
'''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,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)


@flax.struct.dataclass
class  _UpperCAmelCase ( __lowerCamelCase    ):

     """simple docstring"""

     a_    	=					42
     a_    	=					42







class  _UpperCAmelCase ( nn.Module    ):

     """simple docstring"""

     a_    	=					42
     a_    	=					(16, 32, 96, 256)
     a_    	=					jnp.floataa


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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

       a_      :	Optional[int]      =							[]
       for i in range(len(self.block_out_channels ) - 1 ):
         a_      :	str      =							self.block_out_channels[i]
         a_      :	List[Any]      =							self.block_out_channels[i + 1]
         a_      :	Tuple      =							nn.Conv(
             a_							,		kernel_size=(3, 3)							,		padding=((1, 1), (1, 1))							,		dtype=self.dtype							,		)
         blocks.append(a_ )
         a_      :	Optional[Any]      =							nn.Conv(
             a_							,		kernel_size=(3, 3)							,		strides=(2, 2)							,		padding=((1, 1), (1, 1))							,		dtype=self.dtype							,		)
         blocks.append(a_ )
       a_      :	Union[str, Any]      =							blocks

       a_      :	int      =							nn.Conv(
           self.conditioning_embedding_channels							,		kernel_size=(3, 3)							,		padding=((1, 1), (1, 1))							,		kernel_init=nn.initializers.zeros_init()							,		bias_init=nn.initializers.zeros_init()							,		dtype=self.dtype							,		)






     def __call__(      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Optional[Any]      =							self.conv_in(a_ )
       a_      :	Optional[Any]      =							nn.silu(a_ )

       for block in self.blocks:
         a_      :	str      =							block(a_ )
         a_      :	str      =							nn.silu(a_ )

       a_      :	Any      =							self.conv_out(a_ )

       return embedding







@flax_register_to_config
class  _UpperCAmelCase ( nn.Module	,__lowerCamelCase	,__lowerCamelCase    ):

     """simple docstring"""

     a_    	=					32
     a_    	=					4
     a_    	=					(
         "CrossAttnDownBlock2D",
         "CrossAttnDownBlock2D",
         "CrossAttnDownBlock2D",
         "DownBlock2D",
     )
     a_    	=					False
     a_    	=					(320, 640, 1_280, 1_280)
     a_    	=					2
     a_    	=					8
     a_    	=					None
     a_    	=					1_280
     a_    	=					0.0
     a_    	=					False
     a_    	=					jnp.floataa
     a_    	=					True
     a_    	=					0
     a_    	=					"rgb"
     a_    	=					(16, 32, 96, 256)


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Tuple      =							(1, self.in_channels, self.sample_size, self.sample_size)
       a_      :	Union[str, Any]      =							jnp.zeros(a_							,		dtype=jnp.floataa )
       a_      :	int      =							jnp.ones((1,)							,		dtype=jnp.intaa )
       a_      :	int      =							jnp.zeros((1, 1, self.cross_attention_dim)							,		dtype=jnp.floataa )
       a_      :	Union[str, Any]      =							(1, 3, self.sample_size * 8, self.sample_size * 8)
       a_      :	List[str]      =							jnp.zeros(a_							,		dtype=jnp.floataa )

       a_      :	List[str]      =							jax.random.split(a_ )
       a_      :	Dict      =							{"params": params_rng, "dropout": dropout_rng}

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


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Tuple      =							self.block_out_channels
       a_      :	Optional[Any]      =							block_out_channels[0] * 4

       # 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.
       a_      :	List[str]      =							self.num_attention_heads or self.attention_head_dim

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

       # time
       a_      :	Union[str, Any]      =							FlaxTimesteps(
           block_out_channels[0]							,		flip_sin_to_cos=self.flip_sin_to_cos							,		freq_shift=self.config.freq_shift )
       a_      :	int      =							FlaxTimestepEmbedding(a_							,		dtype=self.dtype )

       a_      :	List[str]      =							FlaxControlNetConditioningEmbedding(
           conditioning_embedding_channels=block_out_channels[0]							,		block_out_channels=self.conditioning_embedding_out_channels							,		)

       a_      :	Dict      =							self.only_cross_attention
       if isinstance(a_							,		a_ ):
         a_      :	List[Any]      =							(only_cross_attention,) * len(self.down_block_types )

       if isinstance(a_							,		a_ ):
         a_      :	Optional[Any]      =							(num_attention_heads,) * len(self.down_block_types )

