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

# General docstring
lowercase			:				Optional[Any]    		=			'UperNetConfig'





class  A				(							nn.Module						):





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = 0						,			SCREAMING_SNAKE_CASE = False						,			SCREAMING_SNAKE_CASE = 1						,			) ->				None:






         """simple docstring"""






         super().__init__()
         A       :   Optional[int]					=						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       :   List[Any]					=						nn.BatchNormad(SCREAMING_SNAKE_CASE	)
         A       :   List[str]					=						nn.ReLU()





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				torch.Tensor:






         """simple docstring"""






         A       :   Tuple					=						self.conv(SCREAMING_SNAKE_CASE	)
         A       :   Optional[int]					=						self.batch_norm(SCREAMING_SNAKE_CASE	)
         A       :   Optional[Any]					=						self.activation(SCREAMING_SNAKE_CASE	)

         return output





class  A				(							nn.Module						):





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				None:






         """simple docstring"""






         super().__init__()
         A       :   str					=						[
             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						,			SCREAMING_SNAKE_CASE	) ->				torch.Tensor:






         """simple docstring"""






         A       :   Any					=						input
         for layer in self.layers:
             A       :   Union[str, Any]					=						layer(SCREAMING_SNAKE_CASE	)
         return hidden_state





class  A				(							nn.Module						):





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				None:






         """simple docstring"""






         super().__init__()
         A       :   Dict					=						pool_scales
         A       :   Union[str, Any]					=						align_corners
         A       :   int					=						in_channels
         A       :   List[str]					=						channels
         A       :   List[Any]					=						[]
         for i, pool_scale in enumerate(SCREAMING_SNAKE_CASE	):
             A       :   str					=						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						,			SCREAMING_SNAKE_CASE	) ->				List[torch.Tensor]:






         """simple docstring"""






         A       :   int					=						[]
         for ppm in self.blocks:
             A       :   Tuple					=						ppm(SCREAMING_SNAKE_CASE	)
             A       :   List[Any]					=						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						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				Optional[Any]:






         """simple docstring"""






         super().__init__()

         A       :   List[str]					=						config
         A       :   Union[str, Any]					=						config.pool_scales  # e.g. (1, 2, 3, 6)
         A       :   Optional[int]					=						in_channels
         A       :   List[str]					=						config.hidden_size
         A       :   str					=						False
         A       :   str					=						nn.Convad(self.channels						,			config.num_labels						,			kernel_size=1	)

         # PSP Module
         A       :   Optional[int]					=						UperNetPyramidPoolingModule(
             self.pool_scales						,			self.in_channels[-1]						,			self.channels						,			align_corners=self.align_corners						,			)
         A       :   Union[str, Any]					=						UperNetConvModule(
             self.in_channels[-1] + len(self.pool_scales	) * self.channels						,			self.channels						,			kernel_size=3						,			padding=1						,			)
         # FPN Module
         A       :   Dict					=						nn.ModuleList()
         A       :   List[str]					=						nn.ModuleList()
         for in_channels in self.in_channels[:-1]:  # skip the top layer
             A       :   int					=						UperNetConvModule(SCREAMING_SNAKE_CASE						,			self.channels						,			kernel_size=1	)
             A       :   Union[str, Any]					=						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       :   Dict					=						UperNetConvModule(
             len(self.in_channels	) * self.channels						,			self.channels						,			kernel_size=3						,			padding=1						,			)





     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         self.apply(self._init_weights	)





     def 				__lowerCAmelCase							(							self						,			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						,			SCREAMING_SNAKE_CASE	) ->				List[str]:






         """simple docstring"""






         A       :   Union[str, Any]					=						inputs[-1]
         A       :   Optional[int]					=						[x]
         psp_outs.extend(self.psp_modules(SCREAMING_SNAKE_CASE	)	)
         A       :   str					=						torch.cat(SCREAMING_SNAKE_CASE						,			dim=1	)
         A       :   str					=						self.bottleneck(SCREAMING_SNAKE_CASE	)

         return output





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				torch.Tensor:






         """simple docstring"""






         A       :   Union[str, Any]					=						[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       :   int					=						len(SCREAMING_SNAKE_CASE	)
         for i in range(used_backbone_levels - 1						,			0						,			-1	):
             A       :   Union[str, Any]					=						laterals[i - 1].shape[2:]
             A       :   Optional[Any]					=						laterals[i - 1] + nn.functional.interpolate(
                 laterals[i]						,			size=SCREAMING_SNAKE_CASE						,			mode='''bilinear'''						,			align_corners=self.align_corners	)

         # build outputs
         A       :   List[str]					=						[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       :   Any					=						nn.functional.interpolate(
                 fpn_outs[i]						,			size=fpn_outs[0].shape[2:]						,			mode='''bilinear'''						,			align_corners=self.align_corners	)
         A       :   Tuple					=						torch.cat(SCREAMING_SNAKE_CASE						,			dim=1	)
         A       :   Any					=						self.fpn_bottleneck(SCREAMING_SNAKE_CASE	)
         A       :   Optional[int]					=						self.classifier(SCREAMING_SNAKE_CASE	)

         return output





class  A				(							nn.Module						):





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = 2						,			SCREAMING_SNAKE_CASE = 3						,			SCREAMING_SNAKE_CASE = 1	) ->				None:






