



'''simple docstring'''


import copy

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


_lowerCamelCase	:       Optional[int]      	=	logging.get_logger(__name__)

_lowerCamelCase	:       Optional[Any]      	=	{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  """deta"""
    _SCREAMING_SNAKE_CASE	  =  {
        """hidden_size""": """d_model""",
        """num_attention_heads""": """encoder_attention_heads""",
    }






    def __init__(  self      :					str  ,						UpperCamelCase__      :					Union[str, Any]=None  ,						UpperCamelCase__      :					List[Any]=9_0_0  ,						UpperCamelCase__      :					str=2_0_4_8  ,						UpperCamelCase__      :					int=6  ,						UpperCamelCase__      :					List[Any]=2_0_4_8  ,						UpperCamelCase__      :					Union[str, Any]=8  ,						UpperCamelCase__      :					Optional[int]=6  ,						UpperCamelCase__      :					Optional[int]=1_0_2_4  ,						UpperCamelCase__      :					Union[str, Any]=8  ,						UpperCamelCase__      :					Dict=0.0  ,						UpperCamelCase__      :					Union[str, Any]=True  ,						UpperCamelCase__      :					Any="relu"  ,						UpperCamelCase__      :					Tuple=2_5_6  ,						UpperCamelCase__      :					int=0.1  ,						UpperCamelCase__      :					Any=0.0  ,						UpperCamelCase__      :					Optional[Any]=0.0  ,						UpperCamelCase__      :					Tuple=0.0_2  ,						UpperCamelCase__      :					Optional[Any]=1.0  ,						UpperCamelCase__      :					Union[str, Any]=True  ,						UpperCamelCase__      :					List[Any]=False  ,						UpperCamelCase__      :					int="sine"  ,						UpperCamelCase__      :					Tuple=5  ,						UpperCamelCase__      :					List[Any]=4  ,						UpperCamelCase__      :					Any=4  ,						UpperCamelCase__      :					List[str]=True  ,						UpperCamelCase__      :					str=3_0_0  ,						UpperCamelCase__      :					Optional[Any]=True  ,						UpperCamelCase__      :					str=True  ,						UpperCamelCase__      :					Tuple=1  ,						UpperCamelCase__      :					str=5  ,						UpperCamelCase__      :					List[str]=2  ,						UpperCamelCase__      :					Any=1  ,						UpperCamelCase__      :					int=1  ,						UpperCamelCase__      :					List[Any]=5  ,						UpperCamelCase__      :					Any=2  ,						UpperCamelCase__      :					Any=0.1  ,						UpperCamelCase__      :					Union[str, Any]=0.2_5  ,						**UpperCamelCase__      :					int  ,						):


       """simple docstring"""







       if backbone_config is None:
          logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.' )
          UpperCamelCase					=  CONFIG_MAPPING['resnet'](out_features=['stage2', 'stage3', 'stage4'] )
       else:
          if isinstance(UpperCamelCase__  ,						UpperCamelCase__ ):
             UpperCamelCase					=  backbone_config.pop('model_type' )
             UpperCamelCase					=  CONFIG_MAPPING[backbone_model_type]
             UpperCamelCase					=  config_class.from_dict(UpperCamelCase__ )

       UpperCamelCase					=  backbone_config
       UpperCamelCase					=  num_queries
       UpperCamelCase					=  max_position_embeddings
       UpperCamelCase					=  d_model
       UpperCamelCase					=  encoder_ffn_dim
       UpperCamelCase					=  encoder_layers
       UpperCamelCase					=  encoder_attention_heads
       UpperCamelCase					=  decoder_ffn_dim
       UpperCamelCase					=  decoder_layers
       UpperCamelCase					=  decoder_attention_heads
       UpperCamelCase					=  dropout
       UpperCamelCase					=  attention_dropout
       UpperCamelCase					=  activation_dropout
       UpperCamelCase					=  activation_function
       UpperCamelCase					=  init_std
       UpperCamelCase					=  init_xavier_std
       UpperCamelCase					=  encoder_layerdrop
       UpperCamelCase					=  auxiliary_loss
       UpperCamelCase					=  position_embedding_type
       # deformable attributes
       UpperCamelCase					=  num_feature_levels
       UpperCamelCase					=  encoder_n_points
       UpperCamelCase					=  decoder_n_points
       UpperCamelCase					=  two_stage
       UpperCamelCase					=  two_stage_num_proposals
       UpperCamelCase					=  with_box_refine
       UpperCamelCase					=  assign_first_stage
       if two_stage is True and with_box_refine is False:
          raise ValueError('If two_stage is True, with_box_refine must be True.' )
       # Hungarian matcher
       UpperCamelCase					=  class_cost
       UpperCamelCase					=  bbox_cost
       UpperCamelCase					=  giou_cost
       # Loss coefficients
       UpperCamelCase					=  mask_loss_coefficient
       UpperCamelCase					=  dice_loss_coefficient
       UpperCamelCase					=  bbox_loss_coefficient
       UpperCamelCase					=  giou_loss_coefficient
       UpperCamelCase					=  eos_coefficient
       UpperCamelCase					=  focal_alpha
       super().__init__(is_encoder_decoder=UpperCamelCase__  ,						**UpperCamelCase__ )






