






def __A    (      __lowerCAmelCase	)->  list:





						"""simple docstring"""



						if len(__lowerCAmelCase	) < 2:
												return collection

						def circle_sort_util(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	) -> bool:
												_UpperCAmelCase									=			False

												if low == high:
																		return swapped

												_UpperCAmelCase									=			low
												_UpperCAmelCase									=			high

												while left < right:
																		if collection[left] > collection[right]:
																								_UpperCAmelCase									=			(
																								    collection[right],
																								    collection[left],
																								)
																								_UpperCAmelCase									=			True

																		left += 1
																		right -= 1

												if left == right and collection[left] > collection[right + 1]:
																		_UpperCAmelCase									=			(
																		    collection[right + 1],
																		    collection[left],
																		)

																		_UpperCAmelCase									=			True

												_UpperCAmelCase									=			low + int((high - low) / 2	)
												_UpperCAmelCase									=			circle_sort_util(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)
												_UpperCAmelCase									=			circle_sort_util(__lowerCAmelCase ,		mid + 1 ,		__lowerCAmelCase	)

												return swapped or left_swap or right_swap

						_UpperCAmelCase									=			True

						while is_not_sorted is True:
												_UpperCAmelCase									=			circle_sort_util(__lowerCAmelCase ,		0 ,		len(__lowerCAmelCase	) - 1	)

						return collection


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




from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


__lowerCAmelCase		       =							logging.get_logger(__name__)

# General docstring
__lowerCAmelCase		       =							'''RegNetConfig'''

# Base docstring
__lowerCAmelCase		       =							'''facebook/regnet-y-040'''
__lowerCAmelCase		       =							[1, 10_88, 7, 7]

# Image classification docstring
__lowerCAmelCase		       =							'''facebook/regnet-y-040'''
__lowerCAmelCase		       =							'''tabby, tabby cat'''

__lowerCAmelCase		       =							[
    '''facebook/regnet-y-040''',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]





class   __a			(    nn.Module      ):
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ = 3				, lowerCAmelCase__ = 1				, lowerCAmelCase__ = 1				, lowerCAmelCase__ = "relu"				, )					->				Optional[Any]:




        '''simple docstring'''
        super().__init__()
        lowercase__:   Any  			=   nn.Convad(
            lowerCAmelCase__				, lowerCAmelCase__				, kernel_size=lowerCAmelCase__				, stride=lowerCAmelCase__				, padding=kernel_size // 2				, groups=lowerCAmelCase__				, bias=lowerCAmelCase__				, )
        lowercase__:   str  			=   nn.BatchNormad(lowerCAmelCase__ )
        lowercase__:   Union[str, Any]  			=   ACTaFN[activation] if activation is not None else nn.Identity()
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				Dict:




        '''simple docstring'''
        lowercase__:   List[str]  			=   self.convolution(lowerCAmelCase__ )
        lowercase__:   Optional[Any]  			=   self.normalization(lowerCAmelCase__ )
        lowercase__:   Union[str, Any]  			=   self.activation(lowerCAmelCase__ )
        return hidden_state





class   __a			(    nn.Module      ):
 def __init__(   self				, lowerCAmelCase__ )					->				Union[str, Any]:




        '''simple docstring'''
        super().__init__()
        lowercase__:   Dict  			=   RegNetConvLayer(
            config.num_channels				, config.embedding_size				, kernel_size=3				, stride=2				, activation=config.hidden_act )
        lowercase__:   Dict  			=   config.num_channels
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				int:




        '''simple docstring'''
        lowercase__:   Tuple  			=   pixel_values.shape[1]
        if num_channels != self.num_channels:
               raise ValueError(
                   'Make sure that the channel dimension of the pixel values match with the one set in the configuration.' )
        lowercase__:   Optional[int]  			=   self.embedder(lowerCAmelCase__ )
        return hidden_state





class   __a			(    nn.Module      ):
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ = 2 )					->				Optional[Any]:




        '''simple docstring'''
        super().__init__()
        lowercase__:   Optional[Any]  			=   nn.Convad(lowerCAmelCase__				, lowerCAmelCase__				, kernel_size=1				, stride=lowerCAmelCase__				, bias=lowerCAmelCase__ )
        lowercase__:   Union[str, Any]  			=   nn.BatchNormad(lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				Tensor:




