

'''simple docstring'''





import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowercase_	 =						logging.get_logger(__name__)

lowercase_	 =						{
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}






class __A    (						__UpperCAmelCase		):



 '''simple docstring'''







 __lowerCamelCase					: Optional[Any]    = '''conditional_detr'''
 __lowerCamelCase					: str    = ['''past_key_values''']
 __lowerCamelCase					: Union[str, Any]    = {
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''encoder_attention_heads''',
 }



 def __init__(self				,     A=True				,     A=None				,     A=3				,     A=300				,     A=6				,     A=2_048				,     A=8				,     A=6				,     A=2_048				,     A=8				,     A=0.0				,     A=0.0				,     A=True				,     A="relu"				,     A=256				,     A=0.1				,     A=0.0				,     A=0.0				,     A=0.02				,     A=1.0				,     A=False				,     A="sine"				,     A="resnet50"				,     A=True				,     A=False				,     A=2				,     A=5				,     A=2				,     A=1				,     A=1				,     A=2				,     A=5				,     A=2				,     A=0.25				,     **A				,     )					->  Tuple:


      """simple docstring"""



      if backbone_config is not None and use_timm_backbone:
           raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''' )

      if not use_timm_backbone:
           if backbone_config is None:
                logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
                _a											=						CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
           elif isinstance(__SCREAMING_SNAKE_CASE				,     __SCREAMING_SNAKE_CASE ):
                _a											=						backbone_config.get('''model_type''' )
                _a											=						CONFIG_MAPPING[backbone_model_type]
                _a											=						config_class.from_dict(__SCREAMING_SNAKE_CASE )

      _a											=						use_timm_backbone
      _a											=						backbone_config
      _a											=						num_channels
      _a											=						num_queries
      _a											=						d_model
      _a											=						encoder_ffn_dim
      _a											=						encoder_layers
      _a											=						encoder_attention_heads
      _a											=						decoder_ffn_dim
      _a											=						decoder_layers
      _a											=						decoder_attention_heads
      _a											=						dropout
      _a											=						attention_dropout
      _a											=						activation_dropout
      _a											=						activation_function
      _a											=						init_std
      _a											=						init_xavier_std
      _a											=						encoder_layerdrop
      _a											=						decoder_layerdrop
      _a											=						encoder_layers
      _a											=						auxiliary_loss
      _a											=						position_embedding_type
      _a											=						backbone
      _a											=						use_pretrained_backbone
      _a											=						dilation
      # Hungarian matcher
      _a											=						class_cost
      _a											=						bbox_cost
      _a											=						giou_cost
      # Loss coefficients
      _a											=						mask_loss_coefficient
      _a											=						dice_loss_coefficient
      _a											=						cls_loss_coefficient
      _a											=						bbox_loss_coefficient
      _a											=						giou_loss_coefficient
      _a											=						focal_alpha
      super().__init__(is_encoder_decoder=__SCREAMING_SNAKE_CASE				,     **__SCREAMING_SNAKE_CASE )



 @property
 def       a__						(self )					->  List[Any]:


      """simple docstring"""



      return self.encoder_attention_heads



 @property
 def       a__						(self )					->  Dict:


      """simple docstring"""



      return self.d_model



 def       a__						(self )					->  int:


      """simple docstring"""



      _a											=						copy.deepcopy(self.__dict__ )
      if self.backbone_config is not None:
           _a											=						self.backbone_config.to_dict()
      _a											=						self.__class__.model_type
      return output






class __A    (						__UpperCAmelCase		):



 '''simple docstring'''







 __lowerCamelCase					: Any    = version.parse('1.11'		)



 @property
 def       a__						(self )					->  Tuple:


      """simple docstring"""



      return OrderedDict(
          [
              ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
              ('''pixel_mask''', {0: '''batch'''}),
          ] )



 @property
 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      return 1E-5



 @property
 def       a__						(self )					->  Dict:


      """simple docstring"""



      return 12








'''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 __A    (						unittest.TestCase		):



 '''simple docstring'''







 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



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

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

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



 def       a__						(self )					->  Any:


      """simple docstring"""



      _a											=						[[1, 2], [1, 2, 3, 4]]

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



 def       a__						(self )					->  Dict:


      """simple docstring"""



      _a											=						[[1, 2, 3], [1, 2, 4]]

      _a											=						DisjunctiveConstraint(A )

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

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

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



 def       a__						(self )					->  List[Any]:


      """simple docstring"""



      _a											=						[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

      _a											=						DisjunctiveConstraint(A )

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

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

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

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

      dc.reset()

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

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

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



