


from datetime import datetime as dt
import os

from github import Github


__lowerCamelCase        =     [
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''feature request''',
    '''new model''',
    '''wip''',
]



def  _a			(						):
     a_    :     List[str]										=    Github(os.environ["""GITHUB_TOKEN"""] )
     a_    :     str										=    g.get_repo("""huggingface/transformers""" )
     a_    :     List[str]										=    repo.get_issues(state="""open""" )

     for issue in open_issues:
          a_    :     Optional[Any]										=    sorted([comment for comment in issue.get_comments()]  ,  key=lambda __UpperCamelCase : i.created_at  ,  reverse=__UpperCamelCase )
          a_    :     List[Any]										=    comments[0] if len(__UpperCamelCase ) > 0 else None
          if (
              last_comment is not None
              and last_comment.user.login == "github-actions[bot]"
              and (dt.utcnow() - issue.updated_at).days > 7
              and (dt.utcnow() - issue.created_at).days >= 3_0
              and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
          ):
               # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
               issue.edit(state="""closed""" )
          elif (
              (dt.utcnow() - issue.updated_at).days > 2_3
              and (dt.utcnow() - issue.created_at).days >= 3_0
              and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
          ):
               # print(f"Would add stale comment to {issue.number}")
               issue.create_comment(
                   """This issue has been automatically marked as stale because it has not had """
                   """recent activity. If you think this still needs to be addressed """
                   """please comment on this thread.\n\nPlease note that issues that do not follow the """
                   """[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) """
                   """are likely to be ignored.""" )


if __name__ == "__main__":
       main()







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


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

# General docstring
__lowerCamelCase        =     '''UperNetConfig'''


class   a__	(						nn.Module    ):

       def __init__(   self      :     Any      ,							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      ,							):
            super().__init__()
            a_    :     str										=    nn.Convad(
                in_channels=lowerCamelCase_      ,							out_channels=lowerCamelCase_      ,							kernel_size=lowerCamelCase_      ,							padding=lowerCamelCase_      ,							bias=lowerCamelCase_      ,							dilation=lowerCamelCase_      ,							)
            a_    :     Dict										=    nn.BatchNormad(lowerCamelCase_    )
            a_    :     Union[str, Any]										=    nn.ReLU()

       def 						UpperCAmelCase(   self      :     str      ,							lowerCamelCase_      :     torch.Tensor    ):
            a_    :     Tuple										=    self.conv(lowerCamelCase_    )
            a_    :     int										=    self.batch_norm(lowerCamelCase_    )
            a_    :     Optional[int]										=    self.activation(lowerCamelCase_    )

            return output


class   a__	(						nn.Module    ):

       def __init__(   self      :     List[str]      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     int    ):
            super().__init__()
            a_    :     Union[str, Any]										=    [
                nn.AdaptiveAvgPoolad(lowerCamelCase_    ),
                UperNetConvModule(lowerCamelCase_      ,							lowerCamelCase_      ,							kernel_size=1    ),
            ]
            for i, layer in enumerate(self.layers    ):
                 self.add_module(str(lowerCamelCase_    )      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     List[Any]      ,							lowerCamelCase_      :     torch.Tensor    ):
            a_    :     Optional[int]										=    input
            for layer in self.layers:
                 a_    :     Dict										=    layer(lowerCamelCase_    )
            return hidden_state


class   a__	(						nn.Module    ):

       def __init__(   self      :     Union[str, Any]      ,							lowerCamelCase_      :     Tuple[int, ...]      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     bool    ):
            super().__init__()
            a_    :     Optional[int]										=    pool_scales
            a_    :     List[Any]										=    align_corners
            a_    :     Union[str, Any]										=    in_channels
            a_    :     List[str]										=    channels
            a_    :     Optional[int]										=    []
            for i, pool_scale in enumerate(lowerCamelCase_    ):
                 a_    :     Optional[int]										=    UperNetPyramidPoolingBlock(pool_scale=lowerCamelCase_      ,							in_channels=lowerCamelCase_      ,							channels=lowerCamelCase_    )
                 self.blocks.append(lowerCamelCase_    )
                 self.add_module(str(lowerCamelCase_    )      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     List[str]      ,							lowerCamelCase_      :     torch.Tensor    ):
            a_    :     List[str]										=    []
            for ppm in self.blocks:
                 a_    :     Any										=    ppm(lowerCamelCase_    )
                 a_    :     str										=    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      :     Any      ,							lowerCamelCase_      :     Union[str, Any]      ,							lowerCamelCase_      :     int    ):
            super().__init__()

