






"""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,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)


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





		'''simple docstring'''






		a_	:				jnp.ndarray







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





		'''simple docstring'''






		a_	:				int            =							32
		a_	:				int            =							4
		a_	:				int            =							4
		a_	:				Tuple[str]            =							(
		    "CrossAttnDownBlock2D",
		    "CrossAttnDownBlock2D",
		    "CrossAttnDownBlock2D",
		    "DownBlock2D",
		)
		a_	:				Tuple[str]            =							("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
		a_	:				Union[bool, Tuple[bool]]            =							False
		a_	:				Tuple[int]            =							(320, 640, 1280, 1280)
		a_	:				int            =							2
		a_	:				Union[int, Tuple[int]]            =							8
		a_	:				Optional[Union[int, Tuple[int]]]            =							None
		a_	:				int            =							1280
		a_	:				float            =							0.0
		a_	:				bool            =							False
		a_	:				jnp.dtype            =							jnp.floataa
		a_	:				bool            =							True
		a_	:				int            =							0
		a_	:				bool            =							False





		def    lowerCamelCase       (   self							:	Optional[Any]						,			a_							:	Union[str, Any]	):
				# init input tensors
				lowerCAmelCase_   :							int									=			(1, self.in_channels, self.sample_size, self.sample_size)
				lowerCAmelCase_   :							Dict									=			jnp.zeros(lowerCAmelCase__						,			dtype=jnp.floataa	)
				lowerCAmelCase_   :							Tuple									=			jnp.ones((1,)						,			dtype=jnp.intaa	)
				lowerCAmelCase_   :							Dict									=			jnp.zeros((1, 1, self.cross_attention_dim)						,			dtype=jnp.floataa	)

				lowerCAmelCase_   :							str									=			jax.random.split(lowerCAmelCase__	)
				lowerCAmelCase_   :							Dict									=			{"params": params_rng, "dropout": dropout_rng}

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





		def    lowerCamelCase       (   self							:	Union[str, Any]	):
				lowerCAmelCase_   :							List[str]									=			self.block_out_channels
				lowerCAmelCase_   :							Dict									=			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_   :							Tuple									=			self.num_attention_heads or self.attention_head_dim

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

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

				lowerCAmelCase_   :							Any									=			self.only_cross_attention
				if isinstance(lowerCAmelCase__						,			lowerCAmelCase__	):
						lowerCAmelCase_   :							List[str]									=			(only_cross_attention,) * len(self.down_block_types	)

				if isinstance(lowerCAmelCase__						,			lowerCAmelCase__	):
						lowerCAmelCase_   :							List[str]									=			(num_attention_heads,) * len(self.down_block_types	)

				# down
				lowerCAmelCase_   :							Optional[Any]									=			[]
				lowerCAmelCase_   :							str									=			block_out_channels[0]
				for i, down_block_type in enumerate(self.down_block_types	):
						lowerCAmelCase_   :							Optional[Any]									=			output_channel
						lowerCAmelCase_   :							str									=			block_out_channels[i]
						lowerCAmelCase_   :							int									=			i == len(lowerCAmelCase__	) - 1

						if down_block_type == "CrossAttnDownBlock2D":
								lowerCAmelCase_   :							Dict									=			FlaxCrossAttnDownBlockaD(
								    in_channels=lowerCAmelCase__						,			out_channels=lowerCAmelCase__						,			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_   :							Dict									=			FlaxDownBlockaD(
								    in_channels=lowerCAmelCase__						,			out_channels=lowerCAmelCase__						,			dropout=self.dropout						,			num_layers=self.layers_per_block						,			add_downsample=not is_final_block						,			dtype=self.dtype						,			)

						down_blocks.append(lowerCAmelCase__	)
				lowerCAmelCase_   :							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_   :							Any									=			[]
				lowerCAmelCase_   :							Optional[int]									=			list(reversed(lowerCAmelCase__	)	)
				lowerCAmelCase_   :							Tuple									=			list(reversed(lowerCAmelCase__	)	)
				lowerCAmelCase_   :							Dict									=			list(reversed(lowerCAmelCase__	)	)
				lowerCAmelCase_   :							List[str]									=			reversed_block_out_channels[0]
				for i, up_block_type in enumerate(self.up_block_types	):
						lowerCAmelCase_   :							Optional[int]									=			output_channel
						lowerCAmelCase_   :							str									=			reversed_block_out_channels[i]
						lowerCAmelCase_   :							List[Any]									=			reversed_block_out_channels[min(i + 1						,			len(lowerCAmelCase__	) - 1	)]

						lowerCAmelCase_   :							Optional[Any]									=			i == len(lowerCAmelCase__	) - 1

						if up_block_type == "CrossAttnUpBlock2D":
								lowerCAmelCase_   :							str									=			FlaxCrossAttnUpBlockaD(
								    in_channels=lowerCAmelCase__						,			out_channels=lowerCAmelCase__						,			prev_output_channel=lowerCAmelCase__						,			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_   :							Dict									=			FlaxUpBlockaD(
								    in_channels=lowerCAmelCase__						,			out_channels=lowerCAmelCase__						,			prev_output_channel=lowerCAmelCase__						,			num_layers=self.layers_per_block + 1						,			add_upsample=not is_final_block						,			dropout=self.dropout						,			dtype=self.dtype						,			)

						up_blocks.append(lowerCAmelCase__	)
						lowerCAmelCase_   :							Union[str, Any]									=			output_channel
				lowerCAmelCase_   :							List[Any]									=			up_blocks

