


'''simple docstring'''






import copy

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


lowercase							=logging.get_logger(__name__)

lowercase							={
    'SenseTime/deformable-detr': 'https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="deformable_detr"
       UpperCAmelCase										={
           "hidden_size": "d_model",
           "num_attention_heads": "encoder_attention_heads",
       }






       def __init__(      self  ,							snake_case=True  ,							snake_case=None  ,							snake_case=3  ,							snake_case=3_0_0  ,							snake_case=1_0_2_4  ,							snake_case=6  ,							snake_case=1_0_2_4  ,							snake_case=8  ,							snake_case=6  ,							snake_case=1_0_2_4  ,							snake_case=8  ,							snake_case=0.0  ,							snake_case=True  ,							snake_case="relu"  ,							snake_case=2_5_6  ,							snake_case=0.1  ,							snake_case=0.0  ,							snake_case=0.0  ,							snake_case=0.02  ,							snake_case=1.0  ,							snake_case=True  ,							snake_case=False  ,							snake_case="sine"  ,							snake_case="resnet50"  ,							snake_case=True  ,							snake_case=False  ,							snake_case=4  ,							snake_case=4  ,							snake_case=4  ,							snake_case=False  ,							snake_case=3_0_0  ,							snake_case=False  ,							snake_case=1  ,							snake_case=5  ,							snake_case=2  ,							snake_case=1  ,							snake_case=1  ,							snake_case=5  ,							snake_case=2  ,							snake_case=0.1  ,							snake_case=0.25  ,							snake_case=False  ,							**snake_case  ,							)						->  Any:




          '''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.')
                _UpperCAmelCase       :       Optional[Any]							   =CONFIG_MAPPING['resnet'](out_features=['stage4'])
             elif isinstance(snake_case  ,							snake_case):
                _UpperCAmelCase       :       List[Any]							   =backbone_config.get('model_type')
                _UpperCAmelCase       :       Dict							   =CONFIG_MAPPING[backbone_model_type]
                _UpperCAmelCase       :       Optional[Any]							   =config_class.from_dict(snake_case)
          _UpperCAmelCase       :       Tuple							   =use_timm_backbone
          _UpperCAmelCase       :       List[str]							   =backbone_config
          _UpperCAmelCase       :       Optional[Any]							   =num_channels
          _UpperCAmelCase       :       List[Any]							   =num_queries
          _UpperCAmelCase       :       Optional[int]							   =max_position_embeddings
          _UpperCAmelCase       :       List[str]							   =d_model
          _UpperCAmelCase       :       List[Any]							   =encoder_ffn_dim
          _UpperCAmelCase       :       List[Any]							   =encoder_layers
          _UpperCAmelCase       :       Tuple							   =encoder_attention_heads
          _UpperCAmelCase       :       List[str]							   =decoder_ffn_dim
          _UpperCAmelCase       :       Union[str, Any]							   =decoder_layers
          _UpperCAmelCase       :       Union[str, Any]							   =decoder_attention_heads
          _UpperCAmelCase       :       int							   =dropout
          _UpperCAmelCase       :       str							   =attention_dropout
          _UpperCAmelCase       :       Tuple							   =activation_dropout
          _UpperCAmelCase       :       Any							   =activation_function
          _UpperCAmelCase       :       str							   =init_std
          _UpperCAmelCase       :       List[Any]							   =init_xavier_std
          _UpperCAmelCase       :       Dict							   =encoder_layerdrop
          _UpperCAmelCase       :       Union[str, Any]							   =auxiliary_loss
          _UpperCAmelCase       :       int							   =position_embedding_type
          _UpperCAmelCase       :       Tuple							   =backbone
          _UpperCAmelCase       :       Optional[int]							   =use_pretrained_backbone
          _UpperCAmelCase       :       Any							   =dilation
          # deformable attributes
          _UpperCAmelCase       :       int							   =num_feature_levels
          _UpperCAmelCase       :       str							   =encoder_n_points
          _UpperCAmelCase       :       List[str]							   =decoder_n_points
          _UpperCAmelCase       :       Dict							   =two_stage
          _UpperCAmelCase       :       Any							   =two_stage_num_proposals
          _UpperCAmelCase       :       str							   =with_box_refine
          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       :       List[str]							   =class_cost
          _UpperCAmelCase       :       Optional[Any]							   =bbox_cost
          _UpperCAmelCase       :       List[str]							   =giou_cost
          # Loss coefficients
          _UpperCAmelCase       :       str							   =mask_loss_coefficient
          _UpperCAmelCase       :       Tuple							   =dice_loss_coefficient
          _UpperCAmelCase       :       str							   =bbox_loss_coefficient
          _UpperCAmelCase       :       Optional[int]							   =giou_loss_coefficient
          _UpperCAmelCase       :       Optional[int]							   =eos_coefficient
          _UpperCAmelCase       :       List[Any]							   =focal_alpha
          _UpperCAmelCase       :       Any							   =disable_custom_kernels
          super().__init__(is_encoder_decoder=snake_case  ,							**snake_case)