        '''simple docstring'''
        lowercase__:   Any  			=   self.convolution(lowerCAmelCase__ )
        lowercase__:   str  			=   self.normalization(lowerCAmelCase__ )
        return hidden_state





class   __a			(    nn.Module      ):
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__ )					->				List[str]:




        '''simple docstring'''
        super().__init__()

        lowercase__:   Any  			=   nn.AdaptiveAvgPoolad((1, 1) )
        lowercase__:   str  			=   nn.Sequential(
            nn.Convad(lowerCAmelCase__				, lowerCAmelCase__				, kernel_size=1 )				, nn.ReLU()				, nn.Convad(lowerCAmelCase__				, lowerCAmelCase__				, kernel_size=1 )				, nn.Sigmoid()				, )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				Optional[Any]:




        '''simple docstring'''
        # b c h w -> b c 1 1
        lowercase__:   str  			=   self.pooler(lowerCAmelCase__ )
        lowercase__:   List[str]  			=   self.attention(lowerCAmelCase__ )
        lowercase__:   List[Any]  			=   hidden_state * attention
        return hidden_state





class   __a			(    nn.Module      ):
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ = 1 )					->				Dict:




        '''simple docstring'''
        super().__init__()
        lowercase__:   str  			=   in_channels != out_channels or stride != 1
        lowercase__:   Optional[int]  			=   max(1				, out_channels // config.groups_width )
        lowercase__:   Union[str, Any]  			=   (
            RegNetShortCut(lowerCAmelCase__				, lowerCAmelCase__				, stride=lowerCAmelCase__ ) if should_apply_shortcut else nn.Identity()
        )
        lowercase__:   Dict  			=   nn.Sequential(
            RegNetConvLayer(lowerCAmelCase__				, lowerCAmelCase__				, kernel_size=1				, activation=config.hidden_act )				, RegNetConvLayer(lowerCAmelCase__				, lowerCAmelCase__				, stride=lowerCAmelCase__				, groups=lowerCAmelCase__				, activation=config.hidden_act )				, RegNetConvLayer(lowerCAmelCase__				, lowerCAmelCase__				, kernel_size=1				, activation=lowerCAmelCase__ )				, )
        lowercase__:   Tuple  			=   ACTaFN[config.hidden_act]
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				int:




        '''simple docstring'''
        lowercase__:   Dict  			=   hidden_state
        lowercase__:   Union[str, Any]  			=   self.layer(lowerCAmelCase__ )
        lowercase__:   int  			=   self.shortcut(lowerCAmelCase__ )
        hidden_state += residual
        lowercase__:   Optional[int]  			=   self.activation(lowerCAmelCase__ )
        return hidden_state





class   __a			(    nn.Module      ):
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ = 1 )					->				Dict:




        '''simple docstring'''
        super().__init__()
        lowercase__:   Optional[int]  			=   in_channels != out_channels or stride != 1
        lowercase__:   List[str]  			=   max(1				, out_channels // config.groups_width )
        lowercase__:   Any  			=   (
            RegNetShortCut(lowerCAmelCase__				, lowerCAmelCase__				, stride=lowerCAmelCase__ ) if should_apply_shortcut else nn.Identity()
        )
        lowercase__:   str  			=   nn.Sequential(
            RegNetConvLayer(lowerCAmelCase__				, lowerCAmelCase__				, kernel_size=1				, activation=config.hidden_act )				, RegNetConvLayer(lowerCAmelCase__				, lowerCAmelCase__				, stride=lowerCAmelCase__				, groups=lowerCAmelCase__				, activation=config.hidden_act )				, RegNetSELayer(lowerCAmelCase__				, reduced_channels=int(round(in_channels / 4 ) ) )				, RegNetConvLayer(lowerCAmelCase__				, lowerCAmelCase__				, kernel_size=1				, activation=lowerCAmelCase__ )				, )
        lowercase__:   Union[str, Any]  			=   ACTaFN[config.hidden_act]
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				List[str]:




        '''simple docstring'''
        lowercase__:   Optional[Any]  			=   hidden_state
        lowercase__:   Optional[int]  			=   self.layer(lowerCAmelCase__ )
        lowercase__:   str  			=   self.shortcut(lowerCAmelCase__ )
        hidden_state += residual
        lowercase__:   Optional[int]  			=   self.activation(lowerCAmelCase__ )
        return hidden_state





class   __a			(    nn.Module      ):
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ = 2				, lowerCAmelCase__ = 2				, )					->				Tuple:




