




'''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


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

# General docstring
a    :							Optional[int]										=    'UperNetConfig'






class 		a							(  nn.Module	):


				def __init__(						self       :   List[Any]			,							lowercase_       :   int			,							lowercase_       :   int			,							lowercase_       :   Union[int, Tuple[int, int]]			,							lowercase_       :   Union[int, Tuple[int, int], str] = 0			,							lowercase_       :   bool = False			,							lowercase_       :   Union[int, Tuple[int, int]] = 1			,							):
									super().__init__()
									snake_case_		     =							nn.Convad(
									    in_channels=lowercase_			,							out_channels=lowercase_			,							kernel_size=lowercase_			,							padding=lowercase_			,							bias=lowercase_			,							dilation=lowercase_			,							)
									snake_case_		     =							nn.BatchNormad(lowercase_   )
									snake_case_		     =							nn.ReLU()


				def     A_					(						self       :   Optional[int]			,							lowercase_       :   torch.Tensor   ):
									snake_case_		     =							self.conv(lowercase_   )
									snake_case_		     =							self.batch_norm(lowercase_   )
									snake_case_		     =							self.activation(lowercase_   )

									return output







class 		a							(  nn.Module	):


				def __init__(						self       :   Optional[int]			,							lowercase_       :   int			,							lowercase_       :   int			,							lowercase_       :   int   ):
									super().__init__()
									snake_case_		     =							[
									    nn.AdaptiveAvgPoolad(lowercase_   ),
									    UperNetConvModule(lowercase_			,							lowercase_			,							kernel_size=1   ),
									]
									for i, layer in enumerate(self.layers   ):
														self.add_module(str(lowercase_   )			,							lowercase_   )


				def     A_					(						self       :   Dict			,							lowercase_       :   torch.Tensor   ):
									snake_case_		     =							input
									for layer in self.layers:
														snake_case_		     =							layer(lowercase_   )
									return hidden_state







class 		a							(  nn.Module	):


				def __init__(						self       :   Dict			,							lowercase_       :   Tuple[int, ...]			,							lowercase_       :   int			,							lowercase_       :   int			,							lowercase_       :   bool   ):
									super().__init__()
									snake_case_		     =							pool_scales
									snake_case_		     =							align_corners
									snake_case_		     =							in_channels
									snake_case_		     =							channels
									snake_case_		     =							[]
									for i, pool_scale in enumerate(lowercase_   ):
														snake_case_		     =							UperNetPyramidPoolingBlock(pool_scale=lowercase_			,							in_channels=lowercase_			,							channels=lowercase_   )
														self.blocks.append(lowercase_   )
														self.add_module(str(lowercase_   )			,							lowercase_   )


				def     A_					(						self       :   List[str]			,							lowercase_       :   torch.Tensor   ):
									snake_case_		     =							[]
									for ppm in self.blocks:
														snake_case_		     =							ppm(lowercase_   )
														snake_case_		     =							nn.functional.interpolate(
														    lowercase_			,							size=x.size()[2:]			,							mode='''bilinear'''			,							align_corners=self.align_corners   )
														ppm_outs.append(lowercase_   )
									return ppm_outs







class 		a							(  nn.Module	):


				def __init__(						self       :   List[Any]			,							lowercase_       :   str			,							lowercase_       :   Optional[int]   ):
									super().__init__()

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

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

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


				def     A_					(						self       :   List[str]   ):
									self.apply(self._init_weights   )


				def     A_					(						self       :   str			,							lowercase_       :   Optional[int]   ):
									if isinstance(lowercase_			,							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     A_					(						self       :   Optional[Any]			,							lowercase_       :   Tuple   ):
									snake_case_		     =							inputs[-1]
									snake_case_		     =							[x]
									psp_outs.extend(self.psp_modules(lowercase_   )   )
									snake_case_		     =							torch.cat(lowercase_			,							dim=1   )
									snake_case_		     =							self.bottleneck(lowercase_   )

									return output


				def     A_					(						self       :   List[Any]			,							lowercase_       :   torch.Tensor   ):
									# build laterals
									snake_case_		     =							[lateral_conv(encoder_hidden_states[i]   ) for i, lateral_conv in enumerate(self.lateral_convs   )]

									laterals.append(self.psp_forward(lowercase_   )   )

