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


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

# General docstring
SCREAMING_SNAKE_CASE :	List[Any]							     =		"""UperNetConfig"""
class    A_ (       nn.Module ):

  def __init__(		self :      Union[str, 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     ,							):
   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 		_UpperCAmelCase   (		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      torch.Tensor       ):
   __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       ):
   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 		_UpperCAmelCase   (		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      torch.Tensor       ):
   __a       =  input
   for layer in self.layers:
    __a       =  layer(__SCREAMING_SNAKE_CASE       )
   return hidden_state
class    A_ (       nn.Module ):

  def __init__(		self :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Tuple[int, ...]     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      bool       ):
   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 		_UpperCAmelCase   (		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      torch.Tensor       ):
   __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 :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Any     ,							__SCREAMING_SNAKE_CASE :      List[str]       ):
   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 		_UpperCAmelCase   (		self :      str       ):
   self.apply(self._init_weights       )

  def 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      int       ):
   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 		_UpperCAmelCase   (		self :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]       ):
   __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 		_UpperCAmelCase   (		self :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      torch.Tensor       ):
   # build laterals
   __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 :      Dict     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      int = 2     ,							__SCREAMING_SNAKE_CASE :      int = 3     ,							__SCREAMING_SNAKE_CASE :      Union[int, Tuple[int, int]] = 1       ):
   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 		_UpperCAmelCase   (		self :      Dict       ):
   self.apply(self._init_weights       )

  def 		_UpperCAmelCase   (		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]       ):
   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 		_UpperCAmelCase   (		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      torch.Tensor       ):
   # just take the relevant feature maps
   __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_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			UperNetConfig
  _SCREAMING_SNAKE_CASE    				=			"""pixel_values"""
  _SCREAMING_SNAKE_CASE    				=			True

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]       ):
   if isinstance(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ):
    module.backbone.init_weights()
    module.decode_head.init_weights()
    module.auxiliary_head.init_weights()

  def 		_UpperCAmelCase   (		self :      int       ):
   self.backbone.init_weights()
   self.decode_head.init_weights()
   self.auxiliary_head.init_weights()

  def 		_UpperCAmelCase   (		self :      str     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Tuple=False       ):
   if isinstance(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ):
    __a       =  value
SCREAMING_SNAKE_CASE :	Union[str, 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.
"""

SCREAMING_SNAKE_CASE :	Optional[int]							     =		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."""			,       a_			,       )
class    A_ (       a_ ):

  def __init__(		self :      Any     ,							__SCREAMING_SNAKE_CASE :      Any       ):
   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 		_UpperCAmelCase   (		self :      int     ,							__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     ,							):
   __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     ,							)



from __future__ import annotations



def   __A		(			_A					):



 """simple docstring"""


 __a       =  [True] * limit
 __a       =  False
 __a       =  False
 __a       =  True

 for i in range(3							,				int(limit**0.5 + 1					)							,				2					):
  __a       =  i * 2
  while index < limit:
   __a       =  False
   __a       =  index + i

 __a       =  [2]

 for i in range(3							,				_A							,				2					):
  if is_prime[i]:
   primes.append(_A					)

 return primes



def   __A		(			_A = 100_0000					):



 """simple docstring"""


 __a       =  prime_sieve(_A					)
 __a       =  0
 __a       =  0

 for i in range(len(_A					)					):
  for j in range(i + length							,				len(_A					)					):
   __a       =  sum(primes[i:j]					)
   if sol >= ceiling:
    break

   if sol in primes:
    __a       =  j - i
    __a       =  sol

 return largest


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


