"""simple docstring"""






import copy

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


__A			   = logging.get_logger(__name__)

__A			   = {
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}


class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Dict						    = "deta"
    _UpperCAmelCase	:Any						    = {
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }






    def __init__(   self    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=900    ,			_UpperCAmelCase=2048    ,			_UpperCAmelCase=6    ,			_UpperCAmelCase=2048    ,			_UpperCAmelCase=8    ,			_UpperCAmelCase=6    ,			_UpperCAmelCase=1024    ,			_UpperCAmelCase=8    ,			_UpperCAmelCase=0.0    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase="relu"    ,			_UpperCAmelCase=256    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=0.0    ,			_UpperCAmelCase=0.0    ,			_UpperCAmelCase=0.02    ,			_UpperCAmelCase=1.0    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=False    ,			_UpperCAmelCase="sine"    ,			_UpperCAmelCase=5    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=300    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=5    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=5    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=0.25    ,			**_UpperCAmelCase    ,			):
       if backbone_config is None:
          logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''							)
          lowercase__:   str					=      CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4''']							)
       else:
          if isinstance(_UpperCAmelCase    ,			_UpperCAmelCase							):
             lowercase__:   Tuple					=      backbone_config.pop('''model_type'''							)
             lowercase__:   str					=      CONFIG_MAPPING[backbone_model_type]
             lowercase__:   Optional[int]					=      config_class.from_dict(_UpperCAmelCase							)

       lowercase__:   int					=      backbone_config
       lowercase__:   Any					=      num_queries
       lowercase__:   List[str]					=      max_position_embeddings
       lowercase__:   Optional[Any]					=      d_model
       lowercase__:   List[Any]					=      encoder_ffn_dim
       lowercase__:   Tuple					=      encoder_layers
       lowercase__:   Dict					=      encoder_attention_heads
       lowercase__:   Any					=      decoder_ffn_dim
       lowercase__:   Union[str, Any]					=      decoder_layers
       lowercase__:   List[Any]					=      decoder_attention_heads
       lowercase__:   int					=      dropout
       lowercase__:   List[str]					=      attention_dropout
       lowercase__:   Tuple					=      activation_dropout
       lowercase__:   Tuple					=      activation_function
       lowercase__:   int					=      init_std
       lowercase__:   Optional[Any]					=      init_xavier_std
       lowercase__:   Optional[Any]					=      encoder_layerdrop
       lowercase__:   Optional[int]					=      auxiliary_loss
       lowercase__:   Union[str, Any]					=      position_embedding_type
       # deformable attributes
       lowercase__:   List[str]					=      num_feature_levels
       lowercase__:   Optional[Any]					=      encoder_n_points
       lowercase__:   int					=      decoder_n_points
       lowercase__:   str					=      two_stage
       lowercase__:   Optional[int]					=      two_stage_num_proposals
       lowercase__:   Tuple					=      with_box_refine
       lowercase__:   str					=      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
       lowercase__:   Union[str, Any]					=      class_cost
       lowercase__:   Optional[int]					=      bbox_cost
       lowercase__:   int					=      giou_cost
       # Loss coefficients
       lowercase__:   Optional[int]					=      mask_loss_coefficient
       lowercase__:   List[str]					=      dice_loss_coefficient
       lowercase__:   str					=      bbox_loss_coefficient
       lowercase__:   Union[str, Any]					=      giou_loss_coefficient
       lowercase__:   Optional[int]					=      eos_coefficient
       lowercase__:   str					=      focal_alpha
       super().__init__(is_encoder_decoder=_UpperCAmelCase    ,			**_UpperCAmelCase							)






    @property
    def _snake_case			(   self							):
       return self.encoder_attention_heads






    @property
    def _snake_case			(   self							):
       return self.d_model






    def _snake_case			(   self							):
       lowercase__:   Union[str, Any]					=      copy.deepcopy(self.__dict__							)
       lowercase__:   Dict					=      self.backbone_config.to_dict()
       lowercase__:   Union[str, Any]					=      self.__class__.model_type
       return output