         """simple docstring"""






         super().__init__()

         A       :   List[Any]					=						config
         A       :   Optional[Any]					=						config.auxiliary_in_channels
         A       :   Optional[Any]					=						config.auxiliary_channels
         A       :   Union[str, Any]					=						config.auxiliary_num_convs
         A       :   List[str]					=						config.auxiliary_concat_input
         A       :   Optional[Any]					=						in_index

         A       :   str					=						(kernel_size // 2) * dilation
         A       :   Tuple					=						[]
         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       :   Optional[Any]					=						nn.Identity()
         else:
             A       :   Tuple					=						nn.Sequential(*SCREAMING_SNAKE_CASE	)
         if self.concat_input:
             A       :   Any					=						UperNetConvModule(
                 self.in_channels + self.channels						,			self.channels						,			kernel_size=SCREAMING_SNAKE_CASE						,			padding=kernel_size // 2	)

         A       :   Optional[Any]					=						nn.Convad(self.channels						,			config.num_labels						,			kernel_size=1	)





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         self.apply(self._init_weights	)





     def 				__lowerCAmelCase							(							self						,			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						,			SCREAMING_SNAKE_CASE	) ->				torch.Tensor:






         """simple docstring"""






         A       :   Union[str, Any]					=						encoder_hidden_states[self.in_index]
         A       :   Optional[Any]					=						self.convs(SCREAMING_SNAKE_CASE	)
         if self.concat_input:
             A       :   Optional[Any]					=						self.conv_cat(torch.cat([hidden_states, output]						,			dim=1	)	)
         A       :   str					=						self.classifier(SCREAMING_SNAKE_CASE	)
         return output





class  A				(							__snake_case						):
     __magic_name__						=		UperNetConfig
     __magic_name__						=		'''pixel_values'''
     __magic_name__						=		True





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				List[Any]:






         """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[Any]:






         """simple docstring"""






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





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=False	) ->				List[str]:






         """simple docstring"""






         if isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	):
             A       :   int					=						value





lowercase			:				List[str]    		=			R'\n    Parameters:\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n        config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

lowercase			:				Union[str, Any]    		=			R'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using\n            [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.\n        output_attentions (`bool`, *optional*):\n            Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See\n            `attentions` under returned tensors for more detail.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under\n            returned tensors for more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'





@add_start_docstrings(
    '''UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.'''    ,			__snake_case    ,			)
class  A				(							__snake_case						):





     def __init__(							self						,			SCREAMING_SNAKE_CASE	) ->				Any:






         """simple docstring"""






         super().__init__(SCREAMING_SNAKE_CASE	)

         A       :   List[Any]					=						AutoBackbone.from_config(config.backbone_config	)

         # Semantic segmentation head(s)
         A       :   List[Any]					=						UperNetHead(SCREAMING_SNAKE_CASE						,			in_channels=self.backbone.channels	)
         A       :   str					=						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						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			) ->				Union[tuple, SemanticSegmenterOutput]:






         """simple docstring"""






         A       :   List[Any]					=						return_dict if return_dict is not None else self.config.use_return_dict
         A       :   Optional[Any]					=						(
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         A       :   Any					=						output_attentions if output_attentions is not None else self.config.output_attentions

         A       :   Optional[int]					=						self.backbone.forward_with_filtered_kwargs(
             SCREAMING_SNAKE_CASE						,			output_hidden_states=SCREAMING_SNAKE_CASE						,			output_attentions=SCREAMING_SNAKE_CASE	)
         A       :   int					=						outputs.feature_maps

         A       :   Any					=						self.decode_head(SCREAMING_SNAKE_CASE	)
         A       :   str					=						nn.functional.interpolate(SCREAMING_SNAKE_CASE						,			size=pixel_values.shape[2:]						,			mode='''bilinear'''						,			align_corners=SCREAMING_SNAKE_CASE	)

         A       :   List[Any]					=						None
         if self.auxiliary_head is not None:
             A       :   List[str]					=						self.auxiliary_head(SCREAMING_SNAKE_CASE	)
             A       :   Any					=						nn.functional.interpolate(
                 SCREAMING_SNAKE_CASE						,			size=pixel_values.shape[2:]						,			mode='''bilinear'''						,			align_corners=SCREAMING_SNAKE_CASE	)

         A       :   Optional[Any]					=						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       :   Optional[int]					=						CrossEntropyLoss(ignore_index=self.config.loss_ignore_index	)
                 A       :   str					=						loss_fct(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
                 A       :   List[str]					=						loss_fct(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
                 A       :   Optional[int]					=						main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

         if not return_dict:
             if output_hidden_states:
                 A       :   Optional[Any]					=						(logits,) + outputs[1:]
             else:
                 A       :   Tuple					=						(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						,			)








from math import isqrt







def        _lowerCamelCase(	lowercase__	)						->	bool:

		'''simple docstring'''







		return all(number % divisor != 0 for divisor in range(2   , isqrt(lowercase__	) + 1	)	)







def        _lowerCamelCase(	lowercase__ = 1_0**6	)						->	int:

		'''simple docstring'''







		__lowercase=			0
		__lowercase=			1
		__lowercase=			7
		while prime_candidate < max_prime:
				primes_count += is_prime(lowercase__	)

				cube_index += 1
				prime_candidate += 6 * cube_index

		return primes_count


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