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

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

									return output







class 		a							(  nn.Module	):


				def __init__(						self       :   Dict			,							lowercase_       :   Any			,							lowercase_       :   int = 2			,							lowercase_       :   int = 3			,							lowercase_       :   Union[int, Tuple[int, int]] = 1   ):
									super().__init__()

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

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

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


				def     A_					(						self       :   Union[str, Any]   ):
									self.apply(self._init_weights   )


				def     A_					(						self       :   Union[str, Any]			,							lowercase_       :   Optional[int]   ):
									if isinstance(lowercase_			,							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     A_					(						self       :   List[str]			,							lowercase_       :   torch.Tensor   ):
									# just take the relevant feature maps
									snake_case_		     =							encoder_hidden_states[self.in_index]
									snake_case_		     =							self.convs(lowercase_   )
									if self.concat_input:
														snake_case_		     =							self.conv_cat(torch.cat([hidden_states, output]			,							dim=1   )   )
									snake_case_		     =							self.classifier(lowercase_   )
									return output







class 		a							(  _lowerCamelCase	):
				snake_case_           =						UperNetConfig
				snake_case_           =						"pixel_values"
				snake_case_           =						True


				def     A_					(						self       :   Optional[int]			,							lowercase_       :   Dict   ):
									if isinstance(lowercase_			,							lowercase_   ):
														module.backbone.init_weights()
														module.decode_head.init_weights()
														module.auxiliary_head.init_weights()


				def     A_					(						self       :   List[str]   ):
									self.backbone.init_weights()
									self.decode_head.init_weights()
									self.auxiliary_head.init_weights()


				def     A_					(						self       :   Optional[int]			,							lowercase_       :   Optional[Any]			,							lowercase_       :   Union[str, Any]=False   ):
									if isinstance(lowercase_			,							lowercase_   ):
														snake_case_		     =							value







a    :							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'

a    :							Optional[int]										=    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."  ,	_lowerCamelCase  ,	)
class 		a							(  _lowerCamelCase	):


				def __init__(						self       :   Dict			,							lowercase_       :   Optional[int]   ):
									super().__init__(lowercase_   )

									snake_case_		     =							AutoBackbone.from_config(config.backbone_config   )

									# Semantic segmentation head(s)
									snake_case_		     =							UperNetHead(lowercase_			,							in_channels=self.backbone.channels   )
									snake_case_		     =							UperNetFCNHead(lowercase_   ) 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=lowercase_			,							config_class=_CONFIG_FOR_DOC   )
				def     A_					(						self       :   Optional[int]			,							lowercase_       :   Optional[torch.Tensor] = None			,							lowercase_       :   Optional[bool] = None			,							lowercase_       :   Optional[bool] = None			,							lowercase_       :   Optional[torch.Tensor] = None			,							lowercase_       :   Optional[bool] = None			,							):
									snake_case_		     =							return_dict if return_dict is not None else self.config.use_return_dict
									snake_case_		     =							(
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									snake_case_		     =							output_attentions if output_attentions is not None else self.config.output_attentions

									snake_case_		     =							self.backbone.forward_with_filtered_kwargs(
									    lowercase_			,							output_hidden_states=lowercase_			,							output_attentions=lowercase_   )
									snake_case_		     =							outputs.feature_maps

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

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

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

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

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







'''simple docstring'''

def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   int:

					'''simple docstring'''

					return int((input_a, input_a).count(0      ) == 0      )


def 						__magic_name__       (      )						->   None:

					'''simple docstring'''

					assert and_gate(0, 0      ) == 0
					assert and_gate(0, 1      ) == 0
					assert and_gate(1, 0      ) == 0
					assert and_gate(1, 1      ) == 1


if __name__ == "__main__":
							test_and_gate()
							print(and_gate(1, 0))
							print(and_gate(0, 0))
							print(and_gate(0, 1))
							print(and_gate(1, 1))

