



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_					( a):
							lowerCamelCase__         =	42
							lowerCamelCase__         =	42



class 						UpperCAmelCase_					( nn.Module):
							lowerCamelCase__         =	42
							lowerCamelCase__         =	(16, 32, 96, 256)
							lowerCamelCase__         =	jnp.floataa







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[Any]				     =					nn.Conv(
														    self.block_out_channels[0],    kernel_size=(3, 3),    padding=((1, 1), (1, 1)),    dtype=self.dtype,    )

														_lowerCAmelCase  :							List[Any]				     =					[]
														for i in range(len(self.block_out_channels) - 1):
																					_lowerCAmelCase  :							List[Any]				     =					self.block_out_channels[i]
																					_lowerCAmelCase  :							Dict				     =					self.block_out_channels[i + 1]
																					_lowerCAmelCase  :							Any				     =					nn.Conv(
																					    __a,    kernel_size=(3, 3),    padding=((1, 1), (1, 1)),    dtype=self.dtype,    )
																					blocks.append(__a)
																					_lowerCAmelCase  :							Any				     =					nn.Conv(
																					    __a,    kernel_size=(3, 3),    strides=(2, 2),    padding=((1, 1), (1, 1)),    dtype=self.dtype,    )
																					blocks.append(__a)
														_lowerCAmelCase  :							Tuple				     =					blocks

														_lowerCAmelCase  :							Union[str, Any]				     =					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,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[Any]				     =					self.conv_in(__a)
														_lowerCAmelCase  :							str				     =					nn.silu(__a)

														for block in self.blocks:
																					_lowerCAmelCase  :							str				     =					block(__a)
																					_lowerCAmelCase  :							Dict				     =					nn.silu(__a)

														_lowerCAmelCase  :							str				     =					self.conv_out(__a)

														return embedding



@flax_register_to_config
class 						UpperCAmelCase_					( nn.Module     ,      a     ,      a):
							lowerCamelCase__         =	32
							lowerCamelCase__         =	4
							lowerCamelCase__         =	(
							    "CrossAttnDownBlock2D",
							    "CrossAttnDownBlock2D",
							    "CrossAttnDownBlock2D",
							    "DownBlock2D",
							)
							lowerCamelCase__         =	False
							lowerCamelCase__         =	(320, 640, 1280, 1280)
							lowerCamelCase__         =	2
							lowerCamelCase__         =	8
							lowerCamelCase__         =	None
							lowerCamelCase__         =	1280
							lowerCamelCase__         =	0.0
							lowerCamelCase__         =	False
							lowerCamelCase__         =	jnp.floataa
							lowerCamelCase__         =	True
							lowerCamelCase__         =	0
							lowerCamelCase__         =	"rgb"
							lowerCamelCase__         =	(16, 32, 96, 256)







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

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

														_lowerCAmelCase     ,					_lowerCAmelCase  :							Union[str, Any]				     =					jax.random.split(__a)
														_lowerCAmelCase  :							List[str]				     =					{"params": params_rng, "dropout": dropout_rng}

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







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Any				     =					self.block_out_channels
														_lowerCAmelCase  :							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.
														_lowerCAmelCase  :							Union[str, Any]				     =					self.num_attention_heads or self.attention_head_dim

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

														# time
														_lowerCAmelCase  :							List[str]				     =					FlaxTimesteps(
														    block_out_channels[0],    flip_sin_to_cos=self.flip_sin_to_cos,    freq_shift=self.config.freq_shift)
														_lowerCAmelCase  :							Tuple				     =					FlaxTimestepEmbedding(__a,    dtype=self.dtype)

														_lowerCAmelCase  :							List[Any]				     =					FlaxControlNetConditioningEmbedding(
														    conditioning_embedding_channels=block_out_channels[0],    block_out_channels=self.conditioning_embedding_out_channels,    )

														_lowerCAmelCase  :							List[str]				     =					self.only_cross_attention
														if isinstance(__a,    __a):
																					_lowerCAmelCase  :							List[str]				     =					(only_cross_attention,) * len(self.down_block_types)

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

														# down
														_lowerCAmelCase  :							Any				     =					[]
														_lowerCAmelCase  :							Optional[int]				     =					[]