				# out
				lowerCAmelCase_   :							int									=			nn.GroupNorm(num_groups=32						,			epsilon=1e-5	)
				lowerCAmelCase_   :							Union[str, Any]									=			nn.Conv(
				    self.out_channels						,			kernel_size=(3, 3)						,			strides=(1, 1)						,			padding=((1, 1), (1, 1))						,			dtype=self.dtype						,			)





		def __call__(   self							:	int						,			a_							:	Tuple						,			a_							:	Optional[Any]						,			a_							:	List[str]						,			a_							:	int=None						,			a_							:	str=None						,			a_							:	Optional[Any] = True						,			a_							:	Optional[Any] = False						,			):
				# 1. time
				if not isinstance(lowerCAmelCase__						,			jnp.ndarray	):
						lowerCAmelCase_   :							int									=			jnp.array([timesteps]						,			dtype=jnp.intaa	)
				elif isinstance(lowerCAmelCase__						,			jnp.ndarray	) and len(timesteps.shape	) == 0:
						lowerCAmelCase_   :							Any									=			timesteps.astype(dtype=jnp.floataa	)
						lowerCAmelCase_   :							int									=			jnp.expand_dims(lowerCAmelCase__						,			0	)

				lowerCAmelCase_   :							Any									=			self.time_proj(lowerCAmelCase__	)
				lowerCAmelCase_   :							Union[str, Any]									=			self.time_embedding(lowerCAmelCase__	)

				# 2. pre-process
				lowerCAmelCase_   :							Tuple									=			jnp.transpose(lowerCAmelCase__						,			(0, 2, 3, 1)	)
				lowerCAmelCase_   :							Optional[int]									=			self.conv_in(lowerCAmelCase__	)

				# 3. down
				lowerCAmelCase_   :							Dict									=			(sample,)
				for down_block in self.down_blocks:
						if isinstance(lowerCAmelCase__						,			lowerCAmelCase__	):
								lowerCAmelCase_   :							Optional[Any]									=			down_block(lowerCAmelCase__						,			lowerCAmelCase__						,			lowerCAmelCase__						,			deterministic=not train	)
						else:
								lowerCAmelCase_   :							Optional[int]									=			down_block(lowerCAmelCase__						,			lowerCAmelCase__						,			deterministic=not train	)
						down_block_res_samples += res_samples

				if down_block_additional_residuals is not None:
						lowerCAmelCase_   :							Optional[Any]									=			()

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

						lowerCAmelCase_   :							List[str]									=			new_down_block_res_samples

				# 4. mid
				lowerCAmelCase_   :							Optional[Any]									=			self.mid_block(lowerCAmelCase__						,			lowerCAmelCase__						,			lowerCAmelCase__						,			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_   :							Union[str, Any]									=			down_block_res_samples[-(self.layers_per_block + 1) :]
						lowerCAmelCase_   :							Any									=			down_block_res_samples[: -(self.layers_per_block + 1)]
						if isinstance(lowerCAmelCase__						,			lowerCAmelCase__	):
								lowerCAmelCase_   :							Optional[int]									=			up_block(
								    lowerCAmelCase__						,			temb=lowerCAmelCase__						,			encoder_hidden_states=lowerCAmelCase__						,			res_hidden_states_tuple=lowerCAmelCase__						,			deterministic=not train						,			)
						else:
								lowerCAmelCase_   :							Optional[Any]									=			up_block(lowerCAmelCase__						,			temb=lowerCAmelCase__						,			res_hidden_states_tuple=lowerCAmelCase__						,			deterministic=not train	)

        # 6. post-process
				lowerCAmelCase_   :							List[Any]									=			self.conv_norm_out(lowerCAmelCase__	)
				lowerCAmelCase_   :							str									=			nn.silu(lowerCAmelCase__	)
				lowerCAmelCase_   :							List[Any]									=			self.conv_out(lowerCAmelCase__	)
				lowerCAmelCase_   :							List[Any]									=			jnp.transpose(lowerCAmelCase__						,			(0, 3, 1, 2)	)

				if not return_dict:
						return (sample,)

				return FlaxUNetaDConditionOutput(sample=lowerCAmelCase__	)







"""simple docstring"""




from __future__ import annotations






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    list[int]    ,       _lowercase						:    int					) ->int:






	'''simple docstring'''






	if len(_lowercase					) < k or k < 0:
		raise ValueError("Invalid Input"					)
	a      :		Optional[Any]	=				sum(array[:k]					)
	for i in range(len(_lowercase					) - k					):
		a      :		Optional[Any]	=				current_sum - array[i] + array[i + k]
		a      :		Union[str, Any]	=				max(_lowercase    ,       _lowercase					)
	return max_sum


if __name__ == "__main__":
		from doctest import testmod
		from random import randint

		testmod()
		a						: Any     	=			[randint(-1000, 1000) for i in range(100)]
		a						: List[str]     	=			randint(0, 110)
		print(F'''The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}''')


