




"""simple docstring"""




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 ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


lowerCAmelCase							:			Optional[int]     							=							logging.get_logger(__name__)

# General docstring
lowerCAmelCase							:			int     							=							"""ResNetConfig"""

# Base docstring
lowerCAmelCase							:			Any     							=							"""microsoft/resnet-50"""
lowerCAmelCase							:			Any     							=							[1, 2048, 7, 7]

# Image classification docstring
lowerCAmelCase							:			int     							=							"""microsoft/resnet-50"""
lowerCAmelCase							:			Tuple     							=							"""tiger cat"""

lowerCAmelCase							:			Optional[Any]     							=							[
    """microsoft/resnet-50""",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]






class     __magic_name__							(       nn.Module				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a		, _a = 3		, _a = 1		, _a = "relu" ):





							"""simple docstring"""
							super().__init__()
							lowerCamelCase			      =					nn.Convad(
							    _a		, _a		, kernel_size=_a		, stride=_a		, padding=kernel_size // 2		, bias=_a )
							lowerCamelCase			      =					nn.BatchNormad(_a )
							lowerCamelCase			      =					ACTaFN[activation] if activation is not None else nn.Identity()





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					self.convolution(_a )
							lowerCamelCase			      =					self.normalization(_a )
							lowerCamelCase			      =					self.activation(_a )
							return hidden_state






class     __magic_name__							(       nn.Module				):






				'''simple docstring'''





				def __init__(       self		, _a ):





							"""simple docstring"""
							super().__init__()
							lowerCamelCase			      =					ResNetConvLayer(
							    config.num_channels		, config.embedding_size		, kernel_size=7		, stride=2		, activation=config.hidden_act )
							lowerCamelCase			      =					nn.MaxPoolad(kernel_size=3		, stride=2		, padding=1 )
							lowerCamelCase			      =					config.num_channels





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					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.""" )
							lowerCamelCase			      =					self.embedder(_a )
							lowerCamelCase			      =					self.pooler(_a )
							return embedding






class     __magic_name__							(       nn.Module				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a		, _a = 2 ):





							"""simple docstring"""
							super().__init__()
							lowerCamelCase			      =					nn.Convad(_a		, _a		, kernel_size=1		, stride=_a		, bias=_a )
							lowerCamelCase			      =					nn.BatchNormad(_a )





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					self.convolution(_a )
							lowerCamelCase			      =					self.normalization(_a )
							return hidden_state






class     __magic_name__							(       nn.Module				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a		, _a = 1		, _a = "relu" ):





							"""simple docstring"""
							super().__init__()
							lowerCamelCase			      =					in_channels != out_channels or stride != 1
							lowerCamelCase			      =					(
							    ResNetShortCut(_a		, _a		, stride=_a ) if should_apply_shortcut else nn.Identity()
							)
							lowerCamelCase			      =					nn.Sequential(
							    ResNetConvLayer(_a		, _a		, stride=_a )		, ResNetConvLayer(_a		, _a		, activation=_a )		, )
							lowerCamelCase			      =					ACTaFN[activation]





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					hidden_state
							lowerCamelCase			      =					self.layer(_a )
							lowerCamelCase			      =					self.shortcut(_a )
							hidden_state += residual
							lowerCamelCase			      =					self.activation(_a )
							return hidden_state






class     __magic_name__							(       nn.Module				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a		, _a = 1		, _a = "relu"		, _a = 4 ):





							"""simple docstring"""
							super().__init__()
							lowerCamelCase			      =					in_channels != out_channels or stride != 1
							lowerCamelCase			      =					out_channels // reduction
							lowerCamelCase			      =					(
							    ResNetShortCut(_a		, _a		, stride=_a ) if should_apply_shortcut else nn.Identity()
							)
							lowerCamelCase			      =					nn.Sequential(
							    ResNetConvLayer(_a		, _a		, kernel_size=1 )		, ResNetConvLayer(_a		, _a		, stride=_a )		, ResNetConvLayer(_a		, _a		, kernel_size=1		, activation=_a )		, )
							lowerCamelCase			      =					ACTaFN[activation]





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					hidden_state
							lowerCamelCase			      =					self.layer(_a )
							lowerCamelCase			      =					self.shortcut(_a )
							hidden_state += residual
							lowerCamelCase			      =					self.activation(_a )
							return hidden_state






class     __magic_name__							(       nn.Module				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a		, _a		, _a = 2		, _a = 2		, ):