														_lowerCAmelCase  :							Any				     =					block_out_channels[0]

														_lowerCAmelCase  :							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):
																					_lowerCAmelCase  :							Optional[Any]				     =					output_channel
																					_lowerCAmelCase  :							Optional[int]				     =					block_out_channels[i]
																					_lowerCAmelCase  :							List[Any]				     =					i == len(__a) - 1

																					if down_block_type == "CrossAttnDownBlock2D":
																												_lowerCAmelCase  :							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:
																												_lowerCAmelCase  :							Optional[Any]				     =					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):
																												_lowerCAmelCase  :							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,    )
																												controlnet_down_blocks.append(__a)

																					if not is_final_block:
																												_lowerCAmelCase  :							int				     =					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)

														_lowerCAmelCase  :							Optional[int]				     =					down_blocks
														_lowerCAmelCase  :							int				     =					controlnet_down_blocks

														# mid
														_lowerCAmelCase  :							Dict				     =					block_out_channels[-1]
														_lowerCAmelCase  :							Dict				     =					FlaxUNetMidBlockaDCrossAttn(
														    in_channels=__a,    dropout=self.dropout,    num_attention_heads=num_attention_heads[-1],    use_linear_projection=self.use_linear_projection,    dtype=self.dtype,    )

														_lowerCAmelCase  :							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,    __a,    __a,    __a,    __a,    __a = 1.0,    __a = True,    __a = False,    ):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					self.controlnet_conditioning_channel_order
														if channel_order == "bgr":
																					_lowerCAmelCase  :							List[str]				     =					jnp.flip(__a,    axis=1)

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

														_lowerCAmelCase  :							Union[str, Any]				     =					self.time_proj(__a)
														_lowerCAmelCase  :							Optional[int]				     =					self.time_embedding(__a)

														# 2. pre-process
														_lowerCAmelCase  :							List[str]				     =					jnp.transpose(__a,    (0, 2, 3, 1))
														_lowerCAmelCase  :							int				     =					self.conv_in(__a)

														_lowerCAmelCase  :							Optional[Any]				     =					jnp.transpose(__a,    (0, 2, 3, 1))
														_lowerCAmelCase  :							Tuple				     =					self.controlnet_cond_embedding(__a)
														sample += controlnet_cond

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

														# 4. mid
														_lowerCAmelCase  :							Any				     =					self.mid_block(__a,    __a,    __a,    deterministic=not train)

														# 5. contronet blocks
														_lowerCAmelCase  :							str				     =					()
														for down_block_res_sample, controlnet_block in zip(__a,    self.controlnet_down_blocks):
																					_lowerCAmelCase  :							str				     =					controlnet_block(__a)
																					controlnet_down_block_res_samples += (down_block_res_sample,)

														_lowerCAmelCase  :							int				     =					controlnet_down_block_res_samples

														_lowerCAmelCase  :							List[Any]				     =					self.controlnet_mid_block(__a)

														# 6. scaling
														_lowerCAmelCase  :							List[str]				     =					[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)





def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							int				     =					len(_lowerCamelCase						)
							for i in range(1       ,				_lowerCamelCase						):
														_lowerCAmelCase  :							List[Any]				     =					collection[i]
														_lowerCAmelCase  :							str				     =					0
														_lowerCAmelCase  :							Union[str, Any]				     =					i - 1

														while low <= high:
																					_lowerCAmelCase  :							List[str]				     =					(low + high) // 2
																					if val < collection[mid]:
																												_lowerCAmelCase  :							Optional[int]				     =					mid - 1
																					else:
																												_lowerCAmelCase  :							List[str]				     =					mid + 1
														for j in range(_lowerCamelCase       ,				_lowerCamelCase       ,				-1						):
																					_lowerCAmelCase  :							int				     =					collection[j - 1]
														_lowerCAmelCase  :							Optional[int]				     =					val
							return collection


if __name__ == "__main__":
						_snake_case	=						input("Enter numbers separated by a comma:\n").strip()
						_snake_case	=						[int(item) for item in user_input.split(",")]
						print(binary_insertion_sort(unsorted))