"""simple docstring"""






import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.roformer.modeling_flax_roformer import (
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerModel,
       )


class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=13    ,			_UpperCAmelCase=7    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=99    ,			_UpperCAmelCase=32    ,			_UpperCAmelCase=5    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=37    ,			_UpperCAmelCase="gelu"    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=512    ,			_UpperCAmelCase=16    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=0.02    ,			_UpperCAmelCase=4    ,			):
       lowercase__:   Dict					=      parent
       lowercase__:   List[str]					=      batch_size
       lowercase__:   Optional[Any]					=      seq_length
       lowercase__:   List[Any]					=      is_training
       lowercase__:   int					=      use_attention_mask
       lowercase__:   Tuple					=      use_token_type_ids
       lowercase__:   Union[str, Any]					=      use_labels
       lowercase__:   str					=      vocab_size
       lowercase__:   str					=      hidden_size
       lowercase__:   str					=      num_hidden_layers
       lowercase__:   Optional[int]					=      num_attention_heads
       lowercase__:   List[str]					=      intermediate_size
       lowercase__:   List[str]					=      hidden_act
       lowercase__:   Tuple					=      hidden_dropout_prob
       lowercase__:   int					=      attention_probs_dropout_prob
       lowercase__:   int					=      max_position_embeddings
       lowercase__:   Union[str, Any]					=      type_vocab_size
       lowercase__:   List[Any]					=      type_sequence_label_size
       lowercase__:   Any					=      initializer_range
       lowercase__:   str					=      num_choices






    def _snake_case			(   self							):
       lowercase__:   int					=      ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size							)

       lowercase__:   List[Any]					=      None
       if self.use_attention_mask:
          lowercase__:   Tuple					=      random_attention_mask([self.batch_size, self.seq_length]							)

       lowercase__:   List[Any]					=      None
       if self.use_token_type_ids:
          lowercase__:   str					=      ids_tensor([self.batch_size, self.seq_length]    ,			self.type_vocab_size							)

       lowercase__:   Optional[int]					=      RoFormerConfig(
           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=_UpperCAmelCase    ,			initializer_range=self.initializer_range    ,			)

       return config, input_ids, token_type_ids, attention_mask






    def _snake_case			(   self							):
       lowercase__:   str					=      self.prepare_config_and_inputs()
       lowercase__, lowercase__, lowercase__, lowercase__:   Optional[Any]					=      config_and_inputs
       lowercase__:   Dict					=      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict



@require_flax
class     UpperCAmelCase						(_UpperCAmelCase    ,unittest.TestCase  ):



    """simple docstring"""

    _UpperCAmelCase	:List[str]						    = True

    _UpperCAmelCase	:Union[str, Any]						    = (
        (
            FlaxRoFormerModel,
            FlaxRoFormerForMaskedLM,
            FlaxRoFormerForSequenceClassification,
            FlaxRoFormerForTokenClassification,
            FlaxRoFormerForMultipleChoice,
            FlaxRoFormerForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )






    def _snake_case			(   self							):
       lowercase__:   str					=      FlaxRoFormerModelTester(self							)






    @slow
    def _snake_case			(   self							):
       for model_class_name in self.all_model_classes:
          lowercase__:   Dict					=      model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''    ,			from_pt=_UpperCAmelCase							)
          lowercase__:   int					=      model(np.ones((1, 1)							)							)
          self.assertIsNotNone(_UpperCAmelCase							)



@require_flax
class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    @slow
    def _snake_case			(   self							):
       lowercase__:   Any					=      FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''							)
       lowercase__:   Optional[int]					=      jnp.array([[0, 1, 2, 3, 4, 5]]							)
       lowercase__:   List[Any]					=      model(_UpperCAmelCase							)[0]

       lowercase__:   str					=      50000

       lowercase__:   Tuple					=      (1, 6, vocab_size)
       self.assertEqual(output.shape    ,			_UpperCAmelCase							)

       lowercase__:   List[Any]					=      jnp.array(
           [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]							)

       self.assertTrue(jnp.allclose(output[:, :3, :3]    ,			_UpperCAmelCase    ,			atol=1e-4							)							)