            a_    :     Tuple										=    config
            a_    :     int										=    config.pool_scales  # e.g. (1, 2, 3, 6)
            a_    :     str										=    in_channels
            a_    :     str										=    config.hidden_size
            a_    :     int										=    False
            a_    :     int										=    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_    :     int										=    UperNetConvModule(
                self.in_channels[-1] + len(self.pool_scales    ) * self.channels      ,							self.channels      ,							kernel_size=3      ,							padding=1      ,							)
            # FPN Module
            a_    :     List[Any]										=    nn.ModuleList()
            a_    :     Optional[int]										=    nn.ModuleList()
            for in_channels in self.in_channels[:-1]:  # skip the top layer
                 a_    :     List[str]										=    UperNetConvModule(lowerCamelCase_      ,							self.channels      ,							kernel_size=1    )
                 a_    :     Union[str, Any]										=    UperNetConvModule(self.channels      ,							self.channels      ,							kernel_size=3      ,							padding=1    )
                 self.lateral_convs.append(lowerCamelCase_    )
                 self.fpn_convs.append(lowerCamelCase_    )

            a_    :     str										=    UperNetConvModule(
                len(self.in_channels    ) * self.channels      ,							self.channels      ,							kernel_size=3      ,							padding=1      ,							)

       def 						UpperCAmelCase(   self      :     str    ):
            self.apply(self._init_weights    )

       def 						UpperCAmelCase(   self      :     Any      ,							lowerCamelCase_      :     Union[str, Any]    ):
            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 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     Optional[int]    ):
            a_    :     Optional[int]										=    inputs[-1]
            a_    :     List[str]										=    [x]
            psp_outs.extend(self.psp_modules(lowerCamelCase_    )    )
            a_    :     int										=    torch.cat(lowerCamelCase_      ,							dim=1    )
            a_    :     Tuple										=    self.bottleneck(lowerCamelCase_    )

            return output

       def 						UpperCAmelCase(   self      :     List[str]      ,							lowerCamelCase_      :     torch.Tensor    ):
            # build laterals
            a_    :     List[Any]										=    [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
            a_    :     Tuple										=    len(lowerCamelCase_    )
            for i in range(used_backbone_levels - 1      ,							0      ,							-1    ):
                 a_    :     List[Any]										=    laterals[i - 1].shape[2:]
                 a_    :     int										=    laterals[i - 1] + nn.functional.interpolate(
                     laterals[i]      ,							size=lowerCamelCase_      ,							mode="""bilinear"""      ,							align_corners=self.align_corners    )

            # build outputs
            a_    :     int										=    [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_    :     Union[str, Any]										=    nn.functional.interpolate(
                     fpn_outs[i]      ,							size=fpn_outs[0].shape[2:]      ,							mode="""bilinear"""      ,							align_corners=self.align_corners    )
            a_    :     Optional[Any]										=    torch.cat(lowerCamelCase_      ,							dim=1    )
            a_    :     Tuple										=    self.fpn_bottleneck(lowerCamelCase_    )
            a_    :     str										=    self.classifier(lowerCamelCase_    )

            return output


class   a__	(						nn.Module    ):

       def __init__(   self      :     Union[str, Any]      ,							lowerCamelCase_      :     Optional[Any]      ,							lowerCamelCase_      :     int = 2      ,							lowerCamelCase_      :     int = 3      ,							lowerCamelCase_      :     Union[int, Tuple[int, int]] = 1    ):
            super().__init__()

