


'''simple docstring'''

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


a_  :				Union[str, Any]						= {
    """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
}

a_  :				Tuple						= logging.get_logger(__name__)





class      snake_case    (    lowercase			):





  """simple docstring"""




  _lowerCamelCase		    =  "maskformer"
  _lowerCamelCase		    =  {"hidden_size": "mask_feature_size"}
  _lowerCamelCase		    =  ["resnet", "swin"]
  _lowerCamelCase		    =  ["detr"]






  def __init__(    self			,  UpperCamelCase = 256			,  UpperCamelCase = 256			,  UpperCamelCase = 0.1			,  UpperCamelCase = False			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = 0.02			,  UpperCamelCase = 1.0			,  UpperCamelCase = 1.0			,  UpperCamelCase = 1.0			,  UpperCamelCase = 20.0			,  UpperCamelCase = None			,  **UpperCamelCase			,  ):







   """simple docstring"""
   if backbone_config is None:
    # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
    lowerCamelCase_      =  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(UpperCamelCase			,  UpperCamelCase     ):
    lowerCamelCase_      =  backbone_config.pop("model_type"     )
    lowerCamelCase_      =  CONFIG_MAPPING[backbone_model_type]
    lowerCamelCase_      =  config_class.from_dict(UpperCamelCase     )

   # 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
    lowerCamelCase_      =  DetrConfig()
   else:
    # verify that the decoder is supported
    lowerCamelCase_      =  (
        decoder_config.pop("model_type"     ) if isinstance(UpperCamelCase			,  UpperCamelCase     ) 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(UpperCamelCase			,  UpperCamelCase     ):
     lowerCamelCase_      =  CONFIG_MAPPING[decoder_type]
     lowerCamelCase_      =  config_class.from_dict(UpperCamelCase     )

   lowerCamelCase_      =  backbone_config
   lowerCamelCase_      =  decoder_config
   # main feature dimension for the model
   lowerCamelCase_      =  fpn_feature_size
   lowerCamelCase_      =  mask_feature_size
   # initializer
   lowerCamelCase_      =  init_std
   lowerCamelCase_      =  init_xavier_std
   # Hungarian matcher && loss
   lowerCamelCase_      =  cross_entropy_weight
   lowerCamelCase_      =  dice_weight
   lowerCamelCase_      =  mask_weight
   lowerCamelCase_      =  use_auxiliary_loss
   lowerCamelCase_      =  no_object_weight
   lowerCamelCase_      =  output_auxiliary_logits

   lowerCamelCase_      =  self.decoder_config.encoder_attention_heads
   lowerCamelCase_      =  self.decoder_config.num_hidden_layers
   super().__init__(**UpperCamelCase     )






  @classmethod
  def   snake_case		(    cls			,  UpperCamelCase			,  UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   return cls(
       backbone_config=UpperCamelCase			,  decoder_config=UpperCamelCase			,  **UpperCamelCase			,  )







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  copy.deepcopy(self.__dict__     )
   lowerCamelCase_      =  self.backbone_config.to_dict()
   lowerCamelCase_      =  self.decoder_config.to_dict()
   lowerCamelCase_      =  self.__class__.model_type
   return output







"""simple docstring"""







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
		import torch

		from transformers.generation import DisjunctiveConstraint




@require_torch
class  UpperCamelCase_	( unittest.TestCase):

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	List[str]:
						# For consistency across different places the DisjunctiveConstraint is called,
						# dc.token_ids is a list of integers. It is also initialized only by integers.

						__SCREAMING_SNAKE_CASE						   =				[[1, 2, 4], [1, 2, 3, 4]]
						__SCREAMING_SNAKE_CASE						   =				DisjunctiveConstraint(UpperCAmelCase__   )
						self.assertTrue(isinstance(dc.token_ids			,			UpperCAmelCase__   )   )

						with self.assertRaises(UpperCAmelCase__   ):
											DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]   )   )

						with self.assertRaises(UpperCAmelCase__   ):
											DisjunctiveConstraint([torch.LongTensor([1, 2, 4]   ), torch.LongTensor([1, 2, 3, 4, 5]   )]   )




	def   UpperCAmelCase_      ( self			:				Any   )       ->	int:
						# We can't have constraints that are complete subsets of another. This leads to a preverse
						# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
						# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
						# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
						# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
						__SCREAMING_SNAKE_CASE						   =				[[1, 2], [1, 2, 3, 4]]

						with self.assertRaises(UpperCAmelCase__   ):
											DisjunctiveConstraint(UpperCAmelCase__   )  # fails here




	def   UpperCAmelCase_      ( self			:				List[Any]   )       ->	Any:
						__SCREAMING_SNAKE_CASE						   =				[[1, 2, 3], [1, 2, 4]]

						__SCREAMING_SNAKE_CASE						   =				DisjunctiveConstraint(UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(1   )
						__SCREAMING_SNAKE_CASE						   =				stepped is True and completed is False and reset is False
						self.assertTrue(UpperCAmelCase__   )
						self.assertTrue(not dc.completed   )
						self.assertTrue(dc.current_seq == [1]   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(2   )
						__SCREAMING_SNAKE_CASE						   =				stepped is True and completed is False and reset is False
						self.assertTrue(UpperCAmelCase__   )
						self.assertTrue(not dc.completed   )
						self.assertTrue(dc.current_seq == [1, 2]   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(3   )
						__SCREAMING_SNAKE_CASE						   =				stepped is True and completed is True and reset is False
						self.assertTrue(UpperCAmelCase__   )
						self.assertTrue(dc.completed   )  # Completed!
						self.assertTrue(dc.current_seq == [1, 2, 3]   )




	def   UpperCAmelCase_      ( self			:				str   )       ->	List[str]:
						__SCREAMING_SNAKE_CASE						   =				[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

						__SCREAMING_SNAKE_CASE						   =				DisjunctiveConstraint(UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(1   )
						self.assertTrue(not dc.completed   )
						self.assertTrue(dc.current_seq == [1]   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(2   )
						self.assertTrue(not dc.completed   )
						self.assertTrue(dc.current_seq == [1, 2]   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(4   )
						self.assertTrue(not dc.completed   )
						self.assertTrue(dc.current_seq == [1, 2, 4]   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(5   )
						self.assertTrue(dc.completed   )  # Completed!
						self.assertTrue(dc.current_seq == [1, 2, 4, 5]   )

						dc.reset()

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(1   )
						self.assertTrue(not dc.completed   )
						self.assertTrue(dc.remaining() == 3   )
						self.assertTrue(dc.current_seq == [1]   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(2   )
						self.assertTrue(not dc.completed   )
						self.assertTrue(dc.remaining() == 2   )
						self.assertTrue(dc.current_seq == [1, 2]   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(5   )
						self.assertTrue(dc.completed   )  # Completed!
						self.assertTrue(dc.remaining() == 0   )
						self.assertTrue(dc.current_seq == [1, 2, 5]   )