       @property
       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          return self.encoder_attention_heads






       @property
       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          return self.d_model






       def       lowerCAmelCase		(      self)						->  Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[int]							   =copy.deepcopy(self.__dict__)
          if self.backbone_config is not None:
             _UpperCAmelCase       :       Dict							   =self.backbone_config.to_dict()
          _UpperCAmelCase       :       Optional[Any]							   =self.__class__.model_type
          return output





'''simple docstring'''






import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
 import jax.numpy as jnp

 from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
     FlaxRobertaPreLayerNormForCausalLM,
     FlaxRobertaPreLayerNormForMaskedLM,
     FlaxRobertaPreLayerNormForMultipleChoice,
     FlaxRobertaPreLayerNormForQuestionAnswering,
     FlaxRobertaPreLayerNormForSequenceClassification,
     FlaxRobertaPreLayerNormForTokenClassification,
     FlaxRobertaPreLayerNormModel,
 )






class  __magic_name__							(		unittest.TestCase    ):






       def __init__(      self  ,							snake_case  ,							snake_case=1_3  ,							snake_case=7  ,							snake_case=True  ,							snake_case=True  ,							snake_case=True  ,							snake_case=True  ,							snake_case=9_9  ,							snake_case=3_2  ,							snake_case=5  ,							snake_case=4  ,							snake_case=3_7  ,							snake_case="gelu"  ,							snake_case=0.1  ,							snake_case=0.1  ,							snake_case=5_1_2  ,							snake_case=1_6  ,							snake_case=2  ,							snake_case=0.02  ,							snake_case=4  ,							)						->  Dict:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =parent
          _UpperCAmelCase       :       Dict							   =batch_size
          _UpperCAmelCase       :       List[Any]							   =seq_length
          _UpperCAmelCase       :       List[str]							   =is_training
          _UpperCAmelCase       :       Optional[int]							   =use_attention_mask
          _UpperCAmelCase       :       Dict							   =use_token_type_ids
          _UpperCAmelCase       :       Dict							   =use_labels
          _UpperCAmelCase       :       Optional[Any]							   =vocab_size
          _UpperCAmelCase       :       str							   =hidden_size
          _UpperCAmelCase       :       Dict							   =num_hidden_layers
          _UpperCAmelCase       :       Tuple							   =num_attention_heads
          _UpperCAmelCase       :       List[str]							   =intermediate_size
          _UpperCAmelCase       :       List[str]							   =hidden_act
          _UpperCAmelCase       :       int							   =hidden_dropout_prob
          _UpperCAmelCase       :       Optional[int]							   =attention_probs_dropout_prob
          _UpperCAmelCase       :       Optional[Any]							   =max_position_embeddings
          _UpperCAmelCase       :       Union[str, Any]							   =type_vocab_size
          _UpperCAmelCase       :       Dict							   =type_sequence_label_size
          _UpperCAmelCase       :       Union[str, Any]							   =initializer_range
          _UpperCAmelCase       :       Any							   =num_choices






       def       lowerCAmelCase		(      self)						->  Dict:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =ids_tensor([self.batch_size, self.seq_length]  ,							self.vocab_size)

          _UpperCAmelCase       :       str							   =None
          if self.use_attention_mask:
             _UpperCAmelCase       :       Dict							   =random_attention_mask([self.batch_size, self.seq_length])

