



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


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

# General docstring
__snake_case						:Any						=					'''UperNetConfig'''

class    _A	(     nn.Module   ):



       def __init__(		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					Union[int, Tuple[int, int]]					,		__SCREAMING_SNAKE_CASE							:					Union[int, Tuple[int, int], str] = 0					,		__SCREAMING_SNAKE_CASE							:					bool = False					,		__SCREAMING_SNAKE_CASE							:					Union[int, Tuple[int, int]] = 1					,		):


           '''simple docstring'''

           super().__init__()
           __a							=    nn.Convad(
               in_channels=__SCREAMING_SNAKE_CASE					,		out_channels=__SCREAMING_SNAKE_CASE					,		kernel_size=__SCREAMING_SNAKE_CASE					,		padding=__SCREAMING_SNAKE_CASE					,		bias=__SCREAMING_SNAKE_CASE					,		dilation=__SCREAMING_SNAKE_CASE					,		)
           __a							=    nn.BatchNormad(__SCREAMING_SNAKE_CASE)
           __a							=    nn.ReLU()





       def 		_lowerCamelCase      (		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					torch.Tensor):


           '''simple docstring'''

           __a							=    self.conv(__SCREAMING_SNAKE_CASE)
           __a							=    self.batch_norm(__SCREAMING_SNAKE_CASE)
           __a							=    self.activation(__SCREAMING_SNAKE_CASE)

           return output


class    _A	(     nn.Module   ):



       def __init__(		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					int):


           '''simple docstring'''

           super().__init__()
           __a							=    [
               nn.AdaptiveAvgPoolad(__SCREAMING_SNAKE_CASE),
               UperNetConvModule(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		kernel_size=1),
           ]
           for i, layer in enumerate(self.layers):
               self.add_module(str(__SCREAMING_SNAKE_CASE)					,		__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					torch.Tensor):


           '''simple docstring'''

           __a							=    input
           for layer in self.layers:
               __a							=    layer(__SCREAMING_SNAKE_CASE)
           return hidden_state


class    _A	(     nn.Module   ):



       def __init__(		self							:					str					,		__SCREAMING_SNAKE_CASE							:					Tuple[int, ...]					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					bool):


           '''simple docstring'''

           super().__init__()
           __a							=    pool_scales
           __a							=    align_corners
           __a							=    in_channels
           __a							=    channels
           __a							=    []
           for i, pool_scale in enumerate(__SCREAMING_SNAKE_CASE):
               __a							=    UperNetPyramidPoolingBlock(pool_scale=__SCREAMING_SNAKE_CASE					,		in_channels=__SCREAMING_SNAKE_CASE					,		channels=__SCREAMING_SNAKE_CASE)
               self.blocks.append(__SCREAMING_SNAKE_CASE)
               self.add_module(str(__SCREAMING_SNAKE_CASE)					,		__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					torch.Tensor):


           '''simple docstring'''

           __a							=    []
           for ppm in self.blocks:
               __a							=    ppm(__SCREAMING_SNAKE_CASE)
               __a							=    nn.functional.interpolate(
                   __SCREAMING_SNAKE_CASE					,		size=x.size()[2:]					,		mode='''bilinear'''					,		align_corners=self.align_corners)
               ppm_outs.append(__SCREAMING_SNAKE_CASE)
           return ppm_outs


class    _A	(     nn.Module   ):



       def __init__(		self							:					int					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Dict):


           '''simple docstring'''

           super().__init__()

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

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

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



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           self.apply(self._init_weights)



       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE					,		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 		_lowerCamelCase      (		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           __a							=    inputs[-1]
           __a							=    [x]
           psp_outs.extend(self.psp_modules(__SCREAMING_SNAKE_CASE))
           __a							=    torch.cat(__SCREAMING_SNAKE_CASE					,		dim=1)
           __a							=    self.bottleneck(__SCREAMING_SNAKE_CASE)

           return output





       def 		_lowerCamelCase      (		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					torch.Tensor):


           '''simple docstring'''

           __a							=    [lateral_conv(encoder_hidden_states[i]) for i, lateral_conv in enumerate(self.lateral_convs)]

           laterals.append(self.psp_forward(__SCREAMING_SNAKE_CASE))

