




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 		_snake_case  (  A__					):
			_lowercase		:    jnp.ndarray



@flax_register_to_config
class 		_snake_case  (  nn.Module   ,    A__   ,    A__					):
			_lowercase		:    int			     =							32
			_lowercase		:    int			     =							4
			_lowercase		:    int			     =							4
			_lowercase		:    Tuple[str]			     =							(
			    "CrossAttnDownBlock2D",
			    "CrossAttnDownBlock2D",
			    "CrossAttnDownBlock2D",
			    "DownBlock2D",
			)
			_lowercase		:    Tuple[str]			     =							("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
			_lowercase		:    Union[bool, Tuple[bool]]			     =							False
			_lowercase		:    Tuple[int]			     =							(3_20, 6_40, 12_80, 12_80)
			_lowercase		:    int			     =							2
			_lowercase		:    Union[int, Tuple[int]]			     =							8
			_lowercase		:    Optional[Union[int, Tuple[int]]]			     =							None
			_lowercase		:    int			     =							12_80
			_lowercase		:    float			     =							0.0
			_lowercase		:    bool			     =							False
			_lowercase		:    jnp.dtype			     =							jnp.floataa
			_lowercase		:    bool			     =							True
			_lowercase		:    int			     =							0
			_lowercase		:    bool			     =							False






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       FrozenDict:
						# init input tensors
						SCREAMING_SNAKE_CASE						= (1, self.in_channels, self.sample_size, self.sample_size)
						SCREAMING_SNAKE_CASE						= jnp.zeros(a		,					dtype=jnp.floataa)
						SCREAMING_SNAKE_CASE						= jnp.ones((1,)		,					dtype=jnp.intaa)
						SCREAMING_SNAKE_CASE						= jnp.zeros((1, 1, self.cross_attention_dim)		,					dtype=jnp.floataa)

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

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






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= self.block_out_channels
						SCREAMING_SNAKE_CASE						= 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.
						SCREAMING_SNAKE_CASE						= self.num_attention_heads or self.attention_head_dim

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

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

						SCREAMING_SNAKE_CASE						= self.only_cross_attention
						if isinstance(a		,					a):
									SCREAMING_SNAKE_CASE						= (only_cross_attention,) * len(self.down_block_types)

						if isinstance(a		,					a):
									SCREAMING_SNAKE_CASE						= (num_attention_heads,) * len(self.down_block_types)

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

									if down_block_type == "CrossAttnDownBlock2D":
												SCREAMING_SNAKE_CASE						= 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]		,					use_memory_efficient_attention=self.use_memory_efficient_attention		,					dtype=self.dtype		,					)
									else:
												SCREAMING_SNAKE_CASE						= 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)
						SCREAMING_SNAKE_CASE						= down_blocks

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

									SCREAMING_SNAKE_CASE						= i == len(a) - 1

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

									up_blocks.append(a)
									SCREAMING_SNAKE_CASE						= output_channel
						SCREAMING_SNAKE_CASE						= up_blocks

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







			def __call__(       self		,					a		,					a		,					a		,					a=None		,					a=None		,					a = True		,					a = False		,					)      ->       Union[FlaxUNetaDConditionOutput, Tuple]:
						# 1. time
						if not isinstance(a		,					jnp.ndarray):
									SCREAMING_SNAKE_CASE						= jnp.array([timesteps]		,					dtype=jnp.intaa)
						elif isinstance(a		,					jnp.ndarray) and len(timesteps.shape) == 0:
									SCREAMING_SNAKE_CASE						= timesteps.astype(dtype=jnp.floataa)
									SCREAMING_SNAKE_CASE						= jnp.expand_dims(a		,					0)

						SCREAMING_SNAKE_CASE						= self.time_proj(a)
						SCREAMING_SNAKE_CASE						= self.time_embedding(a)

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

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

						if down_block_additional_residuals is not None:
									SCREAMING_SNAKE_CASE						= ()

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

									SCREAMING_SNAKE_CASE						= new_down_block_res_samples

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

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

						if not return_dict:
									return (sample,)

						return FlaxUNetaDConditionOutput(sample=a)












from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

a_		:						str             =    (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
a_		:						list[int]             =    [ord(letter) for letter in string.ascii_lowercase]
a_		:						set[int]             =    {ord(char) for char in VALID_CHARS}

a_		:						list[str]             =    ["the", "be", "to", "of", "and", "in", "that", "have"]






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= ""
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= 42

			for keychar, cipherchar in zip(cycle(_UpperCAmelCase)							,       _UpperCAmelCase):
						SCREAMING_SNAKE_CASE						= cipherchar ^ keychar
						if decodedchar not in VALID_INTS:
									return None
						decoded += chr(_UpperCAmelCase)

			return decoded






def 				lowerCamelCase__				(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= []
			for key in product(_UpperCAmelCase							,       repeat=3):
						SCREAMING_SNAKE_CASE						= try_key(_UpperCAmelCase							,       _UpperCAmelCase)
						if encoded is not None:
									possibles.append(_UpperCAmelCase)
			return possibles






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			return [possible for possible in possibles if common_word in possible.lower()]






def 				lowerCamelCase__				(_UpperCAmelCase = "p059_cipher.txt"):
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= Path(_UpperCAmelCase).parent.joinpath(_UpperCAmelCase).read_text(encoding='utf-8')

			SCREAMING_SNAKE_CASE						= [int(_UpperCAmelCase) for number in data.strip().split(',')]

			SCREAMING_SNAKE_CASE						= filter_valid_chars(_UpperCAmelCase)
			for common_word in COMMON_WORDS:
						SCREAMING_SNAKE_CASE						= filter_common_word(_UpperCAmelCase							,       _UpperCAmelCase)
						if len(_UpperCAmelCase) == 1:
									break

			SCREAMING_SNAKE_CASE						= possibles[0]
			return sum(ord(_UpperCAmelCase) for char in decoded_text)


if __name__ == "__main__":
				print(f"""{solution() = }""")