        '''simple docstring'''
        super().__init__()

        lowercase__:   Optional[int]  			=   RegNetXLayer if config.layer_type == 'x' else RegNetYLayer

        lowercase__:   str  			=   nn.Sequential(
            # downsampling is done in the first layer with stride of 2
            layer(
                lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, stride=lowerCAmelCase__				, )				, *[layer(lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ ) for _ in range(depth - 1 )]				, )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				Optional[Any]:




        '''simple docstring'''
        lowercase__:   str  			=   self.layers(lowerCAmelCase__ )
        return hidden_state





class   __a			(    nn.Module      ):
 def __init__(   self				, lowerCAmelCase__ )					->				Dict:




        '''simple docstring'''
        super().__init__()
        lowercase__:   int  			=   nn.ModuleList([] )
        # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
        self.stages.append(
            RegNetStage(
                lowerCAmelCase__				, config.embedding_size				, config.hidden_sizes[0]				, stride=2 if config.downsample_in_first_stage else 1				, depth=config.depths[0]				, ) )
        lowercase__:   int  			=   zip(config.hidden_sizes				, config.hidden_sizes[1:] )
        for (in_channels, out_channels), depth in zip(lowerCAmelCase__				, config.depths[1:] ):
               self.stages.append(RegNetStage(lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, depth=lowerCAmelCase__ ) )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ = False				, lowerCAmelCase__ = True )					->				BaseModelOutputWithNoAttention:




        '''simple docstring'''
        lowercase__:   List[str]  			=   () if output_hidden_states else None

        for stage_module in self.stages:
               if output_hidden_states:
                      lowercase__:   Optional[Any]  			=   hidden_states + (hidden_state,)

               lowercase__:   List[Any]  			=   stage_module(lowerCAmelCase__ )

        if output_hidden_states:
               lowercase__:   Optional[Any]  			=   hidden_states + (hidden_state,)

        if not return_dict:
               return tuple(v for v in [hidden_state, hidden_states] if v is not None )

        return BaseModelOutputWithNoAttention(last_hidden_state=lowerCAmelCase__				, hidden_states=lowerCAmelCase__ )





class   __a			(    __UpperCamelCase      ):
 __lowercase :							Dict        = RegNetConfig
 __lowercase :							Dict        = 'regnet'
 __lowercase :							str        = 'pixel_values'
 __lowercase :							List[str]        = True
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				List[str]:




        '''simple docstring'''
        if isinstance(lowerCAmelCase__				, nn.Convad ):
               nn.init.kaiming_normal_(module.weight				, mode='fan_out'				, nonlinearity='relu' )
        elif isinstance(lowerCAmelCase__				, (nn.BatchNormad, nn.GroupNorm) ):
               nn.init.constant_(module.weight				, 1 )
               nn.init.constant_(module.bias				, 0 )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__=False )					->				Optional[Any]:




        '''simple docstring'''
        if isinstance(lowerCAmelCase__				, lowerCAmelCase__ ):
               lowercase__:   Any  			=   value





__lowerCAmelCase		       =							r'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''

__lowerCAmelCase		       =							r'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
'''





@add_start_docstrings(
    'The bare RegNet model outputting raw features without any specific head on top.'       ,       __UpperCamelCase       ,       )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class   __a			(    __UpperCamelCase      ):
 def __init__(   self				, lowerCAmelCase__ )					->				List[str]:




        '''simple docstring'''
        super().__init__(lowerCAmelCase__ )
        lowercase__:   Tuple  			=   config
        lowercase__:   List[str]  			=   RegNetEmbeddings(lowerCAmelCase__ )
        lowercase__:   Optional[int]  			=   RegNetEncoder(lowerCAmelCase__ )
        lowercase__:   Optional[Any]  			=   nn.AdaptiveAvgPoolad((1, 1) )
        # Initialize weights and apply final processing
        self.post_init()
 @add_start_docstrings_to_model_forward(lowerCAmelCase__ )
 @add_code_sample_docstrings(
     checkpoint=_CHECKPOINT_FOR_DOC				, output_type=lowerCAmelCase__				, config_class=_CONFIG_FOR_DOC				, modality='vision'				, expected_output=_EXPECTED_OUTPUT_SHAPE				, )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None )					->				BaseModelOutputWithPoolingAndNoAttention:




