr[i] > arr[h]:
		_lowercase				,      _lowercase :    List[str]							=							arr[h], arr[i]

	# If there are more than 2 elements in the array
	if h - i + 1 > 2:
		_lowercase :    Optional[int]							=							(int)((h - i + 1) / 3			)

		# Recursively sort first 2/3 elements
		stooge(__UpperCAmelCase     ,__UpperCAmelCase     ,(h - t)			)

		# Recursively sort last 2/3 elements
		stooge(__UpperCAmelCase     ,i + t     ,(__UpperCAmelCase)			)

		# Recursively sort first 2/3 elements
		stooge(__UpperCAmelCase     ,__UpperCAmelCase     ,(h - t)			)


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE									=						input('Enter numbers separated by a comma:\n').strip()
						SCREAMING_SNAKE_CASE									=						[int(item) for item in user_input.split(',')]
						print(stooge_sort(unsorted))



