



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					( __snake_case      ):
			UpperCamelCase					:Any     =			42







@flax_register_to_config
class 				__A					( nn.Module					, __snake_case					, __snake_case      ):
			UpperCamelCase					:List[str]     =			32
			UpperCamelCase					:Optional[int]     =			4
			UpperCamelCase					:Optional[Any]     =			4
			UpperCamelCase					:Optional[Any]     =			(
			    '''CrossAttnDownBlock2D''',
			    '''CrossAttnDownBlock2D''',
			    '''CrossAttnDownBlock2D''',
			    '''DownBlock2D''',
			)
			UpperCamelCase					:Optional[Any]     =			('''UpBlock2D''', '''CrossAttnUpBlock2D''', '''CrossAttnUpBlock2D''', '''CrossAttnUpBlock2D''')
			UpperCamelCase					:Optional[Any]     =			False
			UpperCamelCase					:Union[str, Any]     =			(320, 640, 1280, 1280)
			UpperCamelCase					:int     =			2
			UpperCamelCase					:Optional[Any]     =			8
			UpperCamelCase					:Optional[int]     =			None
			UpperCamelCase					:Optional[int]     =			1280
			UpperCamelCase					:List[str]     =			0.0
			UpperCamelCase					:List[Any]     =			False
			UpperCamelCase					:List[str]     =			jnp.floataa
			UpperCamelCase					:int     =			True
			UpperCamelCase					:List[Any]     =			0
			UpperCamelCase					:List[Any]     =			False





			def        _snake_case    (self							,    __magic_name__				):
						lowerCamelCase__ :      Dict								=		(1, self.in_channels, self.sample_size, self.sample_size)
						lowerCamelCase__ :      Optional[int]								=		jnp.zeros(_lowercase							,    dtype=jnp.floataa				)
						lowerCamelCase__ :      List[str]								=		jnp.ones((1,)							,    dtype=jnp.intaa				)
						lowerCamelCase__ :      str								=		jnp.zeros((1, 1, self.cross_attention_dim)							,    dtype=jnp.floataa				)

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

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





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[Any]								=		self.block_out_channels
						lowerCamelCase__ :      List[Any]								=		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__ :      Union[str, Any]								=		self.num_attention_heads or self.attention_head_dim

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

						# time
						lowerCamelCase__ :      Optional[Any]								=		FlaxTimesteps(
						    block_out_channels[0]							,    flip_sin_to_cos=self.flip_sin_to_cos							,    freq_shift=self.config.freq_shift				)
						lowerCamelCase__ :      Optional[Any]								=		FlaxTimestepEmbedding(_lowercase							,    dtype=self.dtype				)

						lowerCamelCase__ :      str								=		self.only_cross_attention
						if isinstance(_lowercase							,    _lowercase				):
									lowerCamelCase__ :      Dict								=		(only_cross_attention,) * len(self.down_block_types				)

						if isinstance(_lowercase							,    _lowercase				):
									lowerCamelCase__ :      int								=		(num_attention_heads,) * len(self.down_block_types				)

						# down
						lowerCamelCase__ :      Union[str, Any]								=		[]
						lowerCamelCase__ :      Optional[Any]								=		block_out_channels[0]
						for i, down_block_type in enumerate(self.down_block_types				):
									lowerCamelCase__ :      List[Any]								=		output_channel
									lowerCamelCase__ :      Dict								=		block_out_channels[i]
									lowerCamelCase__ :      Dict								=		i == len(_lowercase				) - 1

									if down_block_type == "CrossAttnDownBlock2D":
												lowerCamelCase__ :      Optional[Any]								=		FlaxCrossAttnDownBlockaD(
												    in_channels=_lowercase							,    out_channels=_lowercase							,    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__ :      Optional[Any]								=		FlaxDownBlockaD(
												    in_channels=_lowercase							,    out_channels=_lowercase							,    dropout=self.dropout							,    num_layers=self.layers_per_block							,    add_downsample=not is_final_block							,    dtype=self.dtype							,    )

									down_blocks.append(_lowercase				)
						lowerCamelCase__ :      Optional[int]								=		down_blocks

						# mid
						lowerCamelCase__ :      Tuple								=		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__ :      Optional[int]								=		[]
						lowerCamelCase__ :      Union[str, Any]								=		list(reversed(_lowercase				)				)
						lowerCamelCase__ :      List[str]								=		list(reversed(_lowercase				)				)
						lowerCamelCase__ :      Tuple								=		list(reversed(_lowercase				)				)
						lowerCamelCase__ :      Dict								=		reversed_block_out_channels[0]
						for i, up_block_type in enumerate(self.up_block_types				):
									lowerCamelCase__ :      Union[str, Any]								=		output_channel
									lowerCamelCase__ :      Tuple								=		reversed_block_out_channels[i]
									lowerCamelCase__ :      Optional[int]								=		reversed_block_out_channels[min(i + 1							,    len(_lowercase				) - 1				)]