          _UpperCAmelCase       :       Optional[Any]							   =None
          if self.use_token_type_ids:
             _UpperCAmelCase       :       Tuple							   =ids_tensor([self.batch_size, self.seq_length]  ,							self.type_vocab_size)

          _UpperCAmelCase       :       Union[str, Any]							   =RobertaPreLayerNormConfig(
              vocab_size=self.vocab_size  ,							hidden_size=self.hidden_size  ,							num_hidden_layers=self.num_hidden_layers  ,							num_attention_heads=self.num_attention_heads  ,							intermediate_size=self.intermediate_size  ,							hidden_act=self.hidden_act  ,							hidden_dropout_prob=self.hidden_dropout_prob  ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,							max_position_embeddings=self.max_position_embeddings  ,							type_vocab_size=self.type_vocab_size  ,							is_decoder=snake_case  ,							initializer_range=self.initializer_range  ,							)

          return config, input_ids, token_type_ids, attention_mask






       def       lowerCAmelCase		(      self)						->  str:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =self.prepare_config_and_inputs()
          _UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase       :       str							   =config_and_inputs
          _UpperCAmelCase       :       List[Any]							   ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
          return config, inputs_dict






       def       lowerCAmelCase		(      self)						->  Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Tuple							   =self.prepare_config_and_inputs()
          _UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase       :       Optional[int]							   =config_and_inputs

          _UpperCAmelCase       :       Tuple							   =True
          _UpperCAmelCase       :       Any							   =floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
          _UpperCAmelCase       :       Optional[Any]							   =ids_tensor([self.batch_size, self.seq_length]  ,							vocab_size=2)

          return (
              config,
              input_ids,
              token_type_ids,
              encoder_hidden_states,
              encoder_attention_mask,
          )






@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class  __magic_name__							(		lowerCAmelCase ,unittest.TestCase    ):
       UpperCAmelCase										=True

       UpperCAmelCase										=(
           (
               FlaxRobertaPreLayerNormModel,
               FlaxRobertaPreLayerNormForCausalLM,
               FlaxRobertaPreLayerNormForMaskedLM,
               FlaxRobertaPreLayerNormForSequenceClassification,
               FlaxRobertaPreLayerNormForTokenClassification,
               FlaxRobertaPreLayerNormForMultipleChoice,
               FlaxRobertaPreLayerNormForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )






       def       lowerCAmelCase		(      self)						->  List[str]:




          '''simple docstring'''
          _UpperCAmelCase       :       int							   =FlaxRobertaPreLayerNormModelTester(self)






       @slow
       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          for model_class_name in self.all_model_classes:
             _UpperCAmelCase       :       List[str]							   =model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'  ,							from_pt=snake_case)
             _UpperCAmelCase       :       Dict							   =model(np.ones((1, 1)))
             self.assertIsNotNone(snake_case)






@require_flax
class  __magic_name__							(		unittest.TestCase    ):






       @slow
       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          _UpperCAmelCase       :       Tuple							   =FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'  ,							from_pt=snake_case)

          _UpperCAmelCase       :       Optional[int]							   =np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]  ,							dtype=jnp.intaa)
          _UpperCAmelCase       :       str							   =model(snake_case)[0]
          _UpperCAmelCase       :       int							   =[1, 1_1, 5_0_2_6_5]
          self.assertEqual(list(output.shape)  ,							snake_case)
          # compare the actual values for a slice.
          _UpperCAmelCase       :       List[str]							   =np.array(
              [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]]  ,							dtype=np.floataa)
          self.assertTrue(np.allclose(output[:, :3, :3]  ,							snake_case  ,							atol=1E-4))






       @slow
       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'  ,							from_pt=snake_case)

          _UpperCAmelCase       :       List[str]							   =np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]  ,							dtype=jnp.intaa)
          _UpperCAmelCase       :       Tuple							   =model(snake_case)[0]
          # compare the actual values for a slice.
          _UpperCAmelCase       :       List[str]							   =np.array(
              [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]]  ,							dtype=np.floataa)
          self.assertTrue(np.allclose(output[:, :3, :3]  ,							snake_case  ,							atol=1E-4))

