





import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


_lowerCAmelCase 				=     {
    """facebook/maskformer-swin-base-ade""": (
        """https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"""
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

_lowerCAmelCase 				=     logging.get_logger(__name__)

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							'''maskformer'''
      a						       =							{'''hidden_size''': '''mask_feature_size'''}
      a						       =							['''resnet''', '''swin''']
      a						       =							['''detr''']
      def __init__(		self      ,       a__ = 256      ,       a__ = 256      ,       a__ = 0.1      ,       a__ = False      ,       a__ = None      ,       a__ = None      ,       a__ = 0.02      ,       a__ = 1.0      ,       a__ = 1.0      ,       a__ = 1.0      ,       a__ = 20.0      ,       a__ = None      ,       **a__      ,       ):
         if backbone_config is None:
            # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
            A_     :				Optional[int]			      =	SwinConfig(
                image_size=384      ,       in_channels=3      ,       patch_size=4      ,       embed_dim=128      ,       depths=[2, 2, 18, 2]      ,       num_heads=[4, 8, 16, 32]      ,       window_size=12      ,       drop_path_rate=0.3      ,       out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]      ,       )

         if isinstance(a__      ,       a__       ):
            A_     :				Optional[Any]			      =	backbone_config.pop("""model_type"""       )
            A_     :				Union[str, Any]			      =	CONFIG_MAPPING[backbone_model_type]
            A_     :				Optional[int]			      =	config_class.from_dict(a__       )

         # verify that the backbone is supported
         if backbone_config.model_type not in self.backbones_supported:
            logger.warning_once(
                F"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
                F"""Supported model types: {','.join(self.backbones_supported       )}"""       )

         if decoder_config is None:
            # fall back to https://huggingface.co/facebook/detr-resnet-50
            A_     :				Optional[Any]			      =	DetrConfig()
         else:
            # verify that the decoder is supported
            A_     :				str			      =	(
                decoder_config.pop("""model_type"""       ) if isinstance(a__      ,       a__       ) else decoder_config.model_type
            )
            if decoder_type not in self.decoders_supported:
               raise ValueError(
                   F"""Transformer Decoder {decoder_type} not supported, please use one of"""
                   F""" {','.join(self.decoders_supported       )}"""       )
            if isinstance(a__      ,       a__       ):
               A_     :				Optional[Any]			      =	CONFIG_MAPPING[decoder_type]
               A_     :				Tuple			      =	config_class.from_dict(a__       )

         A_     :				List[Any]			      =	backbone_config
         A_     :				Union[str, Any]			      =	decoder_config
         # main feature dimension for the model
         A_     :				Optional[Any]			      =	fpn_feature_size
         A_     :				Any			      =	mask_feature_size
         # initializer
         A_     :				Tuple			      =	init_std
         A_     :				int			      =	init_xavier_std
         # Hungarian matcher && loss
         A_     :				Any			      =	cross_entropy_weight
         A_     :				Any			      =	dice_weight
         A_     :				Dict			      =	mask_weight
         A_     :				Any			      =	use_auxiliary_loss
         A_     :				Optional[int]			      =	no_object_weight
         A_     :				Any			      =	output_auxiliary_logits

         A_     :				Tuple			      =	self.decoder_config.encoder_attention_heads
         A_     :				Optional[Any]			      =	self.decoder_config.num_hidden_layers
         super().__init__(**a__       )
      @classmethod
      def 		_lowerCamelCase      (		cls      ,       a__      ,       a__      ,       **a__       ):
         return cls(
             backbone_config=a__      ,       decoder_config=a__      ,       **a__      ,       )






      def 		_lowerCamelCase      (		self       ):
         A_     :				Union[str, Any]			      =	copy.deepcopy(self.__dict__       )
         A_     :				Tuple			      =	self.backbone_config.to_dict()
         A_     :				Optional[Any]			      =	self.decoder_config.to_dict()
         A_     :				Union[str, Any]			      =	self.__class__.model_type
         return output













from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,):
   '''simple docstring'''


   A_      ,				A_     :				int			      =	coefficient_matrix.shape
   A_      ,				A_     :				Tuple			      =	constant_matrix.shape

   if rowsa != colsa:
      A_     :				int			      =	f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
      raise ValueError(_lowerCAmelCase     )

   if colsa != 1:
      A_     :				List[Any]			      =	f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
      raise ValueError(_lowerCAmelCase     )

   if rowsa != rowsa:
      A_     :				str			      =	(
          """Coefficient and constant matrices dimensions must be nxn and nx1 but """
          f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
      )
      raise ValueError(_lowerCAmelCase     )

   if len(_lowerCAmelCase     ) != rowsa:
      A_     :				Any			      =	(
          """Number of initial values must be equal to number of rows in coefficient """
          f"""matrix but received {len(_lowerCAmelCase     )} and {rowsa}"""
      )
      raise ValueError(_lowerCAmelCase     )

   if iterations <= 0:
      raise ValueError("""Iterations must be at least 1"""     )

   A_     :				NDArray[floataa]			      =	np.concatenate(
       (coefficient_matrix, constant_matrix)  ,axis=1     )

   A_      ,				A_     :				str			      =	table.shape

   strictly_diagonally_dominant(_lowerCAmelCase     )

   # Iterates the whole matrix for given number of times
   for _ in range(_lowerCAmelCase     ):
      A_     :				Union[str, Any]			      =	[]
      for row in range(_lowerCAmelCase     ):
         A_     :				str			      =	0
         for col in range(_lowerCAmelCase     ):
            if col == row:
               A_     :				Optional[Any]			      =	table[row][col]
            elif col == cols - 1:
               A_     :				List[str]			      =	table[row][col]
            else:
               temp += (-1) * table[row][col] * init_val[col]
         A_     :				str			      =	(temp + val) / denom
         new_val.append(_lowerCAmelCase     )
      A_     :				List[str]			      =	new_val

   return [float(_lowerCAmelCase     ) for i in new_val]



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_      ,				A_     :				str			      =	table.shape

   A_     :				Any			      =	True

   for i in range(0  ,_lowerCAmelCase     ):
      A_     :				Optional[Any]			      =	0
      for j in range(0  ,cols - 1     ):
         if i == j:
            continue
         else:
            total += table[i][j]

      if table[i][i] <= total:
         raise ValueError("""Coefficient matrix is not strictly diagonally dominant"""     )

   return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
      import doctest

      doctest.testmod()






