






"""simple docstring"""

from typing import List, Optional, Tuple, Union

import torch
from torch import nn
from torch.nn import CrossEntropyLoss

from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig


lowercase    			=				[
    '''openmmlab/upernet-convnext-tiny''',
    # See all UperNet models at https://huggingface.co/models?filter=upernet
]

# General docstring
lowercase    			=				'''UperNetConfig'''

class 						A_							(      nn.Module  ):







				def __init__(							self							:						Dict						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						Union[int, Tuple[int, int]]						,    __lowerCamelCase							:						Union[int, Tuple[int, int], str] = 0						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						Union[int, Tuple[int, int]] = 1						,    )				->					None:
										super().__init__()
										__magic_name__            =     nn.Convad(
										    in_channels=__lowerCamelCase						,    out_channels=__lowerCamelCase						,    kernel_size=__lowerCamelCase						,    padding=__lowerCamelCase						,    bias=__lowerCamelCase						,    dilation=__lowerCamelCase						,    )
										__magic_name__            =     nn.BatchNormad(__lowerCamelCase	)
										__magic_name__            =     nn.ReLU()







				def     _snake_case					(							self							:						Any						,    __lowerCamelCase							:						torch.Tensor	)				->					torch.Tensor:
										__magic_name__            =     self.conv(__lowerCamelCase	)
										__magic_name__            =     self.batch_norm(__lowerCamelCase	)
										__magic_name__            =     self.activation(__lowerCamelCase	)

										return output





class 						A_							(      nn.Module  ):







				def __init__(							self							:						Dict						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						int	)				->					None:
										super().__init__()
										__magic_name__            =     [
										    nn.AdaptiveAvgPoolad(__lowerCamelCase	),
										    UperNetConvModule(__lowerCamelCase						,    __lowerCamelCase						,    kernel_size=1	),
										]
										for i, layer in enumerate(self.layers	):
																self.add_module(str(__lowerCamelCase	)						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						int						,    __lowerCamelCase							:						torch.Tensor	)				->					torch.Tensor:
										__magic_name__            =     input
										for layer in self.layers:
																__magic_name__            =     layer(__lowerCamelCase	)
										return hidden_state





class 						A_							(      nn.Module  ):







				def __init__(							self							:						Union[str, Any]						,    __lowerCamelCase							:						Tuple[int, ...]						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						bool	)				->					None:
										super().__init__()
										__magic_name__            =     pool_scales
										__magic_name__            =     align_corners
										__magic_name__            =     in_channels
										__magic_name__            =     channels
										__magic_name__            =     []
										for i, pool_scale in enumerate(__lowerCamelCase	):
																__magic_name__            =     UperNetPyramidPoolingBlock(pool_scale=__lowerCamelCase						,    in_channels=__lowerCamelCase						,    channels=__lowerCamelCase	)
																self.blocks.append(__lowerCamelCase	)
																self.add_module(str(__lowerCamelCase	)						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						Dict						,    __lowerCamelCase							:						torch.Tensor	)				->					List[torch.Tensor]:
										__magic_name__            =     []
										for ppm in self.blocks:
																__magic_name__            =     ppm(__lowerCamelCase	)
																__magic_name__            =     nn.functional.interpolate(
																    __lowerCamelCase						,    size=x.size()[2:]						,    mode="bilinear"						,    align_corners=self.align_corners	)
																ppm_outs.append(__lowerCamelCase	)
										return ppm_outs





class 						A_							(      nn.Module  ):







				def __init__(							self							:						Union[str, Any]						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						str	)				->					List[Any]:
										super().__init__()

										__magic_name__            =     config
										__magic_name__            =     config.pool_scales  # e.g. (1, 2, 3, 6)
										__magic_name__            =     in_channels
										__magic_name__            =     config.hidden_size
										__magic_name__            =     False
										__magic_name__            =     nn.Convad(self.channels						,    config.num_labels						,    kernel_size=1	)

										# PSP Module
										__magic_name__            =     UperNetPyramidPoolingModule(
										    self.pool_scales						,    self.in_channels[-1]						,    self.channels						,    align_corners=self.align_corners						,    )
										__magic_name__            =     UperNetConvModule(
										    self.in_channels[-1] + len(self.pool_scales	) * self.channels						,    self.channels						,    kernel_size=3						,    padding=1						,    )
										# FPN Module
										__magic_name__            =     nn.ModuleList()
										__magic_name__            =     nn.ModuleList()
										for in_channels in self.in_channels[:-1]:  # skip the top layer
																__magic_name__            =     UperNetConvModule(__lowerCamelCase						,    self.channels						,    kernel_size=1	)
																__magic_name__            =     UperNetConvModule(self.channels						,    self.channels						,    kernel_size=3						,    padding=1	)
																self.lateral_convs.append(__lowerCamelCase	)
																self.fpn_convs.append(__lowerCamelCase	)

