


'''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 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     42
@flax_register_to_config
class 						UpperCamelCase__       (			nn.Module     ,       SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     3_2
     __magic_name__           =     4
     __magic_name__           =     4
     __magic_name__           =     (
         "CrossAttnDownBlock2D",
         "CrossAttnDownBlock2D",
         "CrossAttnDownBlock2D",
         "DownBlock2D",
     )
     __magic_name__           =     ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
     __magic_name__           =     False
     __magic_name__           =     (3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
     __magic_name__           =     2
     __magic_name__           =     8
     __magic_name__           =     None
     __magic_name__           =     1_2_8_0
     __magic_name__           =     0.0
     __magic_name__           =     False
     __magic_name__           =     jnp.floataa
     __magic_name__           =     True
     __magic_name__           =     0
     __magic_name__           =     False







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							(1, self.in_channels, self.sample_size, self.sample_size)
            _lowerCAmelCase     :				Optional[int]       =							jnp.zeros(snake_case__   ,						dtype=jnp.floataa )
            _lowerCAmelCase     :				Any       =							jnp.ones((1,)   ,						dtype=jnp.intaa )
            _lowerCAmelCase     :				str       =							jnp.zeros((1, 1, self.cross_attention_dim)   ,						dtype=jnp.floataa )

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

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







     def a						(			self ):

            '''simple docstring'''

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

            _lowerCAmelCase     :				Union[str, Any]       =							self.only_cross_attention
            if isinstance(snake_case__   ,						snake_case__ ):
                   _lowerCAmelCase     :				Any       =							(only_cross_attention,) * len(self.down_block_types )

            if isinstance(snake_case__   ,						snake_case__ ):
                   _lowerCAmelCase     :				Tuple       =							(num_attention_heads,) * len(self.down_block_types )

            # down
            _lowerCAmelCase     :				Tuple       =							[]
            _lowerCAmelCase     :				Any       =							block_out_channels[0]
            for i, down_block_type in enumerate(self.down_block_types ):
                   _lowerCAmelCase     :				Tuple       =							output_channel
                   _lowerCAmelCase     :				Optional[int]       =							block_out_channels[i]
                   _lowerCAmelCase     :				List[Any]       =							i == len(snake_case__ ) - 1

                   if down_block_type == "CrossAttnDownBlock2D":
                          _lowerCAmelCase     :				Optional[Any]       =							FlaxCrossAttnDownBlockaD(
                              in_channels=snake_case__   ,						out_channels=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:
                          _lowerCAmelCase     :				List[str]       =							FlaxDownBlockaD(
                              in_channels=snake_case__   ,						out_channels=snake_case__   ,						dropout=self.dropout   ,						num_layers=self.layers_per_block   ,						add_downsample=not is_final_block   ,						dtype=self.dtype   ,						)

                   down_blocks.append(snake_case__ )
            _lowerCAmelCase     :				Union[str, Any]       =							down_blocks

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

                   _lowerCAmelCase     :				str       =							i == len(snake_case__ ) - 1

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

                   up_blocks.append(snake_case__ )
                   _lowerCAmelCase     :				Dict       =							output_channel
            _lowerCAmelCase     :				Optional[int]       =							up_blocks

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







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

            '''simple docstring'''

            if not isinstance(snake_case__   ,						jnp.ndarray ):
                   _lowerCAmelCase     :				Tuple       =							jnp.array([timesteps]   ,						dtype=jnp.intaa )
            elif isinstance(snake_case__   ,						jnp.ndarray ) and len(timesteps.shape ) == 0:
                   _lowerCAmelCase     :				int       =							timesteps.astype(dtype=jnp.floataa )
                   _lowerCAmelCase     :				Optional[int]       =							jnp.expand_dims(snake_case__   ,						0 )

            _lowerCAmelCase     :				Optional[Any]       =							self.time_proj(snake_case__ )
            _lowerCAmelCase     :				int       =							self.time_embedding(snake_case__ )

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

            # 3. down
            _lowerCAmelCase     :				str       =							(sample,)
            for down_block in self.down_blocks:
                   if isinstance(snake_case__   ,						snake_case__ ):
                          _lowerCAmelCase      ,  _lowerCAmelCase     :				int       =							down_block(snake_case__   ,						snake_case__   ,						snake_case__   ,						deterministic=not train )
                   else:
                          _lowerCAmelCase      ,  _lowerCAmelCase     :				Union[str, Any]       =							down_block(snake_case__   ,						snake_case__   ,						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(
                       snake_case__   ,						snake_case__ ):
                          down_block_res_sample += down_block_additional_residual
                          new_down_block_res_samples += (down_block_res_sample,)

                   _lowerCAmelCase     :				Tuple       =							new_down_block_res_samples

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

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

            if not return_dict:
                   return (sample,)

            return FlaxUNetaDConditionOutput(sample=snake_case__ )





'''simple docstring'''







def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Union[str, Any]       =							len(_A       )
       for i in range(length - 1       ):
              _lowerCAmelCase     :				List[Any]       =							i
              for k in range(i + 1 ,    _A       ):
                     if collection[k] < collection[least]:
                            _lowerCAmelCase     :				List[str]       =							k
              if least != i:
                     _lowerCAmelCase      ,  _lowerCAmelCase     :				Union[str, Any]       =							(collection[i], collection[least])
       return collection


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

