


'''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							):





							def __init__(  self		:Any  ,_UpperCamelCase		:Optional[int]  ,_UpperCamelCase		:Optional[int]=1_3  ,_UpperCamelCase		:List[str]=7  ,_UpperCamelCase		:List[str]=True  ,_UpperCamelCase		:int=True  ,_UpperCamelCase		:Dict=True  ,_UpperCamelCase		:Dict=True  ,_UpperCamelCase		:Union[str, Any]=9_9  ,_UpperCamelCase		:Optional[Any]=3_2  ,_UpperCamelCase		:Tuple=5  ,_UpperCamelCase		:Tuple=4  ,_UpperCamelCase		:Union[str, Any]=3_7  ,_UpperCamelCase		:List[str]="gelu"  ,_UpperCamelCase		:Optional[int]=0.1  ,_UpperCamelCase		:List[str]=0.1  ,_UpperCamelCase		:Any=5_1_2  ,_UpperCamelCase		:int=1_6  ,_UpperCamelCase		:Tuple=2  ,_UpperCamelCase		:str=0.02  ,_UpperCamelCase		:Dict=4  ,):
									snake_case_		:							Optional[int]			       =						parent
									snake_case_		:							Union[str, Any]			       =						batch_size
									snake_case_		:							Dict			       =						seq_length
									snake_case_		:							Any			       =						is_training
									snake_case_		:							Dict			       =						use_attention_mask
									snake_case_		:							int			       =						use_token_type_ids
									snake_case_		:							Optional[int]			       =						use_labels
									snake_case_		:							int			       =						vocab_size
									snake_case_		:							Any			       =						hidden_size
									snake_case_		:							List[Any]			       =						num_hidden_layers
									snake_case_		:							Optional[int]			       =						num_attention_heads
									snake_case_		:							Optional[int]			       =						intermediate_size
									snake_case_		:							Optional[int]			       =						hidden_act
									snake_case_		:							List[str]			       =						hidden_dropout_prob
									snake_case_		:							List[str]			       =						attention_probs_dropout_prob
									snake_case_		:							Any			       =						max_position_embeddings
									snake_case_		:							Any			       =						type_vocab_size
									snake_case_		:							Union[str, Any]			       =						type_sequence_label_size
									snake_case_		:							Any			       =						initializer_range
									snake_case_		:							Optional[Any]			       =						num_choices





							def 		a__   (  self		:Optional[Any]			):
									snake_case_		:							Optional[Any]			       =						ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size			)

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

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

									snake_case_		:							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 		a__   (  self		:int			):
									snake_case_		:							Any			       =						self.prepare_config_and_inputs()
									snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_		:							Dict			       =						config_and_inputs
									snake_case_		:							Dict			       =						{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
									return config, inputs_dict





@require_flax
class 						__UpperCamelCase	(						lowercase__						,  unittest.TestCase							):
							lowercase			:		Union[str, Any]   		=  True

							lowercase			:		Optional[Any]   		=  (
							    (
							        FlaxRoFormerModel,
							        FlaxRoFormerForMaskedLM,
							        FlaxRoFormerForSequenceClassification,
							        FlaxRoFormerForTokenClassification,
							        FlaxRoFormerForMultipleChoice,
							        FlaxRoFormerForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)





							def 		a__   (  self		:Optional[Any]			):
									snake_case_		:							Dict			       =						FlaxRoFormerModelTester(self			)





							@slow
							def 		a__   (  self		:Any			):
									for model_class_name in self.all_model_classes:
											snake_case_		:							List[Any]			       =						model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""  ,from_pt=_UpperCamelCase			)
											snake_case_		:							Tuple			       =						model(np.ones((1, 1)			)			)
											self.assertIsNotNone(_UpperCamelCase			)





@require_flax
class 						__UpperCamelCase	(						unittest.TestCase							):





							@slow
							def 		a__   (  self		:int			):
									snake_case_		:							int			       =						FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""			)
									snake_case_		:							List[Any]			       =						jnp.array([[0, 1, 2, 3, 4, 5]]			)
									snake_case_		:							Dict			       =						model(_UpperCamelCase			)[0]

									snake_case_		:							Any			       =						5_0_0_0_0

									snake_case_		:							Dict			       =						(1, 6, vocab_size)
									self.assertEqual(output.shape  ,_UpperCamelCase			)

									snake_case_		:							Any			       =						jnp.array(
									    [[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]]			)

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



'''simple docstring'''






from abc import ABC, abstractmethod
from argparse import ArgumentParser
class 						__UpperCamelCase	(						lowercase__							):





							@staticmethod
							@abstractmethod
							def 		a__   (  _UpperCamelCase		:ArgumentParser			):
									raise NotImplementedError()





							@abstractmethod
							def 		a__   (  self		:Optional[Any]			):
									raise NotImplementedError()