										__magic_name__            =     UperNetConvModule(
										    len(self.in_channels	) * self.channels						,    self.channels						,    kernel_size=3						,    padding=1						,    )







				def     _snake_case					(							self							:						Any	)				->					int:
										self.apply(self._init_weights	)







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						Dict	)				->					int:
										if isinstance(__lowerCamelCase						,    nn.Convad	):
																module.weight.data.normal_(mean=0.0						,    std=self.config.initializer_range	)
																if module.bias is not None:
																						module.bias.data.zero_()







				def     _snake_case					(							self							:						str						,    __lowerCamelCase							:						str	)				->					Optional[Any]:
										__magic_name__            =     inputs[-1]
										__magic_name__            =     [x]
										psp_outs.extend(self.psp_modules(__lowerCamelCase	)	)
										__magic_name__            =     torch.cat(__lowerCamelCase						,    dim=1	)
										__magic_name__            =     self.bottleneck(__lowerCamelCase	)

										return output







				def     _snake_case					(							self							:						List[Any]						,    __lowerCamelCase							:						torch.Tensor	)				->					torch.Tensor:
										# build laterals
										__magic_name__            =     [lateral_conv(encoder_hidden_states[i]	) for i, lateral_conv in enumerate(self.lateral_convs	)]

										laterals.append(self.psp_forward(__lowerCamelCase	)	)

										# build top-down path
										__magic_name__            =     len(__lowerCamelCase	)
										for i in range(used_backbone_levels - 1						,    0						,    -1	):
																__magic_name__            =     laterals[i - 1].shape[2:]
																__magic_name__            =     laterals[i - 1] + nn.functional.interpolate(
																    laterals[i]						,    size=__lowerCamelCase						,    mode="bilinear"						,    align_corners=self.align_corners	)

										# build outputs
										__magic_name__            =     [self.fpn_convs[i](laterals[i]	) for i in range(used_backbone_levels - 1	)]
										# append psp feature
										fpn_outs.append(laterals[-1]	)

										for i in range(used_backbone_levels - 1						,    0						,    -1	):
																__magic_name__            =     nn.functional.interpolate(
																    fpn_outs[i]						,    size=fpn_outs[0].shape[2:]						,    mode="bilinear"						,    align_corners=self.align_corners	)
										__magic_name__            =     torch.cat(__lowerCamelCase						,    dim=1	)
										__magic_name__            =     self.fpn_bottleneck(__lowerCamelCase	)
										__magic_name__            =     self.classifier(__lowerCamelCase	)

										return output





class 						A_							(      nn.Module  ):







				def __init__(							self							:						List[Any]						,    __lowerCamelCase							:						Optional[int]						,    __lowerCamelCase							:						int = 2						,    __lowerCamelCase							:						int = 3						,    __lowerCamelCase							:						Union[int, Tuple[int, int]] = 1	)				->					None:
										super().__init__()

										__magic_name__            =     config
										__magic_name__            =     config.auxiliary_in_channels
										__magic_name__            =     config.auxiliary_channels
										__magic_name__            =     config.auxiliary_num_convs
										__magic_name__            =     config.auxiliary_concat_input
										__magic_name__            =     in_index

										__magic_name__            =     (kernel_size // 2) * dilation
										__magic_name__            =     []
										convs.append(
										    UperNetConvModule(
										        self.in_channels						,    self.channels						,    kernel_size=__lowerCamelCase						,    padding=__lowerCamelCase						,    dilation=__lowerCamelCase	)	)
										for i in range(self.num_convs - 1	):
																convs.append(
																    UperNetConvModule(
																        self.channels						,    self.channels						,    kernel_size=__lowerCamelCase						,    padding=__lowerCamelCase						,    dilation=__lowerCamelCase	)	)
										if self.num_convs == 0:
																__magic_name__            =     nn.Identity()
										else:
																__magic_name__            =     nn.Sequential(*__lowerCamelCase	)
										if self.concat_input:
																__magic_name__            =     UperNetConvModule(
																    self.in_channels + self.channels						,    self.channels						,    kernel_size=__lowerCamelCase						,    padding=kernel_size // 2	)

										__magic_name__            =     nn.Convad(self.channels						,    config.num_labels						,    kernel_size=1	)







				def     _snake_case					(							self							:						Dict	)				->					Optional[int]:
										self.apply(self._init_weights	)







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						List[Any]	)				->					Optional[int]:
										if isinstance(__lowerCamelCase						,    nn.Convad	):
																module.weight.data.normal_(mean=0.0						,    std=self.config.initializer_range	)
																if module.bias is not None:
																						module.bias.data.zero_()







				def     _snake_case					(							self							:						Union[str, Any]						,    __lowerCamelCase							:						torch.Tensor	)				->					torch.Tensor:
										# just take the relevant feature maps
										__magic_name__            =     encoder_hidden_states[self.in_index]
										__magic_name__            =     self.convs(__lowerCamelCase	)
										if self.concat_input:
																__magic_name__            =     self.conv_cat(torch.cat([hidden_states, output]						,    dim=1	)	)
										__magic_name__            =     self.classifier(__lowerCamelCase	)
										return output





class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				UperNetConfig
				UpperCAmelCase__        =				'''pixel_values'''
				UpperCAmelCase__        =				True







				def     _snake_case					(							self							:						Optional[Any]						,    __lowerCamelCase							:						Optional[int]	)				->					int:
										if isinstance(__lowerCamelCase						,    __lowerCamelCase	):
																module.backbone.init_weights()
																module.decode_head.init_weights()
																module.auxiliary_head.init_weights()







				def     _snake_case					(							self							:						Tuple	)				->					Tuple:
										self.backbone.init_weights()
										self.decode_head.init_weights()
										self.auxiliary_head.init_weights()