        '''simple docstring'''
        lowercase__:   List[Any]  			=   (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        lowercase__:   Union[str, Any]  			=   return_dict if return_dict is not None else self.config.use_return_dict

        lowercase__:   Any  			=   self.embedder(lowerCAmelCase__ )

        lowercase__:   List[Any]  			=   self.encoder(
            lowerCAmelCase__				, output_hidden_states=lowerCAmelCase__				, return_dict=lowerCAmelCase__ )

        lowercase__:   Optional[Any]  			=   encoder_outputs[0]

        lowercase__:   Optional[int]  			=   self.pooler(lowerCAmelCase__ )

        if not return_dict:
               return (last_hidden_state, pooled_output) + encoder_outputs[1:]

        return BaseModelOutputWithPoolingAndNoAttention(
            last_hidden_state=lowerCAmelCase__				, pooler_output=lowerCAmelCase__				, hidden_states=encoder_outputs.hidden_states				, )





@add_start_docstrings(
    '\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '       ,       __UpperCamelCase       ,       )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class   __a			(    __UpperCamelCase      ):
 def __init__(   self				, lowerCAmelCase__ )					->				str:




        '''simple docstring'''
        super().__init__(lowerCAmelCase__ )
        lowercase__:   Dict  			=   config.num_labels
        lowercase__:   Dict  			=   RegNetModel(lowerCAmelCase__ )
        # classification head
        lowercase__:   str  			=   nn.Sequential(
            nn.Flatten()				, nn.Linear(config.hidden_sizes[-1]				, config.num_labels ) if config.num_labels > 0 else nn.Identity()				, )
        # initialize weights and apply final processing
        self.post_init()
 @add_start_docstrings_to_model_forward(lowerCAmelCase__ )
 @add_code_sample_docstrings(
     checkpoint=_IMAGE_CLASS_CHECKPOINT				, output_type=lowerCAmelCase__				, config_class=_CONFIG_FOR_DOC				, expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT				, )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, )					->				ImageClassifierOutputWithNoAttention:




        '''simple docstring'''
        lowercase__:   str  			=   return_dict if return_dict is not None else self.config.use_return_dict

        lowercase__:   Optional[int]  			=   self.regnet(lowerCAmelCase__				, output_hidden_states=lowerCAmelCase__				, return_dict=lowerCAmelCase__ )

        lowercase__:   Dict  			=   outputs.pooler_output if return_dict else outputs[1]

        lowercase__:   List[str]  			=   self.classifier(lowerCAmelCase__ )

        lowercase__:   Optional[Any]  			=   None

        if labels is not None:
               if self.config.problem_type is None:
                      if self.num_labels == 1:
                             lowercase__:   Dict  			=   'regression'
                      elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                             lowercase__:   Optional[int]  			=   'single_label_classification'
                      else:
                             lowercase__:   Tuple  			=   'multi_label_classification'
               if self.config.problem_type == "regression":
                      lowercase__:   List[Any]  			=   MSELoss()
                      if self.num_labels == 1:
                             lowercase__:   Optional[int]  			=   loss_fct(logits.squeeze()				, labels.squeeze() )
                      else:
                             lowercase__:   int  			=   loss_fct(lowerCAmelCase__				, lowerCAmelCase__ )
               elif self.config.problem_type == "single_label_classification":
                      lowercase__:   Dict  			=   CrossEntropyLoss()
                      lowercase__:   Optional[int]  			=   loss_fct(logits.view(-1				, self.num_labels )				, labels.view(-1 ) )
               elif self.config.problem_type == "multi_label_classification":
                      lowercase__:   List[Any]  			=   BCEWithLogitsLoss()
                      lowercase__:   Any  			=   loss_fct(lowerCAmelCase__				, lowerCAmelCase__ )

        if not return_dict:
               lowercase__:   int  			=   (logits,) + outputs[2:]
               return (loss,) + output if loss is not None else output

        return ImageClassifierOutputWithNoAttention(loss=lowerCAmelCase__				, logits=lowerCAmelCase__				, hidden_states=outputs.hidden_states )