       # down
       a_      :	Tuple      =							[]
       a_      :	List[str]      =							[]

       a_      :	Dict      =							block_out_channels[0]

       a_      :	Any      =							nn.Conv(
           a_							,		kernel_size=(1, 1)							,		padding="""VALID"""							,		kernel_init=nn.initializers.zeros_init()							,		bias_init=nn.initializers.zeros_init()							,		dtype=self.dtype							,		)
       controlnet_down_blocks.append(a_ )

       for i, down_block_type in enumerate(self.down_block_types ):
         a_      :	List[str]      =							output_channel
         a_      :	Any      =							block_out_channels[i]
         a_      :	Optional[Any]      =							i == len(a_ ) - 1

         if down_block_type == "CrossAttnDownBlock2D":
           a_      :	Dict      =							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]							,		dtype=self.dtype							,		)
         else:
           a_      :	Dict      =							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_ )

         for _ in range(self.layers_per_block ):
           a_      :	Dict      =							nn.Conv(
               a_							,		kernel_size=(1, 1)							,		padding="""VALID"""							,		kernel_init=nn.initializers.zeros_init()							,		bias_init=nn.initializers.zeros_init()							,		dtype=self.dtype							,		)
           controlnet_down_blocks.append(a_ )

         if not is_final_block:
           a_      :	Union[str, Any]      =							nn.Conv(
               a_							,		kernel_size=(1, 1)							,		padding="""VALID"""							,		kernel_init=nn.initializers.zeros_init()							,		bias_init=nn.initializers.zeros_init()							,		dtype=self.dtype							,		)
           controlnet_down_blocks.append(a_ )

       a_      :	List[str]      =							down_blocks
       a_      :	Union[str, Any]      =							controlnet_down_blocks

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

       a_      :	str      =							nn.Conv(
           a_							,		kernel_size=(1, 1)							,		padding="""VALID"""							,		kernel_init=nn.initializers.zeros_init()							,		bias_init=nn.initializers.zeros_init()							,		dtype=self.dtype							,		)






     def __call__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ = 1.0							,		lowerCAmelCase_ = True							,		lowerCAmelCase_ = False							,		):

       '''simple docstring'''

       a_      :	int      =							self.controlnet_conditioning_channel_order
       if channel_order == "bgr":
         a_      :	int      =							jnp.flip(a_							,		axis=1 )

       # 1. time
       if not isinstance(a_							,		jnp.ndarray ):
         a_      :	List[str]      =							jnp.array([timesteps]							,		dtype=jnp.intaa )
       elif isinstance(a_							,		jnp.ndarray ) and len(timesteps.shape ) == 0:
         a_      :	str      =							timesteps.astype(dtype=jnp.floataa )
         a_      :	Any      =							jnp.expand_dims(a_							,		0 )

       a_      :	Optional[int]      =							self.time_proj(a_ )
       a_      :	Any      =							self.time_embedding(a_ )

       # 2. pre-process
       a_      :	Union[str, Any]      =							jnp.transpose(a_							,		(0, 2, 3, 1) )
       a_      :	Tuple      =							self.conv_in(a_ )

       a_      :	Tuple      =							jnp.transpose(a_							,		(0, 2, 3, 1) )
       a_      :	int      =							self.controlnet_cond_embedding(a_ )
       sample += controlnet_cond

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

       # 4. mid
       a_      :	List[str]      =							self.mid_block(a_							,		a_							,		a_							,		deterministic=not train )

       # 5. contronet blocks
       a_      :	Union[str, Any]      =							()
       for down_block_res_sample, controlnet_block in zip(a_							,		self.controlnet_down_blocks ):
         a_      :	Optional[int]      =							controlnet_block(a_ )
         controlnet_down_block_res_samples += (down_block_res_sample,)

       a_      :	List[Any]      =							controlnet_down_block_res_samples

       a_      :	Any      =							self.controlnet_mid_block(a_ )

       # 6. scaling
       a_      :	Optional[int]      =							[sample * conditioning_scale for sample in down_block_res_samples]
       mid_block_res_sample *= conditioning_scale

       if not return_dict:
         return (down_block_res_samples, mid_block_res_sample)

       return FlaxControlNetOutput(
           down_block_res_samples=a_							,		mid_block_res_sample=a_ )




'''simple docstring'''

def 	_snake_case						(      A_       :    list   ):
  """simple docstring"""







  if len(A_   ) <= 1:
    return [tuple(A_   )]

  a_      :	List[Any]      =							[]

  def generate(A_       :    int     ,		A_       :    list   ):
    a_      :	List[Any]      =							[0] * n
    res.append(tuple(A_   )   )

    a_      :	List[str]      =							0
    while i < n:
      if c[i] < i:
        if i % 2 == 0:
          a_      , a_      :	Union[str, Any]      =							arr[i], arr[0]
        else:
          a_      , a_      :	List[str]      =							arr[i], arr[c[i]]
        res.append(tuple(A_   )   )
        c[i] += 1
        a_      :	Optional[Any]      =							0
      else:
        a_      :	Union[str, Any]      =							0
        i += 1

  generate(len(A_   )     ,		A_   )
  return res


if __name__ == "__main__":
    __snake_case:  Dict								=			input("Enter numbers separated by a comma:\n").strip()
    __snake_case:  Optional[Any]								=			[int(item) for item in user_input.split(",")]
    print(heaps(arr))