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

            a_    :     str										=    (kernel_size // 2) * dilation
            a_    :     Union[str, Any]										=    []
            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:
                 a_    :     Dict										=    nn.Identity()
            else:
                 a_    :     Optional[Any]										=    nn.Sequential(*lowerCamelCase_    )
            if self.concat_input:
                 a_    :     List[str]										=    UperNetConvModule(
                     self.in_channels + self.channels      ,							self.channels      ,							kernel_size=lowerCamelCase_      ,							padding=kernel_size // 2    )

            a_    :     str										=    nn.Convad(self.channels      ,							config.num_labels      ,							kernel_size=1    )

       def 						UpperCAmelCase(   self      :     Optional[int]    ):
            self.apply(self._init_weights    )

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


class   a__	(						lowerCAmelCase_    ):
       lowerCamelCase__:     Tuple							    =     UperNetConfig
       lowerCamelCase__:     Tuple							    =     """pixel_values"""
       lowerCamelCase__:     Optional[Any]							    =     True

       def 						UpperCAmelCase(   self      :     Optional[int]      ,							lowerCamelCase_      :     str    ):
            if isinstance(lowerCamelCase_      ,							lowerCamelCase_    ):
                 module.backbone.init_weights()
                 module.decode_head.init_weights()
                 module.auxiliary_head.init_weights()

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

       def 						UpperCAmelCase(   self      :     Optional[Any]      ,							lowerCamelCase_      :     List[Any]      ,							lowerCamelCase_      :     str=False    ):
            if isinstance(lowerCamelCase_      ,							lowerCamelCase_    ):
                 a_    :     List[Any]										=    value

__lowerCamelCase        =     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.
'''

__lowerCamelCase        =     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."""						,       lowerCAmelCase_						,       )
class   a__	(						lowerCAmelCase_    ):

       def __init__(   self      :     Optional[int]      ,							lowerCamelCase_      :     Optional[Any]    ):
            super().__init__(lowerCamelCase_    )

            a_    :     str										=    AutoBackbone.from_config(config.backbone_config    )

            # Semantic segmentation head(s)
            a_    :     List[Any]										=    UperNetHead(lowerCamelCase_      ,							in_channels=self.backbone.channels    )
            a_    :     List[str]										=    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 						UpperCAmelCase(   self      :     Union[str, Any]      ,							lowerCamelCase_      :     Optional[torch.Tensor] = None      ,							lowerCamelCase_      :     Optional[bool] = None      ,							lowerCamelCase_      :     Optional[bool] = None      ,							lowerCamelCase_      :     Optional[torch.Tensor] = None      ,							lowerCamelCase_      :     Optional[bool] = None      ,							):
            a_    :     List[str]										=    return_dict if return_dict is not None else self.config.use_return_dict
            a_    :     Any										=    (
                output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
            )
            a_    :     Tuple										=    output_attentions if output_attentions is not None else self.config.output_attentions

            a_    :     List[Any]										=    self.backbone.forward_with_filtered_kwargs(
                lowerCamelCase_      ,							output_hidden_states=lowerCamelCase_      ,							output_attentions=lowerCamelCase_    )
            a_    :     str										=    outputs.feature_maps

            a_    :     Tuple										=    self.decode_head(lowerCamelCase_    )
            a_    :     Optional[Any]										=    nn.functional.interpolate(lowerCamelCase_      ,							size=pixel_values.shape[2:]      ,							mode="""bilinear"""      ,							align_corners=lowerCamelCase_    )

            a_    :     List[Any]										=    None
            if self.auxiliary_head is not None:
                 a_    :     Dict										=    self.auxiliary_head(lowerCamelCase_    )
                 a_    :     List[Any]										=    nn.functional.interpolate(
                     lowerCamelCase_      ,							size=pixel_values.shape[2:]      ,							mode="""bilinear"""      ,							align_corners=lowerCamelCase_    )

            a_    :     Dict										=    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_    :     int										=    CrossEntropyLoss(ignore_index=self.config.loss_ignore_index    )
                      a_    :     List[Any]										=    loss_fct(lowerCamelCase_      ,							lowerCamelCase_    )
                      a_    :     Optional[int]										=    loss_fct(lowerCamelCase_      ,							lowerCamelCase_    )
                      a_    :     Tuple										=    main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

            if not return_dict:
                 if output_hidden_states:
                      a_    :     str										=    (logits,) + outputs[1:]
                 else:
                      a_    :     Optional[int]										=    (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      ,							)