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Dict=False	)				->					Any:
										if isinstance(__lowerCamelCase						,    __lowerCamelCase	):
																__magic_name__            =     value





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

lowercase    			=				r'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
            [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See
            `attentions` under returned tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers of the backbone. 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(
    '''UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.'''					,							snake_case_					,							)
class 						A_							(      snake_case_  ):







				def __init__(							self							:						Tuple						,    __lowerCamelCase							:						int	)				->					List[Any]:
										super().__init__(__lowerCamelCase	)

										__magic_name__            =     AutoBackbone.from_config(config.backbone_config	)

										# Semantic segmentation head(s)
										__magic_name__            =     UperNetHead(__lowerCamelCase						,    in_channels=self.backbone.channels	)
										__magic_name__            =     UperNetFCNHead(__lowerCamelCase	) if config.use_auxiliary_head else None

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







				@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"	)	)
				@replace_return_docstrings(output_type=__lowerCamelCase						,    config_class=_CONFIG_FOR_DOC	)
				def     _snake_case					(							self							:						Any						,    __lowerCamelCase							:						Optional[torch.Tensor] = None						,    __lowerCamelCase							:						Optional[bool] = None						,    __lowerCamelCase							:						Optional[bool] = None						,    __lowerCamelCase							:						Optional[torch.Tensor] = None						,    __lowerCamelCase							:						Optional[bool] = None						,    )				->					Union[tuple, SemanticSegmenterOutput]:
										__magic_name__            =     return_dict if return_dict is not None else self.config.use_return_dict
										__magic_name__            =     (
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										__magic_name__            =     output_attentions if output_attentions is not None else self.config.output_attentions

										__magic_name__            =     self.backbone.forward_with_filtered_kwargs(
										    __lowerCamelCase						,    output_hidden_states=__lowerCamelCase						,    output_attentions=__lowerCamelCase	)
										__magic_name__            =     outputs.feature_maps

										__magic_name__            =     self.decode_head(__lowerCamelCase	)
										__magic_name__            =     nn.functional.interpolate(__lowerCamelCase						,    size=pixel_values.shape[2:]						,    mode="bilinear"						,    align_corners=__lowerCamelCase	)

										__magic_name__            =     None
										if self.auxiliary_head is not None:
																__magic_name__            =     self.auxiliary_head(__lowerCamelCase	)
																__magic_name__            =     nn.functional.interpolate(
																    __lowerCamelCase						,    size=pixel_values.shape[2:]						,    mode="bilinear"						,    align_corners=__lowerCamelCase	)

										__magic_name__            =     None
										if labels is not None:
																if self.config.num_labels == 1:
																						raise ValueError("The number of labels should be greater than one"	)
																else:
																						# compute weighted loss
																						__magic_name__            =     CrossEntropyLoss(ignore_index=self.config.loss_ignore_index	)
																						__magic_name__            =     loss_fct(__lowerCamelCase						,    __lowerCamelCase	)
																						__magic_name__            =     loss_fct(__lowerCamelCase						,    __lowerCamelCase	)
																						__magic_name__            =     main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

										if not return_dict:
																if output_hidden_states:
																						__magic_name__            =     (logits,) + outputs[1:]
																else:
																						__magic_name__            =     (logits,) + outputs[2:]
																return ((loss,) + output) if loss is not None else output

										return SemanticSegmenterOutput(
										    loss=__lowerCamelCase						,    logits=__lowerCamelCase						,    hidden_states=outputs.hidden_states						,    attentions=outputs.attentions						,    )











"""simple docstring"""

def _lowerCAmelCase	(  __lowerCamelCase:list   ):

						'''simple docstring'''






						__magic_name__            =     len(__lowerCamelCase   )
						for i in range(1 ,						__lowerCamelCase   ):
												__magic_name__            =     collection[i]
												__magic_name__            =     0
												__magic_name__            =     i - 1

												while low <= high:
																		__magic_name__            =     (low + high) // 2
																		if val < collection[mid]:
																								__magic_name__            =     mid - 1
																		else:
																								__magic_name__            =     mid + 1
												for j in range(__lowerCamelCase ,						__lowerCamelCase ,						-1   ):
																		__magic_name__            =     collection[j - 1]
												__magic_name__            =     val
						return collection


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