    @property
    def  A       (  self      :					List[str] ):


       """simple docstring"""







       return self.encoder_attention_heads






    @property
    def  A       (  self      :					Tuple ):


       """simple docstring"""







       return self.d_model






    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       UpperCamelCase					=  copy.deepcopy(self.__dict__ )
       UpperCamelCase					=  self.backbone_config.to_dict()
       UpperCamelCase					=  self.__class__.model_type
       return output



import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	def 						_lowercase  (       self  :			List[str]       )       ->     List[Any]:
					lowercase_       =					1_0
	def 						_lowercase  (       self  :			int       )       ->     List[str]:
					lowercase_       =					[1, 2, 3, 4]
					lowercase_       =					[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
					self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_       ,							self.block_size       ,							0       )       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			int       )       ->     Optional[Any]:
					lowercase_       =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
					lowercase_       =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
					self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_       ,							self.block_size       ,							0       )       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Optional[int]:
					lowercase_       =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0, 1_1, 1_2, 1_3]
					lowercase_       =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
					self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_       ,							self.block_size       ,							0       )       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Any       )       ->     List[Any]:
					lowercase_       =					'''It was the year of Our Lord one thousand seven hundred and
        seventy-five.\n\nSpiritual revelations were conceded to England at that
        favoured period, as at this.'''
					lowercase_  ,	lowercase_       =					process_story(SCREAMING_SNAKE_CASE_       )
					self.assertEqual(SCREAMING_SNAKE_CASE_       ,							[]       )
	def 						_lowercase  (       self  :			List[str]       )       ->     List[str]:
					lowercase_       =					''''''
					lowercase_  ,	lowercase_       =					process_story(SCREAMING_SNAKE_CASE_       )
					self.assertEqual(SCREAMING_SNAKE_CASE_       ,							[]       )
					self.assertEqual(SCREAMING_SNAKE_CASE_       ,							[]       )
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Union[str, Any]:
					lowercase_       =					(
					    '''It was the year of Our Lord one thousand seven hundred and '''
					    '''seventy-five\n\nSpiritual revelations were conceded to England '''
					    '''at that favoured period, as at this.\n@highlight\n\nIt was the best of times'''
					)
					lowercase_  ,	lowercase_       =					process_story(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					[
					    '''It was the year of Our Lord one thousand seven hundred and seventy-five.''',
					    '''Spiritual revelations were conceded to England at that favoured period, as at this.''',
					]
					self.assertEqual(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

					lowercase_       =					['''It was the best of times.''']
					self.assertEqual(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Optional[Any]:
					lowercase_       =					torch.tensor([1, 2, 3, 4]       )
					lowercase_       =					torch.tensor([1, 1, 1, 1]       )
					np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_       ,							0       ).numpy()       ,							expected.numpy()       )
	def 						_lowercase  (       self  :			List[Any]       )       ->     Tuple:
					lowercase_       =					torch.tensor([1, 2, 3, 4, 2_3, 2_3, 2_3]       )
					lowercase_       =					torch.tensor([1, 1, 1, 1, 0, 0, 0]       )
					np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_       ,							2_3       ).numpy()       ,							expected.numpy()       )
	def 						_lowercase  (       self  :			int       )       ->     Dict:
					lowercase_       =					torch.tensor([8, 2, 3, 4, 1, 1, 1]       )
					lowercase_       =					torch.tensor([1, 1, 1, 1, 0, 0, 0]       )
					np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_       ,							1       ).numpy()       ,							expected.numpy()       )




	def 						_lowercase  (       self  :			List[str]       )       ->     Tuple:
					lowercase_       =					1_0_1
					lowercase_       =					torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 1_0_1, 5, 6], [1, 1_0_1, 3, 4, 1_0_1, 6]]       )
					lowercase_       =					torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]       )

					lowercase_       =					compute_token_type_ids(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
					np.testing.assert_array_equal(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )




