

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    _A	(     __UpperCAmelCase   ):
       UpperCamelCase__  :   jnp.ndarray


@flax_register_to_config
class    _A	(     nn.Module				,__UpperCAmelCase				,__UpperCAmelCase   ):
       UpperCamelCase__  :   int						 =						32
       UpperCamelCase__  :   int						 =						4
       UpperCamelCase__  :   int						 =						4
       UpperCamelCase__  :   Tuple[str]						 =						(
           "CrossAttnDownBlock2D",
           "CrossAttnDownBlock2D",
           "CrossAttnDownBlock2D",
           "DownBlock2D",
       )
       UpperCamelCase__  :   Tuple[str]						 =						("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
       UpperCamelCase__  :   Union[bool, Tuple[bool]]						 =						False
       UpperCamelCase__  :   Tuple[int]						 =						(320, 640, 1_280, 1_280)
       UpperCamelCase__  :   int						 =						2
       UpperCamelCase__  :   Union[int, Tuple[int]]						 =						8
       UpperCamelCase__  :   Optional[Union[int, Tuple[int]]]						 =						None
       UpperCamelCase__  :   int						 =						1_280
       UpperCamelCase__  :   float						 =						0.0
       UpperCamelCase__  :   bool						 =						False
       UpperCamelCase__  :   jnp.dtype						 =						jnp.floataa
       UpperCamelCase__  :   bool						 =						True
       UpperCamelCase__  :   int						 =						0
       UpperCamelCase__  :   bool						 =						False



       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					jax.random.KeyArray):


           '''simple docstring'''

           __a							=    (1, self.in_channels, self.sample_size, self.sample_size)
           __a							=    jnp.zeros(__SCREAMING_SNAKE_CASE					,		dtype=jnp.floataa)
           __a							=    jnp.ones((1,)					,		dtype=jnp.intaa)
           __a							=    jnp.zeros((1, 1, self.cross_attention_dim)					,		dtype=jnp.floataa)

           __a	,       __a							=    jax.random.split(__SCREAMING_SNAKE_CASE)
           __a							=    {'''params''': params_rng, '''dropout''': dropout_rng}

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



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __a							=    self.block_out_channels
           __a							=    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.
           __a							=    self.num_attention_heads or self.attention_head_dim

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

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

           __a							=    self.only_cross_attention
           if isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE):
               __a							=    (only_cross_attention,) * len(self.down_block_types)

           if isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE):
               __a							=    (num_attention_heads,) * len(self.down_block_types)

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

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

               down_blocks.append(__SCREAMING_SNAKE_CASE)
           __a							=    down_blocks

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

               __a							=    i == len(__SCREAMING_SNAKE_CASE) - 1

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

               up_blocks.append(__SCREAMING_SNAKE_CASE)
               __a							=    output_channel
           __a							=    up_blocks

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





       def __call__(		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Tuple=None					,		__SCREAMING_SNAKE_CASE							:					int=None					,		__SCREAMING_SNAKE_CASE							:					bool = True					,		__SCREAMING_SNAKE_CASE							:					bool = False					,		):


           '''simple docstring'''

           if not isinstance(__SCREAMING_SNAKE_CASE					,		jnp.ndarray):
               __a							=    jnp.array([timesteps]					,		dtype=jnp.intaa)
           elif isinstance(__SCREAMING_SNAKE_CASE					,		jnp.ndarray) and len(timesteps.shape) == 0:
               __a							=    timesteps.astype(dtype=jnp.floataa)
               __a							=    jnp.expand_dims(__SCREAMING_SNAKE_CASE					,		0)

           __a							=    self.time_proj(__SCREAMING_SNAKE_CASE)
           __a							=    self.time_embedding(__SCREAMING_SNAKE_CASE)

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

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

           if down_block_additional_residuals is not None:
               __a							=    ()

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

               __a							=    new_down_block_res_samples

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

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

           if not return_dict:
               return (sample,)

           return FlaxUNetaDConditionOutput(sample=__SCREAMING_SNAKE_CASE)










from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass

@dataclass
class    _A	:
       UpperCamelCase__  :   int
       UpperCamelCase__  :   TreeNode | None						 =						None
       UpperCamelCase__  :   TreeNode | None						 =						None


__snake_case						:Optional[Any]						=					namedtuple('''CoinsDistribResult''', '''moves excess''')





def 					__snake_case     (      _UpperCAmelCase    ):

    if root is None:
        return 0

    # Validation
    def count_nodes(_UpperCAmelCase    ) -> int:
        if node is None:
            return 0

        return count_nodes(node.left    ) + count_nodes(node.right    ) + 1

    def count_coins(_UpperCAmelCase    ) -> int:
        if node is None:
            return 0

        return count_coins(node.left    ) + count_coins(node.right    ) + node.data

    if count_nodes(_UpperCAmelCase    ) != count_coins(_UpperCAmelCase    ):
        raise ValueError('''The nodes number should be same as the number of coins'''    )

    # Main calculation
    def get_distrib(_UpperCAmelCase    ) -> CoinsDistribResult:

        if node is None:
            return CoinsDistribResult(0       ,			1    )

        __a	,       __a							=    get_distrib(node.left    )
        __a	,       __a							=    get_distrib(node.right    )

        __a							=    1 - left_distrib_excess
        __a							=    1 - right_distrib_excess

        __a							=    (
            left_distrib_moves
            + right_distrib_moves
            + abs(_UpperCAmelCase    )
            + abs(_UpperCAmelCase    )
        )
        __a							=    node.data - coins_to_left - coins_to_right

        return CoinsDistribResult(_UpperCAmelCase       ,			_UpperCAmelCase    )

    return get_distrib(_UpperCAmelCase    )[0]


if __name__ == "__main__":
 import doctest

 doctest.testmod()