									lowerCamelCase__ :      List[str]								=		i == len(_lowercase				) - 1

									if up_block_type == "CrossAttnUpBlock2D":
												lowerCamelCase__ :      List[Any]								=		FlaxCrossAttnUpBlockaD(
												    in_channels=_lowercase							,    out_channels=_lowercase							,    prev_output_channel=_lowercase							,    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__ :      Any								=		FlaxUpBlockaD(
												    in_channels=_lowercase							,    out_channels=_lowercase							,    prev_output_channel=_lowercase							,    num_layers=self.layers_per_block + 1							,    add_upsample=not is_final_block							,    dropout=self.dropout							,    dtype=self.dtype							,    )

									up_blocks.append(_lowercase				)
									lowerCamelCase__ :      Union[str, Any]								=		output_channel
						lowerCamelCase__ :      int								=		up_blocks

						# out
						lowerCamelCase__ :      Optional[Any]								=		nn.GroupNorm(num_groups=32							,    epsilon=1E-5				)
						lowerCamelCase__ :      Optional[Any]								=		nn.Conv(
						    self.out_channels							,    kernel_size=(3, 3)							,    strides=(1, 1)							,    padding=((1, 1), (1, 1))							,    dtype=self.dtype							,    )





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

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

						lowerCamelCase__ :      Any								=		self.time_proj(_lowercase				)
						lowerCamelCase__ :      Tuple								=		self.time_embedding(_lowercase				)

						# 2. pre-process
						lowerCamelCase__ :      Optional[Any]								=		jnp.transpose(_lowercase							,    (0, 2, 3, 1)				)
						lowerCamelCase__ :      List[str]								=		self.conv_in(_lowercase				)

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

						if down_block_additional_residuals is not None:
									lowerCamelCase__ :      Dict								=		()

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

									lowerCamelCase__ :      Dict								=		new_down_block_res_samples

						# 4. mid
						lowerCamelCase__ :      Dict								=		self.mid_block(_lowercase							,    _lowercase							,    _lowercase							,    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__ :      List[str]								=		down_block_res_samples[-(self.layers_per_block + 1) :]
									lowerCamelCase__ :      Optional[Any]								=		down_block_res_samples[: -(self.layers_per_block + 1)]
									if isinstance(_lowercase							,    _lowercase				):
												lowerCamelCase__ :      List[str]								=		up_block(
												    _lowercase							,    temb=_lowercase							,    encoder_hidden_states=_lowercase							,    res_hidden_states_tuple=_lowercase							,    deterministic=not train							,    )
									else:
												lowerCamelCase__ :      Tuple								=		up_block(_lowercase							,    temb=_lowercase							,    res_hidden_states_tuple=_lowercase							,    deterministic=not train				)

        # 6. post-process
						lowerCamelCase__ :      Optional[Any]								=		self.conv_norm_out(_lowercase				)
						lowerCamelCase__ :      Optional[Any]								=		nn.silu(_lowercase				)
						lowerCamelCase__ :      Any								=		self.conv_out(_lowercase				)
						lowerCamelCase__ :      int								=		jnp.transpose(_lowercase							,    (0, 3, 1, 2)				)

						if not return_dict:
									return (sample,)

						return FlaxUNetaDConditionOutput(sample=_lowercase				)








from __future__ import annotations






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      list[str] | None = None )  ->list[list[str]]:



			'''simple docstring'''







			lowerCamelCase__ :      List[str]								=		word_bank or []
			# create a table
			lowerCamelCase__ :      int								=		len(UpperCamelCase ) + 1

			lowerCamelCase__ :      list[list[list[str]]]								=		[]
			for _ in range(UpperCamelCase ):
						table.append([] )
			# seed value
			lowerCamelCase__ :      Tuple								=		[[]]  # because empty string has empty combination

			# iterate through the indices
			for i in range(UpperCamelCase ):
						# condition
						if table[i] != []:
									for word in word_bank:
												# slice condition
												if target[i : i + len(UpperCamelCase )] == word:
															lowerCamelCase__ :      list[list[str]]								=		[
															    [word, *way] for way in table[i]
															]
															# adds the word to every combination the current position holds
															# now,push that combination to the table[i+len(word)]
															table[i + len(UpperCamelCase )] += new_combinations

    # combinations are in reverse order so reverse for better output
			for combination in table[len(UpperCamelCase )]:
						combination.reverse()

			return table[len(UpperCamelCase )]


if __name__ == "__main__":
				print(all_construct('''jwajalapa''', ['''jwa''', '''j''', '''w''', '''a''', '''la''', '''lapa''']))
				print(all_construct('''rajamati''', ['''s''', '''raj''', '''amat''', '''raja''', '''ma''', '''i''', '''t''']))
				print(
				    all_construct(
				        '''hexagonosaurus''',
				        ['''h''', '''ex''', '''hex''', '''ag''', '''ago''', '''ru''', '''auru''', '''rus''', '''go''', '''no''', '''o''', '''s'''],
				    )
				)



