






"""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        a     (   unittest.TestCase	):







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase=1_3							,		_lowerCamelCase=7							,		_lowerCamelCase=True							,		_lowerCamelCase=True							,		_lowerCamelCase=True							,		_lowerCamelCase=True							,		_lowerCamelCase=9_9							,		_lowerCamelCase=3_2							,		_lowerCamelCase=5							,		_lowerCamelCase=4							,		_lowerCamelCase=3_7							,		_lowerCamelCase="gelu"							,		_lowerCamelCase=0.1							,		_lowerCamelCase=0.1							,		_lowerCamelCase=5_1_2							,		_lowerCamelCase=1_6							,		_lowerCamelCase=2							,		_lowerCamelCase=0.0_2							,		_lowerCamelCase=4							,		):
      lowercase							    =       parent
      lowercase							    =       batch_size
      lowercase							    =       seq_length
      lowercase							    =       is_training
      lowercase							    =       use_attention_mask
      lowercase							    =       use_token_type_ids
      lowercase							    =       use_labels
      lowercase							    =       vocab_size
      lowercase							    =       hidden_size
      lowercase							    =       num_hidden_layers
      lowercase							    =       num_attention_heads
      lowercase							    =       intermediate_size
      lowercase							    =       hidden_act
      lowercase							    =       hidden_dropout_prob
      lowercase							    =       attention_probs_dropout_prob
      lowercase							    =       max_position_embeddings
      lowercase							    =       type_vocab_size
      lowercase							    =       type_sequence_label_size
      lowercase							    =       initializer_range
      lowercase							    =       num_choices







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       ids_tensor([self.batch_size, self.seq_length]							,		self.vocab_size       )

      lowercase							    =       None
      if self.use_attention_mask:
        lowercase							    =       random_attention_mask([self.batch_size, self.seq_length]       )

      lowercase							    =       None
      if self.use_token_type_ids:
        lowercase							    =       ids_tensor([self.batch_size, self.seq_length]							,		self.type_vocab_size       )

      lowercase							    =       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=snake_case_							,		initializer_range=self.initializer_range							,		)

      return config, input_ids, token_type_ids, attention_mask







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.prepare_config_and_inputs()
      lowercase    ,     lowercase    ,     lowercase    ,     lowercase							    =       config_and_inputs
      lowercase							    =       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
      return config, inputs_dict


@require_flax
class        a     (   lowerCAmelCase_,				unittest.TestCase	):
    UpperCAmelCase_ :     Tuple	     =True

    UpperCAmelCase_ :     int	     =(
        (
            FlaxRoFormerModel,
            FlaxRoFormerForMaskedLM,
            FlaxRoFormerForSequenceClassification,
            FlaxRoFormerForTokenClassification,
            FlaxRoFormerForMultipleChoice,
            FlaxRoFormerForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       FlaxRoFormerModelTester(self       )







    @slow
    def 			UpperCamelCase_      (			self       ):
      for model_class_name in self.all_model_classes:
        lowercase							    =       model_class_name.from_pretrained('junnyu/roformer_chinese_small'							,		from_pt=snake_case_       )
        lowercase							    =       model(np.ones((1, 1)       )       )
        self.assertIsNotNone(snake_case_       )


@require_flax
class        a     (   unittest.TestCase	):







    @slow
    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'       )
      lowercase							    =       jnp.array([[0, 1, 2, 3, 4, 5]]       )
      lowercase							    =       model(snake_case_       )[0]

      lowercase							    =       5_0_0_0_0

      lowercase							    =       (1, 6, vocab_size)
      self.assertEqual(output.shape							,		snake_case_       )

      lowercase							    =       jnp.array(
          [[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]]       )

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









'''simple docstring'''




from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


__SCREAMING_SNAKE_CASE     :int       =				logging.get_logger(__name__)







class   A_	:
 _lowerCamelCase  : str
 _lowerCamelCase  : str           =							None




 @staticmethod
 def      lowercase	(					):
      raise NotImplementedError




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    Optional[int]	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    List[Any]    ):
      raise NotImplementedError




 def      lowercase	(					self       :    Any	,			snake_case_       :    int    ):
      raise NotImplementedError




 def      lowercase	(					self       :    List[str]    ):
      if not self.is_available():
           raise RuntimeError(
               f'You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.'    )




 @classmethod
 def      lowercase	(					cls       :    List[Any]    ):
      return f'`pip install {cls.pip_package or cls.name}`'







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : int           =							"""optuna"""




 @staticmethod
 def      lowercase	(					):
      return is_optuna_available()




 def      lowercase	(					self       :    List[str]	,			snake_case_       :    Any	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    Tuple    ):
      return run_hp_search_optuna(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    int	,			snake_case_       :    Optional[int]    ):
      return default_hp_space_optuna(snake_case_    )







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Any           =							"""ray"""
 _lowerCamelCase  : Tuple           =							"""'ray[tune]'"""




 @staticmethod
 def      lowercase	(					):
      return is_ray_available()




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    Any	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    List[str]    ):
      return run_hp_search_ray(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    str    ):
      return default_hp_space_ray(snake_case_    )







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : int           =							"""sigopt"""




 @staticmethod
 def      lowercase	(					):
      return is_sigopt_available()




 def      lowercase	(					self       :    Any	,			snake_case_       :    int	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    Dict    ):
      return run_hp_search_sigopt(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    Dict	,			snake_case_       :    Optional[Any]    ):
      return default_hp_space_sigopt(snake_case_    )







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Optional[int]           =							"""wandb"""




 @staticmethod
 def      lowercase	(					):
      return is_wandb_available()




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    Optional[Any]	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    Optional[Any]    ):
      return run_hp_search_wandb(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    Union[str, Any]    ):
      return default_hp_space_wandb(snake_case_    )





__SCREAMING_SNAKE_CASE     :Dict       =				{
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def 	UpperCAmelCase_							(					)		->       str:


     '''simple docstring'''



     _UpperCAmelCase     							=   [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
     if len(__lowercase       ) > 0:
          _UpperCAmelCase     							=   available_backends[0].name
          if len(__lowercase       ) > 1:
               logger.info(
                   f'{len(__lowercase       )} hyperparameter search backends available. Using {name} as the default.'       )
          return name
     raise RuntimeError(
         "No hyperparameter search backend available.\n"
         + "\n".join(
             f' - To install {backend.name} run {backend.pip_install()}'
             for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()       )       )




