import copy

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


_lowerCAmelCase       =		logging.get_logger(__name__)

_lowerCAmelCase       =		{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}
class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     "deta"
							UpperCAmelCase				   =     {
							    "hidden_size": "d_model",
							    "num_attention_heads": "encoder_attention_heads",
							}







							def __init__(   self      :     Optional[int] ,			_A      :     Tuple=None ,			_A      :     Dict=900 ,			_A      :     Union[str, Any]=2048 ,			_A      :     Union[str, Any]=6 ,			_A      :     List[str]=2048 ,			_A      :     str=8 ,			_A      :     Optional[int]=6 ,			_A      :     List[str]=1024 ,			_A      :     Optional[int]=8 ,			_A      :     List[str]=0.0 ,			_A      :     List[str]=True ,			_A      :     Any="relu" ,			_A      :     Any=256 ,			_A      :     Optional[int]=0.1 ,			_A      :     str=0.0 ,			_A      :     Dict=0.0 ,			_A      :     str=0.02 ,			_A      :     Union[str, Any]=1.0 ,			_A      :     Union[str, Any]=True ,			_A      :     Any=False ,			_A      :     Union[str, Any]="sine" ,			_A      :     int=5 ,			_A      :     Optional[Any]=4 ,			_A      :     Any=4 ,			_A      :     Union[str, Any]=True ,			_A      :     Dict=300 ,			_A      :     List[Any]=True ,			_A      :     Any=True ,			_A      :     Tuple=1 ,			_A      :     Optional[int]=5 ,			_A      :     str=2 ,			_A      :     Tuple=1 ,			_A      :     Tuple=1 ,			_A      :     Any=5 ,			_A      :     Tuple=2 ,			_A      :     str=0.1 ,			_A      :     List[str]=0.25 ,			**_A      :     Dict ,			):
												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(_A ,			_A		):
																						_UpperCamelCase			     =     backbone_config.pop('''model_type'''		)
																						_UpperCamelCase			     =     CONFIG_MAPPING[backbone_model_type]
																						_UpperCamelCase			     =     config_class.from_dict(_A		)

												_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=_A ,			**_A		)







							@property
							def  UpperCamelCase_						(   self      :     Optional[int]		):
												return self.encoder_attention_heads







							@property
							def  UpperCamelCase_						(   self      :     int		):
												return self.d_model







							def  UpperCamelCase_						(   self      :     int		):
												_UpperCamelCase			     =     copy.deepcopy(self.__dict__		)
												_UpperCamelCase			     =     self.backbone_config.to_dict()
												_UpperCamelCase			     =     self.__class__.model_type
												return output








"""simple docstring"""



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
 import torch

 from transformers import XLMRobertaModel




@require_sentencepiece
@require_tokenizers
@require_torch
class 			lowerCAmelCase__			(    unittest.TestCase   ):
   @slow
   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[str]							=							XLMRobertaModel.from_pretrained("xlm-roberta-base"							)
        UpperCAmelCase__						:    str							=							torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]							)
        # The dog is cute and lives in the garden house

        UpperCAmelCase__						:    Tuple							=							torch.Size((1, 1_2, 7_6_8)							)  # batch_size, sequence_length, embedding_vector_dim
        UpperCAmelCase__						:    Optional[int]							=							torch.tensor(
            [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]							)
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
             UpperCAmelCase__						:    int							=							model(snake_case__							)["last_hidden_state"].detach()
        self.assertEqual(output.shape							,			snake_case__							)
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]							,			snake_case__							,			atol=1e-3							)							)




   @slow
   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							XLMRobertaModel.from_pretrained("xlm-roberta-large"							)
        UpperCAmelCase__						:    Tuple							=							torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]							)
        # The dog is cute and lives in the garden house

        UpperCAmelCase__						:    Union[str, Any]							=							torch.Size((1, 1_2, 1_0_2_4)							)  # batch_size, sequence_length, embedding_vector_dim
        UpperCAmelCase__						:    Optional[Any]							=							torch.tensor(
            [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]							)
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
             UpperCAmelCase__						:    Optional[Any]							=							model(snake_case__							)["last_hidden_state"].detach()
        self.assertEqual(output.shape							,			snake_case__							)
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]							,			snake_case__							,			atol=1e-3							)							)


