'''simple docstring'''




import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__SCREAMING_SNAKE_CASE :  Any  =						logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE :  Optional[Any]  =						{
    """shi-labs/oneformer_ade20k_swin_tiny""": (
        """https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"""
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}

class 			lowerCamelCase_			(snake_case__					):
   '''simple docstring'''

   __UpperCamelCase:						Dict      			=		"oneformer"
   __UpperCamelCase:						List[Any]      			=		{"hidden_size": "hidden_dim"}




   def __init__( self		:       Tuple		,      A		:       Union[str, Any] = None		,      A		:       Union[str, Any] = 255		,      A		:       Optional[int] = 150		,      A		:       Tuple = 0.1		,      A		:       Tuple = 2.0		,      A		:       Optional[int] = 5.0		,      A		:       str = 5.0		,      A		:       str = 0.5		,      A		:       Optional[int] = 0.07		,      A		:       List[str] = 12544		,      A		:       int = 3.0		,      A		:       Dict = 0.75		,      A		:       Optional[int] = 0.02		,      A		:       str = 1.0		,      A		:       List[str] = 1E-05		,      A		:       Union[str, Any] = False		,      A		:       List[str] = True		,      A		:       Optional[int] = True		,      A		:       List[Any] = [4, 8, 16, 32]		,      A		:       Optional[Any] = 77		,      A		:       Tuple = 256		,      A		:       int = 77		,      A		:       Any = 6		,      A		:       int = 49408		,      A		:       List[Any] = 2		,      A		:       Tuple = 16		,      A		:       Dict = 256		,      A		:       Optional[Any] = 256		,      A		:       Tuple = 256		,      A		:       Any = 1024		,      A		:       Tuple = "GN"		,      A		:       Optional[int] = 6		,      A		:       Dict = 10		,      A		:       Dict = True		,      A		:       Any = 8		,      A		:       List[Any] = 0.1		,      A		:       int = 2048		,      A		:       List[Any] = False		,      A		:       Union[str, Any] = False		,      A		:       Dict = 2		,      A		:       Optional[int] = 4		,      **A		:       Optional[Any]		,      ):






          if backbone_config is None:
                 logger.info("`backbone_config` is unset. Initializing the config with the default `Swin` backbone." )
                 _UpperCAmelCase  :						Union[str, Any]							   =					CONFIG_MAPPING["swin"](
                     image_size=224		,      in_channels=3		,      patch_size=4		,      embed_dim=96		,      depths=[2, 2, 6, 2]		,      num_heads=[3, 6, 12, 24]		,      window_size=7		,      drop_path_rate=0.3		,      use_absolute_embeddings=A		,      out_features=["stage1", "stage2", "stage3", "stage4"]		,      )
          elif isinstance(A		,      A ):
                 _UpperCAmelCase  :						Union[str, Any]							   =					backbone_config.get("model_type" )
                 _UpperCAmelCase  :						Optional[Any]							   =					CONFIG_MAPPING[backbone_model_type]
                 _UpperCAmelCase  :						Any							   =					config_class.from_dict(A )

          _UpperCAmelCase  :						List[Any]							   =					backbone_config

          _UpperCAmelCase  :						List[str]							   =					ignore_value
          _UpperCAmelCase  :						List[Any]							   =					num_queries
          _UpperCAmelCase  :						Any							   =					no_object_weight
          _UpperCAmelCase  :						Any							   =					class_weight
          _UpperCAmelCase  :						Dict							   =					mask_weight
          _UpperCAmelCase  :						Optional[Any]							   =					dice_weight
          _UpperCAmelCase  :						Tuple							   =					contrastive_weight
          _UpperCAmelCase  :						Union[str, Any]							   =					contrastive_temperature
          _UpperCAmelCase  :						List[Any]							   =					train_num_points
          _UpperCAmelCase  :						Any							   =					oversample_ratio
          _UpperCAmelCase  :						Optional[Any]							   =					importance_sample_ratio
          _UpperCAmelCase  :						List[Any]							   =					init_std
          _UpperCAmelCase  :						List[str]							   =					init_xavier_std
          _UpperCAmelCase  :						List[Any]							   =					layer_norm_eps
          _UpperCAmelCase  :						Optional[Any]							   =					is_training
          _UpperCAmelCase  :						List[Any]							   =					use_auxiliary_loss
          _UpperCAmelCase  :						List[Any]							   =					output_auxiliary_logits
          _UpperCAmelCase  :						List[str]							   =					strides
          _UpperCAmelCase  :						Union[str, Any]							   =					task_seq_len
          _UpperCAmelCase  :						List[Any]							   =					text_encoder_width
          _UpperCAmelCase  :						Dict							   =					text_encoder_context_length
          _UpperCAmelCase  :						Dict							   =					text_encoder_num_layers
          _UpperCAmelCase  :						List[str]							   =					text_encoder_vocab_size
          _UpperCAmelCase  :						List[Any]							   =					text_encoder_proj_layers
          _UpperCAmelCase  :						int							   =					text_encoder_n_ctx
          _UpperCAmelCase  :						str							   =					conv_dim
          _UpperCAmelCase  :						Optional[int]							   =					mask_dim
          _UpperCAmelCase  :						Dict							   =					hidden_dim
          _UpperCAmelCase  :						str							   =					encoder_feedforward_dim
          _UpperCAmelCase  :						Optional[int]							   =					norm
          _UpperCAmelCase  :						Optional[Any]							   =					encoder_layers
          _UpperCAmelCase  :						Dict							   =					decoder_layers
          _UpperCAmelCase  :						Union[str, Any]							   =					use_task_norm
          _UpperCAmelCase  :						Dict							   =					num_attention_heads
          _UpperCAmelCase  :						Optional[int]							   =					dropout
          _UpperCAmelCase  :						str							   =					dim_feedforward
          _UpperCAmelCase  :						Optional[Any]							   =					pre_norm
          _UpperCAmelCase  :						str							   =					enforce_input_proj
          _UpperCAmelCase  :						int							   =					query_dec_layers
          _UpperCAmelCase  :						str							   =					common_stride
          _UpperCAmelCase  :						List[Any]							   =					decoder_layers

          super().__init__(**A )





   def   _A			( self		:       Optional[int] ):
          _UpperCAmelCase  :						List[str]							   =					copy.deepcopy(self.__dict__ )
          _UpperCAmelCase  :						Dict							   =					self.backbone_config.to_dict()
          _UpperCAmelCase  :						str							   =					self.__class__.model_type
          return output


'''simple docstring'''




from typing import Any

import numpy as np

class 			lowerCamelCase_			:
      '''simple docstring'''

      def __init__( self		:       Optional[int]		,      A		:       np.ndarray		,      A		:       int ):
             # Check if RHS is negative
             if np.any(tableau[:, -1]		,      where=tableau[:, -1] < 0 ):
                    raise ValueError("RHS must be > 0" )

             _UpperCAmelCase  :						Union[str, Any]							   =					tableau
             _UpperCAmelCase   ,						_UpperCAmelCase  :						str							   =					tableau.shape

             # Number of decision variables x1, x2, x3...
             _UpperCAmelCase  :						Any							   =					n_vars

             # Number of artificial variables to be minimised
             _UpperCAmelCase  :						List[str]							   =					len(np.where(tableau[self.n_vars : -1] == -1 )[0] )

             # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
             _UpperCAmelCase  :						List[Any]							   =					(self.n_art_vars > 0) + 1

             # Number of slack variables added to make inequalities into equalities
             _UpperCAmelCase  :						int							   =					self.n_rows - self.n_stages

             # Objectives for each stage
             _UpperCAmelCase  :						Optional[Any]							   =					["max"]

             # In two stage simplex, first minimise then maximise
             if self.n_art_vars:
                    self.objectives.append("min" )

             _UpperCAmelCase  :						Any							   =					[""]

             # Index of current pivot row and column
             _UpperCAmelCase  :						Optional[Any]							   =					None
             _UpperCAmelCase  :						Dict							   =					None

             # Does objective row only contain (non)-negative values?
             _UpperCAmelCase  :						List[str]							   =					False




      @staticmethod
      def   _A			( *A		:       int ):
             if len(A ) != 3:
                    raise ValueError("Must provide n_vars, n_slack, and n_art_vars" )

             if not all(x >= 0 and isinstance(A		,      A ) for x in args ):
                    raise ValueError("All arguments must be non-negative integers" )

             # decision | slack | artificial
             _UpperCAmelCase  :						Optional[Any]							   =					["x", "s", "a"]
             _UpperCAmelCase  :						int							   =					[]
             for i in range(3 ):
                    for j in range(args[i] ):
                           titles.append(string_starts[i] + str(j + 1 ) )
             titles.append("RHS" )
             return titles




      def   _A			( self		:       List[str]		,      A		:       np.ndarray ):
             _UpperCAmelCase  :						str							   =					self.objectives[-1]

             # Find entries of highest magnitude in objective rows
             _UpperCAmelCase  :						List[str]							   =					(objective == "min") - (objective == "max")
             _UpperCAmelCase  :						Optional[Any]							   =					np.argmax(sign * tableau[0, : self.n_vars] )

             # Choice is only valid if below 0 for maximise, and above for minimise
             if sign * self.tableau[0, col_idx] <= 0:
                    _UpperCAmelCase  :						Dict							   =					True
                    return 0, 0

             # Pivot row is chosen as having the lowest quotient when elements of
             # the pivot column divide the right-hand side

             # Slice excluding the objective rows
             _UpperCAmelCase  :						Any							   =					slice(self.n_stages		,      self.n_rows )

             # RHS
             _UpperCAmelCase  :						Optional[Any]							   =					tableau[s, -1]

             # Elements of pivot column within slice
             _UpperCAmelCase  :						Optional[Any]							   =					tableau[s, col_idx]

             # Array filled with nans
             _UpperCAmelCase  :						List[Any]							   =					np.full(self.n_rows - self.n_stages		,      np.nan )

             # If element in pivot column is greater than zeron_stages, return
             # quotient or nan otherwise
             _UpperCAmelCase  :						str							   =					np.divide(A		,      A		,      out=A		,      where=divisor > 0 )

             # Arg of minimum quotient excluding the nan values. n_stages is added
             # to compensate for earlier exclusion of objective columns
             _UpperCAmelCase  :						Optional[Any]							   =					np.nanargmin(A ) + self.n_stages
             return row_idx, col_idx




      def   _A			( self		:       List[Any]		,      A		:       np.ndarray		,      A		:       int		,      A		:       int ):
             _UpperCAmelCase  :						Union[str, Any]							   =					tableau[row_idx].copy()

             _UpperCAmelCase  :						Optional[Any]							   =					piv_row[col_idx]

             # Entry becomes 1
             piv_row *= 1 / piv_val

             # Variable in pivot column becomes basic, ie the only non-zero entry
             for idx, coeff in enumerate(tableau[:, col_idx] ):
                    tableau[idx] += -coeff * piv_row
             _UpperCAmelCase  :						Optional[Any]							   =					piv_row
             return tableau




      def   _A			( self		:       Optional[Any]		,      A		:       np.ndarray ):
             self.objectives.pop()

             if not self.objectives:
                    return tableau

             # Slice containing ids for artificial columns
             _UpperCAmelCase  :						Union[str, Any]							   =					slice(-self.n_art_vars - 1		,      -1 )

             # Delete the artificial variable columns
             _UpperCAmelCase  :						List[Any]							   =					np.delete(A		,      A		,      axis=1 )

             # Delete the objective row of the first stage
             _UpperCAmelCase  :						Union[str, Any]							   =					np.delete(A		,      0		,      axis=0 )

             _UpperCAmelCase  :						Any							   =					1
             self.n_rows -= 1
             _UpperCAmelCase  :						Dict							   =					0
             _UpperCAmelCase  :						List[str]							   =					False
             return tableau




      def   _A			( self		:       Optional[Any] ):
             # Stop simplex algorithm from cycling.
             for _ in range(100 ):
                    # Completion of each stage removes an objective. If both stages
                    # are complete, then no objectives are left
                    if not self.objectives:
                           _UpperCAmelCase  :						Any							   =					self.generate_col_titles(
                               self.n_vars		,      self.n_slack		,      self.n_art_vars )

                           # Find the values of each variable at optimal solution
                           return self.interpret_tableau(self.tableau		,      self.col_titles )

                    _UpperCAmelCase   ,						_UpperCAmelCase  :						List[str]							   =					self.find_pivot(self.tableau )

                    # If there are no more negative values in objective row
                    if self.stop_iter:
                           # Delete artificial variable columns and rows. Update attributes
                           _UpperCAmelCase  :						int							   =					self.change_stage(self.tableau )
                    else:
                           _UpperCAmelCase  :						Optional[Any]							   =					self.pivot(self.tableau		,      A		,      A )
             return {}





      def   _A			( self		:       List[Any]		,      A		:       np.ndarray		,      A		:       list[str] ):
             _UpperCAmelCase  :						str							   =					{"P": abs(tableau[0, -1] )}

             for i in range(self.n_vars ):
                    # Gives ids of nonzero entries in the ith column
                    _UpperCAmelCase  :						Dict							   =					np.nonzero(tableau[:, i] )
                    _UpperCAmelCase  :						Dict							   =					len(nonzero[0] )

                    # First entry in the nonzero ids
                    _UpperCAmelCase  :						str							   =					nonzero[0][0]
                    _UpperCAmelCase  :						str							   =					tableau[nonzero_rowidx, i]

                    # If there is only one nonzero value in column, which is one
                    if n_nonzero == nonzero_val == 1:
                           _UpperCAmelCase  :						Optional[Any]							   =					tableau[nonzero_rowidx, -1]
                           _UpperCAmelCase  :						Any							   =					rhs_val

        # Check for basic variables
             for title in col_titles:
                    # Don't add RHS or slack variables to output dict
                    if title[0] not in "R-s-a":
                           output_dict.setdefault(A		,      0 )
             return output_dict


if __name__ == "__main__":
  import doctest

  doctest.testmod()