							"""simple docstring"""
							super().__init__()

							lowerCamelCase			      =					ResNetBottleNeckLayer if config.layer_type == """bottleneck""" else ResNetBasicLayer

							lowerCamelCase			      =					nn.Sequential(
							    # downsampling is done in the first layer with stride of 2
							    layer(_a		, _a		, stride=_a		, activation=config.hidden_act )		, *[layer(_a		, _a		, activation=config.hidden_act ) for _ in range(depth - 1 )]		, )





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					input
							for layer in self.layers:
										lowerCamelCase			      =					layer(_a )
							return hidden_state






class     __magic_name__							(       nn.Module				):






				'''simple docstring'''





				def __init__(       self		, _a ):





							"""simple docstring"""
							super().__init__()
							lowerCamelCase			      =					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(
							    ResNetStage(
							        _a		, config.embedding_size		, config.hidden_sizes[0]		, stride=2 if config.downsample_in_first_stage else 1		, depth=config.depths[0]		, ) )
							lowerCamelCase			      =					zip(config.hidden_sizes		, config.hidden_sizes[1:] )
							for (in_channels, out_channels), depth in zip(_a		, config.depths[1:] ):
										self.stages.append(ResNetStage(_a		, _a		, _a		, depth=_a ) )





				def    _lowerCAmelCase				(       self		, _a		, _a = False		, _a = True ):





							"""simple docstring"""
							lowerCamelCase			      =					() if output_hidden_states else None

							for stage_module in self.stages:
										if output_hidden_states:
													lowerCamelCase			      =					hidden_states + (hidden_state,)

										lowerCamelCase			      =					stage_module(_a )

							if output_hidden_states:
										lowerCamelCase			      =					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=_a		, hidden_states=_a		, )






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				__UpperCamelCase			  =						ResNetConfig
				__UpperCamelCase			  =						"resnet"
				__UpperCamelCase			  =						"pixel_values"
				__UpperCamelCase			  =						True





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							if isinstance(_a		, nn.Convad ):
										nn.init.kaiming_normal_(module.weight		, mode="""fan_out"""		, nonlinearity="""relu""" )
							elif isinstance(_a		, (nn.BatchNormad, nn.GroupNorm) ):
										nn.init.constant_(module.weight		, 1 )
										nn.init.constant_(module.bias		, 0 )





				def    _lowerCAmelCase				(       self		, _a		, _a=False ):





							"""simple docstring"""
							if isinstance(_a		, _a ):
										lowerCamelCase			      =					value




lowerCAmelCase							:			int     							=							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 ([`ResNetConfig`]): 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							:			Optional[int]     							=							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 [`~utils.ModelOutput`] instead of a plain tuple.
"""






@add_start_docstrings(
    "The bare ResNet model outputting raw features without any specific head on top."						,					UpperCAmelCase__						,					)
class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __init__(       self		, _a ):





							"""simple docstring"""
							super().__init__(_a )
							lowerCamelCase			      =					config
							lowerCamelCase			      =					ResNetEmbeddings(_a )
							lowerCamelCase			      =					ResNetEncoder(_a )
							lowerCamelCase			      =					nn.AdaptiveAvgPoolad((1, 1) )
							# Initialize weights and apply final processing
							self.post_init()





				@add_start_docstrings_to_model_forward(_a )
				@add_code_sample_docstrings(
				    checkpoint=_CHECKPOINT_FOR_DOC		, output_type=_a		, config_class=_CONFIG_FOR_DOC		, modality="""vision"""		, expected_output=_EXPECTED_OUTPUT_SHAPE		, )
				def    _lowerCAmelCase				(       self		, _a		, _a = None		, _a = None ):





							"""simple docstring"""
							lowerCamelCase			      =					(
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							lowerCamelCase			      =					return_dict if return_dict is not None else self.config.use_return_dict

							lowerCamelCase			      =					self.embedder(_a )

							lowerCamelCase			      =					self.encoder(
							    _a		, output_hidden_states=_a		, return_dict=_a )

							lowerCamelCase			      =					encoder_outputs[0]

							lowerCamelCase			      =					self.pooler(_a )

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

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






@add_start_docstrings(
    "\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "						,					UpperCAmelCase__						,					)
class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __init__(       self		, _a ):