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

           # build outputs
           __a							=    [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):
               __a							=    nn.functional.interpolate(
                   fpn_outs[i]					,		size=fpn_outs[0].shape[2:]					,		mode='''bilinear'''					,		align_corners=self.align_corners)
           __a							=    torch.cat(__SCREAMING_SNAKE_CASE					,		dim=1)
           __a							=    self.fpn_bottleneck(__SCREAMING_SNAKE_CASE)
           __a							=    self.classifier(__SCREAMING_SNAKE_CASE)

           return output


class    _A	(     nn.Module   ):



       def __init__(		self							:					str					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					int = 2					,		__SCREAMING_SNAKE_CASE							:					int = 3					,		__SCREAMING_SNAKE_CASE							:					Union[int, Tuple[int, int]] = 1):


           '''simple docstring'''

           super().__init__()

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

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

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



       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           self.apply(self._init_weights)



       def 		_lowerCamelCase      (		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE					,		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 		_lowerCamelCase      (		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					torch.Tensor):


           '''simple docstring'''

           __a							=    encoder_hidden_states[self.in_index]
           __a							=    self.convs(__SCREAMING_SNAKE_CASE)
           if self.concat_input:
               __a							=    self.conv_cat(torch.cat([hidden_states, output]					,		dim=1))
           __a							=    self.classifier(__SCREAMING_SNAKE_CASE)
           return output


class    _A	(     __UpperCAmelCase   ):
       UpperCamelCase__  :   Optional[Any]						 =						UperNetConfig
       UpperCamelCase__  :   int						 =						'''pixel_values'''
       UpperCamelCase__  :   Dict						 =						True



       def 		_lowerCamelCase      (		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE):
               module.backbone.init_weights()
               module.decode_head.init_weights()
               module.auxiliary_head.init_weights()



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           self.backbone.init_weights()
           self.decode_head.init_weights()
           self.auxiliary_head.init_weights()





       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=False):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE):
               __a							=    value


__snake_case						:List[Any]						=					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.
'''

__snake_case						:List[str]						=					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.'''				,__UpperCAmelCase				,)
class    _A	(     __UpperCAmelCase   ):



       def __init__(		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]):


           '''simple docstring'''

           super().__init__(__SCREAMING_SNAKE_CASE)

           __a							=    AutoBackbone.from_config(config.backbone_config)

           # Semantic segmentation head(s)
           __a							=    UperNetHead(__SCREAMING_SNAKE_CASE					,		in_channels=self.backbone.channels)
           __a							=    UperNetFCNHead(__SCREAMING_SNAKE_CASE) 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=__SCREAMING_SNAKE_CASE					,		config_class=_CONFIG_FOR_DOC)
       def 		_lowerCamelCase      (		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[torch.Tensor] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[bool] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[bool] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[torch.Tensor] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[bool] = None					,		):


           '''simple docstring'''

           __a							=    return_dict if return_dict is not None else self.config.use_return_dict
           __a							=    (
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           __a							=    output_attentions if output_attentions is not None else self.config.output_attentions

           __a							=    self.backbone.forward_with_filtered_kwargs(
               __SCREAMING_SNAKE_CASE					,		output_hidden_states=__SCREAMING_SNAKE_CASE					,		output_attentions=__SCREAMING_SNAKE_CASE)
           __a							=    outputs.feature_maps

           __a							=    self.decode_head(__SCREAMING_SNAKE_CASE)
           __a							=    nn.functional.interpolate(__SCREAMING_SNAKE_CASE					,		size=pixel_values.shape[2:]					,		mode='''bilinear'''					,		align_corners=__SCREAMING_SNAKE_CASE)

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

           __a							=    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
                   __a							=    CrossEntropyLoss(ignore_index=self.config.loss_ignore_index)
                   __a							=    loss_fct(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
                   __a							=    loss_fct(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
                   __a							=    main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

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

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










def 					__snake_case     (      _UpperCAmelCase = 1000000    ):
    __a							=    limit + 1
    __a							=    [0] * limit
    for first_term in range(1       ,			_UpperCAmelCase    ):
        for n in range(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
            __a							=    first_term + n / first_term
            if common_difference % 4:  # d must be divisble by 4
                continue
            else:
                common_difference /= 4
                if (
                    first_term > common_difference
                    and first_term < 4 * common_difference
                ):  # since x,y,z are positive integers
                    frequency[n] += 1  # so z>0 and a>d ,also 4d<a

    __a							=    sum(1 for x in frequency[1:limit] if x == 10    )

    return count


if __name__ == "__main__":
 print(f'{solution() = }')