							"""simple docstring"""
							super().__init__(_a )
							lowerCamelCase			      =					config.num_labels
							lowerCamelCase			      =					ResNetModel(_a )
							# classification head
							lowerCamelCase			      =					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(_a )
				@add_code_sample_docstrings(
				    checkpoint=_IMAGE_CLASS_CHECKPOINT		, output_type=_a		, config_class=_CONFIG_FOR_DOC		, expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT		, )
				def    _lowerCAmelCase				(       self		, _a = None		, _a = None		, _a = None		, _a = None		, ):





							"""simple docstring"""
							lowerCamelCase			      =					return_dict if return_dict is not None else self.config.use_return_dict

							lowerCamelCase			      =					self.resnet(_a		, output_hidden_states=_a		, return_dict=_a )

							lowerCamelCase			      =					outputs.pooler_output if return_dict else outputs[1]

							lowerCamelCase			      =					self.classifier(_a )

							lowerCamelCase			      =					None

							if labels is not None:
										if self.config.problem_type is None:
													if self.num_labels == 1:
																lowerCamelCase			      =					"""regression"""
													elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																lowerCamelCase			      =					"""single_label_classification"""
													else:
																lowerCamelCase			      =					"""multi_label_classification"""
										if self.config.problem_type == "regression":
													lowerCamelCase			      =					MSELoss()
													if self.num_labels == 1:
																lowerCamelCase			      =					loss_fct(logits.squeeze()		, labels.squeeze() )
													else:
																lowerCamelCase			      =					loss_fct(_a		, _a )
										elif self.config.problem_type == "single_label_classification":
													lowerCamelCase			      =					CrossEntropyLoss()
													lowerCamelCase			      =					loss_fct(logits.view(-1		, self.num_labels )		, labels.view(-1 ) )
										elif self.config.problem_type == "multi_label_classification":
													lowerCamelCase			      =					BCEWithLogitsLoss()
													lowerCamelCase			      =					loss_fct(_a		, _a )

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

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






@add_start_docstrings(
    "\n    ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n    "						,					UpperCAmelCase__						,					)
class     __magic_name__							(       UpperCAmelCase__						,					UpperCAmelCase__				):






				'''simple docstring'''





				def __init__(       self		, _a ):





							"""simple docstring"""
							super().__init__(_a )
							super()._init_backbone(_a )

							lowerCamelCase			      =					[config.embedding_size] + config.hidden_sizes
							lowerCamelCase			      =					ResNetEmbeddings(_a )
							lowerCamelCase			      =					ResNetEncoder(_a )

							# initialize weights and apply final processing
							self.post_init()





				@add_start_docstrings_to_model_forward(_a )
				@replace_return_docstrings(output_type=_a		, config_class=_CONFIG_FOR_DOC )
				def    _lowerCAmelCase				(       self		, _a		, _a = None		, _a = None ):





							"""simple docstring"""
							lowerCamelCase			      =					return_dict if return_dict is not None else self.config.use_return_dict
							lowerCamelCase			      =					(
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)

							lowerCamelCase			      =					self.embedder(_a )

							lowerCamelCase			      =					self.encoder(_a		, output_hidden_states=_a		, return_dict=_a )

							lowerCamelCase			      =					outputs.hidden_states

							lowerCamelCase			      =					()
							for idx, stage in enumerate(self.stage_names ):
										if stage in self.out_features:
													feature_maps += (hidden_states[idx],)

							if not return_dict:
										lowerCamelCase			      =					(feature_maps,)
										if output_hidden_states:
													output += (outputs.hidden_states,)
										return output

							return BackboneOutput(
							    feature_maps=_a		, hidden_states=outputs.hidden_states if output_hidden_states else None		, attentions=_a		, )









"""simple docstring"""




def     a__ (				snake_case__				)						->					list:

			if len(snake_case__				) < 2:
						return collection

			def circle_sort_util(snake_case__       , snake_case__       , snake_case__				) -> bool:
						lowerCamelCase			      =					False

						if low == high:
									return swapped

						lowerCamelCase			      =					low
						lowerCamelCase			      =					high

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

									left += 1
									right -= 1

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

									lowerCamelCase			      =					True

						lowerCamelCase			      =					low + int((high - low) / 2				)
						lowerCamelCase			      =					circle_sort_util(snake_case__       , snake_case__       , snake_case__				)
						lowerCamelCase			      =					circle_sort_util(snake_case__       , mid + 1       , snake_case__				)

						return swapped or left_swap or right_swap

			lowerCamelCase			      =					True

			while is_not_sorted is True:
						lowerCamelCase			      =					circle_sort_util(snake_case__       , 0       , len(snake_case__				) - 1				)

			return collection


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



